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Indicate three types of influences on informatics.

Discuss how one might keep current with upcoming changes in informatics.

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>Define fundamental health informatics principles

> Describe key players involved in health informatics

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Health informatics is a field of study that uses science and engineering to develop technologies used to better facilitate more coherent and accessible patient records, in turn creating better outcomes for patients.
Although not discussed frequently, informatics surrounds every healthcare worker daily and knowing its importance is critical.

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How Virtual Reality Is Expanding Health Care

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Virtual reality technology isn't perfect yet, but it's being used to improve telemedicine, surgery, health care workers' training, and more.

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This scholarly article goes over one of the most used technologies we have that connects patients to healthcare providers. Since COVID, telehealth has exploded in the health informatics field and has done a relatively good job at continuation of care, medication/treatment follow through, and over-all better outcomes for the patients. Although this article states some great information as to why this technology is great, like everything there are some limitations. Some include access to technology, patient confidentiality (in relation to if anyone hacks your system), and over all inability to physically assess you patient such as palpating or listening via stethoscope.

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Telehealth includes health care services provided using audio and video technology. Telehealth was originally developed to provide basic care to rural and underserved patients. Higher rates of use of telehealth are now standard in many practices since…

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George Mason University College of Public Health

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Identify the key stakeholders that developed Informatics Competencies.

Describe goals of the TIGER Initiative.

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QSEN stands for Quality and Safety Education for Nurses. This is a 6 main topic system that reinvents how and what nurses know about delivering care to patients to ensure best possible outcomes.

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Pre-Licensure QSEN Faculty Development Workshops

The American Association of Colleges of Nursing (AACN) and the Robert Wood Johnson Foundation (RWJF) led a national effort designed to enhance the ability of nursing faculty to effectively develop quality and safety competencies among graduates of their programs. These significant contributions assured that nursing professionals are provided the knowledge and tools needed to deliver high quality, safe, effective, and patient-centered care. Beginning with Phase I, the Quality and Safety Education in Nursing (QSEN) project, led by Dr. Linda Cronenwett, identified the knowledge, skills, and attitudes (KSAs) that nurses must possess to deliver safe, effective care. In Phase II, QSEN faculty, a National Advisory Board, and 17 leaders from 11 professional organizations representing advanced nursing practice defined graduate-level quality and safety competencies for nursing education and proposed targets for the KSAs for each competency (Cronenwett, 2009). During Phase III of QSEN that spanned from 2009 to early 2012, AACN hosted eight faculty development institutes to better prepare nurse faculty in undergraduate programs to teach quality and safety content. Regional trainings were held in San Antonio, Washington, DC, Minneapolis, Phoenix, Chicago, Boston, Seattle, and Charleston, SC.

Graduate Faculty Development Workshop

This project aimed to address this issue/opportunity by implementing a series of pre-conference workshops, held in conjunction with national specialty or faculty conferences. The goal of the faculty development workshops was to prepare one or more faculty at a majority of the schools in the U.S. that offer graduate nursing education. Individuals who attended the workshops served as faculty champions for the redesign and implementation of graduate quality and safety curriculum. These conferences, supplemented by web based resources, provided guidance and support for faculty working to teach their colleagues about mechanisms used to prepare graduate-level nursing professionals for high quality and safe practice.

This initiative used a "train the trainer" approach to prepare a cadre of nursing faculty and nurse educators. Faculty and clinical educators who attended this workshop learned quality and safety content, as well as innovative techniques for teaching this content to students and other faculty and colleagues. Strategies for advancing quality and safety content at participants' home schools and organizations were covered, as well as ways to champion quality and safety to implement changes in curriculum. A highly thoughtful, rigorous, and scholarly approach was taken to create a framework for the modules to be used in the faculty/educator development workshops.

The workshops provided innovative curricular design and teaching strategies for faculty and specifically targeted faculty teaching in the full array of graduate nursing programs. The inclusion of hospital educators and practice partners in the workshop helped ensure alignment between the workshop content and needs of clinical sites; in addition, the participating clinical leaders enriched the workshop through discussion of case studies from the practice setting. Moreover, by encouraging the presence of practice partners, this project enhanced the clinical learning environment for students and potentially enhance the capacity of these clinical sites to engage in systematic quality and safety improvement efforts.

Pre-and post-institute evaluation was an important and essential component of this initiative. The impact of the workshop was measured in the following ways:

Number of additional faculty trained at the home institutions

Number of nursing courses with enhanced quality and safety content

Number of students who are enrolled in those modified nursing courses

Examples provided of key initiatives to improve quality and safety among clinical sites, partnered with graduate schools/colleges of nursing

** Attendees agreed to participate in pre-surveys and one-year post-institute surveys. Practice partners were surveyed one-year post-institute to determine how these clinical partners have utilized the resources. **

The American Association of Colleges of Nursing (AACN) is the national voice for university and four-year college education programs in nursing. Representing more than 820 member schools of nursing at public and private institutions nationwide, AACN's educational, research, governmental advocacy, data collection, publications, and other programs work to establish quality standards for bachelor's- and graduate-degree nursing education, assist deans and directors to implement those standards, influence the nursing profession to improve health care, and promote public support of baccalaureate and graduate nursing education, research, and practice.

The Robert Wood Johnson Foundation (RWJF) focuses on the pressing health and health care issues facing our country. As the nation's largest philanthropy devoted exclusively to improving the health and health care of all Americans, the Foundation works with a diverse group of organizations and individuals to identify solutions and achieve comprehensive, meaningful, and timely change. For more than 35 years the Foundation has brought experience, commitment, and a rigorous, balanced approach to the problems that affect the health and health care of those it serves. Helping Americans lead healthier lives and get the care they need, the Foundation expects to make a difference in our lifetime.

Barnsteiner, J., Disch, J., Johnson, J. McGuinn, K. (2013). Special Issue: Quality and Safety Education for Nurses. Journal of Professional Nursing, Volume 29, No. 2, March-April 2013.

Johnson, J., Drenkard, K, Emard, E, McGuinn, K. (2015). Leveraging Quality and Safety Education for Nurses to Enhance Graduate-Level Nursing Education and Practice. Nurse Educator, Volume 40, No. 6, May, 2015

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quality and safety education for nursing

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Quality and Safety Education for Nurses (QSEN)

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Pre-Licensure QSEN Faculty Development Workshops The American Association of Colleges of Nursing (AACN) and the Robert Wood Johnson Foundation (RWJF) led a national effort designed to enhance the ability of nursing faculty to effectively develop quality and safety competencies among graduates of their programs. These significant contributions assured that nursing professionals are provided the knowledge and tools needed to deliver high quality, safe, effective, and patient-centered care.

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The TIGER Initiative is a 3 year action plan with a 10 year vision on helping the nursing profession to adopt informatics, principles, and practices that make healthcare safer/effective and more patient centered. The 9 pillars include:

1. Standards and Interoperability

2. Healthcare Information Technology National Agenda/Policy

3. Informatics Competencies

4. Education and Faculty Development

5. Staff Development/Continuing Education

6. Usability/Clinical Application Design

7. Virtual Demonstration Center

8. Leadership Development

9. Consumer Empowerment/Personal Health Record

This initiative was put into place to make healthcare more streamlined. This means education and knowledge was used to put forth policies and standards to create better outcomes across the board.

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"NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. " As seen from the article above, stakeholders include patient, nurse, all interprofessional healthcare team members and other stakeholders (billing, insurance companies, the hospital itself).

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Abstract

Introduction

Globally, health information technologies are now being used by nurses in a variety of settings. However, nurse leaders often do not have the necessary strategic and tactical informatics competencies to adequately ensure their effective adoption and use. Although informatics competencies and competency frameworks have been identified and developed, to date there has not been review or consolidation of the work completed in this area. In order to address this gap, a scoping review is being conducted. The objectives of this scoping review are to: (1) identify informatics competencies of relevance to nurse leaders, (2) identify frameworks or theories that have been used to develop informatics competencies for nurse leaders, (3) identify instruments used to assess the informatics competencies of nurse leaders and (4) examine the psychometric properties of identified instruments.

Methods

Using the Arksey and O'Malley five-step framework, a literature review will be conducted using a scoping review methodology. The search will encompass academic and grey literature and include two primary databases and five secondary databases. Identified studies and documents will be independently screened for eligibility by two reviewers. Data from the studies and documents will be extracted and compiled into a chart. Qualitative data will be subject to a thematic analysis and descriptive statistics applied to the quantitative data.

Ethics and dissemination

Ethical approval was not required for this study. Results will be used to inform a future study designed to validate an instrument used to evaluate informatics competencies for nurse leaders within a Canadian context.

Keywords: nursing, competency, health informatics, medical informatics, nurse administrators

First known literature review that will consolidate nurse informatics competencies for nurse leaders.

Search strategy developed in collaboration with a research librarian well versed in research databases.

Only studies and documents published in English will be included.

Introduction

Health information technologies are being widely and rapidly implemented in healthcare settings around the world. As nurses represent the largest group of health professionals globally, nurses are also likely to be the largest group of users of these technologies. As such, there has been a concerted effort in several countries to ensure that nurses entering practice settings hold competencies that will allow them to use the technologies in meaningful and productive ways that can have benefits both to patients and professionals. These efforts have typically focused on the development and delivery of informatics competencies in basic nursing education at the entry-to-practice level, and often to refer to these as 'nursing informatics competencies'. Nursing informatics is defined by the American Nurses Association as: "the specialty that integrates nursing science with multiple information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information technology."

At present, it is likely that the majority of nurse leaders (nurses in non-clinical, administrative roles) have neither received education inclusive of entry-to-practice informatics competencies nor have been provided with formal opportunities to develop those competencies within their practice settings. Therefore, nurse leaders today may not hold the informatics knowledge, let alone the more advanced competencies required to be able to effectively provide strategic and tactical leadership related to the adoption and use of information and communications technologies (ICTs). Thus, there are now efforts underway to identify informatics competencies of relevance for nurse leaders, but to date there is no known review or consolidation of the work done in this area.

In order to address this gap, a scoping review will be conducted. The objectives of the scoping review are to: (1) identify informatics competencies of relevance to nurse leaders, (2) identify frameworks or theories that have been used to develop informatics competencies for nurse leaders, (3) identify instruments that can be used to assess the informatics competencies of nurse leaders and (4) examine the psychometric properties of identified instruments.

Methods and analysis

The following scoping review protocol was developed using the five-step framework outlined by Arksey and O'Malley, and advanced by Levac and colleagues. The framework includes (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) extracting the collected data and (5) reporting the results. These steps have been followed in the development of the scoping review protocol and are detailed in subsequent sections. Ethical approval was not required for this study. The authors have used both the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist and A Measurement Tool to Assess Systematic Reviews in the development of this protocol. The scoping review searches will be completed by the end of 2017, and subsequent analysis of the literature search findings will be completed in early 2018.

Step 1: identifying the research question

Four research questions were developed that reflect the objectives of the scoping review. These research questions are as follows:

1. What nurse leader informatics competencies have been identified in the literature?

2. What frameworks or theories have been used in the development of informatics competencies for nurse leaders?

3. What instruments have been developed and are being used to assess the informatics competencies for nurse leaders?

4. What are the psychometric properties of the instruments identified for the assessment of nurse leaders' informatics competencies?

These questions are being asked to solicit answers that will provide readers with a comprehensive understanding of the work that has been done to date in the development of informatics competencies for nurse leaders.

Step 2: identifying relevant studies

Eligibility criteria

All research, theoretical and opinion papers will be included within this scoping review of informatics competencies for nurse leaders. The studies and documents assessed will not be limited to their date of publication, country of origin or setting. Only studies published in English will be included. Informatics competencies described in the included studies and documents must be directed at nurses within leadership positions. Nurse leaders are defined as nurses who do not typically provide direct care to patients and are usually in formal leadership roles with responsibility for both human and financial resource management in healthcare organisations. The job titles of nurses in leadership positions include, but are not limited to, nurse educator, nurse manager, nursing case manager, nurse administrator, nurse executive, clinical nurse leader, director, chief nursing officer, chief nurse executive and vice-president of care services. Documents that focus on informatics competencies directed at direct care nurses or nurse informaticians will not be included.

