# data[].attributes.body
Health Informatics is a digital system that provides electronic access to medical information through resources and devices that allow medical records to be stored and accessed by patients and health care personnel. With the advancement of technology, the quality of patient care and safety has improved significantly.

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Human error is something we as medical professionals work effortlessly to avoid. Many organizations and medical professionals have worked to improve errors and establish a system that applies technology and medical information together to prevent medical errors, ensure patient-centered care and enable medical professionals to provide efficient quality of care.

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Review

¹ Division of General Internal Medicine, Johns Hopkins Bayview Medical Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States

² Department of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, United States

³ Department of Medicine, School of Medicine, Duke University, Durham, NC, United States

Corresponding Author:

Leah Wolfe, MD

Division of General Internal Medicine

Johns Hopkins Bayview Medical Center

Johns Hopkins University School of Medicine

5200 Eastern Avenue, Mason F Lord Building

Center Tower, Suite 2300

Baltimore, MD, 21224-2780

United States

Phone: 1 410 550 0512

Fax:1 410 550 3403

Email: lewolfe@jhmi.edu

Background: The transition to the electronic health record (EHR) has brought forth a rapid cultural shift in the world of medicine, presenting both new challenges as well as opportunities for improving health care. As clinicians work to adapt to the changes imposed by the EHR, identification of best practices around the clinically excellent use of the EHR is needed.

Objective: Using the domains of clinical excellence previously defined by the Johns Hopkins Miller Coulson Academy of Clinical Excellence, this review aims to identify best practices around the clinically excellent use of the EHR.

Methods: The authors searched the PubMed database, using keywords related to clinical excellence domains and the EHR, to capture the English-language, peer-reviewed literature published between January 1, 2000, and August 2, 2016. One author independently reviewed each article and extracted relevant data.

Results: The search identified 606 titles, with the majority (393/606, 64.9%) in the domain of communication and interpersonal skills. Twenty-eight of the 606 (4.6%) titles were excluded from full-text review, primarily due to lack of availability of the full-text article. The remaining 578 full-text articles reviewed were related to clinical excellence generally (3/578, 0.5%) or the specific domains of communication and interpersonal skills (380/578, 65.7%), diagnostic acumen (31/578, 5.4%), skillful negotiation of the health care system (4/578, 0.7%), scholarly approach to clinical practice (41/578, 7.1%), professionalism and humanism (2/578, 0.4%), knowledge (97/578, 16.8%), and passion for clinical medicine (20/578, 3.5%).

Conclusions: Results suggest that as familiarity and expertise are developed, clinicians are leveraging the EHR to provide clinically excellent care. Best practices identified included deliberate physical configuration of the clinical space to involve sharing the screen with patients and limiting EHR use during difficult and emotional topics. Promising horizons for the EHR include the ability to augment participation in pragmatic trials, identify adverse drug effects, correlate genomic data to clinical outcomes, and follow data-driven guidelines. Clinician and patient satisfaction with the EHR has generally improved with time, and hopefully continued clinician, and patient input will lead to a system that satisfies all.

JMIR Hum Factors 2018;5(4):e10426

doi:10.2196/10426

Keywords

Use of the electronic health record (EHR) during clinical encounters is now a standard part of contemporary medical practice. The EHR-like other medical technologies-is designed to optimize the efficiency and quality of health care delivery, and ultimately-one hopes-improve patient outcomes. However, as anyone who has ever used or seen his/her health care provider use the EHR during a clinic visit knows that use of the EHR in a way that preserves or enhances clinical excellence is challenging. The Johns Hopkins Miller-Coulson Academy of Clinical Excellence (MCACE) has previously identified the following domains of clinical excellence: (1) communication and interpersonal skills, (2) diagnostic acumen, (3) skillful negotiation of the health care system, (4) scholarly approach to clinical practice, (5) professionalism and humanism, (6) knowledge, and (7) passion for clinical medicine []. To identify best practices around the clinically excellent use of the EHR, the authors conducted a literature review of the MCACE domains and the EHR.

The concepts of the clinical excellence domains and the EHR were defined using a combination of controlled vocabulary terms applicable to PubMed and keyword terms and phrases to capture the English-language, peer-reviewed literature published between January 1, 2000, and August 2, 2016 (). Citations were imported into a citation management system, and duplicates were removed. The authors ensured the search strategies captured a previously published review [] on the topic. One author (LW, FB, or MSC) independently reviewed each article and extracted relevant data. The study was submitted to the institutional review board and deemed exempt from further review.

Overview

The search identified 606 titles (), the majority (393/606, 64.9%) were in the domain of communication and interpersonal skills. Twenty-eight of the 606 (4.6%) titles were excluded from full-text review, primarily due to lack of availability of the full-text article. The remaining 578 full-text articles reviewed were related to either clinical excellence generally (3/578, 0.5%) or to the specific domains of communication and interpersonal skills (380/578, 65.7%), diagnostic acumen (31/578, 5.4%), skillful negotiation of the health care system (4/578, 0.7%), scholarly approach to clinical practice (41/578, 7.1%), professionalism and humanism (2/578, 0.3%), knowledge (97/578, 16.8%), and passion for clinical medicine (20/578, 3.5%).

‎
Figure 1. Flowchart for search strategy and review of English-language, peer-reviewed articles on clinical excellence and the electronic health record between January 1, 2000 and August 2, 2016.
View this figure

Communication and Interpersonal Skills

Within the communication and interpersonal skills domain, the following practice-based themes emerged from the literature, yielding the following clinical "pearls."

How Clinicians Practice

Impact on Patients

How to Prepare for a Visit

How to Organize the Room

How to Engage Patients with the Electronic Health Record in the Room

How to Use the Electronic Health Record to Enhance Intervisit Communication

Diagnostic Acumen

Review of the literature revealed several ways in which the EHR can assist a clinician's diagnostic acumen, such as instant access to historical records, and automation of risk score algorithms. The EHR makes access to past medical history automatic within the sphere in which the EHR operates. Retrieving outside data are the slowest area of progress, but is still improving with the EHR. The EHR's ability to provide interconnected and immediate point-of-care access adds a new dynamic to the health care system, expanding the background of clinical knowledge and enhancing diagnostic acumen and speed of diagnosis [].

The EHR also brings the potential to use calculated risk scores to the user's fingertips. Physicians admitting a patient with non-ST-elevation myocardial infarction can have immediate access to the thrombolysis in myocardial infarction score. An outpatient provider can have an automated atherosclerotic cardiovascular disease risk score calculated as soon as vital signs are measured. While debate exists around the utility of these scores [,], they have and will continue to be ever-present in our understanding of disease. The EHR gives clinicians the added functionality of automatically calculating and providing this data as an added input to the clinician, another tool in the toolbox.

Skillful Negotiation of the Health Care System

The EHR can help clinicians more deftly navigate the health care system to provide high-quality, cost-conscious care. One way the EHR helps clinicians improve care is by promoting adherence to guidelines. Despite knowing that guideline-directed care improves outcomes, chronic and acute-care patients receive guideline-directed care only about 50% of the time [], and one-third of health care expenditure is wasteful []. Clinical decision support (CDS) is the set of prompts that highlight information that could change clinical care and is the answer to the gap in guideline-based care. CDS relies heavily on input from clinical staff who are up-to-date with guidelines. However, when done correctly, CDS has the potential to facilitate the delivery of high-quality care, improving the health of patients and avoiding unnecessary care [,].

Further, the Office of the National Coordinator for health care information technology is moving toward national knowledge-sharing for CDS prompts with the intent of eventually standardizing and classifying the importance of CDS. Together, these represent methods for ensuring that we are navigating our health care environment to provide succinct and concise care.

As the use of the EHR grows, data-sharing is being enhanced across networks in regional data exchange systems called health information exchanges (HIEs). With these, clinicians can share pertinent patient information, labs, and notes, as well as communicate directly about essential details. HIEs are the vehicle for creating seamless and secure data-sharing between networks.

Scholarly Approach to Clinical Practice

Use of the EHR facilitates the creation of patient databases and undertaking of pragmatic trials []. Through automation of the processes of patient screening, patients can be assessed for participation in pragmatic trials directly through diagnostic codes and demographic information, and messaged at home or asked in the office if they would consent to a study. For patients with the ability to access a computer, investigators have provided informed consent via online videos which can be viewed in the comfort of the patient's own home. Further, the addition of the computer to the clinical setting means that the networks for starting a pragmatic clinical trial are primed and ready. The data are already being collected in the system, and need only to be consented to appropriately and shared.

Professionalism and Humanism

The human price of the EHR is the distraction. CDS popups alert clinicians to a clinical need, an incorrect allergy warning may alarm while entering a prescription, and vital signs may flag a sepsis warning inappropriately. In the rapidly advancing world of the EHR with its increased distractions, it is imperative that clinicians maintain strong bonds with patients and stop the intrusion into clinician-patient relationships []. Best practices described in the Communication and interpersonal skills domain can support humanistic attitudes and professional behaviors in the face of the EHR.

The electronic interface of collection is transforming the field of Patient Reported Outcomes (PROs). Many patient portals are set up to ask and record PROs, which can seamlessly integrate into the patient's record. These PROs provide the ability to compare treatments and add patient-centered outcomes to the research. These data are being mobilized for use in decision making by groups like the Patient-Centered Outcomes Research Institute, the National Institutes of Health Collaboratory, and the American Society of Clinical Oncology.

Knowledge

The EHR brings a new way to interface with the knowledge that clinicians generate. Two of the most exciting changes to knowledge will be the discovery of new patterns and the incorporation of genetic data to patient records via "big data" methodology. In computing, big data refers to the use of extensive datasets that are analyzed computationally to reveal previously unknown trends and associations. With enough data points, data scientists suspect that computers will eventually be able to generate prediction models for individual cases based on repositories of old case data []. For example, a computerized model of hyponatremia correction in newborns has been created based on large numbers of observations by computers [].

On the forefront, data scientists and geneticists hope to incorporate patient genomic information into the EHR to help identify patterns and uncover new genetic connections. Once genetic data has been added to a patient's profile, the EHR could theoretically learn what gene loci predispose a patient to angioedema, interstitial lung disease, or any number of previously poorly understood disease states [,].

In a similar vein, the EHR can automate the reporting of adverse drug reactions to newly prescribed drugs. By reporting early trends in side effects from a new agent, EHRs might accelerate the detection of untoward side effects-like myocardial infarction associated with cyclooxygenase enzyme inhibitors (ie, COX-2) [].

Passion and Professional Satisfaction

The introduction of the EHR was fraught with underprepared EHR platforms and unrealistic expectations. Clinicians were initially confronted with decreased efficiency, increased burnout, and high turnover. Early on, physicians using computerized order entry and electronic documentation were 30% more likely to report burnout after controlling for other variables []. The only intervention that routinely improved satisfaction was employing scribes, which suggests that the only positive experience associated with the EHR was minimizing its use []. Further analysis into trends of physician satisfaction reveals that a more robust platform is more correlated with satisfaction. Clinical notes, diagnosis function, and off-site capability were all associated with higher satisfaction. There was a trend that younger physicians were more likely to be satisfied than their elder peers []. Finally, and most promising of all, physicians who had access to their EHR for at least two years were 2.78 times more likely to be satisfied with their EHR compared to those with less than two years' experience [].

Much of the literature in other domains touched on the EHR's potential to improve the interface with clinicians. Tools are being introduced to provide the clinician with medical references on demand for reading about developing medical data [-]. Finally, natural language processing is another advancing technology in which the computer attempts to interpret the clinician's intention when writing. As an example, when a clinician diagnoses a patient with pneumonia, the EHR could ask if it should open the pneumonia order set [,,]. This technology is still in its infancy and will likely require years to be ready for implementation. That said, it is one of the exciting transformations of the EHR that would produce a more fluent interface between the clinician and computer, allowing clinicians to focus back on the priority-patients.

Many articles published after our literature review cite the EHR as a significant factor in clinician burnout. For example, in their 2017 commentary, Shanafelt and colleagues [] discuss clinician burnout in the era of the EHR and its attendant clerical, regulatory, and workload implications. They outline the potential broader impacts of clinician burnout for the quality of care and the health care system at large. They also emphasize the importance of measures to address the increasing documentation burden especially performance and documentation of components of care that are justifiable for billing purposes alone and do not contribute meaningfully to the episode of care. A recent systematic review by West and colleagues [] highlights the evidence supporting both organizational and individual interventions to address burnout. Though beyond the scope of our review, clinician burnout is critical among factors that should be considered in the design, implementation, and use of the EHR going forward.

The EHR has completely transformed the clinical landscape. Its arrival and integration have been fraught with challenges, including having noticeably altered clinicians' communication with patients. That said, clinicians are gradually transforming their approach to, and interaction with, the EHR in a way that attempts to minimize distraction and enhance the quality of the clinician-patient connection again. Computerizing this work has effectively put clinicians "on the grid" and hopefully will continue to bring positive changes to the way that clinicians gather and interact with patient data to further enhance diagnostic acumen, scholarly approach to medicine, professionalism, knowledge, passion for clinical medicine, and the ability to negotiate the health care system to provide clinically excellent care for patients.

Acknowledgments

We wish to acknowledge the Miller-Coulson Academy of Clinical Excellence and Johns Hopkins University School of Medicine.

Conflicts of Interest

None declared.
‎

Multimedia Appendix 1

PubMed search terms and process for literature review for clinically excellent use of the electronic health record.
PDF File (Adobe PDF File), 46KB

‎

CDS: clinical decision support

EHR: electronic health record

HIE: health information exchange

MCACE: Johns Hopkins Miller-Coulson Academy of Clinical Excellence

PROs: patient reported outcomes

Edited by G Eysenbach; submitted 27.03.18; peer-reviewed by M Alkureishi, C Pearce; comments to author 02.05.18; revised version received 27.06.18; accepted 17.07.18; published 05.10.18

©Leah Wolfe, Margaret Smith Chisolm, Fuad Bohsali. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 05.10.2018.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on http://humanfactors.jmir.org, as well as this copyright and license information must be included.

Background: The transition to the electronic health record (EHR) has brought forth a rapid cultural shift in the world of medicine, presenting both new challenges as well as opportunities for improving health care. As clinicians work to adapt to the changes imposed by the EHR, identification of best practices around the clinically excellent use of the EHR is needed.

Objective: Using the domains of clinical excellence previously defined by the Johns Hopkins Miller Coulson Academy of Clinical Excellence, this review aims to identify best practices around the clinically excellent use of the EHR.

Methods: The authors searched the PubMed database, using keywords related to clinical excellence domains and the EHR, to capture the English-language, peer-reviewed literature published between January 1, 2000, and August 2, 2016. One author independently reviewed each article and extracted relevant data.

Results: The search identified 606 titles, with the majority (393/606, 64.9%) in the domain of communication and interpersonal skills. Twenty-eight of the 606 (4.6%) titles were excluded from full-text review, primarily due to lack of availability of the full-text article. The remaining 578 full-text articles reviewed were related to clinical excellence generally (3/578, 0.5%) or the specific domains of communication and interpersonal skills (380/578, 65.7%), diagnostic acumen (31/578, 5.4%), skillful negotiation of the health care system (4/578, 0.7%), scholarly approach to clinical practice (41/578, 7.1%), professionalism and humanism (2/578, 0.4%), knowledge (97/578, 16.8%), and passion for clinical medicine (20/578, 3.5%).

Conclusions: Results suggest that as familiarity and expertise are developed, clinicians are leveraging the EHR to provide clinically excellent care. Best practices identified included deliberate physical configuration of the clinical space to involve sharing the screen with patients and limiting EHR use during difficult and emotional topics. Promising horizons for the EHR include the ability to augment participation in pragmatic trials, identify adverse drug effects, correlate genomic data to clinical outcomes, and follow data-driven guidelines. Clinician and patient satisfaction with the EHR has generally improved with time, and hopefully continued clinician, and patient input will lead to a system that satisfies all.

JMIR Hum Factors 2018;5(4):e10426

doi:10.2196/10426

Keywords

Use of the electronic health record (EHR) during clinical encounters is now a standard part of contemporary medical practice. The EHR-like other medical technologies-is designed to optimize the efficiency and quality of health care delivery, and ultimately-one hopes-improve patient outcomes. However, as anyone who has ever used or seen his/her health care provider use the EHR during a clinic visit knows that use of the EHR in a way that preserves or enhances clinical excellence is challenging. The Johns Hopkins Miller-Coulson Academy of Clinical Excellence (MCACE) has previously identified the following domains of clinical excellence: (1) communication and interpersonal skills, (2) diagnostic acumen, (3) skillful negotiation of the health care system, (4) scholarly approach to clinical practice, (5) professionalism and humanism, (6) knowledge, and (7) passion for clinical medicine []. To identify best practices around the clinically excellent use of the EHR, the authors conducted a literature review of the MCACE domains and the EHR.

The concepts of the clinical excellence domains and the EHR were defined using a combination of controlled vocabulary terms applicable to PubMed and keyword terms and phrases to capture the English-language, peer-reviewed literature published between January 1, 2000, and August 2, 2016 (). Citations were imported into a citation management system, and duplicates were removed. The authors ensured the search strategies captured a previously published review [] on the topic. One author (LW, FB, or MSC) independently reviewed each article and extracted relevant data. The study was submitted to the institutional review board and deemed exempt from further review.

Overview

The search identified 606 titles (), the majority (393/606, 64.9%) were in the domain of communication and interpersonal skills. Twenty-eight of the 606 (4.6%) titles were excluded from full-text review, primarily due to lack of availability of the full-text article. The remaining 578 full-text articles reviewed were related to either clinical excellence generally (3/578, 0.5%) or to the specific domains of communication and interpersonal skills (380/578, 65.7%), diagnostic acumen (31/578, 5.4%), skillful negotiation of the health care system (4/578, 0.7%), scholarly approach to clinical practice (41/578, 7.1%), professionalism and humanism (2/578, 0.3%), knowledge (97/578, 16.8%), and passion for clinical medicine (20/578, 3.5%).

‎
Figure 1. Flowchart for search strategy and review of English-language, peer-reviewed articles on clinical excellence and the electronic health record between January 1, 2000 and August 2, 2016.
View this figure

Communication and Interpersonal Skills

Within the communication and interpersonal skills domain, the following practice-based themes emerged from the literature, yielding the following clinical "pearls."

How Clinicians Practice

Impact on Patients

How to Prepare for a Visit

How to Organize the Room

How to Engage Patients with the Electronic Health Record in the Room

How to Use the Electronic Health Record to Enhance Intervisit Communication

Diagnostic Acumen

Review of the literature revealed several ways in which the EHR can assist a clinician's diagnostic acumen, such as instant access to historical records, and automation of risk score algorithms. The EHR makes access to past medical history automatic within the sphere in which the EHR operates. Retrieving outside data are the slowest area of progress, but is still improving with the EHR. The EHR's ability to provide interconnected and immediate point-of-care access adds a new dynamic to the health care system, expanding the background of clinical knowledge and enhancing diagnostic acumen and speed of diagnosis [].

