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It is the process of studying data to find out the answers to how and why things happened in the past. Usually, the result of data analysis is the final dataset, i.e., a pattern, or a detailed report that you can further use for Data Analytics.

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Types of Data Analysis methods that we discussed were descriptive analysis, prescriptive analysis, and predictive analysis. However, I found other types of data analysis, which were Diagnostic Analysis (gives a detailed explanation of a particular scenario by understanding behavior patterns), Statistical Analysis (a statistical approach or technique for analyzing data sets to summarize their important and main characteristics generally by using some visual aids).

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This week, we reviewed the importance of legal and ethical laws governing our healthcare system. Ethical and legal principles evolve in healthcare similarly to how they grow in society as a whole because the ethical and legal principles that govern society govern healthcare as well, with some alterations due to the specific legalities involved with the practice of healthcare. Legal principles deals with the practical regulation of morality or behaviors and activities. Laws tell us how to behave under specific circumstances and prescribe remedies or punishments for individuals who do not comply with the law. However, ethical considerations determine what is good and which behaviors are desirable by higher principles.

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This review I found examines confidentiality, consent to health care, and research involving adolescents regarding legal and ethical aspects. Adolescents were more likely to seek health care if their provider assures confidentiality. But there has to be a balance between the needs of the adolescent patient, parents, and provider.  However, providers must seek permission for consent and must consider the adolescent's ability to understand the medical treatment or the research in which the adolescent is requested to be involved. Under all circumstances, adolescents' permission for medical treatment and research is always mandatory. Although it is mandatory, I believe involving adolescents in research should be based on a careful assessment of risks, benefits and an understanding of the developing capabilities of the adolescent.

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A picture displaying how data can be visualized or interpreted.

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Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. It is also a process of unifying diverse medical data from various sources and transforming it into visual content (infographics and mini-infographics, charts, tables, timelines, scatter plots, or others). The purpose of visualization is to help medical staff interpret data analytics results faster, recognize trends, and make better decisions.

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Privacy and security of health information are paramount in today's technologically advancing world. The increased use of electronic health records, smartphones, patient portals, etc., is changing the look of our healthcare system.  This video explains the background and current state of the regulations for protecting patients' health information privacy, confidentiality, and security. Although our healthcare system does its best to maintain privacy, some people that I know (older adults) still do not trust that their data are protected. However, one of the ways we help people with this mindset is to l updated them on technologies or educate them on the security systems that have been put in place to protect their privacy.

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For the week, we discussed how informatics could improve healthcare quality and learned about alert fatigue. The term alert fatigue refers to how busy workers become desensitized to safety alerts and, as a result, ignore or fail to respond appropriately to such warnings. The rapidly increasing computerization of health care has produced benefits for clinicians and patients. Most healthcare technologies—computerized provider order entry systems (CPOE), intravenous infusion pumps, or cardiac monitoring devices—provide auditory or visual warnings to clinicians to prevent or act in unsafe situations. These warnings are well intended and, in isolation, may be helpful. However, in the current highly computerized clinical environment, an individual clinician interacts with many different alert-generating devices—meaning that every day, clinicians are on the receiving end of a staggering number of alerts. Thereby causing clinicians to ignore the bothersome, clinically meaningless alarms and the critical alerts that warn of impending severe patient harm. But one of the ways we could prevent alert fatigue in our healthcare industry is to increase the alert specificity by reducing or eliminating clinically inconsequential alerts.

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Two Minutes: What's the Risk? Alert Fatigue

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This video previews what alert/ alarm fatigue is and describes the types of alarms in the hospital setting. Studies have shown that electronic systems has become more prevalent in the healthcare settings but the number of audio continues to increase dramatically.

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Two Minutes: What's the Risk? Alert Fatigue

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Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation

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This study was done to determine how effective machine learning prediction models can generate automatic alerts from CDSSs to directly assist physicians in making beneficial clinical decisions and help shape prescribing behavior. They had one model, the Artificial Neutral Network(ANN) learning machine, which excellently performed well in predicting individual physician responses to an alert from a disease medication. Although this research is still in progress and has not yet been introduced, I think one of its cons can be clinicians' dependency. Some clinicians may become very dependent on such a model to the point that they might not look at patients' labs to ensure they will getting the right medication. However, I believe one area that this model may succeed will be urgent care clinics.

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Monitoring Editor: Gunther Eysenbach and Rita Kukafka

Reviewed by Liangliang Zhang, Travis Goodwin, Huriye Demir, and Suptendra Sarbadhikari

Tahmina Nasrin Poly, MSc, 1, 2, ³ Md.Mohaimenul Islam, MSc, 1, 2, ³ Muhammad Solihuddin Muhtar, BSc, ² Hsuan-Chia Yang, PhD, 1, 2, ³ Phung Anh (Alex) Nguyen, PhD, 2, ⁴ and Yu-Chuan (Jack) Li, MD, PhD 1, 2, 3, 5, ⁶

Abstract

Background

Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far.

Objective

Our objective was to develop machine learning prediction models to predict physicians' responses in order to reduce alert fatigue from disease medication-related CDSSs.

Methods

We collected data from a disease medication-related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets.

Results

A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively.

Conclusions

In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication-related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

Keywords: clinical decision support system, alert fatigue, machine learning, artificial neural network

Introduction

Initiation of computerized provider order entry (CPOE) systems has allowed physicians to order medications, laboratory tests, and other ancillary services electronically [ 1]. CPOE systems create an opportunity to improve patient care by decreasing medication errors, reducing redundant test orders, and promoting standardized clinical practice [ 2, 3]. However, CPOE is often integrated with a clinical decision support system (CDSS) in order to make better clinical decisions through guidance, alerts, and reminders. A CDSS is always combined with software algorithms that generate alerts during orders entered into a CPOE by physicians [ 4, 5]. Each of these alerts addresses a meaningful clinical issue relevant to the administration process and has a positive impact on identifying and preventing erroneous or less optimal prescription [ 6- 8].

The productivity of CDSSs is often impaired by generating distracting alerts in the system (ie, a high volume of clinically irrelevant alerts) [ 9, 10]. van der Sijs et al [ 11] suggested that an ideal CDSS should have high specificity and sensitivity, provide clear information, and facilitate safe and efficient handling of alerts. A recent study reported that approximately 90%-95% of medication alerts are overridden by providers [ 12, 13], and more than half of overrides are due to alerts being deemed clinically irrelevant [ 14]. The main concern is that these large numbers of clinically irrelevant alerts might cause alert fatigue and consume too much time and mental energy. Moreover, it sometimes leads staff to override both critical warnings and unimportant alerts. Getting frequent false alerts can desensitize physicians so that providers always ignore and mistrust alerts with acceleration [ 15]. Ignoring clinically relevant alerts too much triggers patient harm and is associated with an increased rate of mortality.

Until now, significant efforts and strategies have been implemented in minimizing alert fatigue, such as the administration of highly specific algorithms [ 16], customization of third-party providers' sets of alerts [ 17], and execution of tiered severity grading to stratify and lessen the number of false alerts [ 18]. Several studies suggested turning off frequently overridden alerts [ 19], updating clinical content to deliver the most current evidence at the point of care, and holding consensus meetings between physicians and pharmacists [ 20]. Since physicians increasingly adopt electronic prescribing, the progression and proclamation of CDSS alerts might depend, in part, on whether providers find medication safety alerts valuable.

Machine learning is comprised of a collection of techniques that has the potential to learn complex rules and to identify patterns from multidimensional datasets. It has been effectively employed in many areas, such as disease risk prediction [ 21], classification [ 22], and health care utilization [ 23]. To our knowledge, no studies have examined machine learning techniques regarding medication alert reduction in a large number of alert analyses among physicians of different specialties. We hypothesized that machine learning models could predict physician responses, which would ultimately directly assist in developing a sophisticated CDSS for reducing alert fatigue. Therefore, the primary objective of this study was to develop and validate machine learning models to reduce alert fatigue by predicting physician responses. This study may provide perspective on the perceived usefulness of CDSS alerts in patient care and insights into how to design better alert systems in real-world clinical settings. It can contribute to minimizing the number of alerts in the user interface, ensuring the appropriate prescription, and reducing the severity of unintended consequences.

Methods

Ethical Approval and Study Process

This type of study does not require Institutional Review Board review, following the policy of the National Health Research Institutes in Taiwan, as it provides a large amount of computerized, deidentified data. The entire study process is shown in Figure 1 .

Data Source

We collected data from an electronic health record (EHR)-integrated disease medication-related CDSS from a university teaching hospital in Taiwan. We considered only prescriptions that generated alerts due to a prescription error in the CDSS. The data collection period was between August 2018 and May 2019. During the 10-month study period, 9213 prescriptions generated alerts that accounted for approximately 3% of total prescriptions provided by physicians.