Search strategy

With the aid of an experienced research librarian, a search strategy has been developed using two primary electronic databases: (1) the Cumulative Index to Nursing and Allied Health Literature (CINAHL) and (2) Medline (Ovid interface); and five secondary electronic databases: (1) Education Resources Information Center, (2) Web of Science, (3) PsycINFO, (4) Proquest Dissertations and Theses Database and (5) Theses Canada. The search will not be limited to published peer-reviewed literature, but will also include unpublished grey literature pertaining to informatics competencies for nurse leaders. Grey literature will be searched using Google and the Canadian Agency for Drugs and Technologies in Health search tool. In addition, experts in the field of informatics and nursing will be consulted. These strategies will ensure that competency documents developed by nursing organisations are identified in the search of grey literature. To ensure accuracy when using the search strategy for grey literature, the Peer Review Election Search Strategies Checklist will be used. Peer-reviewed papers, research reports and grey literature that identify an approach for defining and/or measuring informatics leadership competencies for nurse leaders or domains of informatics competencies for nurse leaders will be of primary focus within this study.

A search strategy has been developed for Medline (Ovid interface) and is shown in online supplementary appendix A. This search strategy will be modified as required so that it can be used with CINAHL and the secondary electronic databases. There will be no limitations to the date or year the article was published, nor will there be limitations pertaining to the geographic location of the publication.

Once the initial searches of the databases and grey literature are completed, the research team will review the first 100 citations from each source to ensure that the most appropriate search terms and strategy are used. If required, the search strategy will be modified to improve the quantity and relevance of the findings.

Step 3: study selection

Following execution of the search strategy, the first stage of the selection process will take place during which titles and abstracts of publications will be read independently by two members of the research team and deemed eligible if inclusion and exclusion criteria are met. A primary screening and data extraction software called Covidence will be used (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia). This software supports research teams in tracking the number of duplicate articles, facilitating the independent screening of the articles by two research team members and tracking the eligibility process. Studies that are duplicates, irrelevant or unrelated will be removed from the study at this time. If the relevancy of the publication is unclear from the title or abstract, the reviewer will then read the publication in full to determine the eligibility of the publication. Any further changes to the search criteria to improve the search findings will be made in this stage.

This process will first be completed on the first 100 citations and Cohen's kappa will be calculated. A minimum kappa of 0.80 has been set as a threshold to achieve before continuing with the study selection process. Once completed, the second stage of the selection process will commence. The eligible publications screened in the first stage will be independently read in full by two investigators to further establish whether the publication contains data/research relevant to informatics competencies for nurse leaders. When agreement cannot be reached, a third investigator will be consulted. Finally, the PRISMA flow diagram will be populated with the search and eligibility screening findings, and used to provide a visual of the data selection process.

Step 4: data collection

The identified articles and documents that meet all of the eligibility criteria will be thoroughly read, and relevant data will be extracted by the investigators. On extracting data from each publication, it will be organised into a chart using the descriptive-analytical method described by Arksey and O'Malley. In following with this method, the quality of the publication will not be of importance; rather, the information presented will act as the primary focus.

The chart will be divided into two sections. The first section, 'Overview', will summarise the basic information of the publication. The second section, 'Research Questions', will include the four research questions that should be answered on reviewing each piece of relevant literature. The 'Overview' section will include the name of the authors who conducted the study, the date in which the literature was published, whether the study is published or grey literature, the country in which the study took place or focuses on, the main objectives of the study, the methodology pertaining to how the study was conducted inclusive of the study design (including whether the researchers used quantitative, qualitative or mixed method approaches), the setting of the study, the type of nurse leader the competencies were developed for and the summarised results of the study. The 'Research Questions' section includes four main questions that reflect the objectives of this review. The first question focuses on specific informatics leadership competencies for nurse leaders that are reported in the literature. The second question is focused on the identification of frameworks or theories that have been used to develop informatics competencies for nurse leaders. The third question aims to identify any instruments that have been developed, analysed or used to assess informatics competencies for nurse leaders. Lastly, the fourth question focuses on the identification of demonstrated psychometric properties of instruments developed, analysed or used to assess nurse leader informatics competencies. As such, the following data will be extracted from articles: reported nurse leader informatics competencies, the conceptual/theoretical organisation of reported nurse leader competencies, instruments used to assess nurse leader informatics competencies (name, number of items, what the instrument measures) and the psychometric properties of the instruments (eg, reliability, validity, factor analyses). Online supplementary appendix B shows the proposed data extraction table.

Step 5: reporting results

Data present in the chart will be used for both a qualitative and quantitative analysis, and to support data synthesis. Qualitative data from the data extraction table will be uploaded into QSR International's NVivo 11 qualitative data analysis software (Version 11, 2015) and a thematic analysis completed to synthesise the findings. Specifically, the list of competencies will be thematically analysed using an inductive approach to identify what themes emerge. To enhance the rigour of the analysis process and the trustworthiness of the findings, two members of the research team will complete data analysis independently. These individuals will then meet to discuss and finalise the competency themes. Any discrepancies will be resolved through discussion and when agreement cannot be reached another member of the research team will be consulted. Quantitative data related to the number of competencies identified, the number of frameworks and theories drawn on, the number of instruments used and their psychometric properties will be uploaded into SPSS V.24 for data synthesis (IBM Released 2016; IBM SPSS Statistics for Windows, V.24.0). Frequencies and percentages will be reported on both items from the overview section and research question section of the data extraction table. A list of all of the identified health informatics competencies that are relevant to nursing leaders will be presented in a table.

In addition to reporting the study results, the results of an assessment of the quality of each selected article will be completed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Criteria, when applicable. The score generated from the GRADE assessment will be entered into a column on the data extraction table shown in online supplementary appendix B.

Discussion and dissemination

The proposed scoping review is significant as it will allow for easier access and identification of informatics competencies for nurse leaders, which are not currently summarised in any known document. Results of this scoping review will be of interest to healthcare and academic institutions, nursing leadership organisations (eg, Academy of Canadian Executive Nurses (ACEN), Nursing Leadership Network (NLN)), advanced practice educators, nurse leaders and informaticians in a variety of different roles and settings. Hence, dissemination strategies targeting these particular audiences are being planned.

The dissemination of the scoping review findings will be done through traditional end-of-study knowledge translation strategies. The authors will consider submitting abstracts for presentation to a national informatics conference (ie, eHealth, Vancouver, British Columbia, Canada, 2018), a national nursing conference (ie, Canadian Nurses Association Biennial Convention, Ottawa, Canada, 2018), a nursing leadership conference (ie, NLN, Toronto, Ontario, Canada, 2018), a national nursing informatics conference (ie, Canadian Nursing Informatics Association Conference, Vancouver, British Columbia, Canada, 2018) and an international nursing informatics conference (ie, International Congress on Nursing Informatics, Beijing, China, 2020). In addition, the authors intend to submit a manuscript for publication in a peer-reviewed journal.

Additionally, the results of this scoping review will be used to inform a future study focused on the validation of an existing instrument used to assess informatics competencies for nurse leaders in a Canadian context. This study will be conducted in two phases starting with a modified Delphi approach in phase one and a cross-sectional survey including psychometric testing in phase two. Given the current prevalence of health information technology implementations in Canadian healthcare settings, and that nurses typically represent the largest user group of these technologies, it is important that nursing leaders have the competencies required to support the successful procurement, design, implementation, and use and evaluation of these systems. Without informatics competent nursing leadership, organisations run the risk of not being able to obtain expected returns on investments or garner the potential safety and quality benefits of ICT for clinical care delivery. Moreover, without the informed engagement of nurse leaders throughout the systems life cycle of ICT, patient care and nursing practice may be inadvertently compromised.

Footnotes

Contributors: IK, LN and GS conceptualised this study. IK and GS drafted the manuscript and LN made contributions to its development. All authors approved the final manuscript.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

References

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Informatics competencies for nurse leaders: protocol for a scoping review

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Globally, health information technologies are now being used by nurses in a variety of settings. However, nurse leaders often do not have the necessary strategic and tactical informatics competencies to adequately ensure their effective adoption and use.

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Define what usability means for healthcare workers.

Discuss the effect of interoperability has on health care.

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In this article linked above, EHR usability is discussed. Why is usability important for electronic health records? Well, if you think about it, why is usability useful for anything? When it comes to EHR, if the system is user friendly to all of those who need access it results in less errors, increased patient safety and satisfaction, improved patient follow through, and less clinician burnout. In the long run, if a system is user friendly it saves everyone involved time and money.

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EHR Usability: Overview, Payoffs, Challenges & Solutions

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Electronic health record systems have long been associated with poor usability. We explore design solutions to solve healthcare EHR usability issues.

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Success rate (does patient successfully use system?)

timing of task required

error rate

overall satisfaction

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The picture above demonstrates interoperability, or, the ability for two healthcare entities to exchange past interactions or records.

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When interoperability is used, patient care and data can be seamless. Thus, your PCP will know exactly what your CT from your hospital says, allowing the doctor to set up an appointment for a biopsy. When there is connection between providers, imaging, and labs (not to mention everything else) patient care becomes cohesive.

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"Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various – omics data, biomedical data and electronic health records data." As from article added above, the importance of big data comes from technologies to help collect information. This information is then used to help guide our practices for medical providers.

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Abstract

This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

Keywords: Big Data Analytics, Data Mining, Health Informatics, Healthcare Information Systems

1. Introduction

To obtain the best services and care for the patients, healthcare organizations in many countries have proposed various models of healthcare information systems. These models for personalized, predictive, participatory and preventive medicine are based on using of electronic health records (EHRs) and huge amounts of complex biomedical data and high-quality - omics data [ 1].

Contemporarily genomics and postgenomics technologies produce huge amounts of raw data about complex biochemical and regulatory processes in the living organisms [ 2]. These -omics data are heterogeneous, and very often they are stored in different data formats. Similar to these - omics data, the EHRs data are also in heterogeneous formats. The EHRs data can be structured, semi-structured or unstructured; discrete or continuous.

Big data in healthcare and medicine refers to these various large and complex data, which they are difficult to analyse and manage with traditional software or hardware [ 3], [ 4]. Big data analytics covers integration of heterogeneous data, data quality control, analysis, modeling, interpretation and validation [ 5]. Application of big data analytics provides comprehensive knowledge discovering from the available huge amount of data.

Particularly, big data analytics in medicine and healthcare enables analysis of the large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 2]. Big data analytics in medicine and healthcare integrates analysis of several scientific areas such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics. A survey of big data cases in medical and healthcare institutions/organizations is given in [ 6].

The new knowledge discovered by big data analytics techniques should provide comprehensive benefits to the patients, clinicians and health policy makers [ 7].

The remainder of the paper is organized as follows. Related work is described in the second section. Section 3 describes characteristics of big data, while big data analytics is depicted in the subsequent section. The next section explains some challenging issues about big data analytics techniques, while big data privacy and security are described in Section 6. Last section concludes this paper with discussion and further works.

2. Related Work

The rapid development of the emerging information technologies, experimental technologies and methods, cloud computing, the Internet of Things, social networks supplies the amounts of generated data that is growing tremendously in numerous research fields [ 8].

On this point, contemporarily genomics and postgenomics technologies produce huge amounts of raw data about complex biochemical and regulatory processes in the living organisms [ 2]. These high throughput - omics data provide comprehensive insight towards different kinds of molecular profiles, changes and interactions, such as knowledge allied to the genome, epigenome, transcriptome, proteome, metabolome, interactome, pharmacogenome, diseasome, etc. [ 9]. These - omics data are heterogeneous and very often stored in different data formats. The main aims and characteristics of the different - omics disciplines are tabled in .

Table 1:

Genomics

Study of the set of all genes in an organism, in a broader context non-coding parts of DNA are subject of study

Epigenomics

Study of all epigenomic modifications on the genetic material within a cell

Transcriptomics

Study of the expression level of all RNAs in particular cell, or cell population

Proteomics

Study of all possible interactions that a protein can present, complete set of proteins expressed by a genome in a given cell type or organism, under defined conditions, at a given time

Metalobomics

Study of the whole set of the metabolites (small-molecule compounds) within a cell, an organelle, a tissue, an organ or an organism

Interactomics

Study of the entire set of interactions (both: physical and indirect interactions) between and among proteins and other molecules within a particular cell and consequences of those interactions. These interactions are displayed as graphs and called biological networks

Pharmacogenomics

Study which combines pharmacology and genomics in order to analyse the role of the genome in individual's drug response

Diseasomics

Study of all diseases and disorders of an organism, often focusing on those diseases and disorders caused by genetic modifications

Similar to these - omics data, the EHRs data are also stored in heterogeneous formats. The EHRs data, which can be structured, semi-structured or unstructured; discrete or continuous, contain personal patients' data, clinical notes, diagnoses, administrative data, charts, tables, prescriptions, procedures, lab tests, medical images, magnetic resonance imaging (MRI), ultrasound, computer tomography (CT) data. Some of these data are acquired from wearable sensors or capture from medical monitoring devices, with different collection frequency [ 5] that makes these data to have complex features and high dimensions [ 10]. Dealing with noisiness and incompleteness of EHRs are still challenging task and these shortcomings should be consider while applying data mining techniques [ 11].