The EHR also brings the potential to use calculated risk scores to the user's fingertips. Physicians admitting a patient with non-ST-elevation myocardial infarction can have immediate access to the thrombolysis in myocardial infarction score. An outpatient provider can have an automated atherosclerotic cardiovascular disease risk score calculated as soon as vital signs are measured. While debate exists around the utility of these scores [,], they have and will continue to be ever-present in our understanding of disease. The EHR gives clinicians the added functionality of automatically calculating and providing this data as an added input to the clinician, another tool in the toolbox.

Skillful Negotiation of the Health Care System

The EHR can help clinicians more deftly navigate the health care system to provide high-quality, cost-conscious care. One way the EHR helps clinicians improve care is by promoting adherence to guidelines. Despite knowing that guideline-directed care improves outcomes, chronic and acute-care patients receive guideline-directed care only about 50% of the time [], and one-third of health care expenditure is wasteful []. Clinical decision support (CDS) is the set of prompts that highlight information that could change clinical care and is the answer to the gap in guideline-based care. CDS relies heavily on input from clinical staff who are up-to-date with guidelines. However, when done correctly, CDS has the potential to facilitate the delivery of high-quality care, improving the health of patients and avoiding unnecessary care [,].

Further, the Office of the National Coordinator for health care information technology is moving toward national knowledge-sharing for CDS prompts with the intent of eventually standardizing and classifying the importance of CDS. Together, these represent methods for ensuring that we are navigating our health care environment to provide succinct and concise care.

As the use of the EHR grows, data-sharing is being enhanced across networks in regional data exchange systems called health information exchanges (HIEs). With these, clinicians can share pertinent patient information, labs, and notes, as well as communicate directly about essential details. HIEs are the vehicle for creating seamless and secure data-sharing between networks.

Scholarly Approach to Clinical Practice

Use of the EHR facilitates the creation of patient databases and undertaking of pragmatic trials []. Through automation of the processes of patient screening, patients can be assessed for participation in pragmatic trials directly through diagnostic codes and demographic information, and messaged at home or asked in the office if they would consent to a study. For patients with the ability to access a computer, investigators have provided informed consent via online videos which can be viewed in the comfort of the patient's own home. Further, the addition of the computer to the clinical setting means that the networks for starting a pragmatic clinical trial are primed and ready. The data are already being collected in the system, and need only to be consented to appropriately and shared.

Professionalism and Humanism

The human price of the EHR is the distraction. CDS popups alert clinicians to a clinical need, an incorrect allergy warning may alarm while entering a prescription, and vital signs may flag a sepsis warning inappropriately. In the rapidly advancing world of the EHR with its increased distractions, it is imperative that clinicians maintain strong bonds with patients and stop the intrusion into clinician-patient relationships []. Best practices described in the Communication and interpersonal skills domain can support humanistic attitudes and professional behaviors in the face of the EHR.

The electronic interface of collection is transforming the field of Patient Reported Outcomes (PROs). Many patient portals are set up to ask and record PROs, which can seamlessly integrate into the patient's record. These PROs provide the ability to compare treatments and add patient-centered outcomes to the research. These data are being mobilized for use in decision making by groups like the Patient-Centered Outcomes Research Institute, the National Institutes of Health Collaboratory, and the American Society of Clinical Oncology.

Knowledge

The EHR brings a new way to interface with the knowledge that clinicians generate. Two of the most exciting changes to knowledge will be the discovery of new patterns and the incorporation of genetic data to patient records via "big data" methodology. In computing, big data refers to the use of extensive datasets that are analyzed computationally to reveal previously unknown trends and associations. With enough data points, data scientists suspect that computers will eventually be able to generate prediction models for individual cases based on repositories of old case data []. For example, a computerized model of hyponatremia correction in newborns has been created based on large numbers of observations by computers [].

On the forefront, data scientists and geneticists hope to incorporate patient genomic information into the EHR to help identify patterns and uncover new genetic connections. Once genetic data has been added to a patient's profile, the EHR could theoretically learn what gene loci predispose a patient to angioedema, interstitial lung disease, or any number of previously poorly understood disease states [,].

In a similar vein, the EHR can automate the reporting of adverse drug reactions to newly prescribed drugs. By reporting early trends in side effects from a new agent, EHRs might accelerate the detection of untoward side effects-like myocardial infarction associated with cyclooxygenase enzyme inhibitors (ie, COX-2) [].

Passion and Professional Satisfaction

The introduction of the EHR was fraught with underprepared EHR platforms and unrealistic expectations. Clinicians were initially confronted with decreased efficiency, increased burnout, and high turnover. Early on, physicians using computerized order entry and electronic documentation were 30% more likely to report burnout after controlling for other variables []. The only intervention that routinely improved satisfaction was employing scribes, which suggests that the only positive experience associated with the EHR was minimizing its use []. Further analysis into trends of physician satisfaction reveals that a more robust platform is more correlated with satisfaction. Clinical notes, diagnosis function, and off-site capability were all associated with higher satisfaction. There was a trend that younger physicians were more likely to be satisfied than their elder peers []. Finally, and most promising of all, physicians who had access to their EHR for at least two years were 2.78 times more likely to be satisfied with their EHR compared to those with less than two years' experience [].

Much of the literature in other domains touched on the EHR's potential to improve the interface with clinicians. Tools are being introduced to provide the clinician with medical references on demand for reading about developing medical data [-]. Finally, natural language processing is another advancing technology in which the computer attempts to interpret the clinician's intention when writing. As an example, when a clinician diagnoses a patient with pneumonia, the EHR could ask if it should open the pneumonia order set [,,]. This technology is still in its infancy and will likely require years to be ready for implementation. That said, it is one of the exciting transformations of the EHR that would produce a more fluent interface between the clinician and computer, allowing clinicians to focus back on the priority-patients.

Many articles published after our literature review cite the EHR as a significant factor in clinician burnout. For example, in their 2017 commentary, Shanafelt and colleagues [] discuss clinician burnout in the era of the EHR and its attendant clerical, regulatory, and workload implications. They outline the potential broader impacts of clinician burnout for the quality of care and the health care system at large. They also emphasize the importance of measures to address the increasing documentation burden especially performance and documentation of components of care that are justifiable for billing purposes alone and do not contribute meaningfully to the episode of care. A recent systematic review by West and colleagues [] highlights the evidence supporting both organizational and individual interventions to address burnout. Though beyond the scope of our review, clinician burnout is critical among factors that should be considered in the design, implementation, and use of the EHR going forward.

The EHR has completely transformed the clinical landscape. Its arrival and integration have been fraught with challenges, including having noticeably altered clinicians' communication with patients. That said, clinicians are gradually transforming their approach to, and interaction with, the EHR in a way that attempts to minimize distraction and enhance the quality of the clinician-patient connection again. Computerizing this work has effectively put clinicians "on the grid" and hopefully will continue to bring positive changes to the way that clinicians gather and interact with patient data to further enhance diagnostic acumen, scholarly approach to medicine, professionalism, knowledge, passion for clinical medicine, and the ability to negotiate the health care system to provide clinically excellent care for patients.

# data[].attributes.attachment_link.keywords[]
clinical excellence; electronic health record; electronic medical record; technology; communication skills; interpersonal skills; professionalism; humanism; patient care

# data[].attributes.attachment_link.title
Clinically Excellent Use of the Electronic Health Record: Review

# data[].attributes.attachment_link.description
Background: The transition to the electronic health record (EHR) has brought forth a rapid cultural shift in the world of medicine, presenting both new challenges as well as opportunities for improving health care. As clinicians work to adapt to the changes imposed by the EHR, identification of best practices around the clinically excellent use of the EHR is needed.

# data[].attributes.body
The TIGER initiative was created to help prepare nursing professionals for the evolution of the nursing workforce with the use of technology and information to improve the delivery of patient care.

# data[].attributes.body
Interoperability is the ability of different systems, networks, and devices to exchange, integrate, and collaboratively use data across multiple systems worldwide.

# data[].attributes.attachment_link.title
The Future of Clinical Interoperability in Healthcare

# data[].attributes.attachment_link.description
Researching the future of clinical interoperability in healthcare? Click to learn about healthcare data interoperability and how ForeSee Medical’s coding software positions into the health interoperability ecosystem.

# data[].attributes.attachment_link.provider_name
Public Health EHR Software | Champ Software

# data[].attributes.attachment_link.readable_text
We talked previously about Why Standardized Terminology is Important and now we'll talk about what Standardized Terminology in Healthcare really means for public health agencies, and their clients.

Interoperability

Standardized terminology facilitates interoperability.

Interoperability, at least in terms of healthcare, describes the ability of health information systems to work together, within and across agency borders, to create an Electronic Health Record (EHR).

However, interoperability is not just found in healthcare, but everywhere in the outside world.

Some examples include:

Phones - a phone can make and receive a phone call from any other phone, in any country, whether it's a traditional landline or the latest wireless smartphone.

Electrical items - everything from domestic consumer goods to industrial equipment is designed to have the same plug to fit into standard plug sockets

Web browsers - web browsers support HTML standards so that a web page will display as intended on standard and 3 rd-party browsers on a Windows PC and Mac, as well as tablets and smartphones, anywhere in the world.

In order for systems to be compatible and be able to talk to each other, they rely on standards, which provide the rules and guidelines.

These rules include things like:

The size and shape of a plug and socket

How data is transmitted and received

The format in which data is displayed

In healthcare, like in many other areas, these rules and guidelines are subject to a meticulous review process before they can be used in Electronic Health Records.

Federal initiatives, such as ARRA as well as many state and local requirements increasingly demand that EHR systems are interoperable with other systems.

"We are really trying very hard to stay in alignment with national requirements for the standardization of data and information exchange, so we are definitely sticking with the Omaha System and Nightingale Notes."

Pat Stewart, PHN, Administrator/Director, Des Moines Valley Health and Human Services

If you're a public health agency in the process of choosing a new EHR system, then you need to know that it is capable of communicating with state systems such as immunization registries and HIEs so that your agency can share data with the state and other health departments.

In terms of healthcare, this means that client information only needs to be entered into a system once, and that client data can be made available to caregivers where and when they need it.

Communication

Standardized terminology ensures that systems can talk to each other, but it also means that people can talk to each other too and mean the same thing.

Healthcare professionals in different departments or locations may use different words, terms or language to describe client conditions, or care options.

Think about this:

Somebody who goes to the doctor regularly, might say they are 'not feeling very well'
Which could mean they just have a bad cold

Somebody who never goes to the doctor, might say they are 'not feeling very well'
Wnd have a serious medical condition

Somebody with a long term medical condition such as arthritis or cancer, might say they are 'not feeling very well'
But have been bedridden for days but don't like to cause a fuss

What does something like 'generally healthy' mean when describing a client?

It's likely to mean different things to different people in different public health agencies and different locations

This means it's hard to compare like with like, or see what treatments have proved successful, or determine demographics.

Having a standardized terminology means using the same terms to describe the same things such as:

Medical conditions

Client health

Intervention success

Ensuring that there are standards when it comes to talking about conditions or types of clients, makes it much easier for all involved which leads to improved communication.

Now can you see how improved communication leads to improved care?

Shared data

Shared data can be extremely useful as it enables agencies to see what they can learn from other agencies.

Perhaps another agency:

Treats the same type of clients

Has similar demographics

Has common issues

Standardized terminology means that data from other systems can be combined, and the accuracy and relevancy of the data can be assured.

Knowing how agencies have tackled issues previously or what can be learnt from their experiences means that finding the answers can be much quicker, as the appropriate solution may already be in place in another agency. This obviously benefits clients, and saves time and money.

"The payoff for us with having an EHR built on the Omaha System and having that adopted by Southern Prairie Community Care, which is our ACO, is that we really can exchange data with other ACO members.

"We have a very fluid population in southwest Minnesota. Being able to have that continuity of care when clients move from one place to another, to be able to achieve The Triple Aim (improved population health, improved healthcare quality & experience, reduced per capita cost of healthcare) for the clients we serve, in addition to being able to monitor our own data and measure our own outcomes, is the end result we all desired.

"Nightingale Notes has allowed us to get in the sandbox and play with the other deliverers of healthcare: the clinics, hospitals, mental health facilities; they are all integral."

Pat Stewart, PHN, Administrator/Director, Des Moines Valley Health and Human Services

Evidence-based research and practice becomes much more valuable when professionals can describe the same healthcare needs and conditions using the exact same language.

Could you imagine how much more difficult this would be with paper forms and charts?

Shared pathways

Shared pathways may be a new term for you.

Put simply, pathways are standardized plans of care that define and reinforce best practices.

They are usually defined for a specific client population, such as children with special needs, or mothers with newborns, and designed to ensure that appropriate interventions are identified and performed.

Standardized terminology means that pathways can be shared across agencies, and so can be used to demonstrate that certain processes work, promote the best way to provide client care, and support the client, public health nurse and the administration. They can also help save time and save money too.

"Nightingale Notes has allowed us to get in the sandbox and play with the other deliverers of healthcare: the clinics, hospitals, mental health facilities."

Pat Stewart, PHN, Administrator/Director, Des Moines Valley Health and Human Services

Shared pathways mean that agencies facing similar issues can quickly see if the problem has already been solved.

Ability to evaluate

Standardized terminology also means that reporting is much more beneficial.

Being able to see client outcomes, and compare them to other clients, agencies and locations enables agencies to see what works, and why.

"We are able to get reports from Nightingale Notes that we can use as QI tools. We are able to see where we have missing data and where people aren't accurately using the documentation standards we've developed.

Every time we run a report, it gives us the tools we need to adjust our documentation to ensure it reflects what we are really doing."

Suzanne Plemmons, MN, RN, PHCNS-BC, Community Health Director, Kitsap Public Health District

In addition, it's also much easier to explain a course of action or why a certain shared pathway is the right choice to someone with:

No medical training

No knowledge of treatments, or

No understanding of expected outcomes

Better visibility

Standardized Terminology leads to better visibility.

Showing the value of the work done by community health professionals other than physicians can be difficult. To those looking to impress on policy makers or funding sources, being able to share success stories is vital.

"One crucial benefit we didn't have before using the Omaha System was the ability to use inter-professional teamwork to get a fuller picture.

Our epidemiologists are our data experts; they extract the data from our EHR, Nightingale Notes, but the nurses and social workers are the experts in practice and can help give the full picture."

Suzanne Plemmons, MN, RN, PHCNS-BC, Community Health Director, Kitsap Public Health District

Standardized terminology provides a 'common vocabulary', and a clear and precise way to demonstrate how well the client(s), agency, or other metrics like demographics, or location is performing.

So why Nightingale Notes?

Nightingale Notes EHR from Champ Software uses the internationally used Omaha System, which will be familiar to many healthcare professionals.

If you're not sure what the Omaha System is, or the benefits, then the Omaha System Overview is a good place to start.

Then learn more about what the Omaha System offers you.

We have built Nightingale Notes on top of the Omaha System, which means that it can use the full capability of the Omaha System.

Don't just take our word for it

Although we're bound to be biased, we think our Nightingale Notes EHR is the right choice for many public health clinics like yours across the country.

Here's just two more reasons why:

"Nightingale Notes and the Omaha System have allowed us to develop dashboards to provide to our board to show measurements: where are we with the clients we serve, what kind of improvements are we seeing, what kind of services are we providing, what kinds of increases are we seeing in the needs of the clients we serve."

Pat Stewart, PHN, Administrator/Director, Des Moines Valley Health and Human Services

"We are able to view our outcome data because we have an EHR, Nightingale Notes, that is based on the Omaha System. So we have the outcome rating scale to look at.

We can accurately describe our population: number, age, ethnicity, education, poverty level, etc. This gives us a good picture of who we're serving.

We can also accurately describe our services. We can determine the frequency of problems, which are our most common problems, and where we place our major focus."

Suzanne Plemmons, MN, RN, PHCNS-BC, Community Health Director, Kitsap Public Health District

Next steps

Download our eBook, "Standardized Terminology for EHRs" to learn more about what criteria standardized terminology should meet so that you know what to look for when choosing a standardized terminology for your EHR.

If you're ready to see how Nightingale Notes can help you, Contact Us at (507) 388-4141 today, and tell us what you need.

We're looking forward to hearing from you.

# data[].attributes.attachment_link.title
What does Standardized Terminology in Healthcare really mean?

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We talked previously about Why Standardized Terminology is Important and now we'll talk about what Standardized Terminology in Healthcare really means for public health agencies, and their clients. Interoperability Standardized terminology facilitates interoperability.

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The DIKW hierarchy is a pyramid shaped model that represents the relationship between data, information, knowledge and wisdom. Each level of the pyramid builds upon one another to ultimately reach the goal of wisdom.

# data[].attributes.attachment_link.title
Developing Understanding using the DIKW Pyramid

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The Health Information Technology for Economic and Clinical Health (HITECH Act), was created to improve quality health care, safety, and efficiency by promoting health information technology and electronic exchange of information.

"Meaningful use" is a part of the HITECH act created by the Centers for Medicare and Medicaid (CMS) incentive program that provides financial benefits to health care providers who use EHR technology and ensure that health information is shared and exchanged to improve patient care as well as benefit health care providers. Examples such as E-prescribing, computerized provider orders (x-ray, lab orders etc...), and the ability of patients to send secure messages to their health care team.

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Electronic Health Record Vs. Electronic Medical Record 

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Electronic Health Record Vs. Electronic Medical Record 

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Clinical decision support is a process that helps to enhance health-related decisions and actions with pertinent, organized clinical knowledge and patient information to provide safe patient care and the delivery of health care using technology.

Examples include patient's allergies to certain medications, drug-to-drug allergies, documentation templates, and links in the EHR related to current policies and procedures or knowledge references.

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The purpose of health information exchange is to allow health care professionals to securely share a patient's medical information electronically and provide efficient care. It is imperative for the health systems to be interoperable with each other to exchange patient information.

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Telehealth is the use of digital and communication technology to be able to access health care remotely and manage your healthcare.

Some examples include: video conferencing, patient portals, call centers, remote monitoring, continuing medical education, and transmission of still images.

# data[].attributes.attachment_link.title
Miguel tells his pandemic telehealth story

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$100,000 grant going toward telehealth expansion for children in rural NC schools

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Thousands of dollars in grant money will be going toward expanding COVID-19 response services for children in rural North Carolina schools. A press release says the Baxter International Foundation has awarded Children’s Health Fund (CHF) a $100,000 grant to continue its expansion of telehealth and COVID-19 response services for students in rural McDowell and Avery counties.

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Gamification is applied to health and wellness apps to aid with disease prevention, self-management, medication adherence, and telehealth programs.

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The Gamification of Healthcare: Emergence of the Digital Practitioner?

# data[].attributes.attachment_link.description
Gamification in healthcare is gaining momentum, with attempts to apply gaming principles to improve patient clinical outcomes. This trend establishes the need for a “digital practitioner” who channels these games, monitors progress, and selects the most appropriate ones for a given patient.

# data[].attributes.attachment_link.readable_text
Use Branch, formerly known as Ouchie, to help take control of your chronic pain through data, interactive wellness content, social support, and rewards. This platform was created for pain patients by pain patients with input from best-in-class doctors and pain specialists.

You've struggled with your pain alone for long enough. Better understand your symptoms, manage your condition, and perhaps even have some fun while doing so!