Data Preprocessing

The first step of this study was to clean the data. In the dataset, lots of duplications of prescriptions appeared, which means there were several prescriptions with the same patient's registration number, diagnosis code (ie, International Classification of Diseases, 10th Revision, Clinical Modification [ICD-10-CM]), and drug code (ie, Anatomical Therapeutic Chemical [ATC] classification system code). Therefore, we removed those prescriptions and kept the most recent prescription. A total of 6453 prescriptions were considered to develop machine learning-based prediction models. A prescription with the Taiwan National Health Insurance code as the diagnosis code was mapped to the ICD-10-CM code. Data normalization was carried out by converting all the values between 0 and 1. Finally, the data were converted into a matrix that included the diagnosis code, drug code, department ID, and physician ID.

Feature Selection

There could be more than 20 different clinical variables available in a single prescription. Therefore, feature selection is essential in order to keep the variables within a manageable size to be able to optimize the prediction model. The feature selection process was completed in three stages: (1) consultation with an expert (YL) who is a physician and specialist in CDSSs, (2) automated feature selection via machine learning algorithms, and (3) reduction of the number of input variables by using only the first three digits of the diagnosis code (ie, ICD-10-CM) and the first five digits of the drug code (ie, ATC). The patient's age, the patient's gender, the diagnosis code (ie, ICD-10-CM), the drug code (ie, ATC), the physician ID, and the department ID were considered as input variables. We then created a matrix for the diagnosis code (ie, ICD-10-CM), the drug code (ie, ATC code), the physician ID, and the department ID. A total of 6453 input variables were used to develop a machine learning model with binary outcomes.

Model Development

Overview

The objective of the model was to reliably predict what would be physicians' responses to an alert. We divided the entire dataset into two parts: (1) the training dataset (80% of the dataset) and (2) the testing dataset (20% of the dataset). However, the model was trained using 60% of the dataset as the training set and 20% of the dataset as an internal validation set. The remaining 20% of the dataset was used for testing our model's performance (see Figure 2 ). Model development was carried out using Python 3.6 software (Python Software Foundation). Python is a free and open-source programming language and environment for statistical computing and graphics.

Artificial Neural Network

Artificial neural networks (ANNs) were first introduced in the 1940s; recently, they have become extremely powerful and one of the most popular machine learning models that interconnects with adaptive simple processing elements. They usually work by mimicking the biological nervous systems responsible for knowledge processing and knowledge representation [ 24]. ANN-based algorithms have already shown high performance in terms of accuracy, sensitivity, and specificity for classification problems. Therefore, the application of ANNs has increased globally in recent years in health care research, including in drug development, pattern recognition, disease prediction, disease diagnosis, and disease prognosis. ANNs consist of three layers of neurons: the input layer, the hidden layers, and the output layer. The hidden layer can be a single or multiple layer. Every hidden layer is comprised of an activation function. In our study, we used three hidden layers, with the rectified linear unit (ReLU) activation function in the first and second hidden layers, and the sigmoid activation function in the third hidden layer.

The ReLU is a widely used activation function in the prediction model. It converts input values from 0 to α. In the third layer, we used a sigmoid activation function due to a nonlinear nature. The sigmoid function is also one of the most commonly used activation functions for binary classification. The sigmoid function converts output classes between 0 and 1. The ANN was designed to be a classification model that can predict the responses from multiple physicians while minimizing prediction errors by using binary cross-entropy as loss function and the stochastic gradient descent method for optimization. Moreover, 100 epochs were used in the ANN model where maximum accuracy and minimum loss for training and validation can be achieved.

Random Forest

Random forest (RF) is also known as ensemble learning because it is an ensemble of a large number of individual decision trees [ 25]. Each tree in the RF model spits out a class prediction, and the class with the most votes becomes our model's prediction. However, RF applies to both the classification and regression models.

Naïve Bayes

Naïve Bayes (NB) is a classification model that uses the Bayesian probability theory during prediction [ 26]. It is also known as a probabilistic classifier. In 1960, the NB model was first introduced for text classification by the text retriever community [ 27]. However, there are several types of NB algorithms for parameter estimation and event models, such as Gaussian naïve Bayes, multinomial naïve Bayes, and Bernoulli naïve Bayes. Bayes theorem is expressed as equation 1 in Figure 3 .

The variable y is the class variable that represents whether the alert will be accepted or rejected given the condition. Variable X represents the features like drugs, disease, and demographic. X is given as equation 2 in Figure 3 .

Here, x ₁, x ₂ ... x n represent the features (ie, they can be mapped to outcome: accept or reject alert). By substituting for X and expanding using the chain, the rule is given in equation 3 in Figure 3 . In our model, the class variable y has two outcomes: accept or reject. There could be cases where the classification is multivariable. Therefore, the equation 4 in Figure 3 is used to find the class variable y with maximum probability.

Gradient Boosting

Gradient boosting (GB) is one of the promising machine learning algorithms that has already shown better prediction for classification [ 28]. It can be used both in classification and regression models. Like RF, GB is a set of decision trees, but the main differences are how the trees are built and how the results are combined. In the RF model, each tree is built independently, while in the GB model they are built one tree at a time. The GB model works in a forward stage-wise manner and converts weak learners to strong learners [ 29]. The most interesting part of the GB algorithm is that it can easily fit into the new model. Moreover, the RF model combines results at the end of the process, by averaging or majority rules, while the GB model combines results along the way [ 30].

In the training set, input variables such as drugs and diseases, make a set {( x ₁ y ₁), ... ,( xnyn)} of known values of x and corresponding values of y. The goal is always to find an approximation Â ( x) to find a function A ( x) that minimizes the expected value of the specified loss function L( y, A( x)), as shown in equation 5 in Figure 3 .

The GB model assumes a real-valued y and calculates an approximation Â ( x) in the form of a weighted sum of functions hi( x) from H classes, which are called base or weak learners, as shown in equation 6 in Figure 3 .

Support Vector Machine

Support vector machine (SVM) is a supervised machine learning algorithm. SVM is used both in classification and regression problems [ 31]. It is also used to solve linear and nonlinear problems and works well for many complex problems. The idea of SVM is simple: it creates a line or a hyperplane that separates the data into classes. The hypothesis function h is defined as shown in equation 7 in Figure 3 .

The point above or on the hyperplane is classified as a class +1, and the point below the hyperplane is classified as a class -1. The SVM classifier works in the form shown in equation 8 in Figure 3 .

Model Performances

Overview

To evaluate the performance of five machine learning algorithms, we calculated accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUROC). For calculating those measures, we measured true positive, true negative, false positive, and false negative. The definitions of the six parameters are given below.

Accuracy

Accuracy is the test by which we can see how accurate our model is. The equation to calculate accuracy is shown in equation 9 in Figure 3 .

Sensitivity

Sensitivity is the test by which we can determine a positively identified case. The equation to calculate sensitivity is shown in equation 10 in Figure 3 .

Specificity

Specificity is the measure by which we can measure correctly identified cases from negative cases. The mathematical equation is given in equation 10 in Figure 3 .

PPV and NPV

PPV and NPV are two basic measures in biomedical studies. PPV is the probability that the positively identified case is positive. The mathematical equation for PPV is given in equation 12 in Figure 3 . Similarly, the NPV is the probability that the negatively identified case is negative. The mathematical equation is given in equation 13 in Figure 3 .

AUROC

AUROC is a performance measure by which we can evaluate the performance of the model. AUROC is a performance matrix for discrimination; it shows the predictive model's ability to discriminate between positive and negative cases.

Results

Dataset Characteristics

A total of 9214 prescriptions with an alert were collected during the 10-month study period. After preprocessing and removing duplicate prescriptions with the same registration numbers, 6453 prescriptions were used to develop our models. The neurology department got the highest number of alerts (1039/6453, 16.10%). Of those alerts, 546 (52.55%) were accepted and 493 (47.45%) were rejected by physicians (see Multimedia Appendix 1, Figure S1). The urology, dermatology, chest medicine, family medicine, metabolism, and otolaryngology departments observed higher alert rates of 10.61% (685/6453), 9.80% (633/6453), 6.91% (446/6453), 6.61% (427/6453), 6.52% (421/6453), and 6.50% (420/6453), respectively. Moreover, eight departments, including rehabilitation medicine, infectious disease, and ophthalmology, had alert rates of more than 1%. Gender, diagnosis codes, disease codes, physician IDs, and department IDs were used to develop and validate our prediction model (see Table 1 ).