These growing amounts of various - omics data need to be collect, clean, store, transform, transfer, visualize and deliver in a suitable manner to be represented to the clinicians [ 12]. The processing of these big data in medicine and healthcare can be accelerating by using cloud computing and powerful multicore central processing units (CPUs), graphics processing units (GPU) and field-programmable gate arrays (FPGAs) with parallel processing methods.

3. Big Data Characteristics

The term big data is described by the following characteristics: value, volume, velocity, variety, veracity and variability, denoted as 6 "Vs" [ 13], [ 14], shown in Figure . Besides these 6 "Vs", some authors has defined more than these 6 properties to describe big data characteristics [ 15].

The volume of health and medical data is expected to raise intensely in the years ahead, usually measured in terabytes, petabytes even yottabytes [ 14], [ 16]. Volume refers to the amount of data, while velocity refers to data in motion as well as and to the speed and frequency of data creation, processing and analysis. Complexity and heterogeneity of multiple datasets, which can be structured, semi-structured and unstructured, refer to the variety. Veracity referrers to the data quality, relevance, uncertainty, reliability and predictive value [ 14], while variability regards about consistency of the data over time. The value of the big data refers to their coherent analysis, which should be valuable to the patients and clinicians.

Considering the big data characteristics, data searching, storage and analysis, a very appropriate and promising software platform for development of applications that can handle big data in medicine and healthcare is the open-source distributed data processing platform Apache Hadoop MapReduce [ 1], [ 17] that is based on data-intensive computing and NoSQL data modeling techniques [ 18].

4. Big Data Analytics

Applications of big data analytics can improve the patient-based service, to detect spreading diseases earlier, generate new insights into disease mechanisms, monitor the quality of the medical and healthcare institutions as well as provide better treatment methods [ 19], [ 20], [ 21].

Data mining techniques employed on EHRs, web and social media data enable identifying the optimal practical guidelines in the hospitals, identifying the association rules in the EHRs [ 22] and revealing the disease monitoring and health-based trends. Moreover, integration and analysis of the data with different nature, such as social and scientific, can lead to new knowledge and intelligence, exploring new hypothesis, identifying hidden patterns [ 14].

Nowadays, smart phones are excellent platforms to deliver personal messages to patients to involve them in behavioral changes to improve their wellbeing and health conditions. The mobile phone messages can substitute delivering of medical and motivational advices to the patients [ 14].

5. Challenges in Big Data Analytics

Regarding collection of large amount data, some challenging issues should be considered. Obtaining high-throughput - omics data is tied to the cost of experimental measurements. Concerning heterogeneity of the data sources, the noise of the experimental - omics data and the variety of the experimental techniques, environmental conditions, biological nature should be considered, before integration of these heterogeneous data and before employing of the data mining methods. Different data mining techniques can be applied on these heterogeneous biomedical data sets, such as: anomaly detection, clustering, classification, association rules as well as summarization and visualization of those big data sets.

These shortcomings might lead to the unreliability of some of the data points, such as missing values or outliers. Despite of these drawbacks of the - omics data, EHRs data are very influenced by the staff who entered the patient's data, which can lead to entering missing values, incorrect data as a result of mistakes, misunderstanding or wrong interpretation of the original data [ 5]. Integration of data from various databases and standardization for laboratory protocols and values still remain challenging issues [ 10].

High dimensionality of the - omics data means, that there have many more dimensions or features than the number of samples, and on the other side the EHRs data which regard to the individuals/patients, makes data mining techniques to be more challenging task.

The subsequent stage is the pre-processing of the data, which usually envelop handling noisy data, outliers, missing values, data transformation and normalization. This data pre-processing enables to be applied statistical techniques and data mining methods and thus the big data analytics quality and outcomes can improve and can result with discovering of novel knowledge. This novel knowledge obtained by integration of the - omics and EHRs data should results with improving of the implemented healthcare to the patients as well to advanced decision making by the healthcare decision policy makers.

6. Big Data Privacy and Security

Two important issues towards big data in healthcare and medicine are security and privacy of the individuals/patients [ 14], [ 23]. All medical data are very sensitive and different countries consider these data as legally possessed by the patients [ 2]. To address these security and privacy challenges, the big data analytics software solutions should use advanced encryption algorithms and pseudo-anonymization of the personal data. These software solutions should provide security on the network level and authentication for all involved users, guarantee privacy and security, as well as set up good governance standards and practices.

7. Discussion and Future Work

Big data analytics in medicine and healthcare is very promising process of integrating, exploring and analysing of large amount complex heterogeneous data with different nature: biomedical data, experimental data, electronic health records data and social media data. Integration of such diverse data makes big data analytics to intertwine several fields, such as bioinformatics, medical imaging, sensor informatics, medical informatics, health informatics and computational biomedicine. As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in medicine and healthcare. One such platform is the open-source distributed data processing platform Apache Hadoop MapReduce that use massive parallel processing (MPP) [ 20], [ 24]. These applications should enable applying data mining techniques to these heterogeneous and complex data to reveal hidden patterns and novel knowledge from the data.

Recent hardware innovations in processor technology, newer kinds of memories/network architecture will minimize the time spent in moving the data from storage to the processor in a distributed setting [ 25].

Acknowledgements

This paper was supported by the Ministry of Education and Science of the Republic of Macedonia and the Ministry of Science and Technology (MOST) of the Government of the People's Republic of China.

Conflict of interest statement

Authors state no conflict of interest. All authors have read the journal's Publication ethics and publication malpractice statement available at the journal's website and hereby confirm that they comply with all its parts applicable to the present scientific work.

References

# data[].attributes.attachment_link.title
Big Data Analytics in Medicine and Healthcare

# data[].attributes.attachment_link.description
This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data.

# data[].attributes.headline
Transforming Data, Information, Knowledge and Wisdom

# data[].attributes.subject
Transforming Data, Information, Knowledge and Wisdom

# data[].attributes.body
Describe the components to transform data into wisdom.

Differentiate the relationship between data, information, knowledge and wisdom

# data[].attributes.body
Data: Measurements or numbers on their own, ex. a blood-pressure
Information: Using those data points, you notice what those measurements are telling you, ex. the patients systolic blood pressure has been in the 80's-90's the last few checks
Knowledge: Using the information you have gathered to from the data, and using it to draw a thought-out idea. Ex. the patients blood pressure may d/t low volume.
Wisdom: Taking the knowledge of what you just learned and understanding what it means, thus implementing it. Ex. the blood pressure is low d/t low volume which can be cause by bleeding so checking labs.

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Developing Understanding using the DIKW Pyramid

# data[].attributes.body
Described the four buckets of Meaningful use.

Indicate the influences of EHR adoption.

# data[].attributes.body
This video is a nice visual that depicts
what president Obama had put into place back in 2008 regarding EHR.

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The HITECH Act: Electronic Health Records and Meaningful Use

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A Patient's Journey: Meaningful Use Stage 3 and MIPS

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In the context of health care, meaningful use is a term used to define government standards for EHRs. It outlines how patient data should be passed and collected through different clinics, insurance companies and etc.

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Electronic Health Records and Clinical Decision Support

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Electronic Health Records and Clinical Decision Support

# data[].attributes.body
Identify key components of electronic health records.

Describe issues with accessibility with electronic health records.

Discuss clinical decision support tools.

Describe how clinical decision support can support patient safety and outcomes.

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Poor outcomes due to lack of EHR's

# data[].attributes.attachment_link.authors[].name
Office of the National Coordinator for Health IT

# data[].attributes.attachment_link.title
The Future of Health Care: Electronic Health Records

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The benefits of electronic health records include: Better health care by improving all aspects of patient care, including safety, effectiveness, patient-centeredness, communication, education, timeliness, efficiency, and equity.

# data[].attributes.body
Identify contacts for Health Information Exchanges.

Discuss the benefits of Health Information Exchanges.

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What is Health Information Exchange (HIE)?

# data[].attributes.body
"A doctor can care for you better when he or she has important information about your medical history. This includes knowing about your health problems, test results, allergies, and medications. Doctors sometimes use fax or mail to request and receive health information. But that takes time and it's hard to control who reads a fax or opens a piece of mail, so your information may not always be protected.

Mass HIway (pronounced “mass highway”) is the shortened name for a statewide computer network designed to make this process faster and safer. Mass HIway can help healthcare providers connect with members of your healthcare team, and to securely send and receive your health information. The goal is better care coordination and quality for you and your family."

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The goal of the Mass HIway is to support the electronic exchange of patient information across the healthcare community to achieve the best possible care coordination for Massachusetts residents. This includes enabling care providers in improving their processes of sharing, reconciling, and analyzing Protected Health Information (PHI) to facilitate better management of the quality and cost of care.

For this purpose, the Mass HIway offers a range of services and resources to facilitate the use of Health Information Exchange (HIE) by care providers across the Commonwealth of Massachusetts. The HIE efforts cover Direct Messaging, Query HIE, Event Notification Services (ENS), Public Health Reporting, and methods to enhance the interoperability of healthcare systems and workflows by means of HIE utilization.

The HIway is operated by the Massachusetts Executive Office of Health and Human Services ( EOHHS), and its services are regulated through the Mass HIway Regulations. The HIway is overseen by a Health Information Technology (HIT) Council, and informed by consumer, provider, legal and policy, and technology advisory groups to promote a transparent multi-stakeholder engagement model. Click for meeting dates, presentations, and notes of the HIT Council.

The key services and resources provided by the Mass HIway include:

Continued development of the Mass HIway infrastructures

Provider organization education and outreach

EHR/Practice Management Vendor integration services

Onboarding and Enrollment

Secure Direct Messaging

Account Management and Consulting Services

HIway Adoption and Utilization Support

A Statewide ENS Framework

Onboarding and Enrollment

Toolkits to facilitate the utilization of HIE and Query HIE

Visit the Services and Resources menus to review all the available services and resources. Key aspects are highlighted below.

Mass HIway Direct Messaging

The Mass HIway offers a secure Health Information Exchange (HIE) for all healthcare systems in Massachusetts, regardless of affiliation, location, or differences in technology. The HIway's Direct Messaging mechanism enables healthcare providers to exchange health information for the patients they serve. It is a cost-effective tool to improve care coordination, care quality, patient satisfaction, and public health reporting.

The Mass HIway's Direct Messaging solution offers doctors' offices, hospitals, laboratories, pharmacies, skilled nursing facilities, and other healthcare organizations a cost-effective way to securely and seamlessly exchange vital health data electronically to better serve their shared patients. To learn more about HIway Direct Messaging, click here. To enroll, click here.

HIway Connection Requirement

Massachusetts care providers are subject to the Mass HIway Regulations, including the HIway Connection Requirement. Currently, Acute Care Hospitals, Community Health Centers, and Medium/Large Medical Ambulatory Practices are subject to the HIway Connection Requirement. To learn more about the HIway Regulations and Connection Requirement, click here.

HIway Account Management

The Mass HIway supports providers in connecting and using the Mass HIway. This support is provided by our HIway Account Management team. To learn more, click here.

Statewide ENS Framework

An Event Notification Service (ENS) is a specialized form of HIE used to alert care providers about their patients' Admissions, Discharges, and Transfers (ADT) to hospitals, Emergency Departments, and post-acute care facilities. The Mass HIway created and administers the Statewide ENS Framework, which is an ENS solution that serves all Massachusetts care providers.

Acute Care Hospitals must connect to the Statewide ENS Framework to submit Admission Discharge Transfer notifications (ADTs).

All other care providers are encouraged to participate in the framework as well to receive the alert notifications. To learn more about the Statewide ENS Framework, click here.

History of the Mass HIway

In October 2012, Massachusetts first launched the statewide electronic Health Information Exchange, The Massachusetts Health Information Highway (The Mass HIway). The Mass HIway provided Massachusetts care providers with a cost-effective Direct Messaging alternative.

In 2018, the Mass HIway released HIway 2.0, which is based on Orion Health's Communicate platform. HIway 2.0 aligns the HIway's Direct Messaging capabilities with modern interoperability standards, simplifies the connection process, and improves the user experience. Review the new features in the graphic shown below.

To enable providers to take advantage of the improved HIE capabilities, all HIway 1.0 users have been migrated to HIway 2.0.

To get started with enrolling and onboarding onto Mass HIway 2.0, click here.

Mass HIway Overview Video

Review the animated video below to learn more about the Mass HIway.

Subscribe to Mass HIway Newsletter

To stay up to date with the latest services, resources, regulations, and other aspects pertaining to the Mass HIway and advances in HIE, click here to subscribe to the Mass HIway Newsletter.