If you live with lower back pain, neck pain, knee pain, migraines, fibromyalgia, CRPS, Crohn's Disease, endometriosis, arthritis, lupus, nerve pain related to sciatica, or more, Branch's mission is to help you find a way to best self-manage.

The app is easy to use and offers:

- Tracking
Track your pain. Create your personalized journey by updating your branch profile with your specific pain type or condition. You can record changes to your pain level, medication usage, treatments, mood, sleep patterns, accomplishments, challenges, goals, and more. This app also integrates with Apple HealthKit, so you can seamlessly gain insights on how your pain impacts your movement and heart rate.
Set the privacy levels and share as much or as little as you want. You can now have this information ready to go at appointments with your healthcare team.

- Community Support
Share your story. Connect with others who are also affected by the pain or condition you're living with. Offer support and get supported. The branch community has thousands of patients and you're sure to find someone who can relate to what you're going through. Finding and interacting with others can lessen your suffering.

- Cognitive Behavioral Therapy (CBT)
Celebrate your accomplishments. branch regularly recognizes daily efforts! You can set targeted goals and work toward achieving them. The more you keep track of your progress and support others along the way, the more rewards you can earn.

- Educational Content
Learn more about pain management. branch provides you with valuable resources that can help you discover new ways to treat your pain. We partner with leaders in the pain management space and provide access to trusted, of-the-moment resources and articles, directing you to meditations, support groups, exercises and stretches, meal plans, current research and news, and more.

Join the Branch community and #donthurtalone.

Say hi: hello@branch.health
Get help: support@branch.health
Check us out on the web: www.upside.health/patients

Welcome to the completely redesigned and reimagined Branch 3.0!

- Our new look includes both light and dark modes making the app easier to use and navigate than ever before.
- Enjoy new ways of tracking your pain with our new weekly updates and monitoring your progress through the new weekly pain scores.
- Update your medications and treatments at your convenience and connect and cheer other community members through prompt-based exchanges.

*5.0.1 includes minor bug fixes and improvements.

Ouchie

First off, I have tried many apps like this one. And I love this one in comparison. However, it keeps crashing lately. Also, just a suggestion.. I have multiple health issues. And with each one is multiple symptoms. I might have symptoms with my Chronic Fatigue Syndrome one day and the next it's a different array of symptoms. I also have Fibromyalgia and Scoliosis. Once you create a journey, it seems you cannot go back and add more pain symptoms of any kind. You just update them? So that would mean creating many journeys titled CFS for me haha. Also, I created another journey while trying to figure out what to do, and I cannot delete it.
These are merely suggestions. I love your app so far. Thank you!

Fantastic!

Ouchie is the only chronic pain tracker I've found that has an amazing community, as well as such an easy to follow update system and great brief articles. They do have some bugs and changes coming to the app but they are so responsive to email! It took them less than a day to get back to an issue I was having, and they worked to get it fixed right away. I know they listen to feedback from the community, which means they make changes based on our needs. Absolutely amazing app!

Needs Massive Improvements!!

This app is an excellent concept. But it needs a massive overhaul and tons of improvements for it to be truly helpful and worth using. My pain is not simply pain from a temporary injury. And I think that most people who would gravitate to using an app like this would be in a similar situation as myself. It's part of my life as a result of lots of illnesses. But your app doesn't have the features to support this. Nor does it have the capabilities to really be a supportive community. Which is a shame.
Plus it's constantly crashing and as much as I want to use this app; it's becoming more and more frustrating to use. (And I've got enough frustration with everything else I deal with. ) So I'm deleting and not reinstalling this app until there are major improvements.

Hi, We're sorry you are so unsatisfied with this app experience. We would love the opportunity to speak with you about your concerns and see if there are ways we could help. Please reach out at anytime at hello@ouchie.com.

The developer, Ouchie, LLC, indicated that the app's privacy practices may include handling of data as described below. For more information, see the developer's privacy policy.

Data Linked to You

The following data may be collected and linked to your identity:

Data Not Linked to You

The following data may be collected but it is not linked to your identity:

Privacy practices may vary, for example, based on the features you use or your age. Learn More

# data[].attributes.attachment_link.description
‎Use Branch, formerly known as Ouchie, to help take control of your chronic pain through data, interactive wellness content, social support, and rewards. This platform was created for pain patients by pain patients with input from best-in-class doctors and pain specialists. You've struggled with yo...

# data[].attributes.attachment_link.readable_text
Roberta Pastorino, 1, ⁴ Corrado De Vito, ² Giuseppe Migliara, ² Katrin Glocker, ³ Ilona Binenbaum, 3, ⁴ Walter Ricciardi, 1, ⁵ and Stefania Boccia 1, ⁵

Abstract

Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

Introduction

Data have become an omnipresent concept in our daily lives with the routine collection, storage, processing and analysis of immense amount of data. This characteristic is cross-sectorial, ranging from the domain of machine learning and engineering, to economics and medicine.

Over the last decades, there has been growing enthusiasm of the potential usefulness of these massive quantities of data, called Big Data, in transforming personal care, clinical care and public health.1

An overview of Big Data definitions

Despite the term Big Data having become ubiquitous, there is no universal definition until now on the use of this term. According to McKinsey the term Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse.2 Gartner proposed the popular definition of Big Data with the '3V': Big Data is volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.3 According to other definitions, instead, Big Data is also characterized by a fourth dimension: Veracity, concerning the quality, authenticity, 'trustworthiness' of data. 4

Furthermore, there is an emergent discussion that 'Big' is no longer the defining parameter, but rather how 'smart' the data are, focusing on the insights that the volume of data can reasonably provide.5 This aspect is fundamental in the health sector. The potential of Big Data in improving health is enormous. However, its potential value is unlocked only when leveraged to drive decision making and, to enable such evidence-based decision making, it is necessary to have efficient processes to analyse and turn high volumes of data into meaningful insights.

A specific definition of what Big Data means for health research was proposed by the Health Directorate of the Directorate-General for Research and Innovation of the European Commission: Big Data in health encompasses high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.6

Big Data analytics for health systems

The complexity of Big Data analysis arises from combining different types of information, which are electronically captured. The last years have seen an explosion of new platforms, tools and methodologies in storing, and structuring such data, followed by a growth of publications on Big Data and Health ( figure 1 ). To date, we can collect data from electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, clinical trials, telemedicine, mobile apps, sensors and information on well-being, behaviour and socio-economic indicators.

Healthcare professionals can, therefore, benefit from an incredibly large amount of data. Recent reports suggest that US healthcare system alone stored around a total of 150 exabytes of data in 2011 with the perspective to reach the yottabyte.7

Starting with the collection of individual data elements and moving to the fusion of heterogeneous data coming from different sources, can reveal entirely new approaches to improve health by providing insights into the causes and outcomes of disease, better drug targets for precision medicine, and enhanced disease prediction and prevention.

In light of this, opportunities and potential are enormous for the benefit of patients and, in general, of the healthcare system.

The potential benefits of Big Data for healthcare in the European Union

Big Data is a sensitive issue for European Union (EU) institutions: the availability of health-related Big Data can have a positive impact on medical and healthcare functions. EU is faced with several changes that may affect the sustainability of its healthcare system. By 2025 life expectancy is expected to further increase, and this may result in more people living longer, but not necessarily in a healthy and active condition. This will put further pressure on Europe's healthcare costs and economic productivity.

In this context, the data sharing approach can improve outcomes for patients and evidence-based healthcare decision making as reported during the workshop on 'Digitalisation and Big Data: implications for the health sector', held on 19 June 2018 at the European Parliament.8

The use of Big Data in healthcare, in fact, can contribute at different levels as reported by the Study on Big Data in Public Health, Telemedicine and Healthcare of the European Commission:9 (i) increasing earlier diagnosis and the effectiveness and quality of treatments by the discovery of early signals and disease intervention, reduced probability of adverse reactions, etc. (sector 1); (ii) widening possibilities for prevention of diseases by identification of risk factors for disease (sector 2); (iii) improvement of pharmacovigilance and patient safety through the ability to make more informed medical decisions based on directly delivered information to the patients (sector 3); (iv) prediction of outcomes (sector 4).

In the next paragraphs, examples of EU initiatives in the four macro sectors are listed.

Big Data have the potential to yield new insights into risk factors that lead to disease. There is the possibility to engage with the individual patient more closely and import data from mobile health applications or connected devices. These data have the potential to be analysed and used in real-time to prompt changes in behaviours that can reduce health risks, reduce harmful environmental exposures or optimize health outcomes.

Finally, Big Data can help identify and promptly intervene on high-risk and high-cost patients.10 Effective ways of managing these data can therefore facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of healthcare to the specific needs of individuals.11 All these aspects should eventually lead to a reduction in inefficiency and improvement in cost containment for the healthcare system.

Examples of Big Data analytics for new knowledge generation, improved clinical care and streamlined public health surveillance are already available. Below we report a selection of best practices in Europe in the public health and oncology fields.

Big Data in public health

Efforts to improve the availability and accessibility of data in the EU appear to be driven mainly by socio-economic purposes. However, great importance is placed on the need of using data and new information and communication technology (ICT) in public health to improve quality of prevention and care.

In the next years, European health systems must respond more efficiently to the exponential increase of chronic patients identifying the most efficient interventions and releasing the full potential of ICT. The e-health platforms that many European governments are trying to implement can be effective in improving the management of chronic patients in the community setting by interfacing between different health professionals and specialists and with the patients. Furthermore, a large part of EU citizens uses the internet looking for information on health and access to health services.

Moreover, Big Data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, therefore, in a public health perspective, the gathering of a very large amount of data, constitute an inestimable resource to be used in epidemiological research, analysis of the health needs of the population, evaluation of population-based intervention and informed policy making.9

Many projects across the EU are exploring the potential of available Big Data in a wide range of fields. A systematic review published in 2016 from the European Commission identified at that time 10 priority projects on Big Data implemented in Europe that fall in the four macro sectors described above and are aimed to support the sustainability of health systems by addressing the improvement of the quality and effectiveness of treatment, fighting chronic disease and supporting healthy lifestyles.9 Some of these projects focussed on gathering a very wide range of data types, from GP records, hospitalizations, drug prescription and laboratory and radiology analyses in order to create comprehensive national data warehouses. Among them, the 'Decision Support for Health Policy and Planning: Methods, Models and Technologies based on Existing Health Care Data' (DEXHELPP), the eHealth project in Estonia, the ARNO observatory in Italy and the Hospital Episode Statistics in the United Kingdom. The DEXHELPP project (mainly regarding sectors 1 and 4) used routinely collected health data sources to analyse the performance of the health system, to forecast future changes and to simulate the application of policy and interventions. The Estonian eHealth project (mainly regarding sectors 1, 2 and 3) was more oriented toward the improvement of the quality and efficiency of health services, aiming to digitalize all the information and prescription of each patient. Furthermore, in order to facilitate data collection, they provide an environment called X-Road to which all healthcare providers can link while using their own ICT solutions. The ARNO project (mainly regarding sector 4), was committed to epidemiological research, giving the possibility of deep stratification of the general population. The Hospital Episode Statistics (mainly regarding sector 4) was in charge of the Secondary Uses Service that publishes reports and analyses to support the National Health Service in the delivery of healthcare services.

Beside these projects characterized by a comprehensive approach, other initiatives focused on specific conditions (e.g. chronic conditions, rare diseases and psychiatric disorders) are also available in the EU.9 Among them, the PASSI surveillance project (mainly regarding sectors 1 and 2) in Italy provides large amount of information on the life style of almost the 90% of the population, enabling to individuate specific targets to implement and assess public health actions. The Shared Care Platform (mainly regarding sectors 1 and 3) in Denmark is focused on chronic patients, aiming to harmonize the course of treatment among health and social care providers. The Spanish Rare Diseases Registries Research Network (SpainRDR) (mainly regarding sector 1) focuses on the development of clinical research on rare diseases, providing the harmonization and unification into one comprehensive platform of pre-existing databases and registries of rare diseases. CEPHOS-LINK (mainly regarding sectors 1, 2 and 4), is a platform dedicated to mental health that involves six EU countries. It is committed to collect data on psychiatric hospital admissions and re-admissions, with the aim of finding determinants of re-admissions and to harmonize the psychiatric care pathways across the EU.

In addition to the projects reported above, the EU's framework programmes for research and innovation funded a large number of initiatives on Big Data in public health. In table 1 , we list 11 projects funded from the EU between 2012 and 2018 with a contribution over €499.999 that are captured from the Cordis website (source: cordis.europa.eu).

Table 1

Project acronym

Full title

Coordinator country

Start date

End date

Big Data in oncology

Cancer is one of the major health problems affecting our society, a situation that is set to deteriorate globally as the population grows and ages. According to the State of Health in the EU reports, cancer is recognized as one of the major contributors to premature deaths in the EU. It also has an impact on the economy in terms of lower labour market participation and productivity. Advances in Big Data analytics are given cancer researchers powerful new ways to extract value from diverse sources of data.

These diverse sources include a huge amount of data for one patient. As cancer is a molecularly highly complex disease with an enormous intra- and intertumoral heterogeneity among different cancer types and even patients, the collection of various different types of omics data can provide a unique molecular profile for each patient and significantly aid oncologists in their effort for personalized therapy approaches.12

The approach of combining these sources of data is implemented in Comprehensive Cancer Centres (CCCs).13 One of 13 CCCs in Germany is the National Center of Tumor Diseases, where the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) trial is conducted (mainly regarding sector 1, 2 and 3). In a recent review article,14 this trial was illustrated as an example of a highly successful programme addressing the molecular profiling in cancer patients. Within the MASTER trial data relevant to diagnostic information of young patients with advanced-stage cancer diseases is collected by performance of whole exome or whole genome sequencing and RNA sequencing, analysed and discussed.

Another example for a success story given in the review is the INdividualized therapy FOr Relapsed Malignancies in children (INFORM) (mainly regarding sector 1, 2 and 3) registry which aims to address relapses of high-risk tumours in paediatric patients. Data from whole-exome, low-coverage whole-genome, RNA sequencing and microarray-based DNA methylation profiling are utilized to identify patient-specific therapeutic targets. The INFORM registry started as a national effort in Germany and has been extended with the participation of eight European countries, as well as Australia.

Next to the described projects there are many other initiatives which focus on the value of Big Data in oncology, the EU alone funds more than 90 projects working on this topic (projects with a funding over €499.999 are listed in table 2 ). A potential umbrella for bringing together national efforts such as those mentioned above at the European level is Cancer Core Europe.15

Table 2

Project acronym

Full title

Coordinator country

Start date

End date

Collaborations are of extremely high importance especially in the case of paediatric or other rare types of cancer, where the data collected for one patient is indeed enormous, however the number of patients a single centre can have access to is too low to obtain statistical power high enough to reach meaningful results. One of the main challenges of these collaborations is the access to the data as well as the opportunity to analyse the huge amount of data in an efficient way. Physicians, researchers and informatics experts can only benefit from collected data and expert knowledge when they get easy and intuitive access to own data or data of partners. For example, at the German Cancer Research Center, tools are developed to grant ways to access and analyse own data together with data from partners.

Additionally, international exemplary approaches of sharing data among partners or the public are done by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) which provide researchers with access to thousands of sequenced patients with different types of cancer. The availability of these data jointly with data by other partners has enabled large meta-analyses and machine learning algorithms, integrating different types of cancer that led to the identification of novel cancer driver genes that belong to specific pathways and can be possible therapy targets. Furthermore, there are a number of public databases that provide access to catalogues of mutations found to be involved in cancer, such as the Catalogue of Somatic Mutations in Cancer (COSMIC).

All these multiple sources of information combined and the establishment and support of CCCs across Europe offer the potential to increase the number of patients that can be offered molecular profiling and individualized treatment based on Big Data analysis.

Ethical and legal issues for the effective use of Big Data in healthcare

The use of Big Data in healthcare poses new ethical and legal challenges because of the personal nature of the information enclosed.

Ethical and legal challenges include the risk to compromise privacy, personal autonomy, as well as effects on public demand for transparency, trust and fairness while using Big Data.16

Data heterogeneity, data protection, analytical flows in analysing data and the lack of appropriate infrastructures for data storage emerged as critical technical and infrastructural issues that might endanger a Big-Data-driven healthcare.

Recently, Skovgaard et al.17 explored attitudes among people living in the EU toward the reuse of health data. The review indicates that the use of health data for purposes other than treatment enjoys support among people, as long as the data are expected to further the common good. In this context, the recent call reported in Science from a number of eminent scientists worldwide, for the unrestricted use of public genomic data, finds a fertile ground from the public.18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data.

The recent EU Data Protection Regulation (GDPR) tries to balance patients' privacy while ensuring patient's data can be shared for healthcare and research purposes.

On 23 January 2017 the Consultative Committee of the Council of Europe's data protection convention adopted 'Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data',19 the first document on privacy and Big Data which provides suggested measures for preventing any possible negative effects of the use of Big Data on human rights and freedoms.

Therefore, any government that uses Big Data in the health sector needs to establish affirmative policies to protect the health data of individuals, in terms of confidentiality, privacy and security, while ensuring that advancements in science can take advantage from the open use of data for the community well-being.

Conclusions

Big Data is beginning to revolutionize healthcare in Europe as it offers paths and solutions to improve health of individual persons as well as to improve the performance and outcomes of healthcare systems.

The implementation of precision medicine remains contingent on significant data acquisition and timely analysis to determine the most appropriate basis on which to tailor health optimization for individual prevention, diagnosis and disease treatment. Achieving effective and proportionate governance of health-related data will be essential for the future healthcare systems, and it requires that stakeholders collaborate and adapt the design and performance of their systems to reach the maximum innovative potential of information and innovation technology on health in the EU.

In this context, EU Member States should agree on international technical standards, taking also into account openness that is considered as the basic paradigm for digital transformation. Additionally, new approaches must be found for translating the vast amount of data into meaningful information that healthcare professionals can use. Further efforts must be made to make information for doctors and health professionals more accessible and understandable.

To achieve this, existing training and education programmes for healthcare professionals should integrate the issues of data handling in the curricula to ensure the development of the necessary skills and competencies. This is one of the objectives of the 'European network staff eXchange for integrAting precision health in the health Care sysTems' consortium (ExACT)20 project that aims to integrate precision health in European health systems by training a new generation of healthcare professionals across and outside of the EU.

In conclusion, we are living in fast-moving times, not least in terms of healthcare innovation. Whilst there are pressing needs for more personalized and sustainable health services, science and technology are offering a host of potentially invaluable new tools to deliver them. A cooperation at the EU level is needed to facilitate investments both in new technology and in the human capital, in order to guide citizens into this new frontier of human health and well-being where data are becoming a significant corporate asset, a vital economic input and the foundation of new business models.

Conflicts of interest: None declared.

References

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15. Eggermont AMM, Apolone G, Baumann M, et al. Cancer core Europe: a translational research infrastructure for a European mission on cancer. [[ Mol Oncol2019;13:521-7. PMC free article] [ PubMed] Google Scholar]

19. Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data Consultative Committee of the Convention for the Protection of Individuals with Regard to Automatic Processing of Personal Data (T-PD) Guidelines 1 on the Protection of Individuals with Regard to the Processing of Personal Data in a World of Big Data 2 Directorate General of Human Rights and Rule of Law. www.coe.int/data-protection (12 September 2019, date last accessed).