Table 1

Variable

Input column contents

Input column number

Patient's gender

Male or female

1

Diagnosis code

First 3 digits of the ICD-10-CM a code

822

Drug code

First 5 digits of the ATC b classification system code

262

Physician ID

Physician ID

227

Department ID

Department ID

29

Prediction Performance of Machine Learning Algorithms

We developed five types of machine learning models to predict physician response. To determine the overall performance of predictive models, six evaluation metrics were applied. Among all the machine learning models, ANN showed the best performance (AUROC 0.94) (see Figure 4 and Multimedia Appendix 1, Figure S2).

The accuracy of the ANN, RF, NB, GB, and SVM models were 0.88, 0.85, 0.83, 0.82, and 0.57. The sensitivity and specificity of the ANN, RF, NB, GB, and SVM models were 0.87, 0.88, 0.87, 0.79, and 0.57 and 0.83, 0.82, 0.78, 0.90, and 1.0, respectively (see Table 2 ).

Table 2

Algorithm

Accuracy

Sensitivity, %

Specificity, %

PPV a, %

NPV b, %

F1

Discussion

Principal Findings

CDSSs directly assist physicians in making correct clinical decisions that ultimately reduce prescription errors by generating real-time alerts and lessen probable unwanted consequences. Clinical workflow is often impaired by excessive numbers of alerts; therefore, physicians pay less attention to alerts and even ignore alerts indiscriminately. This study focused on physicians' recent practice patterns and represented the findings of machine learning models to predict physicians' responses to alerts from a disease medication-related CDSS. The key findings are as follows: (1) an ANN model can correctly predict physicians' responses with higher accuracy than other models and (2) we identified potential features that could provide insight into the system design. These findings may contribute to building a sophisticated provider-friendly interface in which a CDSS may offer real-time alerts if the prediction is positive for that individual physician. If the prediction is negative, that means physicians might not accept the alert; therefore, the CDSS will not generate alerts during the prescribing of prescriptions or will provide soft or passive alerts without interruption. However, all the alerts would be recorded and the report sent to the individual physician by email on a weekly basis to inform them of how important the alerts were in order to reduce unwanted consequences.

Clinical Implications

CDSSs have already shown their capability to improve patient safety and quality of care by lowering the number preventable medication errors [ 32- 34]; however, an unreasonable override rate raises questions regarding the quality of CDSSs. Patient safety and effective care could be improved by initiating sophisticated criteria for generating alerts in the CDSS that prevent alert fatigue and minimize the override rate [ 35- 37]. Identification of physicians and departments who override alerts more often would help to reduce the override rate and help us understand how physicians would respond to drug-disease alerts, which would result in immense benefits. There are no previous studies that used a machine learning prediction model to identify physicians and departments who override alerts more often. In this study, machine learning algorithms were used to reduce alert fatigue by identifying physicians and departments who override alerts more often. Our findings are consistent with existing research that showed physicians played a great role in alert override [ 38]. Bell et al showed that alert override can be minimized by physicians' preferences for alert selection [ 39]. There are several reasons that can make physicians override alerts. First, current medication-related CDSSs are not designed to take the patient's previous medication history into account. Sometimes patients are already tolerant of the drug and physicians need to override the alert and prescribe the drug [ 40]. Second, some CDSSs required an entry for the reason for alert override and that lead to an unacceptable time burden for physicians [ 41]. Third, physicians believe that they already know the alert is inappropriate based on their experience, so they are more likely to override the alert [ 13, 42]. Our study also provides a very important point: no matter how accurate the CDSS is according to the most relevant knowledge base, the alert acceptance was highly affected by the individual physician's perspective. Our model will reduce the gap between real-world clinical practice and knowledge-based theory.

Yeh et al [ 43] demonstrated that dermatology, gynecology-obstetrics, family medicine, and ophthalmology departments had higher acceptance rates; however, pediatrics, psychiatry, and internal medicine departments, such as cardiology, endocrinology and metabolism, gastroenterology, hematology, rheumatology, and general medicine, had lower acceptance rates. In our study, we also found that physicians' decisions vary from department to department.

Strengths and Limitations

This study has several strengths. First, this is the first study to use machine learning algorithms to predict physicians' intentions to accept or reject alerts. This model may help to reduce alert fatigue in the current CDSS. Second, this study is personalized for each physician. Third, the performance of the model is satisfactory, such that it would help to reduce alert fatigue. Despite several strengths, our study also has several limitations that need to be addressed. First, we did not include free-text override reasons in our analysis, and free-text reasons could add additional value to our model. However, our model provided the AUROC with decent specificity and sensitivity. Second, we did not include physicians' experiences, working periods, ages, and genders in this prediction model. These data are difficult to collect retrospectively because EHR systems do not record this type of information. Third, we have only used one hospital dataset; multiple hospital datasets would make our model more reliable.

Future Works

This was the first part of our work. In the future, we will integrate our prediction model into the CDSS in order to check the feasibility of our model. It will help to reduce alert fatigue and result in a sophisticated CDSS by providing soft or passive alerts. Moreover, we will also try to get feedback from physicians about our prediction model (see Figure 5 ).

Conclusions

The findings of the study showed the potential for machine learning prediction models to predict physicians' responses with high sensitivity and specificity. Among the five machine learning algorithms, the ANN model showed greater performance than the other models. This model can be a promising tool to reduce alert fatigue from CDSSs in clinical settings and can help to correctly identify an individual's alert acceptance rate.

Acknowledgments

We would like to thank AESOP (AI-Enhanced ​Safety of ​Prescription) Technology for giving us data and technological support to conduct this study. This research was funded, in part, by the Ministry of Education (MOE) (grant numbers MOE 109-6604-001-400 and DP2-109-21121-01-A-01) and the Ministry of Science and Technology (MOST) (grant number MOST109-2823-8-038-004).

Abbreviations

AESOP

AI-Enhanced ​Safety of ​Prescription

ANN

artificial neural network

ATC

Anatomical Therapeutic Chemical

AUROC

area under the receiver operating characteristic curve

CDSS

clinical decision support system

CPOE

computerized physician order entry

EHR

electronic health record

GB

gradient boosting

ICD-10-CM

International Classification of Diseases, 10th Revision, Clinical Modification

MOE

Ministry of Education

MOST

Ministry of Science and Technology

NB

naïve Bayes

NPV

negative predictive value

PPV

positive predictive value

ReLU

rectified linear unit

RF

random forest

SVM

support vector machine

Appendix

Multimedia Appendix 1

Description of alerts in different departments and problem fitting checks.

Footnotes

Conflicts of Interest: None declared.

References

4. Eiermann B, Rahmner P, Korkmaz S, Landberg C, Lilja B, Shemeikka T. Knowledge bases for clinical decision support in drug prescribing: Development, quality assurance, management, integration, implementation and evaluation of clinical value. In: Jao C, editor. Decision Support Systems. London, UK: IntechOpen; 2010. [ Google Scholar]

26. Zhang HJA. The optimality of naive Bayes. Proceedings of the Nineteenth National Conference on Artificial Intelligence; Nineteenth National Conference on Artificial Intelligence; July 25-29, 2004; San Jose, CA. 2004. [ Google Scholar]

Articles from JMIR Medical Informatics are provided here courtesy of JMIR Publications Inc.

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Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation

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Monitoring Editor: Gunther Eysenbach and Rita Kukafka Reviewed by Liangliang Zhang, Travis Goodwin, Huriye Demir, and Suptendra Sarbadhikari Tahmina Nasrin Poly, MSc, 1, 2, 3 Md.Mohaimenul Islam, MSc, 1, 2, 3 Muhammad Solihuddin Muhtar, BSc, 2 Hsuan-Chia Yang, PhD, 1, 2, 3 Phung Anh (Alex) Nguyen, PhD, 2, 4 and Yu-Chuan (Jack) Li, MD, PhD 1, 2, 3, 5, 6 Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety.

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This is a picture of a physician connecting with her patient outside of the hospital.

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For this week, we learned about telehealth. We discussed the benefits and challenges that telehealth can impose on our healthcare system. Telehealth has the potential to improve patient care. But sometimes, it can also be a disadvantage depending on the extent of the care needed. The COVID-19 pandemic forced us to experiment with the use of the telehealth system. We witnessed the benefits and the challenges of utilizing telehealth during the pandemic. Some benefits of telehealth were access to care, its convenience, provider stress reduction, and increased patient satisfaction. Patients living in areas with a shortage of healthcare facilities do not have to travel long distances for care but can access a provider in the comfort of their homes. Regarding the challenges, telehealth has limitations with performing physical exams, technical difficulties, overutilization, etc.