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The goal of the Mass HIway is to support the electronic exchange of patient information across the healthcare community to achieve the best possible care coordination for Massachusetts residents. This includes enabling care providers in improving their processes of sharing, reconciling, and analyzing Protected Health Information (PHI) to facilitate better management of the quality and cost of care.

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This session includes a discussion of the benefits and risks related to the exchange of patient information through a Health Information Exchange. This review includes an analysis of the applicable federal privacy law requirements, including HIPAA and Part 2 regulations. Learn about best practices to capture the proper patient authorizations and to implement safeguards to comply with applicable laws.

This session is recommended for Health Information Management Leaders, Compliance Officers, Privacy Officers, Chief Operating Officers and Executive Team Members responsible for the use and disclosure of patient protected health information.
Learning Objectives:

Understand the benefits and risks associated with a Health Information Exchange (HIE)

Discuss the opt in versus opt out processes related to an HIE

Review the privacy law restrictions and limitations

Determine the best practices for proper process and procedure development to support compliance with the applicable privacy and security regulations

Presenter from Morris Manning & Martin:

Michele Madison is a highly experienced professional, providing legal advice to health systems in a variety of regulatory and business matters. Michele manages a multitude of legal issues including employment, investigations, and risk management assessment, and she often facilitates the implementation of compliance plans for HIPAA privacy and security regulations, and also provides oversight and guidance regarding medical staff governance and credentialing issues.
Sponsor:

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Recording: Understanding the Benefits of a Health Information Exchange - DCHA

# data[].attributes.attachment_link.description
This session includes a discussion of the benefits and risks related to the exchange of patient information through a Health Information Exchange. This review includes an analysis of the applicable federal privacy law requirements, including HIPAA and Part 2 regulations. Learn about best practices to capture the proper patient authorizations and to implement safeguards to comply with applicable laws.

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Identify successful telehealth applications.

Describe types of telehealth

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"Telehealth is the use of digital information and communication technologies to access health care services remotely and manage your health care. Technologies can include computers and mobile devices, such as tablets and smartphones. This may be technology you use from home."

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How many times have you heard it said that the internet has changed modern life? Indeed, it's likely changed how you stay in touch with family and friends and buy goods and services. And it's probably even changed how you search for information about health problems.

Several telehealth tools are offered to help you manage your health care and receive the services you need. During the coronavirus disease 2019 (COVID-19) pandemic, many people used telehealth. People often still use it. Find out more about telehealth.

What is telehealth?

Telehealth is the use of digital information and communication technologies to access health care services remotely and manage your health care. Technologies can include computers and mobile devices, such as tablets and smartphones. This may be technology you use from home. Or a nurse or other health care professional may provide telehealth from a medical office or mobile van, such as in rural areas. Telehealth can also be technology that your health care provider uses to improve or support health care services.

The goals of telehealth, sometimes called e-health or m-health (mobile health), include the following:

Make health care easier to get for people who live in communities that are remote or in the country.

Keep you and others safe if you have an infectious disease such as COVID-19.

Offer primary care for many conditions.

Make services more easily offered or handy for people who have limited ability to move, time or transportation.

Offer access to medical specialists.

Improve communication and coordination of care among health care team members and a person getting care.

Offer advice for self-management of health care.

Many people found telehealth helpful during the COVID-19 pandemic and still use it. Telehealth is being used more often.

Here are many examples of telehealth services that may be helpful for your health care.

Virtual visits

Some clinics may use telemedicine to offer remote care. For example, clinics may offer virtual visits. These can allow you to see a health care provider, mental health counselor or a nurse via online video or phone chats.

Virtual visits can offer care in many conditions such as migraines, skin conditions, diabetes, depression, anxiety, colds, coughs and COVID-19. These visits allow you to get care from a provider when you don't need or can't get an in-person visit.

Before your visit, your health care team may send you information or forms to fill out online and return to them. They may also make sure you have the technology you need. They'll check to see if you need to update or install any software or apps too. And they can tell you how to sign on and join the video chat for your visit. Also, the health care team can explain how to use the microphone, camera and text chat. If needed, ask a family member to help you set up the technology you need.

You only need a smartphone, tablet or computer with internet access to join the virtual visit. You can find a comfortable, quiet, private spot to sit during your visit. Your provider also meets from a private place.

Other options

Some people may use web or phone-based services for medical care or advice. When you log into a web-based service or call a service that offers primary or urgent care, you're guided through many questions. The provider or nurse practitioner can prescribe drugs. Or they may suggest home care tips or more medical care.

While these services are handy, they have drawbacks:

Treatment may not be coordinated with your regular provider.

Important details from your medical history may not be considered.

The computer-driven model used to make decisions may not be right for you if you have a complex medical history.

The service doesn't easily allow for you to make decisions with your provider about treatments.

Remote monitoring

Many technologies allow your provider or health care team to check your health remotely. These technologies include:

Web-based or mobile apps for uploading data to your provider or health care team. For example, if you have diabetes, you may upload food logs, blood sugar levels and drugs that a nurse checks.

Devices that measure and wirelessly send data, such as blood pressure, blood sugar and oxygen levels.

Wearable devices that automatically record and send data. For example, the devices may record data such as heart rate, blood sugar, how you walk, your posture, tremors, physical activity or your sleep.

Home monitoring devices for older people or people with dementia that can find changes in daily activities such as falls.

Devices that send notifications to remind you to do exercises or take drugs.

Providers talking to providers

Providers can also use technology to give people better care. For example, in a virtual consultation, primary care providers can get input from specialists in other locations when they have questions about your diagnosis or treatment.

The primary care provider sends exam notes, history, test results, X-rays or other images to the specialist to review. The specialist may answer by email. Or they may do a virtual visit with you at your provider's office. They may also ask for a face-to-face meeting.

In some cases, a nurse or other health care professional may use technology to provide care from a medical office, clinic or mobile van in a rural area. They may call a specialist or provider at a medical clinic to do a remote consult.

These virtual consultations may prevent unnecessary in-person referrals to a specialist. They may also cut wait times for you to see a specialist. And they may remove the need for you to travel to a specialist.

Patient portal

Your primary care clinic may have an online patient portal. These portals offer a safer way of contacting your provider instead of email. A portal provides a safe online tool to do the following:

Message your provider or a nurse.

Ask for prescription refills.

Review test results and summaries of earlier visits.

Schedule visits or ask for appointment reminders for preventive care.

If your provider is in a large health care system, the portal may also provide one point of contact for any specialists you may see.

Personal health apps

Many apps have been made to help people better organize their medical information in one secure place. These digital tools may help you:

Store personal health information.

Record vital signs.

Calculate and track your calories.

Schedule reminders for taking drugs.

Record physical activity such as your daily step count.

Personal health records

An electronic personal health record system (PHR system) is a collection of information about your health that you control and maintain. A PHR app is easy for you to see anytime via a web-enabled device, such as your computer, laptop, tablet or smartphone. A PHR also allows you to review your lab results, X-rays and notes from your provider. Your provider may give this to other providers with permission.

In an emergency, a personal health record can quickly give emergency staff vital information. For example, it can show your current conditions, drugs, drug allergies and your provider's contact details.

The potential of telehealth

Technology has the potential to improve the quality of health care. And technology can make it easier for more people to get health care.

Telehealth may offer ways to make health care more efficient, better coordinated and closer to home. You can go to a virtual visit anywhere - such as at home or in your car. And you don't need to travel to go to a virtual visit.

Telehealth can be useful so you can stay home if you're sick or if it's hard for you to travel. And you can use telehealth if you live far from a medical center. And many people have been able to keep distance from others at home and still receive care during the COVID-19 pandemic. And providers can diagnose and treat COVID-19 remotely.

Virtual visits can also provide you with the choice to meet with specialists who don't live where you do.

The limitations of telehealth

Telehealth has potential for better coordinated care. But it also runs the risk of gaps in care, overuse of medical care, inappropriate drug use or unnecessary care. Providers can't do a physical exam in-person, which can affect a diagnosis.

The potential benefits of telehealth services may be limited by other factors, such as costs. Insurance reimbursement for telehealth can vary by state and type of insurance in the U.S. But insurance keeps expanding for telehealth services in the U.S. And during the COVID-19 pandemic, insurance restrictions changed for a period of time. Check with your insurance company to see which providers have virtual visits covered by insurance.

Also, some people who need improved access to care may be limited because of not having internet access or a mobile device. People without internet access may be able to access telehealth services by using wireless internet offered at public places. For example, libraries or community centers may offer wireless internet for virtual visits that can take place in private rooms.

Sometimes technology doesn't work well. It's important to have a plan with your provider to call them by phone if there is an issue with the virtual visit.

Show references

See more In-depth

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Managing your health in the age of Wi-Fi

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From patient portals to wearable monitoring devices, telehealth gives you the tools to better manage your health. Are you taking advantage of these tools?

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Hearing a lot about telehealth lately? Telehealth, including telemedicine, is a great way to get health care from the comfort of your home.

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Pro:
-easier to access
-access
-less stigma

Con:
-Overuse
-Unrealistic expectations

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Discuss current consumer technologies.

Describe how gamification can help consumer improve health.

Describe a potential future innovation for healthcare.

Identify innovations that have been successful.

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3 Apps that Will Change the Way You Think About Healthcare

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"Gamification is the use of game-like elements in non-game contexts. When used in healthcare, gamification can improve patient engagement and motivation. Gamification can also be used to promote healthy behaviors, such as taking medication as prescribed."

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The Gamification of Healthcare: Emergence of the Digital Practitioner?

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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.

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What do people get wrong about gamification in healthcare?

# data[].attributes.body
Describe how informatics can improve quality of healthcare.

Identify ways an EHR can help report quality indicators.

Identify key elements to determine quality data.

Evaluate data sets for quality.

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HOW TO IMPROVE PRODUCT USABILITY: 11 Tips to Make Your Product More Usable

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Personal Health Record, Patient Portal Self Check-In | onpatient.com

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Things that patients can see via their patient health records:

The patient's medical history

Applicable diagnoses

Historical and ongoing medications, including over-the-counter and alternative treatments

Past medical and surgical interventions

Immunization status

Allergies and other relevant medical conditions that can impact the delivery of emergency care (e.g., Type 1 diabetes, etc.)

Blood type

Whom to contact in the event of an emergency

Insurance information

Contact information for the patient's regular health providers

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Definition/Introduction

A personal health record (PHR) refers to the collection of an individual's medical documentation maintained by the individual themselves, or a caregiver, in cases where patients are unable to do so themselves. This includes details such as:

The patient's medical history

Historical and ongoing medications, including over-the-counter and alternative treatments

Past medical and surgical interventions

Allergies and other relevant medical conditions that can impact the delivery of emergency care (e.g., Type 1 diabetes, etc.)

Whom to contact in the event of an emergency

Contact information for the patient's regular health providers

Any other information the patient feels is pertinent may also be included. This contrasts with electronic medical records and electronic health records (EMR and EHR), which are usually maintained by the treating physician or hospital to provide medical care and for billing purposes.

A PHR may be either physical or, as has become increasingly common moving forward, electronic. It includes all self-reported and self-recorded health data, including health issues and treatments, records of vital signs and activity recorded with personal devices including smartphones and smartwatches, nutritional data such as diet composition and calorie intake, etc. Several commercial applications are available that allow an individual to maintain a PHR, and some also allow integration of this data with the individual's EMR/HER, allowing them to take better charge of their own health.

The goal of a PHR is for the patient to be able to keep their health data on hand for ready access for both themselves and anyone involved in their care while maintaining the privacy and security of this data. A PHR can hence assist in providing tailored medical care.[1]

Issues of Concern

Benefits of PHR Platforms [2]

A snapshot view of the individual's health.

Objective data points for vital signs, nutrition, physical activity, and disease course, which can allow individuals to follow their health in real-time and quantify the amount of effort and change that has occurred over time.

Gamification of the health data collection and reporting process can serve as a source of motivation in achieving health goals such as weight loss targets, motivational videos, calorie registers, etc.

Greater clarity of medications to be taken and better compliance with them, plus an improved assessment of the same.

Identification of successes and failures in the delivery of care and the underlying reasons for them.

Rapid emergency response with the availability of pertinent health data in the absence of a caregiver or bystanders.

PHRs may be developed as a one-size-fits-all approach that may sometimes fail to take into account individual variations.

Certain advertised benefits of commercial PHR applications may not be supported by concrete evidence and can prove misleading to consumers.

Anxiety may be provoked by an urge to record all personal data.

Individuals may find patterns where none exist, leading to greater false positives and higher healthcare utilization.