20. Boccia S, Pastorino R, Mariani M, Ricciardi W. The European network staff eXchange for integrAting precision health in the health Care sysTems (ExACT): a Marie Curie Research and Innovation Staff Exchange (RISE) project. Epidemiol Biostat Public Heal 2019;16. doi: 10.2427/13122. [ Google Scholar]

Articles from The European Journal of Public Health are provided here courtesy of Oxford University Press

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Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

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Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts ...

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Background

The rapidly increasing computerization of health care has produced benefits for clinicians and patients. Yet the integration of technology into medicine has been anything but smooth, and as newer and more sophisticated devices have been added to the clinical environment, clinicians' workflows have been affected in unanticipated ways. These fundamental shifts have resulted in new threats to patient safety-a cruel irony given that technological solutions have been promoted for many years as the most promising solution to medical errors.

Most health care technologies-be they computerized provider order entry systems (CPOE), smart intravenous infusion pumps, or cardiac monitoring devices-provide auditory or visual warnings to clinicians to prevent or act on unsafe situations. These warnings are well intended and in isolation may be helpful. However, in the current highly computerized clinical environment, an individual clinician interacts with many different alert-generating devices-meaning that every day, clinicians are on the receiving end of a staggering number of alerts. A 2014 study found that the physiologic monitors in an academic hospital's 66 adult intensive care unit beds generated more than 2 million alerts in one month, translating to 187 warnings per patient per day. In a study in the Veterans Affairs primary care, clinicians received more than 100 alerts per day.

The term alert fatigue describes how busy workers (in the case of health care, clinicians) become desensitized to safety alerts, and as a result ignore or fail to respond appropriately to such warnings. This phenomenon occurs because of the sheer number of alerts, and it is compounded by the fact that the vast majority of alerts generated by CPOE systems (and other health care technologies) are clinically inconsequential-meaning that in most cases, clinicians should ignore them. The problem is that clinicians then ignore both the bothersome, clinically meaningless alarms and the critical alerts that warn of impending serious patient harm. In essence, a proliferation of alerts that are intended to improve safety actually results in a paradoxical increase in the chance patients will be harmed. Although little discussed prior to the widespread use of electronic medical records, alert fatigue is now recognized as a major unintended consequence of the computerization of health care and a significant patient safety hazard.

Effect of alert fatigue on patient safety

Much of the literature on alert fatigue derives from studies of CPOE and clinical decision support systems, in which alerts are provided to warn of potentially harmful drug-drug interactions or incorrect medication doses. These studies consistently show three main findings:

Alerts are only modestly effective at best. A systematic review of computerized reminders found only minor improvements in targeted processes of care, and, while CPOE systems have been shown to markedly decrease prescribing errors, this can largely be ascribed to their ability to standardize drug doses, provide decision support, and eliminate errors from poor handwriting or incorrect transcriptions.

Alert fatigue is common. Clinicians generally override the vast majority of CPOE warnings, even "critical" alerts that warn of potentially severe harm. There is less literature on other types of warnings, but it is likely that rates of overriding or ignoring warnings in other settings are also high.

Alert fatigue increases with growing exposure to alerts and heavier use of CPOE systems. This finding is intuitive, but also raises the important implication that without system redesign, the safety consequences of alert fatigue will likely become more serious over time.

Although there are few studies that quantify adverse events related to alert fatigue, this phenomenon has been implicated as a significant cause in several high-profile errors. A 2011 Boston Globe investigation identified more than 200 deaths over a 5-year period attributable to failure to appropriately heed alarms from physiologic monitoring systems. A recent book by a prominent patient safety leader details how a hospitalized teenager received a 38-fold overdose of an antibiotic, in large part because the ordering physician had been advised by colleagues to "just ignore the alerts."

Current context

Although only recently recognized, alert fatigue (and the unintended consequences of the computerization of health care) has become a high profile patient safety issue. The Joint Commission released a sentinel event alert in April 2015 calling for health care organizations to pay close attention to information technology as a safety issue. In order to mitigate these consequences-including alert fatigue-The Joint Commission recommended improving the culture of safety by creating a shared sense of responsibility between users and developers, paying careful attention to safe IT implementation, and engaging leadership to provide oversight of health IT planning, implementation, and evaluation.

There is intense interest in developing specific methods to combat alert fatigue, but as yet, there is no consensus on the optimal approaches. Solving alert fatigue will require use of the principles of human factors engineering as well as those of informatics, as the problem fundamentally arises from both the technology itself and how busy human beings interact with the technology. A WebM&M provided several suggestions on how to minimize alert fatigue in CPOE systems:

Increase alert specificity by reducing or eliminating clinically inconsequential alerts.

Tailor alerts to patient characteristics and critical integrated clusters of physiologic indicators. For example, incorporate renal function test results into the alert system so that alerts for nephrotoxic medications are triggered only for patients at high risk.

Tier alerts according to severity. Warnings could be presented in different ways, in order to key clinicians to alerts that are more clinically consequential.

Make only high-level (severe) alerts interruptive.

Apply human factors principles when designing alerts (e.g., format, content, legibility, and color of alerts).

A quality improvement program in the Veterans Affairs system that incorporated the above principles and provided primary care physicians with education on managing alerts achieved a small but meaningful reduction in alerts.

One limitation to addressing alert fatigue pertains to the legal consequences of removing alerts. A recent commentary pointed out that system developers have thus far been reluctant to remove alerts for fear of being held liable if patients were harmed in the absence of a warning. There has been progress toward developing guidelines for high-severity alerts (which warn of significant risk of harm and should be retained) and low-severity alerts (less clinically consequential warnings, which could be made non-interruptive or removed entirely).

In solving the problem of alert fatigue, health care will need to look to examples from other industries. The aviation industry offers a sharp contrast to health care, because cockpit technology is rigorously designed to provide only highly consequential alerts to pilots, minimizing minor alerts in order to allow pilots to maintain situational awareness. This use of human factors engineering and deep attention to the experience of the end-user has thus far been lacking in health care technology design.

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Alert fatigue (alarm fatigue) is when healthcare workers become desensitized to electronic safety alerts. Alert fatigue can lead to medical errors and adverse events.

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Why the hospital of the future will be your own home

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Why the hospital of the future will be your own home

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Niels van Namen: Why the hospital of the future will be your own home

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Nobody likes going to the hospital, whether it's because of the logistical challenges of getting there, the astronomical costs of procedures or the alarming risks of complications like antibiotic-resistant bacteria. But what if we could get the lifesaving care provided by hospitals in our own homes?

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The HIPPA Privacy Rule creates national standards to protect individuals' medical records and personal health information.

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Health Information Privacy Laws in the Digital Age: HIPAA Doesn’t Apply

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The notion of health information privacy has evolved over time as the healthcare industry has embraced technology. Where once individuals were concerned about the privacy of their conversations and financial information, the digitization of health data…

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Experts say the increased hacking can be attributed to the health care industry's rapid move to digital, particularly amid the Covid-19 pandemic; an increase inremote work, which allows more avenues for attacks with employees using more personal devices; the financially lucrative information for cybercriminals in health care; and greaterawareness of attacks across the industry, thus more reporting.

And that threat is only growing, with President Joe Biden warning Monday of potential Russian cyberattacks against the U.S.

The widespread unauthorized access of this data raises significant privacy and security concerns for consumers and the industry - costing billions every year - and highlights some of the potential consequences as health care modernizes and information flows more seamlessly.

POLITICO analyzed six-plus years of data reported to HHS' Office for Civil Rights through Friday. HIPAA-covered organizations - includinghospitals, insurers and health care systems - must report breaches of protected health information affecting 500 or more people to the office, which posts those incidents publicly on what is known in the industry as the "Wall of Shame." Entities that are attacked are required to notify the people affected.

"Unfortunately, the industry is pretty much easy pickings, and they're hitting it because they're getting paid," said Mac McMillan, CEO of cybersecurity company CynergisTek. "It's [not] gonna slow down until we either get more serious about stopping it, or blocking it, or being more effective at it. From the cybercriminals' perspective, they're being successful, they're getting paid, why would they stop?"

Health care information is highly coveted by hackers, who can sell the data on the dark web or use it fraudulently, including to file false Medicare claims and for identity theft. An individual's health details can be worth more than a credit card, said Cindi Bassford, a partner at Guidehouse focusing on cybersecurity. And the fraudulent use of that information hurts health organizations' bottom lines: IBM found that each data breach cost health care organizations an average of $9.23 million in 2021, more than any industry.

The industry is also particularly vulnerable to ransomware because a potential disruption in care could threaten patients' lives, leaving many health care organizations feeling forced to pay ransoms.

The breaches reported to HHS are categorized by type, with hacking being by far the most prevalent. Other types of breaches reported include data theft - which could mean a stolen laptop - and unauthorized access, which could mean accidentally sending information to the wrong people.

Lee Kim, director of privacy and security at the Healthcare Information and Management Systems Society and a member of DHS' cybersecurity training panel, said that hacking has become easier for cybercriminals. They've been more successful as open source tools allow them to better target vulnerabilities.

And cybercriminals are collaborating with each other, often selling ransomware programs to others, forming a "cottage industry," said John Riggi, the national adviser for cybersecurity and risk at the American Hospital Association.

Not all of the 46 million-plus individuals impacted in 2021 will suffer significant consequences as a result of their information being compromised. Many won't realize it or understand what it means, said Carter Groome, CEO of health care risk management consulting firm First Health Advisory.

Some experts like Kirk Nahra, a privacy attorney at WilmerHale, argue that few people whose information is compromised are meaningfully affected. But others saythe exposure is considerable.

"If you believe that there's confidential medical information about you floating out there, that eats at you, because you really don't know the impact," said Harry Greenspun, partner and chief medical officer at Guidehouse, an advisory firm.

Genomic information could be damaging and potentially used in extortion schemes, Greenspun said. Cybercriminals could potentially use that data to find children a parent has never acknowledged or disclose that a politician might be predisposed to dementia.

The total number of reported breaches is also up because health care organizations have become more aware they are happening, experts say.

The move to remote work in recent years and most recently because of the Covid-19 pandemic is another reason, experts say. With remote work comes a lack of onsite IT support, Greenspun said. The need for companies to move quickly to stand up remote work has prompted many organizations to delay implementing security patches, he said.

Plus, many employees use their own personal devices for work, which can make businesses more vulnerable.

"You've got kids doing Zoom for school, everyone's doing all sorts of stuff on it," Greenspun said. "So it's a much less secure environment and many fewer controls. It opens the door for opportunistic folks."

The effort to let health care data flow more freely is also a factor, experts say.

For years, the industry has pushed to facilitate better health information sharing, which has been historically stymied with data siloed between health organizations. The 21st Century Cures Act, signed into law by former President Barack Obama, mandated that health care organizations share more data to enable better coordination of care.

"Because data starts to move around more freely, this is sort of the cost of doing business," said Aaron Maguregui, senior counsel at Foley & Lardner.

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Health data breaches swell in 2021 amid hacking surge, POLITICO analysis finds

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Experts say the increased hacking can be attributed to the health care industry's rapid move to digital, particularly amid the Covid-19 pandemic; an increase inremote work, which allows more avenues for attacks with employees using more personal devices; the financially lucrative information for cybercriminals in health care; and greaterawareness of attacks across the industry, thus more reporting.

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

The use of health data analytics allows for improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment and more informed decision-making. At the business level, it can lower costs, simplify internal operations and more.

What is health care data?

In order to discuss health data analytics and the role it plays in the health care sector, we must first understand the data that is being collected and analyzed. There is data being collected on the processes and procedures of the business side of health care, but there is also an enormous amount of health data being gathered, stored and analyzed.

Health data is any data relating to the health of an individual patient or collective population. This information is gathered from a series of health information systems (HIS) and other technological tools utilized by health care professionals, insurance companies and government organizations.

We are able to see a holistic view of each individual patient as well as trends tied to location, socioeconomic status, race and predisposition. The information being collected can be broken down into specific datasets that can then be analyzed.

There are a variety of tools and systems used to collect, store, share and analyze health data gathered through various means. These tools include:

Electronic Health Records (EHRs)

Personal Health Records (PHRs)

Electronic Prescription Services (E-prescribing)

Patient Portals

Master Patient Indexes (MPI)

Health-Related Smart Phone Apps and more

With digital data collection, there is more and more health care data to be analyzed every second. With the increase of electronic record keeping, applications and other electronic means of data collection and storage, there is a significant amount of data being collected in real time.

These data sets are so complex that traditional processing software and storage options cannot be used. Cloud storage is a necessity when dealing with "Big Data." Cloud storage is built to be secure, an absolute must when dealing with sensitive patient information. It is also very cost-efficient and has been helpful in lowering the increasing cost of health care.

Big Data Analytics and COVID-19

The impact COVID-19 has had on the health care industry is evident to anyone and everyone. You don't need to be plugged into the world of medicine to see what has been happening worldwide during this pandemic.

What most people don't see, though, is the impact COVID-19 has had on health care data analytics. "Big data tools have played an increasingly significant role in health care decision-making" says HealthITAnalytics. It is not just providers, but lawmakers and researchers who are turning to big data analytics and predictive models to help allocate resources, predict surges, improve patient care and outcomes and employ preventive measures.

Big data and health data analytics have played an integral role in the fight against COVID-19. The data is coming in at a near constant rate. Analyzing that health data has allowed for a better understanding of how to respond and treat patients.

This pandemic has resulted in an enormous surge of health data being recorded and manipulated allowing for bigger and better analytics. Unfortunately, we are also seeing that COVID-19 is " shining a harsh spotlight on health care's biggest issues. " There are a lot of obstacles when it comes to sharing health data across organizations and a distinct lack of standardization in the way that data is collected and analyzed.

This widespread problem was evident in the early days of the pandemic as conflicting and ever-changing information was being presented to the public. We saw a turn towards disbelief when it came to COVID-related information with many still believing misinformation and previously held beliefs on how this virus should be handled.

The spotlight that COVID-19 shined on these problems, however, will allow for them to be rectified. The providers, researchers and policymakers can learn from these mistakes and work towards a better, more standardized solution for big data in health care.

The Importance of Health Care Data Analytics

We can collect all the data we want, but it doesn't do any good if we don't know what to do with that information. We need a centralized, systematic way of collecting, storing and analyzing data so we can use it to our advantage.

The collection of data in health care settings has become more streamlined in recent years. Not only does the data help improve day-to-day operations and better patient care, it can now be better used in predictive modeling. Instead of just looking at historical information or current information, we can use both datasets to track trends and make predictions. We are now able to take preventive measures and track the outcomes.

The fee-for-service style of health care is becoming a thing of the past. There is a growing demand for patient-centric, or value-based, medical care which has led to a considerable shift towards predictive and preventive measures in regards to public health in recent years. Data makes this possible. Instead of simply treating the symptoms as they present, practitioners are able to identify patients at high risk of developing chronic illnesses and help to treat an issue before it surfaces. This helps to lower costs for the practitioner, insurance company and patient as the preventive treatment may help to stave off long-term issues and expensive hospitalizations.

If hospitalization is necessary, data analytics can help practitioners predict risks of infection, deterioration and readmission. This too can help lower costs and improve patient care outcomes.

Consider the impact this has had on the COVID-19 pandemic. The data being collected is analyzed in real time to understand the effects of the virus better and predict future trends so we may slow the spread and prevent future outbreaks.

Types of Health Care Analytics

Not every question can be answered by using the same analysis of the data. Through the use of different types of big data analytics, we can answer many of the questions being asked in health care settings.

Descriptive Analytics

Descriptive analytics uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with descriptive analytics.

Predictive Analytics

Predictive analytics uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with predictive analytics.

Prescriptive Analytics

Prescriptive analytics will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with prescriptive analytics.

How Data Analytics Leads to Health Care Solutions

Health care data management has the potential to lead to better care if used properly. With centralized datasets, there is immediate access to necessary information whenever and wherever it is needed. The addition of big data analytics improves efficiency on all fronts. Better data leads to better care.

Predictive Modeling

Predictive modeling is the process of analyzing current and historical data to predict future outcomes. Models use data mining, machine learning and statistics to identify patterns and predict outcomes. Predictive models built off of the health data being collected provide solutions on the macro and micro level.

The use of predictive analytics can alert health care professionals to potential risks. By analyzing behavioral data, we can predict treatment outcomes, potential risks for chronic illness and even predict risk of self-harm. The health data collected can be used for risk scoring, readmission prediction and prevention, predicting infection and deterioration and so much more at the individual patient level.

Predictive modeling can also be used on a much larger scale. Population health management is impossible without the use of these models. Outbreaks can be predicted, outcomes can be predicted, and in knowing what is to come, preventive measures can be taken.

Predictive modeling can even be used in administrative applications to increase efficiency and lower costs for all.

Reductions in Health Care Costs

Health care is expensive. And, those costs only continue to increase across the board. We are, however, seeing a shift from fee-for-service payment models to value-based care.

Through the use of predictive and prescriptive analytics, health care organizations and practitioners can get detailed models for lowering costs and patient risk. In addition to the patient-centric benefits mentioned above, health data analytics can reduce appointment no-shows, manage supply chain costs, prevent equipment breakdowns and decrease fraud.

The Growing Role of the Health Data Analyst

Health Data Analysts take the health care data being collected and use skills such as data acquisition, data management, data analysis and data interpretation to provide actionable insights. The role of big data in health care and the increasing need for improvement in the health care sector has led to a higher demand for qualified health data analysts.

The role of a health data analyst varies based on their position and industry of choice. Regardless of industry, a health data analyst will need to be able to work with, develop and evaluate health information technology (health IT) and other health information systems (HIS).

They may also be expected to:

Collect or mine data

Examine current and historical data

Evaluate raw data

Build predictive models

Automate reports

Required Skills for Health Data Analysts

Health data analysts solve problems for the organizations that hire them. In order to do so, they should be well versed in several essential skills:

Structured Query Language (SQL)

Excel

Statistical Programming

Data Visualization

You should also have certain soft skills as a health data analyst such as:

Analytical thinking

Creative thinking

Attention to detail

Strong communication skills

Schooling for Health Data Analysts

While it is not a requirement to become a health data analyst, many jobs will require a bachelor's degree in health information management (HIM) or a related field (data science, mathematics, health informatics, statistics, etc.).

A master's degree is, again, not a requirement to work as a health care data analyst, but it will qualify you for higher, more competitive positions. You can earn your master's in data analytics, health informatics, or another related field.

Where Will A Health Data Analyst Be Employed?

Numerous industries and organizations utilize health data and require the help of health data analysts. These include:

Hospitals, private or public

Government health care departments

Diagnostic centers

Health insurance companies

Health care consulting companies

Large medical practices

Health IT vendors

Other health organizations

Depending on the industry or role they choose to take, health care data analysts may work as part of larger teams or alone.

How Much Do Health Data Analysts Make?

Your salary, much like your role responsibilities, will vary based on the industry or organization for which you choose to work. Payscale lists the median salary for a health data analyst as $63,000 per year. The U.S. Bureau of Labor Statistics doesn't have an entry specific to Health Data Analysts, however, the Medical and Health Services Manager page does list "health information manager." The median pay listed here is closer to $100,000.