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This article explains the barriers and benefits that the healthcare industry may face with the use of telehealth and how legislative initiatives have advocated for further telehealth advancements, especially with the rapid implementation of telehealth in the times of coronavirus disease 2019. Upon my search, I found out the the areas that have advanced with the help of telehealth are behavioral health, reproductive and maternal birth, etc.
Even after the pandemic, telehealth has made it convenient to still connect with my therapist on various occasions whenever I need to, and I thank God that Telehealth is here to stay!!!

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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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This game explores foods, especially the number of carbs you can consume and how to manage type 1 diabetes mellitus, and the safe check your blood sugar level, and administer insulin shots. It also incorporates sports activities. Hence, ensuring that diabetics patients understand they do not have to restrict themselves from a workout. But instead, be on the lookout for hyperglycemia or hypoglycemia during the activity, which can be done using a continuous glucose monitor.

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For this week, we learned about games that are available to educate patients with a newly diagnosed specific disease. I understand that gamification is defined as the use of game thinking and game mechanics in a non-game context to engage users and solve problems. Additionally, gamification is also a way to collect data that is costly to manage with other methods.
We were also introduced to different games that may benefit patients during the beginning stages of a specific medical condition. The game I explored was Rufus, the Bear. It was an exciting game. However, it took a while before I understood the game. This game is beneficial to a child after being diagnosed not with T1DM, not only to help learn where the best place to inject and test and the foods to eat but to learn how to inject and test their blood sugar levels. Understanding how and where to test and inject is critical as a person with type 1 diabetes needs multiple injections and measures their blood glucose many times in their lifetime. Overall, the game will benefit children, especially those ages (4-14) who have been newly diagnosed with TD1M.

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Understanding the State's Health Information Exchange (HIE)

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This week, we discussed how health information exchange data is found and collected in the U.S. healthcare system. We learned that health information exchange enables healthcare professionals to securely share patient information to reduce the delay in the treatment process.
When it comes to data, they are raw numbers collected and stored at the level that it was obtained. However, datasets include a vast amount of medical data, measurements, financial data, statistical data, demographics, etc., gathered from various healthcare data sources.
During my search, I found this article that discussed how most studies are done to evaluate the use/impact of HIEs on health care healthcare outcomes, and a few used HIE data to answer a research objective that was not specific to HIE use.

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Highlights

*

Data from HIEs is available and is in limited use by researchers for clinical research.

*

Majority of studies are done to evaluate the use/impact of HIEs on health care delivery/outcomes.

*

Only two studies used HIE data to answer a research objective that was NOT specific to HIE use.

*

Few HIEs are involved in generating published research.

Abstract

Objective

To determine the use of health information exchange organizations (HIEs) to support and conduct clinical research.

Materials and methods

This scoping review included US-based studies published between January 2003 and March 2014 that used data from an HIE to address at least one of three categories of research: clinical or epidemiological research, financial evaluation, or utilization of health services. Eligibility was not restricted to research on HIEs. Studies with research questions outside of the evaluation of HIEs themselves were sought.

Results

Eighteen articles met final study inclusion criteria from an initial list of 847 hits. Fifteen studies addressed a clinical or epidemiological research question, 6 addressed a financial consideration, and 8 addressed a utilization issue. Considerable overlap was found among the research categories: 13 articles addressed more than one category. Of the eighteen included studies, only two used HIE data to answer a research objective that was NOT specific to HIE use. Research designs were varied and ranged from observational studies, such as cohort and cross-sectional studies, to randomized trials. The 18 articles represent the involvement of a small number of HIEs; 7 of the studies were from a single HIE.

Discussion

This review demonstrates that HIE-provided information is available and used to answer clinical or epidemiological, financial, or utilization-based research questions; however, the majority of the studies using HIE data are done with the primary goal of evaluating the use and impact of HIEs on health care delivery and outcomes. As HIEs mature and become integrated parts of the health care industry, the authors anticipate that fewer studies will be published that describe or validate the role of HIEs, and more will use HIEs as multi-institutional data sources for conducting clinical research and improving health services and clinical outcomes.

Conclusion

Articles identified in this review indicate the limited extent that HIE data are being used for clinical research outside of the evaluation of HIEs themselves, as well as the limited number of specific HIEs that are involved in generating published research. Significant barriers exist that prevent HIEs from developing into an invaluable resource for clinical research including technological infrastructure limitations, business processes limiting secondary use of data, and lack of participating provider support. Research to better understand challenges to developing the necessary infrastructure and policies to foster HIE engagement in research would be valuable as HIEs represent an opportunity to engage non-traditional health care provider research partners.

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Health information exchanges-Unfulfilled promise as a data source for clinical research

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Data from HIEs is available and is in limited use by researchers for clinical research. * Majority of studies are done to evaluate the use/impact of HIEs on health care delivery/outcomes. * Only two studies used HIE data to answer a research objective that was NOT specific to HIE use.

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For this week, we went over EHR and EMR and its role in clinical decisions, and I learned the difference between EHR and EMR because I used to use these terms interchangeably. When it comes to electronic medical records, they are digital versions of the paper charts in the clinician’s office. It contains the medical and treatment history of patients in one practice. EMR  also allows providers to track data over time, easily identify patients due for preventive screenings or checkups. But the information in EMRs does not travel easily out of the practice, and patient’s record are sometimes printed out and delivered by mail to members of their care team. When it comes to EHRs, it focuses on the total health of the patient. Thereby going beyond standard clinical data collected in the provider’s office and including a broader view on patient’s care. EHRs are designed to reach out beyond the health organization that initially collects and compiles the information that can be shared with other health care providers, such as laboratories and specialists.

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Embedded Bias: How Medical Records Sow Discrimination

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This study explains how misdiagnosis disproportionately happens to patients whom doctors have labeled as “difficult” in their electronic health records and how black patients may be subjected to systematic bias in physicians’ perceptions of their credibility.

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David Confer, a bicyclist and an audio technician, told his doctor he "used to be Ph.D. level" during a 2019 appointment in Washington, D.C. Confer, then 50, was speaking figuratively: He was experiencing brain fog - a symptom of his liver problems. But did his doctor take him seriously? Now, after his death, Confer's partner, Cate Cohen, doesn't think so.

Confer, who was Black, had been diagnosed with non-Hodgkin lymphoma two years before. His prognosis was positive. But during chemotherapy, his symptoms - brain fog, vomiting, back pain - suggested trouble with his liver, and he was later diagnosed with cirrhosis. He died in 2020, unable to secure a transplant. Throughout, Cohen, now 45, felt her partner's clinicians didn't listen closely to him and had written him off.

That feeling crystallized once she read Confer's records. The doctor described Confer's fuzziness and then quoted his Ph.D. analogy. To Cohen, the language was dismissive, as if the doctor didn't take Confer at his word. It reflected, she thought, a belief that he was likely to be noncompliant with his care - that he was a bad candidate for a liver transplant and would waste the donated organ.

For its part, MedStar Georgetown, where Confer received care, declined to comment on specific cases. But spokesperson Lisa Clough said the medical center considers a variety of factors for transplantation, including "compliance with medical therapy, health of both individuals, blood type, comorbidities, ability to care for themselves and be stable, and post-transplant social support system." Not all potential recipients and donors meet those criteria, Clough said.

Doctors often send signals of their appraisals of patients' personas. Researchers are increasingly finding that doctors can transmit prejudice under the guise of objective descriptions. Clinicians who later read those purportedly objective descriptions can be misled and deliver substandard care.

Discrimination in health care is "the secret, or silent, poison that taints interactions between providers and patients before, during, after the medical encounter," said Dayna Bowen Matthew, dean of George Washington University's law school and an expert in civil rights law and disparities in health care.

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Bias can be seen in the way doctors speak during rounds. Some patients, Matthew said, are described simply by their conditions. Others are characterized by terms that communicate more about their social status or character than their health and what's needed to address their symptoms. For example, a patient could be described as an "80-year-old nice Black gentleman." Doctors mention that patients look well-dressed or that someone is a laborer or homeless.

The stereotypes that can find their way into patients' records sometimes help determine the level of care patients receive. Are they spoken to as equals? Will they get the best, or merely the cheapest, treatment? Bias is "pervasive" and "causally related to inferior health outcomes, period," Matthew said.

Narrow or prejudiced thinking is simple to write down and easy to copy and paste over and over. Descriptions such as "difficult" and "disruptive" can become hard to escape. Once so labeled, patients can experience "downstream effects," said Dr. Hardeep Singh, an expert in misdiagnosis who works at the Michael E. DeBakey Veterans Affairs Medical Center in Houston. He estimates misdiagnosis affects 12 million patients a year.