There may be bias in self-reporting health data.

Lapses in security and confidentiality are a major concern, especially with cloud-based solutions.

Misuse of health data by entities with commercial interests.

Clinical Significance

PHRs are seeing widespread adoption in today's digital age, and physicians must adapt to this new data source. Interpreted correctly, a PHR can provide valuable data points to assess the clinical course before the presentation and provides a level of fidelity that could not be achieved by traditional patient interviews alone. This concept represents a crucial component of shared decision-making by the patient and their physician.[3]

Used correctly, PHRs can improve patient adherence to follow-up, allow patients to better monitor therapeutic goals such as blood pressure or blood glucose thresholds, allow recognition of improvement or worsening of control of existing medical conditions, improve compliance with medication regimens, especially when these regimens are complex - all of which culminate in the achievement of superior management of medical issues.

However, critical analysis is necessary, as many of these devices and applications use one-size-fits-all algorithms that may result in both false positives and false negatives in the interpretation of medical conditions. Patients must be counseled about interpreting and acting on health data in close cooperation with their healthcare provider in order to avoid unwarranted anxiety or concerns over minor normal variations that are interpreted as something more sinister. This is especially of concern in a setting where PHRs are interlinked with EMR/EHR systems that provide patients with real-time test results that are yet to be interpreted and clinically correlated by a physician. Moreover, PHRs may also contribute to a state of "information overload" whereby extraneous data in a PHR becomes noise that can garble the signal of pertinent health information in the healthcare setting.

In this setting, the upcoming use of artificial intelligence (AI) in the processing of patient data may serve as a useful adjunct in highlighting true abnormalities and help determine action signals that necessitate healthcare intervention. This can allow healthcare providers to look past the vast mountain of data and detect significant findings from the repository of information within an individual's PHR.

Nursing, Allied Health, and Interprofessional Team Interventions

A caveat of PHRs is the importance of presenting information to the layperson in a manner that simplifies the concepts while retaining accuracy and veracity of facts. This may depend heavily on the involvement of sensitized healthcare professionals from various disciplines for the development of PHR platforms, as they would be best poised to recognize cognitive errors of reporting and interpretation that may affect the use of PHRs by the public at large.

Allied health providers can significantly contribute to the development of individual PHRs by counseling and educating persons on the benefits and shortcomings of various platforms, the relevance of data recorded to their clinical problems, and by filtering this data to make interpretation easier for the physicians involved in the patient's care. A multi-disciplinary approach to the creation of PHRs, constant study and improvement of PHR design by a team of pan-professional individuals, and post-marketing surveillance of usage data, successes, and pitfalls, all are critical components of developing this modality as a fresh addition to the healthcare armamentarium.

References

1.

Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc. 2006 Mar-Apr;13(2):121-6. [ PMC free article: PMC1447551 ] [ PubMed: 16357345 ]

2.

Roehrs A, da Costa CA, Righi RD, de Oliveira KS. Personal Health Records: A Systematic Literature Review. J Med Internet Res. 2017 Jan 06;19(1):e13. [ PMC free article: PMC5251169 ] [ PubMed: 28062391 ]

3.

Davis S, Roudsari A, Raworth R, Courtney KL, MacKay L. Shared decision-making using personal health record technology: a scoping review at the crossroads. J Am Med Inform Assoc. 2017 Jul 01;24(4):857-866. [ PMC free article: PMC7651971 ] [ PubMed: 28158573 ]

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A personal health record (PHR) refers to the collection of an individual's medical documentation maintained by the individual themselves, or a caregiver, in cases where patients are unable to do so themselves.

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Describe what makes up two step authentication.

Discuss privacy issues in regards to EHRs.

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Patient privacy continues to evolve and practices must follow strict guidelines in order to protect patient information and the practice’s reputation.  In order to comply with HIPAA Law, you must have specific policies and procedures in place to properly control, disclose and protect PHI. HIPAA’s Privacy Rule defines specific rights for individuals regarding their PHI and obligates covered entities and their business associates to comply with protecting their information.

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What is two-factor authentication and why is it used?

Two-factor authentication (2FA), sometimes referred to as two-step verification or dual-factor authentication, is a security process in which users provide two different authentication factors to verify themselves.

2FA is implemented to better protect both a user's credentials and the resources the user can access. Two-factor authentication provides a higher level of security than authentication methods that depend on single-factor authentication (SFA), in which the user provides only one factor -- typically, a password or passcode. Two-factor authentication methods rely on a user providing a password as the first factor and a second, different factor -- usually either a security token or a biometric factor, such as a fingerprint or facial scan.

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What is two-factor authentication and why is it used?

Two-factor authentication (2FA), sometimes referred to as two-step verification or dual-factor authentication, is a security process in which users provide two different authentication factors to verify themselves.

2FA is implemented to better protect both a user's credentials and the resources the user can access. Two-factor authentication provides a higher level of security than authentication methods that depend on single-factor authentication ( SFA), in which the user provides only one factor -- typically, a password or passcode. Two-factor authentication methods rely on a user providing a password as the first factor and a second, different factor -- usually either a security token or a biometric factor, such as a fingerprint or facial scan.

Two-factor authentication adds an additional layer of security to the authentication process by making it harder for attackers to gain access to a person's devices or online accounts because, even if the victim's password is hacked, a password alone is not enough to pass the authentication check.

Two-factor authentication has long been used to control access to sensitive systems and data. Online service providers are increasingly using 2FA to protect their users' credentials from being used by hackers who stole a password database or used phishing campaigns to obtain user passwords.

What are authentication factors?

There are several ways in which someone can be authenticated using more than one authentication method. Currently, most authentication methods rely on knowledge factors, such as a traditional password, while two-factor authentication methods add either a possession factor or an inherence factor.

Authentication factors, listed in approximate order of adoption for computing, include the following:

A knowledge factor is something the user knows, such as a password, a personal identification number (PIN) or some other type of shared secret.

A possession factor is something the user has, such as an ID card, a security token, a cellphone, a mobile device or a smartphone app, to approve authentication requests.

A biometric factor, also known as an inherence factor, is something inherent in the user's physical self. These may be personal attributes mapped from physical characteristics, such as fingerprints authenticated through a fingerprint reader. Other commonly used inherence factors include facial and voice recognition or behavioral biometrics, such as keystroke dynamics, gait or speech patterns.

A location factor is usually denoted by the location from which an authentication attempt is being made. This can be enforced by limiting authentication attempts to specific devices in a particular location or by tracking the geographic source of an authentication attempt based on the source Internet Protocol address or some other geolocation information, such as Global Positioning System (GPS) data, derived from the user's mobile phone or other device.

A time factor restricts user authentication to a specific time window in which logging on is permitted and restricts access to the system outside of that window.

The vast majority of two-factor authentication methods rely on the first three authentication factors, though systems requiring greater security may use them to implement multifactor authentication ( MFA), which can rely on two or more independent credentials for more secure authentication.

How does two-factor authentication work?

Enabling two-factor authentication varies depending on the specific application or vendor. However, two-factor authentication processes involve the same general, multistep process:

1. The user is prompted to log in by the application or the website.

2. The user enters what they know -- usually, username and password. Then, the site's server finds a match and recognizes the user.

3. For processes that don't require passwords, the website generates a unique security key for the user. The authentication tool processes the key, and the site's server validates it.

4. The site then prompts the user to initiate the second login step. Although this step can take a number of forms, the user has to prove that they have something only they would have, such as biometrics, a security token, an ID card, a smartphone or other mobile device. This is the inherence or possession factor.

5. Then, the user may have to enter a one-time code that was generated during step four.

6. After providing both factors, the user is authenticated and granted access to the application or website.

Elements of two-factor authentication

Two-factor authentication is a form of MFA. Technically, it is in use any time two authentication factors are required to gain access to a system or service. However, using two factors from the same category doesn't constitute 2FA. For example, requiring a password and a shared secret is still considered SFA as they both belong to the knowledge authentication factor type.

2FA involves two of three potential authentication factors.

As far as SFA services go, usernames and passwords are not the most secure. One problem with password-based authentication is it requires knowledge and diligence to create and remember strong passwords. Passwords require protection from many insider threats, such as carelessly stored sticky notes with login credentials, old hard drives and social engineering exploits. Passwords are also prey to external threats, such as hackers using brute-force, dictionary or rainbow table attacks.

Given enough time and resources, an attacker can usually breach password-based security systems and steal corporate data. Passwords have remained the most common form of SFA because of their low cost, ease of implementation and familiarity.

Multiple challenge-response questions can provide more security, depending on how they are implemented, and standalone biometric verification methods can also provide a more secure method of SFA.

Types of two-factor authentication products

There are many different devices and services for implementing 2FA -- from tokens to radio frequency identification ( RFID) cards to smartphone apps.

Two-factor authentication products can be divided into two categories:

1. tokens that are given to users to use when logging in; and

2. infrastructure or software that recognizes and authenticates access for users who are using their tokens correctly.

Authentication tokens may be physical devices, such as key fobs or smart cards, or they may exist in software as mobile or desktop apps that generate PIN codes for authentication. These authentication codes, also known as one-time passwords ( OTPs), are usually generated by a server and can be recognized as authentic by an authentication device or app. The authentication code is a short sequence linked to a particular device, user or account and can be used only once as part of an authentication process.

Organizations need to deploy a system to accept, process and allow or deny access to users authenticating with their tokens. This may be deployed in the form of server software or a dedicated hardware server, as well as provided as a service by a third-party vendor.

An important aspect of 2FA is ensuring the authenticated user is given access to all resources the user is approved for and only those resources. As a result, one key function of 2FA is linking the authentication system with an organization's authentication data. Microsoft provides some of the infrastructure necessary for organizations to support 2FA in Windows 10 through Windows Hello, which can operate with Microsoft accounts, as well as authenticate users through Microsoft Active Directory, Azure AD or Fast IDentity Online ( FIDO).

How 2FA hardware tokens work

Hardware tokens for 2FA are available supporting different approaches to authentication. One popular hardware token is the YubiKey, a small Universal Serial Bus (USB) device that supports OTPs, public key encryption and authentication, and the Universal 2nd Factor protocol developed by the FIDO Alliance. YubiKey tokens are sold by Yubico Inc., based in Palo Alto, Calif.

When users with a YubiKey log in to an online service that supports OTPs -- such as Gmail, GitHub or WordPress -- they insert their YubiKey into the USB port of their device, enter their password, click in the YubiKey field and touch the YubiKey button. The YubiKey generates an OTP and enters it in the field.

The OTP is a 44-character, single-use password; the first 12 characters are a unique ID that represents the security key registered with the account. The remaining 32 characters contain information that is encrypted using a key known only to the device and Yubico's servers, established during the initial account registration.

The OTP is sent from the online service to Yubico for authentication checking. Once the OTP is validated, the Yubico authentication server sends back a message confirming this is the right token for this user. 2FA is complete. The user has provided two factors of authentication: The password is the knowledge factor, and the YubiKey is the possession factor.

Two-factor authentication for mobile devices

Smartphones offer a variety of 2FA capabilities, enabling companies to use what works best for them. Some devices can recognize fingerprints, use the built-in camera for facial recognition or iris scanning, and use the microphone for voice recognition. Smartphones equipped with GPS can verify location as an additional factor. Voice or Short Message Service (SMS) may also be used as a channel for out-of-band authentication.

A trusted phone number can be used to receive verification codes by text message or automated phone call. A user has to verify at least one trusted phone number to enroll in mobile 2FA.

Apple iOS, Google Android and Windows 10 all have apps that support 2FA, enabling the phone itself to serve as the physical device to satisfy the possession factor. Duo Security, based in Ann Arbor, Mich., and purchased by Cisco in 2018 for $2.35 billion, has a platform that enables customers to use their trusted devices for 2FA. Duo's platform first establishes that a user is trusted before verifying the mobile device can also be trusted as an authentication factor.

Authenticator apps replace the need to obtain a verification code via text, voice call or email. For example, to access a website or web-based service that supports Google Authenticator, users type in their username and password -- a knowledge factor. Users are then prompted to enter a six-digit number. Instead of having to wait a few seconds to receive a text message, an authenticator generates the number for them. These numbers change every 30 seconds and are different for every login. By entering the correct number, users complete the verification process and prove possession of the correct device -- an ownership factor.

These and other 2FA products offer information on the minimum system requirements necessary to implement 2FA.

Biometric authentication has become an increasingly popular option on mobile devices.

Push notifications for 2FA

A push notification is passwordless authentication that verifies a user by sending a notification directly to a secure app on the user's device, alerting the user that an authentication attempt is happening. The user can view details of the authentication attempt and either approve or deny access -- typically, with a single tap. If the user approves the authentication request, the server receives that request and logs the user in to the web app.