Take the Next Step in Your Career

The University of Pittsburgh School of Health and Rehabilitation Sciences offers a Master of Science in Health Informatics degree with a Data Science track. If you want to enter the field of health informatics or health data analytics or open up your career options, this master's degree can make you a competitive candidate.

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The Role of Data Analytics in Health Care

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Data Analytics is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement. Health care analytics uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

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Technical Evangelist Director, Tableau

Editor's note: This article was originally published on Information Age.

Data storytelling is a skill that has been around for decades. From Hans Rosling’s TED talks, to Cole Knafflic’s best-selling books, people have been rethinking how to use narrative structures to communicate data insights. The recommendations are simple and smart, and should be widely applied.

Good data storytelling is even more important today. Clearly conveying the meaning of data has never been more key than during the Covid-19 crisis. As we demand answers and assessments of the pandemic’s threats, the public have relied on people who can communicate both findings and uncertainties in understandable ways. Great data storytellers have demystified complex concepts such as exponential curves and logarithmic axes. The media have been adept at turning data into stories about trends and movements and the impacts of changing lockdowns. More recently we have moved from data about the disease to the impacts on our economy.

If data-driven responses to protecting public health and repairing our economies are putting data storytelling into the spotlight, how might this discipline evolve over the next handful of years and become more powerful and personalised? Covid-19 provides an opportunity to examine and rethink how we present, share and communicate data in our own work.

You and your colleagues share data in meetings or presentations all the time. What lessons can we take from the media’s analysis of Covid-19 into our virtual meetings?

Visualising your story through data

There is no faster way to lose audience engagement than by not presenting your data in way that can be understood by everyone in the room. Working remotely has exacerbated this challenge: screen fatigue means you have to be even more careful when crafting your communications.

Engaging tactics like ‘progressive reveals’ can help draw your audience’s attention to a data story. Imagine this process:

First, show and explain a single axis.

Then add one data point, explaining what it means.

Next, reveal more data points.

Then, add colour to show different categories.

Finally, consider adding animation to show change over time.

It’s like zooming out from a low level of detail to seeing the whole image. This highly engaging process adds one piece at a time, resulting in an audience that understands a chart. Don’t bamboozle them with the whole thing in one go.

Instead of presenting a singular conclusion, data storytelling methods should be about encouraging a wider and more open conversation about what the data insights are saying. Ask your audience to join in on the conversation; invite them to ask questions, critique and add in their point of view. The “Slow Reveal” work of school teacher Jenna Laib shows an approach adults should adopt. Data storytelling should generate conversations, not just a passive virtual shrug.

Converting analytics into insights

Modern self-service analytics and BI tools do not turn people into data storytellers. Rather, education and a new approach to promoting a data culture within organisations are needed. From the top down, more times than not, key stakeholders don’t fully understand the potential power of data storytelling and more importantly, what it can and can’t do. There is a common thought that data equals fact. Instead, there should be a mind shift towards data as a source of information and insight from which opinions are made.

Data storytelling should be part of the culture of an organisation. People can’t convert findings into valuable insights unless they understand the skills needed for communicating with data or in short, how to tell a story. If a data insight isn’t understood and isn’t compelling, it dies on the spot and no one will act on it. Why bother investing in tools and technology if you can’t use the insights to drive change?

There is not enough investment in the kind of training that encourages exploration and questioning of data. Training that does exist focuses on tools, not on how to share insight, interpret charts or make data-informed decisions. More effort needs to be put into considering how to build a data culture that encourages answers to a series of fruitful questions about data in society and business.

Speaking the language of data

We use data to find and share insights to persuade, inform and drive change. If we don’t do that effectively, what was the purpose of the investment in your analytics platform? Having a full stack of data collection, storage, processing, and analysis is of no use if you and your employees do not know how to use that data to have conversations or to make decisions. Data storytelling turns data from neutral fields in a database into opinions, arguments and insights.

Only by elevating data culture and educating on digital literacy across the board will we enable individuals to do more engaging and meaningful data storytelling, and in turn, to get a better return on your investment.

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The importance of data storytelling in the next decade of data

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Learn more on importance of data storytelling in the next decade of data.

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Monitoring Editor: Gunther Eysenbach

Reviewed by Saif Khairat, Karen Colorafi, and Thiago Cruvinel

Uba Backonja, RN, MS, PhD, 1, ² Sarah C Haynes, MPH, ³ and Katherine K Kim, MBA, MPH, PhD ³

Abstract

Background

There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.

Objective

The aim of the study was to assess the health care practitioners' perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care.

Methods

Medical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners' rankings of overview visualizations.

Results

Themes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data.

Conclusions

Visualizations guided by a framework and literature can support health care practitioners' understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care.

Keywords: cancer care facilities, informatics, patient-centered care, patient-generated health data, precision medicine, visualization

Introduction

Background

About 1 out of every 4 people in the United States lives with multiple chronic conditions (MCC), which include cancer and other conditions such as hypertension, diabetes, and heart disease [ 1]. About 40% of individuals with cancer also live with 1 or more chronic conditions [ 2]. The attendant complexity and potential confounding factors in managing treatment-intensive illnesses such as cancer among individuals with MCC beg for personalized care approaches. Personalized care, as described in precision medicine and personalized medicine, involves the collaboration between health care practitioners (HCPs) and patients and considers the perspectives, experiences, and health-related data of the person receiving care [ 3]. A recent Cochrane review suggests that personalized care can support the physical and psychosocial health of individuals with MCC [ 3], which can include those engaged in cancer care. Person-generated health data (PGHD) such as symptoms, medication use, physical activity, and health goals are important information for personalizing care of MCC. Organizations, including the United States Department of Health and Human Services [ 4, 5] and the Healthcare Information and Management Systems Society [ 6], recommend that PGHD be captured and used in decision making, care planning, and coordination. Furthermore, mobile technologies can support collection and access to PGHD to support the management of chronic conditions and personalized care [ 7- 12].

Although PGHD is quite varied, it may be collected at a different velocity and magnitude and in different and nonstandard formats [ 11]. To be useful to HCPs, individuals with MCC (including those engaged in cancer care), and caregivers, the cognitive burden of understanding and synthesizing this information must be minimized, and the opportunity to make good decisions must be maximized. This is particularly pertinent in the care of individuals with MCC engaging in cancer care, which is the scope of the research described in this manuscript. Coordination of cancer care involves many people-the individual engaging in cancer care, their HCPs, caregivers, family, and health care staff-who need to integrate the large amounts of data; these data are used to support understanding of an individual's health status, completion of health-related tasks, and care-related decision making [ 13].

Data visualization offers an approach to address this challenge of integrating and using large amounts of data collected to support the personalized care of individuals with MCC engaging in cancer care. Data visualizations are representations of data through the application of visual encodings (eg, position and color) [ 14- 17]. Visualization can leverage a user's cognitive strengths such as pattern recognition, and it helps them overcome their cognitive limitations including calculating and remembering strings of numbers. This can ultimately support understanding, task completion, and decision making. Visualizations that are designed with guidance from potential end users can be particularly valuable. User-centered design, within the field of human-centered design, is an approach to systems development that involves potential end users to understand their behaviors, tasks, and needs, among other factors [ 18, 19]. User-centered design can help the designer fully render the users' needs to improve the decisions that the visualizations are meant to support. User-centered design has been used previously in the development of visualizations [ 20, 21] including visualizations of patient-reported outcomes [ 22, 23].

The benefits of data visualizations-supporting understanding, task completion, and decision making-are especially critical in health-related settings such as cancer care facilities. In these settings, data are used to support important, critical, and time-restricted decisions that impact the health of individuals. Data visualizations are increasingly being incorporated into clinical care through integration of dashboards into health record systems. A recent review suggests that dashboards that integrate visualizations have the potential to support the cognitive work and decision making of intensive care unit clinicians [ 24]. However, there have been few examples of the effective use of person-generated data in personalized cancer care, particularly to enable shared decision making or care coordination [ 25]. A recent study found that patients with solid tumors who used a Web-based system to report symptoms experienced longer survival compared with usual care [ 26]. There is also little research on the value of visualizations within systems that integrate patient-generated data in cancer care. A pilot study conducted in Italy suggested that a dashboard that integrated remote monitoring and symptom-tracking data could be useful to HCPs and patients [ 27]. However, this work did not specifically evaluate the visualizations, and rationales for the visualization designs were not described. Therefore, a gap exists in the literature and practice regarding the development of informatics solutions that integrate and visualize person-generated data to support understanding and decision making regarding personalized cancer care among individuals with MCC.

Prior Work

OnPoint is a mobile app developed by the authors to support care coordination for individuals with MCC [ 25, 28, 29]. Previous studies by the authors' research group that were conducted to support heart failure and oncology patients resulted in the development of a mobile app that featured patient health goals, proactive symptom assessment, comprehensive medication list and medication reconciliation, and tracking for patient and caregiver use. On the basis of this prior work, researchers identified the need for visualization of data collected from the app and integrated into the electronic health record for communication to HCPs. This inspired the study described below.

Study Purpose

The purpose of this study was to assess HCPs' perceptions of and feedback regarding visualizations developed to support the personalized care of individuals with MCC engaging in cancer care.

Methods

Design, Time Frame, and Setting

This user-centered study took place from May to June 2017 at a large, urban academic medical center in the western United States.

Recruitment of Participants

We sought 8 medical doctors (MDs) and registered nurses (RNs); a sample size considered adequate for this type of qualitative user-centered design study [ 30- 32]. HCPs were either known to the researchers or identified by referral of HCPs who had participated in previous studies in the development of the OnPoint mobile app [ 25, 28, 29]. HCPs were eligible to participate if they were potential end users of a health information system to support cancer patients with MCC and HCPs currently providing care to cancer patients.

Visualization Development

Paper mock-ups of data visualizations were developed by UB (author) guided by Munzner Nested Model for Visualization Design [ 17]. We applied all model constructs (italicized in the following paragraphs) except for the algorithm design construct, which is suited for software development rather than for our focus on presoftware development.

The domain problem addressed was the need to support the care of individuals with MCC engaging in cancer care-the problem addressed by the OnPoint mobile app [ 25, 28, 29].

For operation and data type abstraction, we identified operations (tasks) and data types from the previous studies [ 25, 28, 29]. These data types were blood pressure (mm Hg), weight (kg), blood glucose levels (mg/dL), medication adherence (medications not taken at the time or frequency as prescribed), and symptoms from the Canadian Oncology Symptom Triage and Remote Support (COSTaRS) practice guides [ 33, 34].

For visual encoding and interaction design, mock-up encodings and designs were guided by literature on (1) visualizing data [ 14- 17, 35- 44]; (2) health data visualization [ 23, 45- 47]; and (3) a mock patient persona and scenario [ 48, 49]. The mock patient was a 56-year-old woman with uterine cancer and type 1 diabetes. She had recurrent work and personal constraints on Thursdays that interfered with taking medications as prescribed and managing her blood glucose levels. This patient also recently experienced weight gain due to fluid retention.

The following visualization mock-ups were created based on the nested model constructs: (1) a 4-week overview of medication adherence, blood pressure, weight, and blood glucose alone ( Figure 1 ) and with pop-ups providing details on demand ( Figure 2 ), (2) a 4-week view of line graphs indicating blood glucose readings alone ( Figure 3 ) and with a pop-up providing details about a specific blood glucose reading ( Figure 4 ), and (3) a 2-week view for blood pressure ( Figure 5 ) and with a pop-up providing details about a specific blood pressure reading ( Figure 6 ). In addition, 3 additional versions of the 4-week overviews of medication adherence, blood pressure, weight, and blood glucose were created ( Figures 7 -). Finally, a visualization of self-reported symptoms generated from the COSTaRS protocols was developed ( Figure 10 ).

Indication of target levels and ranges were shown with lines and colors. For example, in Figures 3 and , the gray bands indicate the target blood glucose range (80-130 mg/dL before a meal and <180 mg/dL 2 hours after the start of the meal [ 50]). In Figures 5 and , lines indicate (1) mock patient average systolic and diastolic blood pressure and target blood pressure (120/80 mm Hg [ 51]). Line graphs were purposefully chosen based on previous research indicating that position and color of dots on a chart (eg, individual glucose readings) can support quantitative interpretation [ 37- 39, 41]. Blue, orange, and yellow colors were used because they can be distinguished by individuals with color blindness [ 52- 54].

Measures and Procedures

This study applied user-centered design methods to engage potential end users early in the design process to understand their needs, priorities, and values. UB and SH conducted one-on-one interviews; participants were provided with pens, colored pencils, and markers and encouraged to draw and take notes on the paper visualizations as they reviewed them. They were also prompted throughout the interview to think aloud about what they saw and thought while reviewing the visualizations [ 55, 56].

Interviews were conducted in 3 steps: (1) going through the scenario during which they thought aloud while viewing paper mock-ups and responding to prompts (see Textbox 1 for the scenario), (2) ranking of alternative visualizations of overview mock-ups, and (3) providing overall impressions and usefulness for care coordination. This step sequence was used to guide the participants in using the visualization as they might in their clinical practice, which can help elicit reflection from participants [ 48].

1. You have arrived at the clinic before you start seeing patients. You want to see how your first patient of the day is doing. Her name is Deb Lee (age 56 years). Three weeks ago she completed chemotherapy for uterine cancer. She also has type 1 diabetes that was diagnosed in childhood.

2. You have done your typical chart review of Deb's clinical data using the clinic's electronic health record. After that chart review of electronic health record data, you want to see how Deb is doing at home. Recently the clinic started supporting patients in collecting data at home. Data include:

if the patient took her medications as prescribed

blood pressure (measured twice a day)

weight (taken once a day)

blood glucose (taken periodically throughout the day using a traditional finger-prick monitor)

3. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot on "This week," Thursday at 12:00 pm]

4. You'd like to see some specific information about a specific data point. You hover over this orange dot. [Researcher points to dot for weight on "This week," Thursday]

In step 1, participants first viewed an overview of visualization ( Figures 1 and ), then specific measures ( Figures 3 and ), then sought details on demand for those measures ( Figures 5 and ). This approach aligns with Shneiderman Visual Information Seeking Mantra of "Overview first, zoom and filter, then details-on-demand" [ 42]. While viewing the visualizations, participants explained what they saw, the impression of the patient, what additional information they would want included, what aspects supported their understanding of the patient, and what aspects were confusing.

In step 2, the researcher presented alternate versions of the 4-week overview ( Figures 7 -). Ordering of the versions was varied from participant to participant so that the order in which the versions were presented did not influence responses. While viewing the alternate versions, participants described their overall impressions, aspects they perceived as helpful, and aspects they perceived as confusing. Then, the then researcher gave the clinicians the original 4-week overview ( Figure 1 ) and asked participants to order this overview and the alternate versions from most helpful (ranked first) to least helpful (ranked fourth). Participants explained aloud their rationale for the ordering while sorting the versions.

In step 3, participants viewed the visualization that provided summaries of longitudinal patient-reported symptoms ( Figure 10 ). Again, the participants were asked to describe their overall impression of the visualization, aspects that they perceived as helpful in understanding the patient, and aspects they perceived as confusing in understanding the patient. At the end of the interview, the researchers asked whether and how the visualizations could help personalize the care and asked for suggestions.

Interviews were recorded, transcribed, and supplemented by field notes taken by researchers during the interviews. This study was approved by the affiliated institutional review board. All participants provided verbal consent after receiving and reading the study consent form. Participants were provided with a US $50 Amazon gift card for engaging in the interview.

Analyses

Transcripts of participant interviews were analyzed independently by 2 researchers using directed content analysis [ 57]. Categories used to guide the development of codes and the content analysis were developed from the data and refined as described below. At first, 2 researchers independently coded 3 randomly selected sections of transcripts from different participants to identify themes. The unit of analysis was a distinct idea within a participant's statement. After each of 3 rounds of independent coding, the researchers discussed how content was coded and any new themes that emerged for which codes needed to be added. After the third round, the researchers concurred that the codes adequately covered all themes, thus yielding the final codebook used for the remainder of transcript coding. Inter-rater reliability was calculated using an estimate of inter-rater reliability as described by Topf [ 58]. The agreement was 84.5%. The researchers discussed the discrepancies in coding and ultimately came to a consensus for a final inter-rater agreement of 100%. Researchers then independently coded the transcripts using NVivo (v11.4.1, QSR International, Melbourne, Australia). Rankings for preferences of the 4 versions of the overview visualization ( Figure 2 ; Figures 7 -) were tabulated, and mean ranks and SD were calculated for each overview version.

Results

Participants

A total of 8 HCPs participated in the interviews. Out of these, 4 were MDs; 1 was a pain management specialist (participant MD1), and 3 were oncologists (participants MD2, MD3, and MD4), and 4 were RNs (participants RN1, RN2, RN3, and RN4). Each participant provided care to cancer patients in the cancer center. Interviews lasted for approximately 25 to 42 min.

Themes

We identified 7 themes. Of these, 2 themes were not directly relevant to the visualizations; therefore, for the purpose of this paper, we report the following 5 themes: data elements, supportive elements, confusing elements, interpretation, and use of visualization. See Table 1 for descriptions and specific content regarding the themes.