Conveying bias can be as simple as a pair of quotation marks. One team of researchers found that Black patients, in particular, were quoted in their records more frequently than other patients when physicians were characterizing their symptoms or health issues. The quotation mark patterns detected by researchers could be a sign of disrespect, used to communicate irony or sarcasm to future clinical readers. Among the types of phrases the researchers spotlighted were colloquial language or statements made in Black or ethnic slang.

"Black patients may be subject to systematic bias in physicians' perceptions of their credibility," the authors of the paper wrote.

That's just one study in an incoming tide focused on the variations in the language that clinicians use to describe patients of different races and genders. In many ways, the research is just catching up to what patients and doctors knew already, that discrimination can be conveyed and furthered by partial accounts.

Confer's MedStar records, Cohen thought, were pockmarked with partial accounts - notes that included only a fraction of the full picture of his life and circumstances.

Cohen pointed to a write-up of a psychosocial evaluation, used to assess a patient's readiness for a transplant. The evaluation stated that Confer drank a 12-pack of beer and perhaps as much as a pint of whiskey daily. But Confer had quit drinking after starting chemotherapy and had been only a social drinker before, Cohen said. It was "wildly inaccurate," Cohen said.

"No matter what he did, that initial inaccurate description of the volume he consumed seemed to follow through his records," she said.

Physicians frequently see a harsh tone in referrals from other programs, said Dr. John Fung, a transplant doctor at the University of Chicago who advised Cohen but didn't review Confer's records. "They kind of blame the patient for things that happen, not really giving credit for circumstances," he said. But, he continued, those circumstances are important - looking beyond them, without bias, and at the patient himself or herself can result in successful transplants.

David Confer and Cate Cohen in summer 2016 visiting the Outer Banks of North Carolina. Throughout Confer's battle with non-Hodgkin lymphoma, Cohen says, she felt her partner's clinicians didn't listen closely to him and had written him off. Confer died in 2020, unable to secure a liver transplant. (Cate Cohen)

The History of One's Medical History
That doctors pass private judgments on their patients has been a source of nervous humor for years. In an episode of the sitcom "Seinfeld," Elaine Benes discovers that a doctor had condescendingly written that she was "difficult" in her file. When she asked about it, the doctor promised to erase it. But it was written in pen.

The jokes reflect long-standing conflicts between patients and doctors. In the 1970s, campaigners pushed doctors to open up records to patients and to use less stereotyping language about the people they treated.

Nevertheless, doctors' notes historically have had a "stilted vocabulary," said Dr. Leonor Fernandez, an internist and researcher at Beth Israel Deaconess Medical Center in Boston. Patients are often described as "denying" facts about their health, she said, as if they're not reliable narrators of their conditions.

One doubting doctor's judgment can alter the course of care for years. When she visited her doctor for kidney stones early in her life, "he was very dismissive about it," recalled Melina Oien, who now lives in Tacoma, Washington. Afterward, when she sought care in the military health care system, providers - whom Oien presumed had read her history - assumed that her complaints were psychosomatic and that she was seeking drugs.

"Every time I had an appointment in that system - there's that tone, that feel. It creates that sense of dread," she said. "You know the doctor has read the records and has formed an opinion of who you are, what you're looking for."

When Oien left military care in the 1990s, her paper records didn't follow her. Nor did those assumptions.
New Technology - Same Biases?
While Oien could leave her problems behind, the health system's shift to electronic medical records and the data-sharing it encourages can intensify misconceptions. It's easier than ever to maintain stale records, rife with false impressions or misreads, and to share or duplicate them with the click of a button.

"This thing perpetuates," Singh said. When his team reviewed records of misdiagnosed cases, he found them full of identical notes. "It gets copy-pasted without freshness of thinking," he said.

Research has found that misdiagnosis disproportionately happens to patients whom doctors have labeled as "difficult" in their electronic health record. Singh cited a pair of studies that presented hypothetical scenarios to doctors.

In the first study, participants reviewed two sets of notes, one in which the patient was described simply by her symptoms and a second in which descriptions of disruptive or difficult behaviors had been added. Diagnostic accuracy dropped with the difficult patients.

The second study assessed treatment decisions and found that medical students and residents were less likely to prescribe pain medications to patients whose records included stigmatizing language.

Digital records can also display prejudice in handy formats. A 2016 paper in JAMA discussed a small example: an unnamed digital record system that affixed an airplane logo to some patients to indicate that they were, in medical parlance, "frequent flyers." That's a pejorative term for patients who need plenty of care or are looking for medications.

But even as tech might amplify these problems, it can also expose them. Digitized medical records are easily shared - and not merely with fellow doctors, but also with patients.

Since the '90s, patients have had the right to request their records, and doctors' offices can charge only reasonable fees to cover the cost of clerical work. Penalties against practices or hospitals that failed to produce records were rarely assessed - at least until the Trump administration, when Roger Severino, previously known as a socially conservative champion of religious freedom, took the helm of the U.S. Department of Health and Human Services' Office for Civil Rights.

During Severino's tenure, the office assessed a spate of monetary fines against some practices. The complaints mostly came from higher-income people, Severino said, citing his own difficulties getting medical records. "I can only imagine how much harder it often is for people with less means and education," he said.

Patients can now read the notes - the doctors' descriptions of their conditions and treatments - because of 2016 legislation. The bill nationalized policies that had started earlier in the decade, in Boston, because of an organization called OpenNotes.

For most patients, most of the time, opening record notes has been beneficial. "By and large, patients wanted to have access to the notes," said Fernandez, who has helped study and roll out the program. "They felt more in control of their health care. They felt they understood things better." Studies suggest that open notes lead to increased compliance, as patients say they're more likely to take medicines.
Conflicts Ahead?
But there's also a darker side to opening records: if patients find something they don't like. Fernandez's research, focusing on some early hospital adopters, has found that slightly more than 1 in 10 patients report being offended by what they find in their notes.

And the wave of computer-driven research focusing on patterns of language has similarly found low but significant numbers of discriminatory descriptions in notes. A study published in the journal Health Affairs found negative descriptors in nearly 1 in 10 records. Another team found stigmatizing language in 2.5% of records.

Patients can also compare what happened in a visit with what was recorded. They can see what was really on doctors' minds.

Oien, who has become a patient advocate since moving on from the military health care system, recalled an incident in which a client fainted while getting a drug infusion - treatments for thin skin, low iron, esophageal tears, and gastrointestinal conditions - and needed to be taken to the emergency room. Afterward, the patient visited a cardiologist. The cardiologist, who hadn't seen her previously, was "very verbally professional," Oien said. But what he wrote in the note - a story based on her ER visit - was very different. "Ninety percent of the record was about her quote-unquote drug use," Oien said, noting that it's rare to see the connection between a false belief about a patient and the person's future care.

Spotting those contradictions will become easier now. "People are going to say, 'The doc said what?'" predicted Singh.

But many patients - even ones with wealth and social standing - may be reluctant to talk to their doctors about errors or bias. Fernandez, the OpenNotes pioneer, didn't. After one visit, she saw a physical exam listed on her record when none had occurred.

"I did not raise that to that clinician. It's really hard to raise things like that," she said. "You're afraid they won't like you and won't take good care of you anymore."

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Embedded Bias: How Medical Records Sow Discrimination

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David Confer, a bicyclist and an audio technician, told his doctor he "used to be Ph.D. level" during a 2019 appointment in Washington, D.C. Confer, then 50, was speaking figuratively: He was experiencing brain fog - a symptom of his liver problems. But did his doctor take him seriously?

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The Evolution of Meaningful Use: Today, Stage 3, and Beyond

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The late-breaking regulations for electronic health records require practices to get up to speed quickly.

Fam Pract Manag. 2016;23(1):17-22

Author disclosures: no relevant financial affiliations disclosed.

Article Sections

Editor's note: The Centers for Medicare & Medicaid Services (CMS) announced in early January 2016 that it will phase out the meaningful use program later this year to make way for new electronic health record regulations created under the Medicare Access and CHIP Reauthorization Act (MACRA). For the time being, however, the current rules remain in effect. Although the meaningful use program may not be a stand-alone program under MACRA, the requirements will likely continue. This article provides information on the requirements for 2015, as eligible professionals have until the end of February 2016 to attest to meaningful use for 2015. The article also lays out the current requirements for 2016 through 2018 to support future planning by practices.

The Centers for Medicare & Medicaid Services (CMS) and the Office of the National Coordinator for Health Information Technology on Oct. 6, 2015, released a final rule governing the electronic health records (EHR) meaningful use (MU) program through 2018. The timing of the rule's release was challenging as it gave practices fewer than 90 days to make adjustments. The good news, if there is any, is that if physicians or other eligible professionals (EPs) were on track to meet the old requirements, they should be able to meet the new ones. The new rule eases the requirements in Stage 2 and changes the reporting period for all EPs to any consecutive 90 days in 2015. More important, the new rule initiated a fundamental change in MU that will roll the program into a single set of requirements, beginning in 2015 and ending in 2018. In fact, it now makes more sense to think of MU in terms of calendar years than stages.