Push notifications authenticate the user by confirming that the device registered with the authentication system -- usually a mobile device -- is in the possession of the user. If an attacker compromises the device, the push notifications are also compromised. Push notifications eliminate threats such as man-in-the-middle attacks, unauthorized access and social engineering attacks.

While push notifications are more secure than other forms of authentication methods, there are still security risks. For example, users could accidentally approve a fraudulent authentication request because they are used to tapping approve when they receive push notifications.

Is two-factor authentication secure?

While two-factor authentication does improve security, 2FA schemes are only as secure as their weakest component. For example, hardware tokens depend on the security of the issuer or manufacturer. One of the most high-profile cases of a compromised two-factor system occurred in 2011 when security company RSA Security reported its SecurID authentication tokens had been hacked.

The account recovery process itself can also be subverted when it is used to defeat two-factor authentication because it often resets a user's current password and emails a temporary password to allow the user to log in again, bypassing the 2FA process. The business Gmail accounts of the chief executive of Cloudflare were hacked in this way.

Although SMS-based 2FA is inexpensive, easy to implement and considered user-friendly, it is vulnerable to numerous attacks. The National Institute of Standards and Technology ( NIST) has discouraged the use of SMS in 2FA services in its Special Publication 800-63-3: Digital Identity Guidelines. NIST concluded that OTPs sent via SMS are too vulnerable due to mobile phone number portability attacks, attacks against the mobile phone network and malware that can be used to intercept or redirect text messages.

Future of authentication

Environments that require higher security may be interested in three-factor authentication, which typically involves possession of a physical token and a password used in conjunction with biometric data, such as fingerprint scans or voiceprints. Factors such as geolocation, type of device and time of day are also being used to help determine whether a user should be authenticated or blocked. Additionally, behavioral biometric identifiers, such as a user's keystroke length, typing speed and mouse movements, can also be discreetly monitored in real time to provide continuous authentication instead of a single one-off authentication check during login.

Relying on passwords as the main method of authentication, while common, often no longer offers the security or user experience that companies and their users demand. And, even though legacy security tools, such as a password manager and MFA, attempt to deal with the problems of usernames and passwords, they depend on an essentially outdated architecture: the password database.

Consequently, many organizations are turning to passwordless authentication. Using methods such as biometrics and secure protocols enables users to securely authenticate themselves in their applications without having to enter passwords. In business, this means employees can access their work without having to enter passwords and IT still maintains total control across every login. The use of blockchain, for example, through decentralized identity or self-sovereign identity, is also gaining attention as an alternative to traditional authentication methods.

Continue Reading About two-factor authentication (2FA)

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What is Two-Factor Authentication (2FA) and How Does It Work?

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Two-factor authentication adds an extra layer of security to identity and access management programs. Learn about 2FA benefits, use cases and products.

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2 minute video about the security and privacy in regards to patients EHR.

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Electronic Health Records: Privacy and Security

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EHR are great because we are able to look up past medical records and histories of our patients. However, like with anything online, it runs the risk of privacy issues.

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Office of the National Coordinator for Health IT

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Worried About Using a Mobile Health Device for Work? Here's What To Do!

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Compare exploratory vs. explanatory analysis.

Discuss fundamental of data analysis.

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The main difference between explanatory and exploratory research is that explanatory research explains why certain phenomena work in the way that they do, whereas exploratory research explores and investigates a problem that is not clearly defined.

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Exploratory vs  Explanatory Analysis wvgBSMks4p8 [Business Analytics Nanodegree]

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Data analytics involves the manipulation and computation of large volumes of data, often from a wide variety of different sources. Manipulation and computation are performed at high velocity to identify patterns, correlations, and other useful information. Data analytics capabilities have been found to enhance the utility of the mass amounts of data that can be collected and communicated through the supply chain on a regular basis. Not only can information be distilled more quickly but models can also be developed to aid decision-making at both higher and lower levels in the supply chain. Examples include:

• understanding buyer/supplier behavior to reduce inventory levels;

• using predictive analytics to predict maintenance events in equipment so that parts and labor resources can be effectively prepositioned for repairs;

• using analytics to support real-time dynamic decision-making as to how to pick and ship orders based on the current situational awareness of demand patterns; and

• creating online promotions hourly, based on buying demand to reduce inventory levels.

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2.1 Introduction

Data analytics is the science of integrating heterogeneous data from diverse sources, drawing inferences, and making predictions to enable innovation, gain competitive business advantage, and help strategic decision-making. The data analytics domain has evolved under various names including online analytical processing (OLAP), data mining, visual analytics, big data analytics, and cognitive analytics. Also the term analytics is used to refer to any data-driven decision-making. In fact analytics is a pervasive term and is used in many different problem domains under different names-road traffic analytics, text analytics, spatial analytics, risk analytics, and graph analytics, for example. In the last 3 years, new academic degree programs at the master's level have been introduced under the name data science.

The recent emergence of Big Data has brought upon the data analytics domain a bigger role as well as greater challenges. The bigger role comes from the strategic initiatives across various organizations, small and big, to leverage big data for innovation and competitive advantage. In addition to the predominantly structured data that the data analytics methods used hitherto, there is a need to incorporate both semistructured and unstructured data into the analytic methods. There is greater value in drawing upon heterogeneous but related data from sources such as social media, geospatial data, and natural language texts. This in itself is a very difficult problem. Among the other challenges, both the data volume and the speed of data generation have increased tremendously in the recent years. From 2008 to 2015 the world-wide data has increased from 50 petabytes (PB) to 200 PB [1].

There is a greater expectation that the data analytics methods not only provide insights into the past, but also provide predictions and testable explanations. Moreover, analytics is not limited to predictive models. The IBM Watson's Jeopardy! game championship in 2011 clearly demonstrated the increased role of data analytics. Watson is a question-answering system [2] and exemplifies cognitive analytics. It generates multiple hypotheses for answering a question and assigns a degree of confidence to each answer.

Data analytics and Business Intelligence (BI) figure in the top spots for CIO's technology priority list in 2013. They also appear in the top 10 CIO business strategies [3]. Analytics are used for solving a range of problems from improving process efficiency to cost reductions, providing superior customer service and experience, identifying new products and services, and enhancing security capabilities.

Data analytics plays a tremendous role in Intelligent Transportation Systems (ITS). The advent of Internet of Things (IoT) ushers in even a greater role for analytics in ITS. Heterogeneous data originates from diverse sources such as weather sensors embedded in roadways, traffic signal control systems, social media, mobile devices such as smart phones, traffic prediction and forecasting models, car navigation systems, and connected car networks. Several software applications driven by this data are emerging. Such applications include emergency vehicle notification systems, automatic enforcement of speed limits, dynamic traffic light sequencing, vehicle-to-vehicle communication and collaboration, and real-time traffic prediction and rerouting.

The goal of this chapter is to provide a comprehensive and unified view of data analytics fundamentals. This exposition is intended to provide the requisite background for reading the chapters that follow. The intent is not to describe rigorous mathematical and algorithmic details about data analytics methods and practices. Entire books have been dedicated to providing that level of detail for topics such as OLAP, data mining, hypothesis testing, predictive analytics, and machine learning, which have implications for ITS.

The chapter is organized as follows. The four functional facets of data analytics from a workflow perspective-descriptive, diagnostic, predictive, and prescriptive-are described in Section 2.2. Next the evolution of data analytics from the late 1980s is traced in Section 2.3. The progression from SQL analytics, to business analytics, visual analytics, big data analytics, cognitive analytics is described. This evolution should be seen as a gradual increase in data analytics functional sophistication and the range of analytics-enabled applications.

Data science as the foundational discipline for the current generation of data analytics systems is discussed in Section 2.4. Data lifecycle, data quality issues, and approaches to building and evaluating data analytics are discussed in this section. An overview of tools and resources for developing data analytic systems is provided in Section 2.5. Future directions in data analytics are listed in Section 2.6. Section 2.7 summarizes and concludes the chapter. Questions and exercise problems are given in Section 2.8. Machine learning algorithms are a critical component of the state-of-the-art data analytics systems, and are discussed in Chapter 12 in this volume.

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URL: https://www.sciencedirect.com/science/article/pii/B978012809715100002X

Data analytics

Data analytics involves the manipulation and computation of large volumes of data, often from a wide variety of different sources. Manipulation and computation are performed at high velocity to identify patterns, correlations, and other useful information. Data analytics capabilities have been found to enhance the utility of the mass amounts of data that can be collected and communicated through the supply chain on a regular basis. Not only can information be distilled more quickly but models can also be developed to aid decision-making at both higher and lower levels in the supply chain. Examples include:

*

understanding buyer/supplier behavior to reduce inventory levels;

*

using predictive analytics to predict maintenance events in equipment so that parts and labor resources can be effectively prepositioned for repairs;

*

using analytics to support real-time dynamic decision-making as to how to pick and ship orders based on the current situational awareness of demand patterns; and

*

creating online promotions hourly, based on buying demand to reduce inventory levels.

The current adoption level for predictive analytics is 25% and is expected to reach 70% in 3-5 years, and up to 77% in 6 years (MHI, 2015). The strategic use of data analytics has been to provide up-to-date information to make decisions and respond appropriately. Doing so can enhance agility to respond to uncertain market conditions. This feature can help reduce risk, especially in emerging markets where volatile conditions can complicate growth and production (Degun, 2014).

In the transportation sector, there has been a shift from using traditional TMS to their integration with decision-making analytics tools, so as not just to record but analyze transportation data and make suggestions accordingly. Predictive analytics can provide critical predictions for field service and can be used strategically to position parts and personnel before problems or issues arise. This not only reduces part inventory but ensures overall service quality and reliability (Brown, Basu, & Worth, 2010).

For example, Accenture was interviewed for its multinational presence, and its consequent knowledge of global trade and trends. Accenture has recently collaborated with General Electric on a predictive analytics project (TALERIS) to provide an intelligent operations service for airline carriers around the globe. Another example is health products retailer GNC, which uses a labor management dashboard in distribution centers to track picking completion times and variances, enabling it to shift resources accordingly to keep up with demand. Using data analytics for supply chain management can also optimize shipping. For example, United Parcel Service (UPS) has been working for a decade on a system called On-Road Integrated Optimization and Navigation that determines the optimal path for road delivery, using big data analytics to handle the data encountered (Noyes, 2014; Rosenbush & Stevens, 2015).

SAP has recently produced a high-performance analytic appliance (HANA) platform that is effectively "live cache" technology. HANA can be used to support an in-memory relational database management system. This technology can dramatically increase the runtime speed of various complex applications in the supply chain and can transform what were once operational tools into decision-making tools. Speed is achieved by virtue of in-core processing and avoiding the need for data aggregation. For example, a materials requirements plan (MRP), which traditionally took hours to run, can now produce results in seconds. Such speed can enable companies to run multiple MRPs to achieve the most desirable scenarios. Another example is in warehouse management, where it is used to support tracking warehouse employees in real time by managing data that tracks the historic movements of employees. Such information can be used to predict task completion times, making labor management more efficient.

The following web link provides additional examples of implementations of data analytics in the supply chain:

SAP HANA-Transforming Business Systems https://www.youtube.com/watch?v=EoPnKSgXzO8

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URL: https://www.sciencedirect.com/science/article/pii/B9780128159569000028

MODA works closely both with agencies across the city government and with New Yorkers directly. Within the city government, MODA convenes data holders and analytics professionals both through our management of the NYC Open Data Program and through convening the Analytics Exchange (AnEx), the City's community of practice for analytics professionals. AnEx has over 600 members and meets quarterly to share success stories and best practices and discuss the environment for analytics in the city and how it could be improved. To celebrate the 10-year anniversary of NYC Open Data, in 2019, MODA conducted its most involved engagement of stakeholders to date to develop a strategic plan for the next decade of the program. We conducted a 2-week public survey and 2-day co-design workshop connecting city agencies, community members, and private organizations who interact with the city's open data to set the priorities that will guide our management of the program going forward. Part of making data open to the public means not asking people how they are using it. Through conducting public engagement events like our annual Open Data Week celebration and co-design workshops like these, we hope to strengthen the feedback loop to make NYC Open Data more useful for all.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128211229000051

Linda A. Winters-Miner PhD, ... on January 8, 2014 at 1:22 pm in Healthcare Datamining, in Practical Predictive Analytics and Decisioning Systems for Medicine , 2015

Traditional Statistics vs Data Mining vs Predictive Analytics

Data Analytic formats probably can be described best by comparing the following methods, all needed at some point in the "Data Scientist's" toolbox:

*

Descriptive statistics (based on traditional "Frequentist" Fischerian statistics)

*

Descriptive graphical visualization of data (generally based on traditional statistics)

*

Traditional Fischerian (Central Limit Theorem) statistics, versus:

*

Data mining modern "Statistical Learning Theory" algorithms, versus:

*

Predictive analytics ... and:

*

Prescriptive analytics ...