Table 1

Theme

Description

Specific content regarding the theme

Data elements

Existing or future or potential data elements (eg, weight, blood pressure, medication adherence, and symptoms)

data useful for specific role in cancer care included weight and medications ( Figures 1 ,, and ) and the list of symptoms ( Figure 10 ) [MD a1 and RN b 2]

suggestions for additional data elements or information included additional measures such as heart rate [MD1 and RN4], temperature [RN1 and RN2], body mass index [MD3], lab values [RN1], meal times or what eaten [RN1, RN2, MD3, and MD4], physical activity engagement [RN1], sleep [MD1], swelling [MD2], symptoms that may be particular or specific to certain cancer therapies [MD2, RN2, and MD3]

patient-identified symptoms [RN2 and MD3]

a legend defining the symptoms [MD1]

meaning of the symptom scale ratings [MD1]

reasons for missed medications [MD2, MD3, and RN1]

values and description of the normal values and ranges for blood glucose and blood pressure [MD2]

goals of care [RN1]

treatments [RN1]

patient-reported reasons for abnormal values [RN2]

Supportive elements

Aspects of the visualization that supported the participant's understanding of the patient or that they thought were helpful

the color orange drew attention and helped participants find data points or patterns in the data that might require attention or indicate something abnormal more easily [MD1, MD3, MD4, RN1, RN3, and RN4]

calendar format and line graphs were helpful because clinicians are accustomed to them [MD1, RN1, RN2, and RN4], are used in practice [MD1 and RN1], and help see trends [MD1-MD4 and RN1-RN4]

having details on demand was helpful [MD1-MD4 and RN1-RN4] and does not to lead to overpopulation of data within the visualization [MD3]

suggestions for additional supportive elements included a pop-up with a numeric scale for normal ranges [MD4] or an indication of how the normal range was derived [MD3]

Confusing elements

Aspects of the visualization that the participant does not understand or finds confusing or unhelpful

Figure 1 : Unsure if blank spaces indicated that measurements were normal or not taken [MD2, MD3, and MD4]

Figure 7 : Circles confusing or overwhelming [MD2, MD4, RN2, RN3, and RN4]

Figure 8 : Unsure if the empty rectangles indicated normal readings or no measures taken that day [RN2, RN3, RN4, and MD4]

Figure 10 : Lack of clarity about the meaning of the numeric scale [RN3 and RN4]; unsure about threshold values for the numeric scores that led to values being highlighted in orange or whether the thresholds were the same across all symptoms [RN1]; miniature line graphs hard to interpret [MD1, MD2, MD4, and RN3]; and hard to understand, compare, and interpret the 2 different ways of quantifying symptoms [RN1, RN2, and RN4]

Interpretation

Information obtained or conclusions drawn about the patient from the visualization

Use of visualization

Ways the participant would or could use the visualization

visualizations could help clinicians gain an understanding of patient outside the clinic [MD1 and MD2], help them prepare specific questions regarding Thursdays [MD1-MD4, and RN4] and the cause of rapid weight gain [MD2], discuss the patient's symptom experiences or management [RN4 and MD4], make clinic time more efficient [MD1 and MD2]

visualizations could help patients remember health experiences [MD2] and empower patients to engage in health management [MD4]. Together clinicians and patients could use visualizations to support personalized cancer care [MD1-MD4, and RN1-RN4], facilitate interactions that focused more on the patient and their specific needs [MD1, MD2, RN2], and better guide conversations between clinicians and patients [RN3, RN4, MD3, and MD4]

Data Elements

There were several data elements within the visualizations that participants indicated as useful and supportive for their collaborations with patients. Participants noted the usefulness of measures and behaviors portrayed in Figures 1 - (eg, weight) and symptoms portrayed in Figure 10 (eg, pain levels). Certain data were noted as being less relevant given their roles (see Table 1 ). Participants indicated several additional data elements that could be helpful (see Table 1 ). This included symptoms identified by patients as relevant or important (n=2). For example, MD3 suggested patient-driven modifications of the symptoms list:

Can we plan another category that I want the patient to monitor? For example, if they're having bleeding-vaginal bleeding-can they use the category of vaginal bleeding to show me...?

Supportive Elements

Participants described several visual elements that supported their understanding of the patient. The elements included color, the calendar format, use of line graphs, and the ability to get "details on demand." All participants (n=8) stated that color supported their understanding of the data. Several indicated that icons helped differentiate graphs (n=4) and that the gray bands indicating normal ranges helped identify abnormal data points (n=4). Participants stated that the calendar format and line graphs were helpful because they are accustomed to them (n=4), are used in practice (n=2), and help them see trends (n=8):

[The line graph] gels with what practitioners could be used to...You don't want to have something too novel where you have some bizarre bar graph or some kind of odd, interesting pattern that's in 3D...that people haven't seen.

Three participants stated that the calendar format allowed them to see trends such as missed medications on Thursdays (RN1, RN2, and RN4):

...you see a pattern...that helps you identify that there is a regimen and that there's a schedule...it enables you to see something missed in the pattern by seeing the...[entire] month.

All participants (n=8) reacted positively to "details on demand" features such as hovering over a data point to get a pop-up with detailed information:

...it's good that it [the visualization] doesn't overpopulate the numbers right there and then because I mean I would just be overwhelmed with actual numbers, so this hovering thing is really good.

Confusing Elements

There were several visual elements in the 4-week overviews that participants found confusing. These included not understanding the meaning of blank spaces in Figure 1 (n=3) or Figure 8 (n=4), being confused or overwhelmed by the circles in Figure 7 (n=5), and lacking clarity about how the weight increase was calculated for Figures 1 and (n=3).

Several participants noted issues with interpreting visualizations for patient-reported symptoms ( Figure 10 ). This included lack of clarity about the meaning of the numeric scale, threshold values for the numeric scores that led to values being highlighted in orange, and whether the thresholds were the same across all symptoms (n=3). Participants also found it difficult to interpret the miniature line graphs (n=4) and to understand, compare, and interpret the 2 different ways of quantifying symptoms (n=3).

Interpretation

All participants (n=8) used visual elements to interpret data-finding patterns and viewing trends-to support understanding and decision making. They identified missed medications including the pattern of missed medications on Thursday using the calendar views (n=6). When seeing the pattern on Thursdays, participants were prompted to think about what could cause the patterns:

I wonder what's going on Thursdays that she always forgets the medications.

Identifying this pattern supported RN4's decision making to investigate the cause of the pattern:

I'm not sure why [she is missing her medications consecutively on Thursdays] so you would have to find out why is she missing her drugs on Thursday.

MD4 similarly described how using the visualizations supported understanding of the patient, reasoning about what might be causing abnormal readings, and ruling out potential causes:

...she just is not taking her medications for some reason...I can use the visualization and the colors to figure out some of her difficulties...[about] why she may not be adherent with her health [behaviors]and medications.

RN4 echoed how the visualizations could facilitate clinician reasoning, stating that a clinician could postulate about what might be causing the issues on Thursdays by bringing in symptoms and other data:

...you could really get I think a good picture.

Trends in line graphs helped participants interpret temporal glucose, blood pressure, and weight data (n=5). Participants reflected on several weeks' worth of data, comparing normal and abnormal points over time as well as visual elements indicating missed medication, to formulate whether they believe the patient had well-controlled blood glucose (n=2). For example, MD4 stated:

[if the medication being missed is] related to her insulin...and her blood sugars aren't controlled, [then] the general impression probably is that her blood sugars aren't controlled and her diabetes isn't controlled.

Participants viewed trends across different measures to infer relationships between measures. When viewing Figures 5 and , MD1, MD2, RN1, and RN4 viewed the line graph trend for blood pressure, guided by the gray bands indicating normal readings and color coding of the data points, to inform their reasoning about what might have caused blood pressure to stay within normal range but weight to increase (as indicated by the line graph trend and color-coded data points). Participants used the calendar structure to see if weekly patterns were consistent:

I also see that the same kind of pattern I've seen on this very day and the week before.

Use of Visualizations

Participants stated that visualizations could help gain an understanding of the patient outside the clinic and prepare specific questions to facilitate discussions with the patient about their self-management outside the clinic. This included discussing circumstances on Thursdays that made health management challenging (n=5), asking questions to help investigate the cause of rapid weight gain (n=1); and understanding symptom experiences or management (n=2). MD2 stated that the visualizations provide insights that "might open up a door to other questions that you normally wouldn't ask if you didn't [see trends]."

In addition, participants noted that visualizations could be helpful for patients. Having the visualizations during clinic visits could help patients remember symptoms; MD2 stated that the visualizations provide "another way to understand if the patient had any symptoms but forget to mention [them] to us or we forgot to ask [about them] during the clinic visit." Visualizations could also support patients feeling empowered; MD4 stated that a visualization tool could empower patients to engage in health behaviors such as taking medications and "be more aware of their symptoms" regularly.

All participants (n=8) mentioned the use of visualization to personalize visits with patients. Visualizations helped them identify issues specific to the patient that needed to be addressed, making interactions more focused on the patient and their specific needs (n=3). Visualizations could also give a clearer and focused picture of the patient, their health status, and needs that can better guide conversations and interactions with patients (n=4):

I think it would cause us to get a good picture, get a fast picture, evaluate that with the patient so we don't walk into an assumption, but dive a little bit quicker if we needed to.

MD3 stated that the visualization could personalize visits by bringing "attention to the important things" and focus "conversations with the patient directly to what's the issues or the problems that now I see [using the visualizations]..." RN4 stated that a benefit of the visualizations through personalizing visits with patients could be earlier identification of issues "rather than waiting until things [snowball]."

Ranking of Overview Visualizations

Participants varied in their preferences for the 4-week overviews. On average, participants ranked Figure 2 as most helpful (mean 1.8 [SD 1.2]) and Figure 7 least helpful (mean 3.9 [SD 0.4]; see Table 2 ).

Table 2

Figure version

MD a 1

MD2

MD3

MD4

RN b 1

RN2

RN3

RN4

Ranking c, mean (SD)

Discussion

Principal Findings and Comparison With Prior Work

In our user-centered design study, we found that MD and RN participants' understanding of physiological and symptom data for individuals with MCC engaged in cancer care was supported by visualizations we developed by applying a visualization framework and relevant literature. Both MD and RN participants found that various visual encodings such as color, and familiar presentation such as calendar formats and line graphs supported their interpretation of the presented data. This research used foundational knowledge in data visualization in a novel way to develop visualizations that both MD and RN participants found helpful and effective in integrating various health-related data. Our MD and RN participants also noted the potential usefulness of the visualization in supporting personalized care. This user-centered design study offers findings from potential clinician users of the output of patient-generated health data from the OnPoint app. These results will be used specifically to inform the integration of visualizations into OnPoint system in the next phase of the project.

We demonstrated that using paper prototypes early in the design process allowed us to engage potential end users, gather useful insights, and explore suggested changes efficiently before investing in technical resources to build the system. We found that there were similar reactions by MDs and RNs to the visualizations. For example, all MD and RN participants found bands representing normal ranges and details on demand to be helpful, and they perceived the visualizations as helpful in providing personalized care. Both MDs and RNs reported that color helped them pick out important data points and that the blood glucose graph dips and peaks helped them think about what might have caused abnormal readings. This suggests that careful design of visualizations that incorporate fundamental guidance of data visualization can support a wide range of users. Although personalization and customization of a visualization interface based on different users' needs could increase usefulness and usability [ 59- 61], it is possible to minimize the extent to which visualization versions differ when they are designed thoughtfully and purposefully.

Although we cannot assume that an interactive tool incorporating visualizations for use by individuals with MCC engaged in cancer care would necessitate the same design as a tool for clinicians, we do believe that this study offers a starting point for features to consider for users who are patients.

This study has the potential to inform the growing domain of research in integrating visualizations into informatics solutions that support personalized patient care [ 45, 62- 71]. This includes work on integrating home monitoring data for individuals engaging in cancer care [ 27] as well as health-related quality of life data for prostate cancer care [ 72].

Our study findings are congruent with guidance and best practices described in the visualization literature. Both MD and RN participants noted that color helped them see patterns in the data or pick out data that require attention, congruent with work described by Ware [ 44]. They also were able to use the line graphs to identify meaningful patterns in the data; this aligns with recommendations based on work by Cleveland and McGill [ 37- 39] and Mackinlay [ 41]. In particular, position rather than other data encodings (eg, area) supports more accurate interpretation of the data being represented by the encoding. Although we did not compare our line graphs with other graph types in this study, participants responded positively to our design choice that was guided by the data visualization literature.

Implications for Developing Health-Related Visualizations

On the basis of the findings from our study and the current literature of integrating visualizations into clinical care, we propose the following design recommendations: (1) applying knowledge from both health informatics and visualization domains to guide the creation of visualizations and (2) applying previous research can facilitate the development and testing of systems that integrate health data visualization. First, using Munzner Nested Model for Visualization Design [ 17] supported the design process by making it efficient, and it can facilitate integration of our findings with other research using the same model [ 73].

Second, providing users with options on how to visualize the same data may support use of the visualization. In our study, we found that among the 4-week calendar view options, there was not 1 that was consistently favored. Following 1 of Nielsen usability heuristics-flexibility and efficiency of use-visualization tool developers could allow users to customize how data and information are displayed [ 59- 61].

Finally, engaging potential end users early in the design ideation process was feasible and insightful. To minimize time and burden on HCP participants, researchers can carefully develop study protocols so they can maximize opportunities for participants to provide insights such as using mock patient personas and scenarios to guide eliciting feedback about mock-ups. Using personas and scenarios is advocated within the human-computer interaction domain [ 48, 49], and it has been used to support the development of health informatics tools [ 74, 75].

Limitations

There were limitations to our study. Our sample was limited to MDs and RNs; these visualizations could be useful to other HCPs supporting individuals with MCC engaging in cancer care, such as care coordinators, dieticians, pharmacists, and social workers. In addition, inputs from patients themselves and their family members must be collected to understand their informational needs. This study was conducted at a single cancer center; therefore, it has limited generalizability to other cancer centers. Mock-ups were on paper rather than on a device that a clinician would use to view visualizations in practice (eg, computer tablet and desktop computer). The data visualization literature used to guide the development of our mock-ups has not been tested extensively and empirically within health-related apps for cancer care; our work can support the building of evidence regarding the application of the visualization literature within this health domain. Although the visualizations are intended to be delivered via the electronic health record, we did not explicitly address how this might be accomplished. This work will be pursued in a future phase.

Conclusions

This study suggests that visualizations guided by a framework and literature can support HCPs' understanding of data to support personalized cancer care for individuals with MCC. By integrating health informatics and visualization literature and applying user-centered design methods, we were able to develop and elicit feedback on visualizations for health-related data including person-reported data. Future research could apply these methods toward the development of visualizations to support the care of other populations and the development of functional systems integrated into clinical and personal health care.

Acknowledgments

Funding for this study was provided in part by the Boston University-Center for the Future of Technology in Cancer Care (National Institute of Biomedical Imaging and Bioengineering award U54 CFTCC U54-EB015403-05) for authors UB, SCH, and KKK; and Gordon and Betty Moore Foundation grant to the Betty Irene Moore School of Nursing for SCH. The authors would like to thank Petra Backonja for her editorial support.

Abbreviations

COSTaRS

Canadian Oncology Symptom Triage and Remote Support

HCPs

health care practitioners

MCC

multiple chronic conditions

MD

medical doctor

PGHD

person-generated health data

RN

registered nurse

Footnotes

Conflicts of Interest: None declared.

References

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Articles from JMIR Human Factors are provided here courtesy of JMIR Publications Inc.

# data[].attributes.attachment_link.title
Data Visualizations to Support Health Practitioners' Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

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Monitoring Editor: Gunther Eysenbach Reviewed by Saif Khairat, Karen Colorafi, and Thiago Cruvinel Uba Backonja, RN, MS, PhD, 1, 2 Sarah C Haynes, MPH, 3 and Katherine K Kim, MBA, MPH, PhD 3 There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care.

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584,464 views | Anders Ynnerman • TEDxGöteborg 2010

Medical scans can produce thousands of images for a single patient in seconds, but how do doctors know what's useful? Scientific visualization expert Anders Ynnerman shows us sophisticated new tools -- like virtual autopsies -- for analyzing our data, and hints at the sci-fi-sounding medical technologies coming up next. This talk contains some graphic medical imagery.

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Medical scans can produce thousands of images for a single patient in seconds, but how do doctors know what's useful? Scientific visualization expert Anders Ynnerman shows us sophisticated new tools -- like virtual autopsies -- for analyzing our data, and hints at the sci-fi-sounding medical technologies coming up next. This talk contains some graphic medical imagery.

This talk was presented to a local audience at TEDxGöteborg 2010, an independent event. TED's editors chose to feature it for you.

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About the speaker

Anders Ynnerman studies the fundamental aspects of computer graphics and visualization, in particular large scale and complex data sets with a focus on volume rendering and multi-modal interaction.

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Anders Ynnerman: Visualizing the medical data explosion

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Medical scans can produce thousands of images for a single patient in seconds, but how do doctors know what's useful? Scientific visualization expert Anders Ynnerman shows us sophisticated new tools -- like virtual autopsies -- for analyzing our data, and hints at the sci-fi-sounding medical technologies coming up next.

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SUMMARY

Infographics are an innovative and engaging method of visually communicating information in a colourful and concise manner. There has been substantial interest in their use within the commercial and health sectors.

Healthcare professionals already use infographics to communicate medical information to their patients. A firm grasp of health information enhances patients' decision making capabilities and may improve the practitioner-patient relationship. Infographics can also be used at the population level for public health messages.

Because healthcare practitioners are likely to be consulted during their creation and use, this article aims to walk the reader through a number of different infographics in order to outline how they may be used to communicate healthcare information.

WHAT IS AN INFOGRAPHIC?

Infographics are an innovative and engaging method of visually communicating information in a colourful and concise manner. Evidence suggests that the representation of information in graphic format enhances our understanding and ability to make decisions1.

The concept of presenting information in graphical format is not new. A number of early examples of data visualisation exist from the eighteenth century; most notably the depiction of the deck layout of the British slave ship Brookes in 17882. This information poster (see figure 1 ) depicted views of the reprehensible living conditions suffered by slaves during their voyage to the colonies and was reproduced countless times by the abolitionist movement in Georgian Britain. The combination of text, images, and narrative arguably make this an early predecessor of the modern charticle, which is a type of infographic discussed later in this article.

Other noteworthy examples of data visualisation can be found in the nineteenth century. Florence Nightingale's graphical representation of causes of mortality amongst British forces fighting in the Crimean war illustrated forcefully that death from preventable disease outnumbered other causes including battle wounds3. This was achieved using a type of infographic called a polar area diagram, which is also discussed later in this article. Nightingale was incredibly successful at reaching her target audience whilst simultaneously triggering attitudinal change. These remain important aims in modern infographic design4. Another historical example of data visualisation came in the early twentieth century in the form of Otto Neurath's isotype picture language5. Neurath and his associates produced a vast library of communicative graphics addressing a variety of socio-political topics, many of which remain in use today. The philosophy of isotype picture language is elaborated upon during discussion of figure 3 .

Whereas Neurath principally designed his isotype language for people who had poor literacy or numeracy, the modern infographic is designed for quick assimilation to match the frenetic lives of a general public who are often unwilling or unable to spend time focusing on a piece of information long enough to understand it6. The commercial sector has enthusiastically taken up infographic design within the last decade or so as a means of communicating large quantities of otherwise difficult to assimilate information in a single easily understood and visually attractive product. There is evidence to suggest that an increasing demand for healthcare information exists, particularly amongst the 'baby boomer' generation who, as they age and develop greater risk of illness, are increasingly demanding access to good quality healthcare information in order to make decisions about their care7, for example, breast screening, prostate specific antigen testing, and decisions surrounding the risk-reward ratio of statin usage.

Infographics may be designed for mass consumption as a digital or print resource, for instance, via social media or traditional mediums such as newspapers. Alternatively, they may be used as a means of communicating with the public on a smaller scale (e.g. as a poster within a general practice). The use of infographics on a one-to-one scale has also been explored. A recent article by Harvard Medical School Professor David P Steensma evaluated the potential role of infographics in counselling patients affected by myelodysplastic syndrome8. Steensma concluded that the representation of outcomes in picture format might be a useful aid when counselling some of his patients about treatment complications. We anticipate that other similarly complex medical or surgical conditions could also benefit from this visually orientated approach to counselling.

Furthermore, infographics can be used to overcome language barriers. Communicating with patients who do not speak English as their first language has become increasingly common in Northern Ireland due to changing trends in migration9. A research team from Columbia University investigated the efficacy of infographics as a primary care communicative tool in the Washington Heights neighbourhood of Northern Manhattan10. Residents of this neighbourhood are majority Latino and overwhelmingly from the Dominican Republic, where the primary language is Spanish. Researchers found that successful infographics are those in which viewers could form symbolic or colour analogies. For example, blood pressure was visually linked with a traffic light system. High blood pressure was shown as a 'red light', therefore informing the viewer of 'danger' or the need to 'stop', whilst the opposite was true of healthy blood pressures and the colour green. Mildly elevated blood pressures were shown in amber to highlight need for caution (see figure 2 ). Similar analogies will likely also work well for communicating other results such as HbA1c or blood cholesterol.