The 2015 reporting period for all EPs is any consecutive 90 days from Jan. 1, 2015, through Dec. 31, 2015. EPs can attest to MU beginning Jan. 4, 2016, and must complete their attestation by Feb. 29 (see " Meaningful use reporting periods ").

MEANINGFUL USE REPORTING PERIODS

2015 Reporting period for payment adjustment year Deadline to attest to avoid payment adjustment in 2016 Deadline to attest to avoid payment adjustment in 2017
2016 Reporting period for payment adjustment year Deadline to attest to avoid payment adjustment in 2017 Deadline to attest to avoid payment adjustment in 2018

2017 Reporting period for payment adjustment year Deadline to attest to avoid payment adjustment in 2018 Deadline to attest to avoid payment adjustment in 2019

If an EP first attested to MU before 2014, then he or she was scheduled to attest to Stage 2 in 2015. These EPs must attest to the new Modified Stage 2 but are not able to use the alternative exclusions except for one involving public health.

Looking at the objectives and measures included in the new Modified Stage 2, a few changes stand out:

The patient electronic access measure requires patients to view, download, or transmit their health information online. This measure previously required at least 5 percent of an EP's unique patients to access their records this way during the attesting period but now requires just one unique patient to do so. The objective still requires that at least 50 percent of unique patients have access to their records, such as through a patient portal.

The measure requiring EPs to receive secure electronic messaging from patients has undergone a similar change, with the requirement shrinking from at least 5 percent of patients to at least one patient.

A number of measures have been eliminated from attestation because CMS either determined them redundant or found that EPs are already tracking them at high levels (see " Redundant and topped out measures "). EPs are still expected to perform those functions, but they will not be required to report a measure for them. One note of caution: Although EPs are no longer required to demonstrate the ability to share summaries of care electronically with other providers, they are still required to send at least 10 percent of summaries of care electronically.

The public health reporting requirement has undergone a major change. Previously, Stage 2 required only the capability to submit electronic data to immunization registries. With this final rule, providers are now expected to report two of the three public health reporting measures (the others including syndromic surveillance reporting and specialized registry reporting). To give providers time to adjust to the increased reporting requirement, CMS is offering alternate exclusions for providers scheduled to attest to Stage 1 or Stage 2. More information about the exclusion is available in a set of frequently asked questions on the CMS website. Providers scheduled to attest to Stage 1 can claim an alternate exclusion for up to two measures, but they must still attest or meet the exclusion requirements for the remaining measure. Providers scheduled to attest to Stage 2 may claim an alternate exclusion for syndromic surveillance or specialized registry measures, but must still attest or meet the exclusion requirements for the immunization registry measure.

MU no longer distinguishes between "core" and "menu" objectives. All measures are now required.

REDUNDANT AND TOPPED OUT MEASURES

The Centers for Medicare & Medicaid Services have eliminated certain measures under Meaningful Use Modified Stage 2 that were determined no longer relevant or were covered by other measures. In most cases, physicians and other eligible professionals (EPs) are still expected to perform these functions but no longer will have to report them:

Record patient demographics,

Record and chart changes in vital signs,

Record smoking status,

Provide clinical summaries for patients for each office visit,

Incorporate clinical laboratory results as structured data,

Generate lists of patients by conditions,

Identify patients needing preventive/follow-up care and send reminders,

Provide summary of care records for more than 50 percent of patient referrals or transitions of care and perform at least one successful exchange electronically with another provider (note: EPs must still provide 10 percent of care summaries through an electronic health record or health information exchange),

Record electronic notes in patient records,

Provide imaging results consisting of the image itself and any explanations or other accompanying information in the electronic health record,

Record patient family history as structured data.

2016 - All aboard Stage 2

For 2016 reporting, all EPs must attest to Modified Stage 2, and no one is able to use the alternative measures or exclusions. A caveat to this is that if an EP was previously scheduled to attest to Stage 1 in 2016, he or she may skip the laboratory and imaging computerized order entry measures. The reporting period is a full calendar year for all EPs, except those who are new to MU or attesting for the first time in 2016.

For 2017, EPs may choose to attest to either Modified Stage 2 or Stage 3. For those who decide to continue with Stage 2, the reporting period is again a full calendar year. For those who decide to attest to Stage 3 instead, the reporting period is 90 consecutive days. If attesting for the first time in 2017, the reporting period is 90 consecutive days for attesting to either Stage 2 or 3.

Additionally, some of the measure thresholds for Stage 2 are higher. For example, EPs are again required to have at least 5 percent of unique patients view, download, or transmit their health information electronically. Five percent of patients must use secure messaging with their physician as well. Also, EPs must attest to two public health reporting measures; alternate exclusions are not available.

As for certified EHR technology (CEHRT), EPs can use a 2014 edition CEHRT to attest to Stage 2. They can also use either a 2015 edition CEHRT or a combination of 2014 and 2015 editions to attest to Stage 2 or 3.

2018 and beyond - All aboard Stage 3 and enter MACRA

Although Stage 3 is intended to be the final stage of MU, it is not the last act. The Medicare Access and CHIP Reauthorization Act (MACRA) rolls the MU program, the Physicians' Quality Reporting System, and the value-based payment modifier program into a new program called the Merit-Based Incentive Payment System (MIPS). Part of the program's scoring is tied to EPs using certified EHR technology and attaining MU.

The Health Information Technology for Economic and Clinical Health (HITECH) Act, which created MU, also created the regional extension centers (RECs) program, which is a state- or region-based support program for eligible professionals working on MU.

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The Evolution of Meaningful Use: Today, Stage 3, and Beyond

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The late-breaking regulations for electronic health records require practices to get up to speed quickly. Fam Pract Manag. 2016;23(1):17-22 Author disclosures: no relevant financial affiliations disclosed. Article Sections Editor's note: The Centers for Medicare & Medicaid Services (CMS) announced in early January 2016 that it will phase out the meaningful use program later this year to make way for new electronic health record regulations created under the Medicare Access and CHIP Reauthorization Act (MACRA).

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Transforming Data, Information, Knowledge, and Wisdom

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Transforming Data, Information, Knowledge, and Wisdom

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The purpose of a quantitative descriptive study conducted in 2012 was to identify the gaps between the informatics knowledge and skills self-reported by novice nurses and the informatics knowledge and skills demonstrated in acute-care settings by novice nurses as reported by nurse managers.

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Novice Nurse Preparedness to Effectively Use Electronic Health Records in Acute Care Settings: Critical Informatics Knowledge and Skill Gaps

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The ability of nurses to use an Electronic Health Record (EHR) effectively is critical to patient safety, decreased facility expenditures for training, and reduced healthcare costs. To use EHRs effectively, nurses must have the required knowledge and

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The focus was not on information technology knowledge and skills, as it was understood that the novice nurse would not have the skills of information technology. At the end of the study, the results showed novice nurses (with less than two years' experience) were most highly skilled in e-mail, internet usage and search engines, word processing, lab results retrieval, keyboarding, and online classrooms/education; they reported novice nurses being least skilled in electronic

publishing, computer languages, graphic development, webpage design, and coding for billing purposes. Thereby revealing informatics knowledge and skills gaps reported by new nurses.
This study did an excellent identifying the gaps in the use of informatics. If nursing programs and clinical facilities collaborate to close these gaps, it will provide trained and novice nurses with safe and efficient patient care and meet documentation requirements.

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AMA Calls for a Pause in the Meaningful Use Program - Medical Software Solutions

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This week, I learned that Meaningful Use sets the specific objectives that eligible professionals and hospitals must achieve to participate in the EHR Incentive Programs. I also learned that the “Meaning Use” program, was designed to be implemented in three stages of legislation to promote a complete and interoperable EHR and HIT systems in U.S. hospitals, and nurses have been involved with every phase of its implementation to ensure usage and adaptation to evolving policies affecting EHR. During my search on the process of meaningful use establishment of this program, I found articles that explained the history of the stages of the meaningful use program, its implementation, and why physicians needed a pause to implement stage 3.
The first(focuses primarily on the CPOE initiative for physicians) and second stage(implementation of quality indicators that required electronic data to be collected as an integral component of HIT systems) of MU was successfully implemented. However, the third stage, which focused on care quality outcome measures, was not executed but replaced by Medicare Access and CHIP Reauthorization Act (MACRA) before it was established in 2017 as a result of the 2015 final rule, which focused on using certified electronic health record (EHR) technology (CEHRT) to improve health outcomes. This delay happened because the American Medical Association (AMA) and 41 medical societies urged the administration to pause the final stages of the federal government’s Meaningful Use program in light of critical changes to Medicare. The coalition of physician organizations believed that locking in Stage 3 rules for the Meaningful Use program would be an unwise step that undermined the implementation of Medicare payment reforms. Moreover, they also believed that pausing Stage 3 would have allowed them to evaluate their environment and work with the administration to implement the needed changes to the Meaningful Use program currently under consideration.