There are minimally four ways "Data Scientists" can analyze data today:

1.

Old-fashioned 20th century p-value "frequentist statistics," which for the most part look at "means" of populations (just what we do not want for personalized medicine - which requires predictions for the individual).

2.

Use of DA (discriminant analysis) and LR (logistic regression) to get at individual scored predictions - i.e., lower-level personalized data analysis, which will generally give the lowest, least powerful scores compared to points 3 and 4, below. ( These two are exceptions among the "traditional methods" because they make predictions for the individual.)

3.

Logistic regression (LR) modified by "standardization," so to speak, with WoE (weight of evidence). This is needed by certain organizations that are "regulated" industries and have to give a set of rules for each score/each decision made; these have more power, thus giving more reliable predictions (compared to using DA and LR in the "traditional format"). ( Note: this third group is included in the domain of predictive analytics, but 1 and 2 above, although useful for visualizing data trends, are not generally defined as being the core part of the field of predictive analytics.)

4.

Data mining algorithms (from statistical learning theory methods) that do not need to meet the assumptions of "traditional statistics" but instead learn from the data and can understand any crazy type of curvilinear distribution. Generally these methods find the highest accuracy scores (or predictions) from the same data that may be barely touched by 20th century Fischerian statistics, or at the best give "predictions for means of populations or sub-populations (groups)" but not for the individual.

In summary, generally today predictive analytics is thought to include only the modern statistical learning theory, plus weighted regression methods, plus discriminant analysis and logistic regression. Thus, much of Fischerian statistics are becoming "outdated" for medicine as today there are much better ways to analyze data.

The final bottom-line criterion must be this: Does the data analysis work and predict for the individual, or for the group? True predictive analytics can do both at the same time; traditional statistics cannot do both but only work at the population level - and not even that is done well in most circumstances, because the assumptions of traditional statistics (which are dependent on the Central Limit Theorem) are usually not met with life science data (and in fact most "real world data"). All kinds of variations of the Generalized Model can be done to attempt to take care of some of these things, but it is a lot of work, it is important that the operator really knows what he or she is doing statistically, and the results are generally inferior to what can be done with true predictive analytics.

Some data scientists, coming from the traditional 20th century statistical domain, may argue that statistical modeling and testing versus predictive modeling via general approximators is not really related to the nomothetic/idiographic distinction of how one conducts science and/or modeling and prediction. Prediction of individuals (every person is unique - idiographic science) or groups (every person is like every other person - nomothetic science) can be done using any modeling tool the data scientist desires. It is just a matter of how many models the scientist is willing to build, or capable of building, if agreeing that the goal is one model per person, to find out exactly what works for Jo[hn] Jones (should Jo[hn] Jones get sick).

However, what this book is all about is data analysis that most efficiently and most accurately predicts what is best for the individual person (patient). The process for this was presented in Chapter 3, and we repeat it here in Figure 21.1 because it is so crucial.

The model shown in Figure 21.1, with a decisioning process added, is where it is at today ... the place where medicine and healthcare delivery are evolving to in order to treat the individual with accuracy, instead of guesswork. We live in a different age today; Bayesian data analysis methods are again coming into use in the 21st century - the past century was an "anomaly" in data science.

Another way of describing the controversy between traditional 20th century Fischerian statistics and modern predictive analytic data analysis is to use the term Global Learning (i.e., a population-based learning where means of the population and corresponding t-tests, p values, and other stats of traditional statistics are used) vs. Individual Statistical Learning Theory methods (i.e., the case-by-case learning approach of data mining algorithms). This distinction is important for the main message of this book, which is personalized and person-centered health care. This distinction is also important for CER (Comparative Effectiveness Research) and HTE (Heterogeneous Treatment Effect), in which some scientists are intent on looking at groups of people (groups classified by age, sex, etc.) but for which the ultimate goal is to look at individuals. CER and HTE will be discussed more thoroughly in Chapter 26: 21st Century Healthcare and Wellness: Getting the Health Care Delivery System That Meets Global Needs.

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URL: https://www.sciencedirect.com/science/article/pii/B9780124116436000491

Abstract

Newark Industrial Data Analytics Platform (NIDAP) is intended to serve as a smart city infrastructure by building an industry network of shared database by major anchor businesses, about 400 small and medium manufacturers, and the city government of Newark, NJ. The partnerships among local university, industry, and government entities were formed to promote Newark's industrial competitiveness and economic development. The NIDAP provides the stakeholders with infrastructure for information sharing to understand local supply-chain and business development, and for data analytics tools to support the performance and sustainability of economic growth, to analyze local workforce trends, and to assess the effectiveness and impact of economic policies by the city government. In this case study, we present the case of developing the NIDAP for providing manufacturing industry with visibility for contract opportunities, the Newark anchor institutions with procurement collaborations, and the city government with the ability to assess the impact of city's economic policies and to dynamically evaluate economic policy alternatives.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128161692000158

The Increasing Application of Data Analytics in M&As

Often described as the identification and interpretation of meaningful patterns in data, data analytics relies on the application of statistics, computer programing, and model building to uncover insights helpful in business decision making. In an age when the cost of data storage, organization, and manipulation has been declining, senior executives often are confronted with vast amounts of data on their customers, suppliers, employees, etc. Just as executives attempt to use such data to describe, predict and improve business performance, data analytics can be applied to all phases of the M&A process described in this book.

Putting a deal together often requires access to large amounts of granular data and the ability to achieve an understanding of historical patterns which underlie predictions of future performance. While the spreadsheet models discussed in of this book are examples of how analytical tools can be used to address value and risk related to M&As, they often fail to identify and explain more complex issues. So-called "structured data" such as 10K financial statements are more easily accessible and manipulated than "unstructured data"which ranges from that gleaned from social media to customer preference survey results, census data, to weather information. Thus, analytical tools which use both structured and unstructured data may provide insights into patterns not observable in the output of financial spreadsheet models.

The application of data analytics to M&A has increased in acceptance and popularity with 40% of 2016 Deloitte & Touche survey respondents viewing data analytics as critical to M&A analysis. Larger firms are more inclined to use data analytics with 58% of firms with revenue exceeding $5 billion viewing data analytics as a core component of M&A analysis; firms with annual revenue under $500 million showed less interest in such tools.

Data analytical tools can be applied throughout the M&A process. For example, text reading software can organize the review of legal contracts through key word (or phrase) searches based on names, locations, and dollar amounts. A page by page review of legal documents, once time consuming, can now be done automatically. Another application is in assessing the target firm's talent pool by identifying the number of employees with certain types of degrees or certifications and length of work experience. Comparisons can be made between the target's and acquirer's compensation structures to determine what changes might have to be made and the potential impact on the combined firm's cost structure. Automated natural language processing (i.e., training computers to understand all forms of human communication) can be used to assess the intellectual property of the target firm and to cross-reference those descriptions of the property with other databases to avoid potential future litigation or regulatory challenges.

While data analytics can provide valuable insights, it is subject to significant limitations. It is predicated implicitly on the notion that more data is better than less. A deluge of data makes it difficult to focus on what is really important. While software can sift through reams of data to identify patterns, it is not always clear that the patterns are not simply an anomaly or whether they are significant and sustainable over time. Furthermore, the management of the target firm is likely to balk at handing over voluminous data during due diligence. As explained elsewhere, it is in the interest of sellers to comply with requests for specific data rather than to go through the exhaustive and expensive process of supplying large volumes of data. Analytical tools can be expensive, difficult to use, and appear to executive decision makers as "black boxes." When executives don't understand how the conclusions were reached, they are less likely to have a high degree of confidence in them.

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URL: https://www.sciencedirect.com/science/article/pii/B978012815075700005X

Similarities and Differences Between Data Analytics and Decision Support

Both data analytics and decision support systems exploit the wealth of data that is increasingly becoming available for global health. However, these disciplines can be considered as sequential steps of a much bigger process. It may seem that there exists some overlap between these two phases, yet some differences are clearly noticeable.

Data analytics concerns mainly the discovery of hidden and meaningful patterns across data sources. The most frequently used methods involve statistical analysis and ML for predictive modeling purposes. Hence, it is grounded on artificial intelligence methods and data science techniques. On the other hand, decision support is a well-known discipline that has been in use since the 1970s. The main objective of decision support systems is to help humans in the decision-making process.

Fig. 9.3 shows a possible workflow from data collection to the suggestion of recommendations. Once data has been collected, data analytics is the first step toward turning data into knowledge. First, descriptive analytics is usually utilized to discover nontrivial trends and patterns. Second, predictive analytics is applied to generate computational models that represent the knowledge behind the data, which is relevant to address a particular health problem. Once we have that, we can create decision support systems that can help improve health by providing the final user with recommendations. This is especially important in low- and middle-income scenarios given their complexity in terms of how big the population is and the high number of diseases to be considered. In addition to this, because of counterfeit and the use of expired antibiotics, leading to antimicrobial resistance, we must face bigger challenges in comparison to first world countries.

In a similar manner to the "Model List of Essential Medicines" (World Health Organization, 2015) that was first launched by the World Health Organization in 1977, Dove and colleagues identify the need of creating an "Essential Diagnostics List" (Dove et al., 2015) that will take advantage of technologies such as those mentioned above. This list will allow establishing connections with health policies that cover multiple sectors, which may involve other sectors than just health (e.g., the use of omic technologies in the pharmaceutical field), as well as with science and ethics that are completely transparent and related to global society.

An overview of the application of informatics to global health is given in the work published by Dixon and collaborators (Dixon et al., 2015). In the rest of this chapter, we will focus on two specific fields, data analytics and decision support, presenting recent approaches that have been applied to global health informatics in relation to them.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128045916000100

Big Data, Not Always so Big

"Big Data Analytics" is one of the "coolest" tech-terms in 2015, referring to algorithms that slice and dice reams of data, to elicit conclusions and relationships not known, not even suspected before. Such "data mining" systems are routinely applied to the mountains of financial data: to catch theft and fraud. The idea being that since so much data accumulates daily, there is more and more "beef" to process, in order to characterize an identity thief, for example. This is indeed so, insightful relationships may be discovered by combing reams of relevant data. Alas, too often the technology is over-applied leading to useless conclusions and unhelpful relationships that while they are covered by a validity fig-leaf, their inference is misleading. This is for the same reason that pure random data would yield "big data" conclusions, and "analytic" relationships, which in fact reflect normal randomized variety. You will hardly hear this admitted by Big Data vendors and proponents, who claim that "big data tools" offer the magic of deriving conclusions over a subject without ever understanding what it is about. It is a triumph of raw data over modeling and case insight: which is both its strength and weakness.

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URL: https://www.sciencedirect.com/science/article/pii/B978012803477400003X

Fitting Criteria

In practical data-analytic situations, an exact representation of the observed proximity data by a distance model is usually impossible due to measurement errors. MDS systematically searches for an object configuration and an optimal data transformation that minimizes the discrepancy between the transformed proximity data and the distance predictions. The discrepancy is quantified as a fit index (e.g., the normalized stress), which is also used as a fitting criterion to be minimized for estimating parameters in the model. Two broad classes of discrepancy functions have been used in MDS. One is the least squares (LS) criterion (Kruskal, 1964a,b), and the other is the maximum likelihood criterion (Ramsay, 1977, 1982). Although the maximum likelihood criterion has some appeals for its statistical inference capabilities with data with a large sample size, the LS criterion has been far more predominantly used in MDS for its simplicity and flexibility, requiring no rigid distributional assumptions. The LS criterion is stated as,

where t indicates a generic data transformation function, whose specific form depends on the scale level of proximity data, as described in the previous section. The LS MDS attempts to find the set of object coordinates x ( i = 1, ..., n, where n is the number of objects and m = 1, ..., M), and data transformation parameters that minimize the discrepancy between the transformed proximity data [ t( o)] and predicted distances ( d). Criterion [2] usually cannot be minimized in closed form, and some sort of iterative algorithms should be used (see Borg and Groenen (2005) for more details of the optimization algorithms used in MDS). The LS criterion used in MDS may have multiple local minima, and iterative optimization procedures may converge to one of them that is not the true minimum of the criterion we wish to find. This is called the local minima problem. To circumvent this difficulty, we may obtain multiple solutions with many different initial estimates and choose the best solution among them. In all results of MDS reported in this article, solutions were obtained with 100 random initial starts.