Five different types of infographics will be discussed within this article and we hope to demonstrate how they may be used to communicate a variety of healthcare information.

ISOTYPE ARRAYS

An isotype array represents increasing quantity as multiple identically sized graphics5. Isotype arrays are a useful means of negating the effects of framing bias, a type of bias that relates to which part of a sentence one decides to place emphasis upon1. For instance, it would be factually correct to state to patients that 90% of women within figure 3 were side effect free after treatment. However, because favourable outcomes are exclusively focused upon and the 10% of women experiencing side effects are not mentioned, evidence suggests many patients will be unable to make a fully informed decision about their care1.

Figure 3 shows fifty female icons, five of which are coloured grey to show how many women experience treatment induced side effects whilst the remainder are coloured pink to demonstrate absence of side effects. Communicating this type of information with the aid of an isotype array allows viewers to judge their future risk by visualising positive and negative outcomes.

However, researchers found that a significant number of people who did not speak English as their first language had a tendency to misunderstand isotype arrays because they interpreted them too literally10. For instance, infographics advising on quantity of fruit to consume each week (with an apple icon depicting fruit) were interpreted to mean apples were the only fruit that should be eaten. Similarly, the presence of male or female stick figures may be mistaken as implying gender specific disease. Both can be addressed by clearly presenting context in infographic design and publication.

POLAR AREA DIAGRAMS

A polar area diagram (also known as a 'coxcomb graph') is a pie chart combined with some of the features of a stacked bar chart11. Unlike pie charts, where slices differ in angle and area but are equal in radius, each slice within a polar area diagram is equal in angle but different in area and radius. Individual slices are colourfully divided in a similar manner to stacked bars. Figure 4 represents fictional virology lab data for the year 2015.

Each slice represents one month of the year and is therefore 30° (360/12). Slice area differs based upon total number of viruses diagnosed each month. Slices are broken down into three different types of viruses, which are always represented in the same order from virus A (innermost) to virus C (outermost). Slices advance in a clockwise direction as we progress through a calendar year.

Three pieces of information are contained within this infographic. Firstly, virus A has a year round incidence and remains the most common of the three viruses. Secondly, there are two annual peaks in incidence (summer and winter). Finally, whilst there is a spike in the incidence of virus B during winter months, it is virus C that increases in incidence during summer months. If a more precise comparison of viral incidence is desired, then this graphic would benefit from inclusion of an explanatory key equating slice area with a numerical value.

WORD CLOUDS

A word cloud is a method of visualising text in a colourful and eye catching manner. Words are clustered together and ranked in importance according to size, prominence, and colour. An earlier draft of this article was analysed for the most frequently used words and after removing conjunctive words like 'and' or 'but', a list of remaining words were used to produce figure 5 . The importance of words such as 'infographic' and 'communicate' is emphasised by their size, location, and colour.

Less prominent words are smaller and coloured differently. Therefore, there is a hierarchy of differently sized and coloured words to highlight the logical order readers should approach our word cloud. In the healthcare setting, word clouds can be used as a means of summarising and linking users to sections of text such as those found within public health documents or clinical guidelines. They may also be used online to link web users to parts of a website based upon previous popular search terms or content.

HUB AND SPOKE DIAGRAMS

These involve the depiction of a central 'hub' surrounded by branching 'spokes' that connects the central topic to a number of peripheral topics. This form of infographic has become a powerful communicative tool in the explanation of a range of scientific phenomenon12. Figure 6 illustrates the link between a fictional type of cancer and a number of aetiological factors. Four short branching spokes surround the central hub 'cancer' linking it to four causative genes. These are the innermost, and by extension, most important risk factors for cancer in this diagram. Each individual gene is directly connected to cancer and capable of causing cancer in isolation. If we progress to the second layer from the central hub we can see two factors that are directly linked to the central hub ('smoking' and 'alcohol') whilst the other two are connected to genes found in the innermost layer ('obesity' and 'infection'). This is a method of representing direct vs. indirect causation. In this diagram smoking has the potential to directly cause cancer but infection will only lead to cancer in the presence of gene B or C (i.e. infection is connected to the central hub but only indirectly through other spokes).

The outermost layer of this diagram contains a number of letters of the Greek alphabet that do not have any attachment (direct or indirect) to the central hub. These are intended to represent risk factors that correlate with but do not cause cancer. This is probably one of the more difficult to understand infographics discussed within this article and, for this reason, it is primarily targeted at members of the public with an interest in healthcare science. However, with the aid of an accompanying text based explanation, hub and spoke diagrams could have a place in the visual explanation of multifactorial disease.

CHARTICLES

A charticle contains a combination of attractive colours, easily understood text, and key graphs or charts that aid in the delivery of a central message. They are excellent alternatives to traditional articles when it is anticipated that viewers may not have the time or patience to read and understand large quantities of text. Figure 7 is a charticle addressing the relationship between cancer and ageing. Colours are an important means of attracting viewers to the infographic and not only grab attention, but also alter mood and energy levels13. We found that pale colours worked best as a background whilst bold colours were suited to emphasising key pieces of information within the charticle. The key 'take home message' was purposefully placed centrally so as to draw the viewer's eye whilst information above and below gives additional detail for those interested.

Psychological studies suggest that terms such as 'doubtful', 'probable', or 'likely' were inconsistently understood amongst the general public14. For this reason, such terms are avoided in this charticle. In addition, it is possible to inadvertently introduce bias into a statement via something called denominator neglect. This is a form of cognitive bias that influences the viewer's ability to correctly understand fractions by focusing attention on the wrong aspect of the ratio, for example mistakenly identifying 1 in 3 as being smaller than 2 in 6. It is recommended that where ratios are used, consistent denominators should be employed15. The denominator is maintained as 100,000 to allow for an unbiased comparison of cancer risk as we age.

Pie charts are familiar and acceptable to the general public1. Therefore, one is included in figure 7 to visually emphasise the fact that most people living with cancer in Northern Ireland are over the age of 70. A Pareto chart is also included. This type of graph combines bars and lines on a double axis grid in order to communicate two different data trends. In this example, we demonstrate that as the population of Northern Ireland ages there is a simultaneous dramatic increase in number of cancer cases diagnosed and number of people living with cancer between the ages of 40 and 80. An explanatory key below reveals the meaning of data point size by representing increased cancer incidence as increasingly large red circles.

CONCLUSION

Infographics are a useful and innovative means of communication with great potential for making medical information more accessible to the general public. Five different infographics have been discussed and we welcome constructive feedback or creative ideas from interested readers.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest. The N.Ireland Cancer Registry is funded by the Public Health Agency Northern Ireland. Infographics were produced using a combination of Microsoft Word 2011, Infographics Word Edition 1.1, and Adobe Photoshop CC 2015. The mention of named computer software is not intended as an endorsement of these products.

Please note, this was the original reference given by the author regarding "Communicating with patients who do not speak English as their first language has become increasingly common in Northern Ireland due to changing trends in migration9."

The reference is exactly as NISRA recommend citations, unfortunately it doesn't provide access to the information as quoted by the author. The reference I've included is easily accessible and gives the same information, namely that Northern Ireland are undergoing changing trends in migration.

Obviously it is up to you, but the following reference doesn't actually do the job of referencing the material, unless the author wants to give a more exact reference.

REFERENCES

1. Spiegelhalter D, Pearson M, Short I. Visualizing uncertainty about the future. Science. 2011;333(6048):1393-400. [[ PubMed] Google Scholar]

2. Plymouth Chapter of the Society for Effecting the Abolition of the Slave Trade. Stowage of the British slave ship Brookes under the regulated slave trade act of 1788. England: Plymouth; 1788. Etching. [Held in the Library of Congress, Prints & Photographs Reading Room. LC-USZ62-34160.] Available at: [ http://www.loc.gov/pictures/item/98504459/ Last accessed February 2016. Google Scholar]

3. Nightingale F. Notes on matters affecting the health, efficiency, and hospital administration of the British Army: founded chiefly on the experience of the late war. London: Harrison; 1858. pp. 310-311. Available at: [ https://archive.org/details/b20387118 Last accessed February 2016. Google Scholar]

5. Burke C, Kindel E, Walker S. Isotype: design and contexts 1925-1971. London: Hyphen Press; 2013. [ Google Scholar]

7. LeRouge C, Van Slyke C, Seale D, Wright K. Baby boomers' adoption of consumer health technologies: survey on readiness and barriers. J Med Internet Res. 2014;16(9):e200. [[ PMC free article] [ PubMed] Google Scholar]

8. Steensma DP. Graphical representation of clinical outcomes for patients with myelodysplastic syndromes. Leuk Lymphoma. 2016;57(1):17-20. [[ PubMed] Google Scholar]

10. Arcia A, Suero-Tejada N, Bales ME, Merrill JA, Yoon S, Woollen J, et al. Sometimes More is More: Iterative Participatory Design of Infographics for Engagement of Community Members with Varying Levels of Health Literacy. J Am Med Inform Assoc. 2015;23(1):174-83. [[ PMC free article] [ PubMed] Google Scholar]

12. Macosko EZ, Pokala N, Feinberg EH, Chalasani SH, Butcher RA, Clardy J, et al. A hub-and-spoke circuit drives pheromone attraction and social behaviour in [[ C.elegans . Nature. 2009;458(7242):1171-5. PMC free article] [ PubMed] Google Scholar]

15. Lipkus IM. Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making. 2007;27(5):696-713. [[ PubMed] Google Scholar]

Articles from The Ulster Medical Journal are provided here courtesy of Ulster Medical Society

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Infographics: Healthcare Communication for the Digital Age

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Infographics are an innovative and engaging method of visually communicating information in a colourful and concise manner. There has been substantial interest in their use within the commercial and health sectors. Healthcare professionals already use infographics to communicate medical information to their patients.

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Journal of Ethics | American Medical Association

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Abstract

Forced sterilization has a long history in the United States. Because sterilization requires surgical skill, physicians have been the lone professionals engaging in this practice, although they were not the only experts or Americans to hold eugenic and neo-eugenic views. But physicians have also been whistleblowers who exposed sterilization abuse and led efforts to end it. The commentary on this case suggests that physicians should respond along those lines.

Case

During her outpatient obstetrics clinic, Dr P saw MT. MT first came to see Dr P at age 24. At that time, she had given birth to 5 children and had normal periods and hormone levels. Now 34 years old and recently released from prison, MT explains that she delivered her most recent child via cesarean delivery at the age of 30, while she was in prison. She wishes to become pregnant again but has been unable to do so for the past 10 months and now seeks Dr P's help.

MT's recent physical examinations and lab test results were normal, so Dr P thought her inability to conceive over the last 10 months might be due to infertility in her new husband. Testing revealed, however, that his sperm's motility and numbers were normal. Dr P then ordered a hysterosalpingography, a fluoroscopic x-ray of the uterus and fallopian tubes, to see if MT had developed an obstruction, perhaps from scarring after a chlamydia infection for which she was successfully treated as a teen. Dr P is shocked to see that the hysterosalpingography reveals that MT had undergone a tubal ligation.

Dr P has no good reason to believe that MT knows that she had a tubal ligation. When Dr P tells MT that she has had a tubal ligation, she sits silently for a moment and then begins to cry. "They made me sign something right before the Caesarean section. I was drugged. That's when they did it. What can we do now?"

Commentary

MT's story is a familiar one. Tens of thousands of Americans have been involuntarily sterilized since the turn of the 20th century. ¹ MT's experience represents the most recent iteration of this practice-forced sterilization of women in prison-but it is not unique. In its earliest form, sterilization in America followed eugenics, the so-called science of racial betterment. Scientists and clinicians engaged in this field of study, and many policymakers drew on eugenic research when calling for an end to immigration in the 1910s and 1920s. 2,3 Eugenicists offered segregation and sterilization as solutions to the so-called problem of the unfit and the social ills they supposedly embodied and reproduced. 2,3 But physicians have also been whistleblowers who exposed sterilization abuse and led efforts to end it.

Coercive Sterilization History

In 1927, the US Supreme Court upheld Virginia's eugenic sterilization law in the landmark case of Buck v Bell, in which Justice Oliver Wendell Holmes Jr famously declared, "Three generations of imbeciles are enough." ⁴ By 1942, 30 states had adopted similar eugenic statutes, ² and sterilization rates skyrocketed. Before Buck, a few hundred sterilizations were performed annually, and 3233 individuals had been sterilized for eugenic reasons. ² After Buck, between 2000 and 3000 Americans were eugenically sterilized every year. ² By 1941, at least 38 087 citizens had been eugenically sterilized. ² However, as historian Paul Lombardo makes clear, we do not know the actual number of eugenic sterilizations performed in the United States, because not all of them were recorded or officially counted. ¹ Most sterilizations were performed on women because women bore children and because, before Buck, men who had been sterilized in prison (to make them more docile) successfully charged that their vasectomies constituted cruel and unusual punishment. 2,5,6

American eugenicists, including health care professionals, positioned themselves as at the forefront of international eugenics. Worldwide, they participated in conferences, published articles, received honorary degrees, and corresponded with eugenicists in other countries-most notably, Germany. ⁷ Nazi policymakers based eugenic laws and programs on American legislation and courted support of leading American eugenicists from the 1930s through the early years of World War II. ⁷ Indeed, Nazi policymakers held up American, eugenics-informed immigration policy as a model of racial preservation. ⁷ The US immigration laws of 1921 and 1924 restricted immigration of persons with hereditary diseases and of persons from Eastern and Southern Europe, Africa, and Asia. Nazis scrutinized American eugenic research, including the infamous family studies of the Jukes and the Kallikaks that purported to prove the hereditability of so-called degenerate traits. Nazi scientists and policymakers used these studies to support precursors to the Final Solution. ⁷

Mutual admiration between German and American eugenicists led Americans drawing on Nazi propaganda to spread ideas of supposed racial betterment. ⁷ American eugenicists applauded German sterilization laws and "were the strongest foreign supporters of Nazi race policies." ⁷ California enacted more sterilization laws than any other US state and sterilized more people than any other US state, ⁷ and its eugenicists enthusiastically expressed support for sterilization laws in Germany. 3,7 Paul Popenoe and Eugene S. Gosney, president of the Human Betterment Foundation, the leading eugenics organization in the California, proved especially influential. Both men corresponded with German eugenicists and supported Nazi policies for years. ⁷

Neo-eugenics and Family Planning

In the 1930s, opposition from the Catholic Church, geneticists, and social scientists stressed the importance of environment rather than genetics-nurture as opposed to nature-in shaping individual behavior. This opposition weakened eugenics. 1,8 The number of eugenic sterilizations also declined when the US joined the allies in World War II, with surgeons now serving in the war. ² Sterilization did not rebound, ² but neither did it end.

Neo-eugenics. Ideas linking reproductive fitness to race continued to seep into clinical practice, public policy, and popular culture after World War II. ⁹ Neo-eugenics framed poverty, illegitimacy, and criminality as culturally-not genetically-reproduced by women of color and drove a new wave of forced sterilizations from the 1950s through the 1970s. ⁹ White postwar social anxiety about civil rights, overpopulation, Mexican and Puerto Rican immigration, and welfare state expansion under President Lyndon Baines Johnson's Great Society program undergirded these surgeries, which were nearly always performed by White male physicians on poor women of color. ⁹ In the 1950s, North Carolina targeted poor Black women for sterilization under its existing eugenics law 10,11 and "Mississippi appendectomies"-forced sterilizations of Black women who entered hospitals to undergo abdominal surgeries and, without their knowledge or informed consent, left without their uteruses-ran rampant across the South, especially in areas of intense civil rights action. Clinicians practiced White supremacy when they employed scalpels as tools of social control to demonstrate their resistance to Brown v Board of Education (1954) and other civil rights victories. 9,12

Loyalty to one's employer in following defined processes can strain one's loyalty to the patient.

Family planning. Transformation of sterilization from a eugenic procedure into a legitimate form of contraception in the late 1960s and early 1970s hid rising rates of forced sterilization among American women. ⁹ In 1960, the pill came on the market, boasting a nearly 100% success rate at preventing pregnancy. ¹³ Despite uncomfortable side effects, millions of American women rushed to have prescriptions filled. Intrauterine devices (IUDs) offered similar benefits a few years later. But the 1969 pill scare ¹³ and the 1974 Dalcon Shield IUD fiasco ¹⁴ revealed deadly risks associated with these methods of contraception. Unwilling to return to messy, less effective diaphragms and condoms, American women and men began requesting sterilization, which many clinicians viewed as a safe, effective method of limiting fertility, preventing unplanned pregnancy, and reducing overpopulation-a new concern in the 1960s. ⁹ Voluntary sterilization rates rose concurrently with federal family planning expansion in the late 1960s, unifying clinicians' belief in sterilization as cost-effective contraception that could alleviate effects of poverty for patients whom they saw as vulnerable-most notably, poor women of color who received state-funded health care. ⁹ When poor women were forcibly sterilized, their surgeries were included in voluntary sterilization tallies and rendered invisible. ⁹

Clinician Intention and Authority

Most clinicians acted with benevolent intentions but often crossed ethical lines when they recommended sterilization to patients, especially poor women of color who did not request them. In an era when informed consent was a topic of conversation among clinicians but not standard practice, thousands of Black, Latina, and Native American women entered public hospitals to give birth and were sterilized after delivery without their informed consent. Some clinicians deceived patients into believing tubal ligation was reversible; some pressured patients to verbally consent during labor or while under the influence of pain medication; and others failed to use interpreters when communicating with patients with limited English language proficiency and pushed those patients to signify their consent on forms they did not understand. ⁹

Occasionally, physicians were malicious, bullying pregnant and laboring women into sterilization by threatening to revoke care. 15,16,17,18,19 Some with malevolent intentions blamed poor women of color for a host of social problems and put their scalpels to punitive use. One North Carolina physician crudely stated: "A doctor who had just got his income tax back and realized it all went to welfare and unemployment was more likely to push it [sterilization] harder." ⁹ Most physicians who sterilized women in the 1960s and 1970s acted within outdated standards of medical paternalism. 9,20 These (mostly) White men viewed reproduction as a public rather than a private issue and believed their medical license gave them the social authority to determine who should bear children and who should not. They saw themselves as uniquely qualified to create social change on an individual level; sterilization required a surgeon's skill, after all.