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WASHINGTON - The American Medical Association (AMA) and 41 medical societies today urged the administration to pause the final stages of the federal government's Meaningful Use program in light of pivotal changes to Medicare. The coalition of physician organizations believe that locking in Stage 3 rules for the Meaningful Use program would be an unwise step that undermines the implementation of Medicare payment reforms.

In letters delivered today to the Secretary of Health and Human Services Sylvia Burwell and Office of Management and Budget Director Shaun Donovan , the physician organizations noted that the proposed Stage 3 regulation was developed prior to and without consideration of the changes enacted by the Medicare Access and Chip Reauthorization Act (MACRA). Given the vital role of the Meaningful Use program in the reformed payment systems introduced by MACRA, proceeding with the proposed Stage 3 rules threatens to impede the health care delivery innovations Congress sought to promote when it passed the law.

"There is growing bipartisan recognition in Congress that the direction of the Meaningful Use program needs to be reassessed in light of usability and interoperability challenges with electronic health record systems," said AMA President Steven J. Stack, M.D. "Poorly performing systems that do not facilitate the seamless exchange of data would severely undermine the ability of the health system to support the implementation of the payment reforms outlined in MACRA."

The AMA believes that pausing Stage 3 at this time will provide the opportunity to evaluate the environment and work with the administration to implement the needed changes to the Meaningful Use program that are currently under consideration.

"The AMA wants the Meaningful Use program to succeed. Pausing to reassess Stage 3 rules will not stop or delay progress with EHRs," said Dr. Stack. "On the contrary, we firmly believe a temporary period of reevaluation will help move the program forward and drive innovation and adoption."

The AMA is committed to supporting policies at every level of government that improve patient health, enhance access to affordable, quality care and that protect the privacy and trusted interactions patients have with physicians. Visit BreaktheRedTape.org to learn more about EHRs and Meaningful Use, contact members of Congress, or share experiences.

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AMA Calls for a Pause in the Meaningful Use Program - Medical Software Solutions

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AMA and Other Medical Societies Call for a Pause in the Meaningful Use Program WASHINGTON - The American Medical Association (AMA) and 41 medical societies today urged the administration to pause the final stages of the federal government's Meaningful Use program in light of pivotal changes to Medicare.

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What is Interoperability?

It is the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.

Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including the individual.

Four Levels of Interoperability

Foundational (Level 1): Establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another

Structural (Level 2): Defines the format, syntax and organization of data exchange including at the data field level for interpretation

Semantic (Level 3): Provides for common underlying models and codification of the data including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user

Organizational (Level 4): Includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These components enable shared consent, trust and integrated end-user processes and workflows

What is Health Information Exchange and Data Sharing?

Health information exchange, or HIE, provides the capability to electronically move clinical information among disparate healthcare information systems and maintain the meaning of the information being exchanged. The goal of health information exchange is to facilitate access to and retrieval of clinical data to provide safe, timely, efficient, effective and equitable patient-centered care. HIE can also be used by public health authorities to assist in the analysis of the health of populations.

The term HIE is generally used as either a verb or a noun.

Verb: The electronic sharing of health-related data between two or more organizations facilitated by applied standards for use by a variety of stakeholders to inform health and care.

Noun: Organizations within the United States that provide health information exchange technology and services at a state, regional or national level and often work directly with communities to promote secure sharing of health data.

In the European Union, the act of information exchange is often referred to as data sharing. The European Commission's Support Centre for Data Sharing uses the term to refer to the collection of practices, technologies, cultural elements and legal frameworks that are relevant to transactions in any kind of information digitally, between different kinds of organizations. This term is more broadly applied to all types of data sharing, but can also be specifically used for health-related data.

Interoperability Standards

Standards provide a common language and a common set of expectations that enable interoperability between systems and/or devices. In order to seamlessly digest information about an individual and improve the overall coordination and delivery of healthcare, standards permit clinicians, labs, hospitals, pharmacies and patients to share data regardless of application or market supplier.

Standards Development

A standards organization, standards body, standards development organization (SDO) or standards setting organization is focused on developing, coordinating, promulgating, revising, amending, reissuing, interpreting, or otherwise producing standards that are intended to address the needs of some relatively wide base of affected adopters.

In order to be recognized as an SDO, an organization may be accredited by the American National Standards Institute (ANSI) or the International Organization for Standardization (ISO). Standards are also developed by other groups such as trade unions or associations. The development and adoption of open, consensus-based standards can be a complex process involving many different stakeholders and subject matter experts.

There are over 40 different SDOs in the health IT arena. Some entities create standards, such as Health Level Seven (HL7), Systematized Nomenclature of Medicine (SNOMED) International, and the Clinical Data Interchange Standards Consortium (CDISC). Others, like Integrating the Healthcare Enterprise (IHE), do not develop new standards, but rather bundle complementary base standards into IHE profiles that are used to define a specific function or use case, and then are balloted. This creates a scenario that helps drive adoption of the base standards by providing implementation guidance that describes how multiple standards can be used together to support interoperable health information exchange.

The different SDOs and profiling organizations have varying compositions and processes, but generally follow shared principles based on developing standards through a multi-stakeholder, consensus-based process to respond to specific industry or market needs.

Types of Standards

In order to understand the types of health data standards available for use, informatics professionals organize these standards into the following specific categories: vocabulary/terminology, content, transport, privacy and security, and identifiers.

Vocabulary/Terminology Standards

Vocabulary/terminology standards address the ability to represent concepts in an unambiguous manner between a sender and receiver of information, a fundamental requirement for effective communication. Health information systems that communicate with each other rely on structured vocabularies, terminologies, code sets and classification systems to represent health concepts. Some common vocabulary standards currently used in the marketplace include:

Current Procedural Terminology (CPT®): A code set, maintained by the American Medical Association (AMA), used to bill outpatient and office procedures.

Healthcare Common Procedure Coding System: A set of healthcare procedure codes based on CPT that is used for Medicare reimbursement.

ICD-10 and ICD-11: The International Statistical Classification of Diseases and Related Health Problems (ICD) is a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. The 11th revision will replace the ICD-10 in January 2022.

Logical Observation Identifiers Names and Codes (LOINC®): A universal code system for identifying health measurements, observations and documents. These codes represent the "question" for a test or measurement. LOINC codes can be grouped into laboratory and clinical tests, measurements and observations.

National Drug Code (NDC): Maintained by the U.S. Food and Drug Administration, NDC provides a list of all drugs manufactured, prepared, propagated, compounded or processed for commercial distribution.

RadLex: A unified language of radiology terms for standardized indexing and retrieval of radiology information resources, managed by the Radiological Society of North America. It unifies and supplements other lexicons and standards, such as SNOMED-Clinical Terms and DICOM.

RxNorm: A terminology used to normalize names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary.

Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT): A comprehensive clinical health terminology product. It enables the consistent, processable representation of clinical content in electronic health records (EHRs). These codes often represent the "answer" for a test or measurement to the LOINC "question" code.

The Centers for Disease Control and Prevention (CDC) provide a number of code sets for vaccines (Vaccines Administered (CVX)) and manufacturers (Manufacturers of Vaccines (MVX)). These codes can be used in immunization messages.

The Unified Code for Units of Measure: A code system intended to include all units of measures used in international science, engineering and business to facilitate unambiguous electronic communication of quantities together with their units.

Content Standards

Content standards relate to the data content within exchanges of information. They define the structure and organization of the electronic message or document's content. This standard category also includes the definition of common sets of data for specific message types.

Consolidated CDA (C-CDA): A library of CDA templates, incorporating and harmonizing previous efforts from HL7, IHE, and Health Information Technology Standards Panel (HITSP). It represents harmonization of the HL7 Health Story guides, HITSP C32, related components of IHE Patient Care Coordination and Continuity of Care Documents, or CCD.

HL7's Version 2.x (V2): A widely implemented messaging standard that allows the exchange of clinical data between systems. It is designed to support a central patient care system as well as a more distributed environment where data resides in departmental systems.

HL7 Version 3 Clinical Document Architecture (CDA®): An XML-based document markup standard that specifies the structure and semantics of "clinical documents" for the purpose of exchange between healthcare providers and patients. It defines a clinical document as having the following six characteristics: persistence, stewardship, potential for authentication, context, wholeness and human readability.