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URL: https://www.sciencedirect.com/science/article/pii/B9780080970868420453

Overall Discussion

Meta-analytic data and a review of the literature suggest that CBM may offer promise as a clinical intervention tool. Potential practical benefits of CBM-transportability, augmentation effects with CBT, and cost-effectiveness (for review, see Beard, 2011)-are among the reasons for the enthusiasm for this approach. The mixed findings in the various meta-analyses suggest using any CBM intervention with any parameters may not be an effective treatment for all disorders for all individuals under all circumstances. To parse apart the complicated interactions that may arise, several issues common across CBM programs require increased attention.

First, an increased focus on mechanisms is needed. As argued elsewhere in the context of ABM (Clarke et al., 2014; Kuckertz & Amir, 2015; MacLeod & Clarke, 2015), one would not expect clinical outcome effects without a corresponding change in bias. Thus, failure to see clinical effects in the absence of bias change does not indicate that the mechanism is unrelated to outcome but, rather, that the experimental method was insufficient to produce mechanistic change. Nonetheless, null findings are ambiguous in that they do not provide information regarding whether researchers were indeed targeting a bias thought to maintain psychopathology. Although studies that change attentional bias have consistently yielded reductions in symptoms in the context of ABM (Clarke et al., 2014), a few exceptions in both the ABM and IBM literatures have demonstrated expected group differences in bias in the absence of expected group differences on symptoms or behavior (e.g., Clerkin, Beard, Fisher, & Schofield, 2015; Reese, McNally, Najmi, & Amir, 2010; Van Bockstaele et al., 2011).

The mechanisms are only as good as the quality of the measurement we use to assess them. To illustrate, since the introduction of the probe detection paradigm in 1986 (MacLeod et al., 1986), 30 years of accumulated research suggests that this task is a valid instrument in differentiating anxious and nonanxious participants (for reviews, see Bar-Haim et al., 2007; Van Bockstaele et al., 2014); yet, reliability of the task is at best moderate (Price et al., 2015). This seemingly defies a basic psychometric tenant-that reliability of an assessment instrument is a pre-requisite for its validity. To reconcile these differences, researchers should more clearly consider the statistical limitations associated with the measures they use (i.e., difference scores between response latencies to two types of stimuli), as well as alternate explanations for the apparent discrepancy between reliability-validity findings (for discussion of this issue, see Iacoviello et al., 2014; Price et al., 2015; Sipos, Bar-Haim, Abend, Adler, & Bliese, 2014). Although several measurement alternatives have been proposed to reconcile these differences and advance the measurement of attentional bias (Iacoviello et al., 2014; Zvielli et al., 2015), these alternatives await further validation to determine whether they represent an improvement over current measures of attentional bias.

Theory is critically important in order to advance the field and avoiding "ad hoc storytelling" (Treat & Weersing, 2005). Data from various ABM trials are available and can be used to replicate and validate any new measures of attentional bias. Such efforts demand a concerted effort and sharing of raw response latencies among investigators in order to advance the field.

In summary, it may be time to examine basic questions about the nature of cognitive biases, their relationship to symptoms, their psychometric properties, and the robustness of our experimental manipulations. In so doing, we must also insist on quality control that is focused not only on issues such an minimizing experimenter bias (Cristea, Kok, et al., 2015; Cristea, Mogoaşe, et al., 2015; Heeren, Mogoaşe, Philippot, et al., 2015), but also on issues related to proper delivery of active ingredient of the intervention. Accumulation of individual findings is important, but any one study can only advance the field so far. Real advancement is likely to come from accumulation of data across multiple studies, as is now becoming common in other areas (e.g., fMRI). It is also important to consider the nascent field of ABM in relation to the larger body of intervention research. In his classic paper entitled In defense of external invalidity, Mook (1983) described the importance of distinguishing between "'What Can' versus 'What Does'" happen in the context of experimental research, while appreciating the importance of research regarding "what can" happen. CBM research to date has demonstrated that brief, computerized interventions can have an effect on cognitive biases and subsequent emotional symptoms. Yet, meta-analyses suggest that this is not always what does happen in the context of CBM programs across a variety of situations using a variety of methods. Given the lack of the access to empirically supported treatments such as CBT (Gunter & Whittal, 2010), the fact that therapist-free, low-cost, and computerized interventions such as CBM can have an effect of mechanisms and symptoms suggests the potential benefit of future research in the CBM field regarding the parameters around which these effects occur.

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URL: https://www.sciencedirect.com/science/article/pii/B9780128034576000192

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Data analytics is the science of integrating heterogeneous data from diverse sources, drawing inferences, and making predictions to enable innovation, gain competitive business advantage, and help strategic decision-making. The data analytics domain has evolved under various names including online analytical processing (OLAP), data mining, visual analytics, big data analytics, and cognitive analytics.

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Data Science In 5 Minutes | Data Science For Beginners | What Is Data Science? | Simplilearn

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Prepare a visualization of data.

Determine components for developing an infographic.

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Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
The utility of data visualization can be divided into three main goals: to explore, to monitor, and to explain. While some visualizations can span more than one of these, most focus on a single goal.

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What is data visualization?

Learn how data visualization can improve understanding and analyses, enabling better and faster decision making

Explore IBM Planning Analytics Explore other solutions

What is data visualization?

Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it's important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends. Harvard Business Review (link resides outside IBM) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We'll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or Design Thinking sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they're looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate workflows. Data modeling also uses abstraction to represent and better understand data flow within an enterprise's information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn't limited to performance dashboards. For example, while text mining an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it's important to remember that it is a skillset that should extend beyond your core analytics team.

Types of data visualizations

The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published The Visual Display of Quantitative Information (link resides outside IBM), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.

Pie charts and stacked bar charts: These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.

Line charts and area charts: These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.

Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.

Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.

Heat maps: These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.

Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Open source visualization tools

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers. D3.js (link resides outside IBM) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.

ECharts: A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc. Echarts (link resides outside IBM) is based in JavaScript and ZRender, a lightweight canvas library.

Vega: Vega (link resides outside IBM) defines itself as "visualization grammar," providing support to customize visualizations across large datasets which are accessible from the web.

deck.gl: It is part of Uber's open source visualization framework suite. deck.gl (link resides outside IBM) is a framework, which is used for exploratory data analysis on big data. It helps build high-performance GPU-powered visualization on the web.

Data visualization best practices

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It's important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company's open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You'll want the data that you provide to motivate people to act within their scope of their role. If you're unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn't mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

While there are various data visualization tools on the market, Cognos Analytics is IBM's business intelligence and data visualization tool. Cognos Analytics's self-service platform integrates cognitive computing technology, including artificial intelligence and machine learning, to make it easy for organizations to visualize data, share new insights, and encourage data-driven decision-making.
Explore IBM Cognos® Analytics today

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Learn how data visualization can improve understanding and analyses, enabling better and faster decision making.

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Visualizing Health and Healthcare Data Book

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Nice video on healthcare visualization

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The First Impression: Gives the reader a first impression of what the text is going to present or argue. ...

The Story: What is the main argument of the infographic? ...

The Data: What are the supporting details of the main arguments? ...

The Strategy: Where does your eye travel?

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Nursing informatics is the specialty that transforms data into
needed information and leverages technologies to improve
health and health care equity, safety, quality, and outcomes.
Data, information and knowledge are the three components for informatics

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"There are several core professional meetings whose sole focus is HIT. While not focused exclusively on pharmacy, the HIMSS annual meeting is arguably the largest, single conference in the United States on HIT. If you like technology as much as we do, the HIMSS exhibit floor is an extremely large playground, with about 15,000 more attendees than the American Society of Health-System Pharmacist's (ASHP) Mid-year Clinical Meeting. Speaking of ASHP's Midyear, we certainly include this conference in our list of key sources of HIT information. Although not as large as HIMSS, Midyear's exclusive focus on the needs of pharmacy make it an invaluable source of information for our specific, professional perspective."

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Outreach is an important component of the life of a faculty member. Our outreach and engagement efforts allow us to connect with professional colleagues from local hospitals and health systems and in international settings. Outreach includes presentations at meetings, consultations for groups of all sizes, and regular columns in journals, such as the one you are reading. Regular readers of this column know that we consistently focus on the role of health information technology (HIT) in pharmacy and in health care. In general, the goal of our outreach is to keep our readers up-to-date on the latest HIT advancements, research, and discoveries that we believe have the potential to impact patient care and pharmacy practice. Information and the technology that manages it are a common denominator in every discipline and specialty. But how do we (and maybe you) keep up with it?

We have many of the same resources for keeping informed as you do: professional meetings, colleagues, journals, webinars, vendors, and more. Sometimes, the information comes to us in a nice package, including all of the related information, so we can interpret it and the impact it may have on pharmacy. More often, however, the information comes to us in silos, requiring us to internalize it, consider it along with other information, and ultimately determine what it means to pharmacy or health systems or physician or nurses. A sizable chunk of the information about which we write comes to us through the Internet, as a byproduct of transactions or innovation occurring in one manner or another.

One of our most useful Internet-based resources was www.ihealthbeat.org from the California Health Care Foundation. We use the past tense because the site (which was more of a service than a static page) stopped adding new content on February 1, 2016. The loss of this extremely valuable tool prompted us to refiect on the strategic level methods we find most useful for keeping up with HIT, which we explore below. Our focus is not necessarily on the functions or features of these methods (eg, a push-email service), but instead on the value and quality of the information provided.

We will begin with www.healthcareitnews.com, one of our favorite alternatives to www.ihealthbeat.org._iHealthBeat did not include advertisements on its site, whereas Healthcare IT News does. We do not necessarily view this as a significant negative; ads have become so pervasive on the Internet that seasoned users are able to unconsciously ignore them to focus on relevant information. Arrangement of information by topical areas is similar across both sites, focusing on technology, policy, and industry news. Other sites that we recommend include www.healthit.gov, the primary governmental site for all information related to HIT, and www.modernhealthcare.com, whose scope is broader than HIT. Of note, Healthcare IT News is part of a larger publishing group affiliated with HIMSS (Healthcare Information and Management System Society), an international organization focused on optimal use of HIT. In many instances, ads can be unrelated and distracting, but when they are complementary they bring a concrete application to bounce off of a focus area.

There are several core professional meetings whose sole focus is HIT. While not focused exclusively on pharmacy, the HIMSS annual meeting is arguably the largest, single conference in the United States on HIT. If you like technology as much as we do, the HIMSS exhibit floor is an extremely large playground, with about 15,000 more attendees than the American Society of Health-System Pharmacist's (ASHP) Mid-year Clinical Meeting. Speaking of ASHP's Midyear, we certainly include this conference in our list of key sources of HIT information. Although not as large as HIMSS, Midyear's exclusive focus on the needs of pharmacy make it an invaluable source of information for our specific, professional perspective.

While the mega-conferences with attendance numbering in the tens of thousands are great for experiencing a great breadth of information, we also find value in smaller conferences. ASHP's Summer Meeting includes several conferences, one of which is called the "Informatics Institute." The educational programming is targeted toward attendees' needs to advance their use of HIT. Like the Midyear, exhibits and networking sessions allow you to connect with colleagues.

We also rely on print media. Journals are an important source of information because they provide a view from the research perspective, showing the why and how of HIT use. As a reader of Hospital Pharmacy, you are aware of the value found in this media. One of our primary journals is the Journal of the American Medical Informatics Association ( JAMIA), which covers the full spectrum of HIT topics. Content is not specific to pharmacy, but JAMIA's systemwide focus is very helpful in terms of providing a big-picture perspective.

We are tuned into the dynamic nature of our health care environment, especially the growing focus on engaging patients in their own care. One of our primary sources of information related to the use of HIT and the Internet to connect with patients and to enable their role in their own care is the Journal of Medical Internet Research ( JMIR). Because JAMIA's focus is on health care in the Internet age, there is considerably variety in article topics. The JMIR publishing group includes other journals with narrowed scope (eg, mHealth, medical informatics, human factors, etc).

This is a brief description of some of the resources we find of most value in helping us keep up with HIT. We encourage you to explore these resources for your own needs. We know that we probably missed some alternative resources that you find most valuable. Please send us an email to let us know what we should check out (Brent at ude.nrubua@nerbxof and Bill at ude.nrubua@gbeklef).

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Keeping Up with Advancements in Health Information Technology

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We all face an onslaught of information on a daily basis in our personal and professional lives. We view it as a significant part of our professional responsibility to help our colleagues stay abreast of the latest advancements related to health information ...

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Future Medicine: Modern Informatics | Richard Frackowiack | TEDxYouth@Zurich

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Health Management and Policy Productions