By the 1950s, most physicians were no longer general practitioners making house calls among patients in their communitiesbut specialists caring for strangers in hospitals and clinics with new lifesaving technologies and drugs. ²¹ Despite this change, many physicians continued to believe in their capacity, authority, and right to make life-changing decisions for their patients. Their patients, however, were beginning to challenge this authority and demand a greater role in the provision of their own health care. 3,22,23,24 This clash of values eventually caused a reassessment of the patient-clinician relationship, but it was not soon enough to end the forced surgeries. ²⁵

Change, but Not Enough

In courtrooms and in the court of public opinion, victims of sterilization abuse bravely challenged the physicians who harmed them, forcing state and federal governments to confront the violence they suffered. The Department of Health, Education and Welfare (HEW) established federal guidelines in February 1974 that required informed consent and prohibited sterilization of persons under 18 years of age. ⁹ The National Welfare Rights Organization opposed the HEW guidelines in court based on their insufficiency to prevent abuse. ⁹ A subsequent court order prompted HEW to prohibit sterilization of persons unable to consent or under 21 years of age. ⁹ New York City established protections for public patients in 1975 that included a mandatory waiting period between consent and surgery. ⁹ In 1976, California adopted similar guidelines that applied to all public and private patients seeking sterilization. ⁹ State guidelines mandated waiting periods of between 14 days (California) to 30 days (New York) between consent and surgery, prohibited sterilization of minors, required consent in a patient's native language, and provided model consent forms. ⁹ In 1978, HEW adopted rules, including a 30-day wait mandate for all patients 21 years or older whose sterilizations were federally funded; continued a moratorium on sterilization persons unable to consent; and forbade sterilization of institutionalized persons unless their informed consent could be secured and reviewed by a board, a rule that sought to prevent the practice of sterilization among persons with intellectual and mental disabilities that dates back to the early 20th century. ⁹

These policy changes significantly reduced, but did not end, sterilization abuse in America. ¹⁸ Forced sterilization made national headlines again in the summer of 2013 when it was revealed that the California Department of Corrections had sterilized 148 female inmates between 2006 and 2010 without state approval-a significant violation because California outlawed forced sterilization in 1979 and in 1994 required clinical officials' approval for elective sterilization of prisoners. ²⁶ MT's sterilization fits the abuse profile of many women incarcerated during this period in California. It occurred in prison during a cesarean delivery and did not involve informed consent.

Whistleblowers and Advocates

A small group of physicians, many of whom were active in the civil rights movement, led efforts to end sterilization abuse. 22,27 One year after news broke of the infamous US Public Health Service Syphilis Study at Tuskegee, ⁹ the Southern Poverty Law Center revealed that its clients in Relf vs Weinberger, Minnie Lee (age 12) and Mary Alice (age 14) Relf were sterilized by a Montgomery, Alabama, family planning agency whose personnel believed these young Black girls-one of whom had a disability-needed permanent pregnancy prevention. ⁹ The Relf case made national headlines, and Senator Edward Kennedy invited the girls' parents to testify at US Senate hearings. ⁹

In the early 1970s, Bernard Rosenfeld, an obstetrics resident physician at the University of California, Los Angeles (UCLA), County Hospital, exposed sterilization abuse of Mexican and Mexican-American women at his institution. Rosenfeld spoke to reporters, contributed to a Health Research Group report that documented abuse, and wrote to civil rights organizations. 15,16 Attorneys Antonia Hernández of the Los Angeles Center for Law and Justice and Charles Nabarette took the case and filed a class action lawsuit against Edward Quilligan, chair of the UCLA Department of Obstetrics and Gynecology. Karen Benker, a UCLA medical student who also witnessed the abuse, testified on behalf of the plaintiffs. She and Rosenfeld directly and publicly challenged a leader in in their field, potentially imperiling their careers. ¹⁵ After his first year of residency, Rosenfeld's contract was not renewed. Rosenfeld believes this was retribution for his activism, although Quilligan cited unsatisfactory job performance. ²⁸ Psychiatrist Terry Kupers served as an expert witness and testified to the psychological damage that plaintiffs suffered as a consequence of their forced surgeries. When Madrigal v Quilligan was decided in favor of Quilligan in 1978, ²⁹ Kupers published an op-ed in the Los Angeles Times decrying the verdict. ³⁰

New York City adopted sterilization guidelines the same year Madrigal was filed. Helen Rodriguez-Trías spearheaded the collaboration between the New York City Health and Hospitals Corporation and the grassroots Committee to End Sterilization Abuse that resulted in the guidelines. Rodriguez-Trías was raised and trained in Puerto Rico. She later moved to New York City, where she practiced at Lincoln Hospital, and became a leader in in the women's health movement. Throughout her long career, which included serving as president of the American Public Health Association in 1993, Rodriguez-Trías promoted social change within medicine and on the streets. 31,32

In the early 1970s, Constance Pinkerton-Uri, a Native American physician, raised public awareness of sterilization abuse on Indian Reservations after a patient requested a "womb transplant." Pinkerton-Uri discovered that this patient, believing the surgery to be reversible, had undergone a hysterectomy 6 years earlier. Suspecting a long-term epidemic of similar sterilization abuse at Indian Health Service (IHS) sites nationwide, Uri consulted US Senator James Abourezk of South Dakota, who requested a federal investigation. 33,34,35 The subsequent Government Accounting Office report revealed that 3000 tubal ligations had been performed between 1973 and 1976, 36 of which violated the 1974 federal prohibition against sterilization of women under age 21. 36,37 Keenly aware of Native American women's reluctance to seek care via the IHS because of the risk of sterilization, Uri and 2 colleagues began to hold clinical appointments in a tepee. ⁹

Dr P's Response

Dr P could stay quiet and focus on helping her patient come to terms with a surgery to which she did not consent and that robbed her of her fertility. But the history just outlined suggests that MT's sterilization abuse was part of a broader nationwide pattern of systemic injustice. Dr P should inform state officials of her patient's situation and suggest a thorough review of former and current prisoners' health records in search of similar cases of abuse. Although Dr P cannot reverse MT's sterilization, she can follow in the footsteps of her socially conscious physician predecessors who helped reduce sterilization abuse in America.

Editor's Note

The case to which this commentary is a response was developed by the editorial staff. Background image of group portrait of Jewish women, most of whom had survived concentration camp, holding their babies. Some of these women were supposed to have been sterilized. 1946. United States Holocaust Memorial Museum, courtesy of American Jewish University, Los Angeles, California.

Citation

AMA J Ethics. 2021;23(1):E18-25.

DOI
10.1001/amajethics.2021.18.
Conflict of Interest Disclosure

The author(s) had no conflicts of interest to disclose.

The people and events in this case are fictional. Resemblance to real events or to names of people, living or dead, is entirely coincidental. The viewpoints expressed in this article are those of the author(s) and do not necessarily reflect the views and policies of the AMA.

Author Information

Rebecca Kluchin, PhD is the author of Fit to Be Tied: Sterilization and Reproductive Rights in America, 1950-19 80 (Rutgers University Press, 2009). She has published articles on abortion, sterilization, disability, and feminism and is currently writing Birth Rights: A History of Personhood and Reproductive Justice. She earned a BA in history and psychology from Washington University in St Louis and MA and PhD degrees in history from Carnegie Mellon University.

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How Should a Physician Respond to Discovering Her Patient Has Been Forcibly Sterilized?

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Forced sterilization has a long history in the United States. Because sterilization requires surgical skill, physicians have been the lone professionals engaging in this practice, although they were not the only experts or Americans to hold eugenic and neo-eugenic views. But physicians have also been whistleblowers who exposed sterilization abuse and led efforts to end it.

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The Future of Healthcare-Why Patients Should Care | Wendy Ward | TEDxUAMS

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Introducti﻿on

Digital healthcare may be defined as the use of advanced technologies including 'big data', artificial intelligence and genomics to achieve health objectives for patients. The key argument for its widespread adoption, qualitatively interpreted as the continued and consistent presence across healthcare systems for a patient, stems not from an arbitrary normative assumption of the need for digitalisation but rather the several benefits it presents across various types of healthcare systems. This includes, but is not limited to, improving access to healthcare services, enabling greater autonomous active patient participation, facilitating better clinical decision making as well as allowing for cost savings and greater efficiency in the delivery of health services and, more broadly, better clinical outcomes [ 1, 2, 3, 4]. The underlying technology of digital healthcare, however, necessitates a discussion into the philosophy underpinning the very nature of technology itself. Whilst some may argue that technologies enable the testing of hypotheses such that findings can be applied to pursue worthy goals, others counter that technologies make things appear which would not have existed without it (e.g. DNA or hypertension) [ 5, 6]. Svenaeus suggests that technology's influence in shaping our experiences can change the very views we possess concerning the goals we pursue [ 7]. It is this concept of displacement that is important to consider within Western medicine's patient-centred framework to ensure that human value is not disregarded in favour of the routinisation of novel digital health devices and procedures.

This tension between the benefits of technology in healthcare and the risks posed presents unique ethical challenges for widespread adoption. Fritz and Cox succinctly summarise the fundamental issue: whilst organisations balance financial as well as clinical obligations and physicians manage inter-patient demands for current and future generations, they all do so in the absence of a philosophical framework to face novel healthcare challenges fairly and justly for the different users of healthcare services [ 8]. To identify the ethical challenges facing adoption, it is necessary to explore the multitude that are currently faced, and to categorise them contextually at multiple levels. We will analyse the challenges to the main stakeholders of the healthcare process as defined by the WHO classification of main users of digital healthcare, i.e. the patient, physician/healthcare providers and organisations [ 9]. Many authors have identified the principles of Rawls as a foundational approach to enable justice for different healthcare users. Whilst it is beyond the scope of this essay to cover the voluminous literature on the application of Rawls' work to healthcare, before undertaking our discussion it is important to briefly summarise the key tenets of the Two Principles of Justice, as developed in A Theory of Justice [ 10]:

1. 1.
The Principle of Greatest Equal Liberty states that all citizens have an equal right to political liberty, freedom of speech and assembly, liberty of conscience, freedom of the person and expression, freedom of thought, and the right to hold personal property;

2. 2.
The Principle of Equality is further sub-divided into:

1. a.
The Difference Principle, which states that socioeconomic inequalities can be just if they are arranged such that the least advantaged members of society receive the greatest benefit from them;

2. b.
The Equal Opportunity Principle, where the arrangement of socioeconomic inequalities is such that they are attached to positions from which no individual is blocked from occupying, regardless of protected class characteristics.

Whilst Rawls did not include the right to health in his theory, academic discourse subsequently has subsequently attempted, and advanced, its coverage to cover the concept of health [ 11, 12, 13]. In the instance where these principles are found to be in conflict with each other, they can be ordered lexically: 1, 2b, 2a. In other words, The Greatest Equal Liberty Principle must be satisfied before The Equal Opportunity Principle which, in turn, must be satisfied before The Difference Principle. A principle cannot be applied till those of higher order are fully met or deemed inapplicable. In tandem with Rawls' principles, which we will use to analyse the problem of achieving a socially just distribution of resources, we further employ the remaining three principles of Beauchamp and Childress's four-principles ethical framework (respect for patient autonomy, beneficence in actions to place patient interests first, non-maleficence to 'do no harm' and justice for equitable distribution of resources) within the context of the intersection of technology with decision making in medicine and its practice, to stimulate debate regarding some of the ethical challenges faced [ 14]. A critical appreciation of said challenges can enable potential effective solutions.

Challenges for healthcare organisations

The cost of technology represents one of the largest challenges to organisations with respect to installation and subsequent maintenance [ 15]. The inability to directly assess intangible patient and cost benefits makes it difficult to value, and subsequently justify, the capital expenditure on new technology. Remote monitoring may be seen as a compromise compared to widespread installations. Proponents argue that reduced unnecessary hospital utilisation can act to lower healthcare costs, which may be especially pertinent in chronic disease management [ 16]. From a utilitarian viewpoint, this is beneficial in maximising effective outcomes for as many patients as possible. However, the literature suggests that remote monitoring does not act to decrease the cost for the vast majority [ 16]. Most studies suggest minimal, if any, cost savings, and sometimes increased costs; however, positive clinical outcomes were reported for the management of chronic conditions [ 17, 18, 19]. As per Rawls' Difference Principle, the literature is not supportive of cost reductions benefiting the majority of those who are most disadvantaged. It has been suggested that underserved populations may even see greater costs in remote monitoring [ 16, 18]. Thus, it is critical to prevent a false sense of organisational autonomy from arising which may, without prudent oversight, lead to a shift in the healthcare organisation's role from a patient-centred objective to a profit-centred agenda [ 16].

The concept of 'new' technology raises the issue of interoperability. Due to the timescales involved, integration and cross-access of data and usage between different systems or even different versions may be difficult [ 20, 21, 22]. Arguably, this stands in direct violation of the ethical obligation of organisations, derived from their fiduciary relationship with patients, to ensure that the incorporation of legacy systems meet integration standards [ 23].

Challenges for healthcare providers

Ethical challenges for physicians are thought to arise due to the "imprinting" of the necessity to provide the best possible medical care during medical training, to the extent that this implies the utilisation of the newest and most technologically advanced care [ 24]. With the advent of new healthcare technologies, this may lead to inappropriately rapid routinisation, defined as the social process which leads to the meaning of a new biomedical technology changing as participants become habituated to its use [ 24].

The role of the jurisdiction is important when economies of scale are considered as part of expansion. In the USA and the EU, doctors are required to obtain full licensing in the state in which the patients reside [ 25]. In the handful of cases so far, it has been deemed that healthcare professionals are 'travelling' to the patient for the virtual delivery of healthcare, thus a licence for that state is required [ 25]. When framed as per Beauchamp and Childress's. Four Principles framework, the moral obligation of 'Justice' to achieve equity in health resource allocation can arguably be translated into a legal duty to not deny patients access to the highest standard of care due to their geographic location, thereby introducing further complications [ 14, 26].

Despite healthcare technology presenting significant opportunities to improve healthcare outcomes, inappropriate uptake must be acted against. The technological imperative refers to the inevitability of new technology and its essential nature which denotes the need for acceptance for societal good. Within healthcare, the domination of technology arguably modulates the purpose of healthcare from preservation and restoration under responsible autonomy to death prevention such that the patient is both '(the) battlefield and (the) prize' [ 27]. This introduces the moral dilemma of using the patient themselves as a means to meet the end of death prevention, thereby violating one of the fundamental guiding principles of medical ethics: Autonomy. To explore this further, we must refer to the derivation of morality itself. Referring to the imperative implies an exigent choice with little room for manoeuvring. This occurs to the extent that it becomes unthinkable for doctors to not perform the treatment [ 24]. Thus, the moral imperative is derived from a social process-mediated origin such that, whilst novel technology is not mandated to be used, the imperative brings about normality as a result of routinisation and is indicative of how the social milieu has influenced the interpretation of its efficacy [ 24].

Challenges for patients

The implications of the moral imperative spill over into challenges presented to patients themselves. The need to ensure appropriate usage of expensive equipment (at least during the early adoption phase) and the moral imperative to utilise the new technology stands as a direct challenge to the need to preserve patient autonomy, as derived from the application of personalism to healthcare systems [ 28]. Their manifestation is their unique value which includes immanent properties such as free will [ 28, 29]. Their causative potential stands to be realised via the participation of the person in decisions centring them [ 28]. Thus, within the patient-centred framework of modern medicine, the challenge of the moral imperative presents a direct violation.

On the other hand, the position of technology does not need to be restricted to a false dichotomy of either the value-neutrality dictum or the value-ladenness thesis. The admittance of technological value does not automatically imply our subjugation to the technological imperative and reduction in autonomy. Cassell [ 5﻿] proposes that the value-ladenness of technology is derived from its intrinsic characteristics which correspond to the deficiencies of human nature. Thus, handling of the value-ladenness of technology necessitates governance of our values and control of ourselves, whilst the management of its ethical challenges require the management of our ethos [ 5].

Despite addressing the minutiae, it is critical to consider the fundamental issue of patient access to digital healthcare technology and services. From a utilitarian viewpoint with regards to if, for example, remote monitoring enables greater access; for the majority, we know that no such improvements in access have been reported [ 16, 17]. Similarly, the data into whether or not remote monitoring promotes greater access for the most disadvantaged provides a similar result in that the Rawlsian perspective is not fulfilled [ 16]. In the context of digital health technology provisions, one can consider the disadvantaged to be the underserved, such as remote and rural communities as well as specific groups within the population as per the socioeconomic divide. Remote communities often lack the necessary financial and technological infrastructure whilst the technology itself may be biased to those with adequate levels of technological literacy [ 30]. This is, of course, under the assumption that the relevant technology has been offered or delivered in the first place. Ethics by design, which imposes a moral obligation on developers to design with aforethought of ethical implications to prevent any discrimination of access, remains a critical consideration for the planning phase of digital healthcare technology [ 31].

Conclusions

In tackling the challenges faced, the prioritarian approach to distributive justice has been proposed as a potential framework. Prioritarianism focuses on prioritising 'the worse off in the distribution of advantages' [ 32]. In the current climate, global healthcare is dominated by utilitarian principles with the rational and adopted action being that which produces the greatest good (e.g. numbers vaccinated and cases averted) [ 33]. In achieving scale and distributing innovative technology, this can be in contradiction to reaching the most disadvantaged [ 33]. On the other hand, prioritarianism is 'sensitive to... (how) advantages are distributed among individuals rather than just concerned with the overall level or sum of them' [ 32]. Ensuring that the needs of the disadvantaged (those who are 'seldom heard') are addressed requires less a concern with scale and rather over-correction to ensure that the privileged early adopters who are easier to reach do not dominate the conversation [ 33]. The principles suggested by Winters et al. include identifying what constitutes disadvantage (such that the social value of digital healthcare can be maximised for the worst off), designing and implementing digital healthcare technologies so that it addresses the needs of the worst off through their participation, and incorporating reflections about ethical obligations as digital healthcare researchers and practitioners [ 33 ﻿].

Multiple challenges exist in this attempt to ensure the widespread and effective adoption of digital healthcare across different stakeholders and regions of a country as well as internationally. Even when Laennec introduced the stethoscope, it was accepted very gradually [ 34]. As per Rogers's seminal 'Diffusion of Innovations'[ 35], we are present in the early adopter phase, with the need, and gradual collection, of data (much so as the Philosophy of Science intended it) moving us along the hypothetical curve [ 36]. One would be correct to question whether, given the multitude of challenges faced, digital healthcare truly enables effective medical outcomes for the good of the patient. Within a Kuhnian context, the integration of digital healthcare acts to advance knowledge and further the physician's role as part of a paradigmatic shift, whilst the empowerment of the patient enhances the overall doctor-patient relationship such that widespread adoption of digital healthcare technology is a goal worth pursuing for the future of the field.

Acknowledgements

I would like to extend my thanks to Nahiyan Raidah for her support in preparing this manuscript.

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Authors and Affiliations

1. University of Cambridge School of Clinical Medicine, Cambridge, UK

Azmaeen Zarif

2. Gonville & Caius College, University of Cambridge, Trinity St, Cambridge, CB2 1TA, United Kingdom

Azmaeen Zarif

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Correspondence to Azmaeen Zarif.

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Zarif, A. The ethical challenges facing the widespread adoption of digital healthcare technology. Health Technol. 12, 175-179 (2022). https://doi.org/10.1007/s12553-021-00596-w
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DOI: https://doi.org/10.1007/s12553-021-00596-w

Keywords

Digital healthcare

Ethics

Prioritarianism

Rawls

Technology

Patients

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The ethical challenges facing the widespread adoption of digital healthcare technology - Health and Technology

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With the rise of telemedicine, wearable healthcare, and the greater leverage of 'big data' for precision medicine, various challenges present themselves to organisations, physicians, and patients. Beyond the practical, financial, and clinical considerations, we must not ignore the ethical imperative for fair and just applications to improve the field of healthcare for all.