Transport Standards

Transport standards address the format of messages exchanged between computer systems, document architecture, clinical templates, user interface and patient data linkage. Standards center on "push" and "pull" methods for exchanging health information.

Digital Imaging and Communications in Medicine (DICOM): The standard for the communication and management of medical imaging information and related data. DICOM enables the transfer of medical images across systems and facilitates the development and expansion of picture archiving and communication systems.

Direct StandardTM: Defines a set of standards and protocols to allow participants to send authenticated, encrypted health information directly to known, trusted recipients over the internet. Two primary specifications are the Applicability Statement for Secure Health Transport v1.2 and the XDR and XDM for Direct Messaging.

Fast Healthcare Interoperability Resources (FHIR®): An HL7 standard for exchanging healthcare information electronically. The basic building blocks of FHIR are "resources," which describe exchangeable health data formats and elements. FHIR also provides standardization for application programming interfaces (APIs). FHIR provides a number of benefits and improvements as a modern healthcare standard including facilitating interoperable exchange with legacy standards, lower overhead, shorter learning curve, an ability to transmit only the necessary pieces of information, potential for patient mediated data, and an energized community of supporters and implementers.

IHE provides a number of specifications that can be used in the exchange of health information.

PCHAlliance co-sponsors the Personal Health Device (PHD) efforts within the IHE Devices Domain, which are focused on developing IHE profiles that leverage and build upon the Continua Design Guidelines and are also updated to support FHIR. These profiles provide guidance to implement globally recognized, consensus-based approaches to connect and test both personal and clinical devices and integrate them into health information systems. The specifications being developed support both medical devices and mainstream consumer facing apps to enable scalable interoperability of the rapidly expanding connected health ecosystem.

Privacy and Security Standards

Privacy standards aim to protect an individual's (or organization's) right to determine whether, what, when, by whom and for what purpose their personal health information is collected, accessed, used or disclosed. Security standards define a set of administrative, physical and technical actions to protect the confidentiality, availability and integrity of health information.

In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) outlines standards that safeguard the privacy and security of protected health information.

HIPAA Privacy Rule: Establishes national standards to protect individuals' medical records and other personal health information. It applies to health plans, healthcare clearinghouses, and healthcare providers that conduct certain healthcare transactions electrically. The rule applies safeguards to protect the privacy of personal health information, and sets limits and conditions on the uses and disclosures of such information without patient authorization. The rule also gives patients rights over their own health information, including the right to examine and obtain a copy of their records, and to request corrections.

HIPAA Security Rule: Sets national standards for protecting the confidentiality, integrity, and availability of electronically protected health information. Compliance with the Security Rule was required as of April 20, 2005 (April 20, 2006 for small health plans). The rule addresses the technical and non-technical safeguards that "covered entities" must have in place to secure an individual's electronic protected health information. Prior to HIPAA, there were no generally accepted requirements or security standards for protecting health information.

In Europe, the General Data Protection Regulation (GDPR) outlines privacy and security regulations for all processing and storage of data relating to data subjects-or people-in the European Union (EU). This regulation extends to health information and any organization that may process or store data on these subjects, meaning it has extensive reach to many organizations worldwide and related to the sharing of data across organizations.

Identifier Standards

Entities use identifier standards to uniquely identify patients or providers.

Enterprise Master Patient Index (EMPI): A data registry used across a healthcare organization to maintain consistent and accurate data on the patients treated and managed within its departments.

Medical Record Number (MRN): An organization specific code used as a systematic documentation of a patient's history and care during a hospital stay.

National Council of State Boards of Nursing ID (NCSBN ID): A unique identifier automatically generated for each registered nurse and licensed practical/vocational nurse, freely available via the Nursys database and maintained by NCSBN.

National Provider ID (NPI): A unique 10-digit number for a healthcare provider to create a standard identification. These NPIs are included in the free NPI Registry.

Object ID (OID): A globally unique ISO identifier and a preferred scheme for unique identifiers in HL7.

Standards, Implementation Guides and Profiles

Standards on their own are helpful because they describe and constrain what data moves.

Implementation guides describe how to implement these standards for a specific use case. An implementation guide is a companion to a standard that describes how to use a standard to satisfy a specific healthcare use case. It should specify which way a standard is to be applied in a particular use case-how to structure the data consistently and what vocabulary to use. While you can implement a standard in a number of different ways, when using an implementation guide to integrate a standard into a health IT system, the guide directs one way to constrain the standard for a particular situation, removing ambiguity and achieving consistency.

A benefit to the use of implementation guides like IHE Profiles is that they provide a common language for purchasers and market suppliers to discuss the integration needs of healthcare sites and the integration capabilities of health IT products. They provide precise definitions of how to implement standards to meet specific clinical needs. They offer developers a clear implementation path for standards that has been carefully documented, reviewed and tested as well as supported by industry partners. They give purchasers a tool that reduces the complexity, cost and anxiety of implementing interoperable systems. IHE profiles, in particular, organize and leverage the integration capabilities that can be achieved by coordinated implementation of communication standards, such as DICOM, HL7 W3C and security standards.

Testing and Conformance Efforts

The adoption and implementation of standards involves a testing ecosystem that spans standards development, implementation and feedback from real-world testing into the development process to support continuous improvement. There are various different testing and compliance efforts that support advancing health IT interoperability, including:

Conformity Assessment: IHE International administers the IHE Conformity Assessment Scheme, which forms the basis for IHE Conformity Assessment Programs and any official certification of conformance to IHE Profiles associated with such testing programs. IHE International authorizes designated test laboratories accredited under this standard to assess the conformity of products with selected IHE profiles.

eHealth Exchange Testing Program: The program was developed to test compliance for HIE standards as required by the eHealth Exchange Coordinating Committee for onboarding to the eHealth Exchange network. Its purpose is to enable organizations that wish to participate in the exchange to validate the compliance of their health IT with the eHealth Exchange Performance and service specifications.

HL7 FHIR® Connectathons: These events provide hands-on FHIR development and testing opportunities held in conjunction with working group meetings. Participants engage in development and testing, working directly with other FHIR developers and senior members of the FHIR standards development team.

IHE Connectathons: A cross-vendor, supervised and structured testing event where the industry tests implementation of IHE Profiles and other standards to successful standards implementation and use. All tests are evaluated on interoperability and conformance to IHE Profiles found in IHE's technical frameworks. The test floor is overseen by IHE's technical project managers, providing a safe, neutral test environment and an opportunity for industry collaboration and problem resolution.

Office of the National Coordinator for Health IT Health IT Certification Program: A voluntary certification program established by the Office of the National Coordinator for Health IT (ONC) to provide for the certification of health IT. Requirements for certification are established by standards, implementation specifications and certification criteria adopted by the ONC secretary. The program supports the availability of certified health IT for its encouraged and required use under other federal, state and private programs. The program is run as a third-party product conformity assessment scheme for health IT based on the principles of the ISO and International Electrotechnical Commission framework.

Organizations like the Global Consortium for eHealth Interoperability, which was co-founded by HIMSS, IHE International and HL7 International, work to amplify and align the work of organizations like IHE and HL7 to increase adoption of emerging and mature health IT standards. The primary goal of the consortium is to coordinate work with governments and national ministries of health to further align existing and emerging standards and implementation guidance with strategic healthcare policy goals to achieve improved health outcomes for people everywhere.

The Interoperability Ecosystem

The health interoperability ecosystem comprises individuals, systems and processes that want to share, exchange and access all forms of health information, including discrete, narrative and multimedia. Individuals, patients, providers, hospitals/health systems, researchers, payers, suppliers and systems are potential stakeholders within this ecosystem. Each is involved in the creation, exchange and use of health information and/or data.

An efficient interoperability ecosystem provides an information infrastructure that uses technical standards, policies and protocols to enable seamless and secure capture, discovery, exchange and utilization of health information.

Exchange Partners and Data Sources

Historically, the focus was on the exchange of clinical information between providers and hospitals. As the technology has progressed, these exchange partners have broadened to include individuals, such as patients and caregivers, and organizations, such as long-term care, public health departments, community-based organizations and payers. With this expansion, along with the shift to value-based care, has also come a broader range of data types that are available to inform health and care, including device data, patient-generated data and data pertaining to the social determinants of health. Social determinants of health can include data points such as an individual's housing status, access to reliable transportation and level of food security.

Network Architectures

There are three primary types of exchange network architecture used to coordinate the exchange of health information across entities.

Centralized: Patient data are collected and stored in a centralized repository, data warehouse or other databases. The exchange organization has full control over the data, including the ability to authenticate, authorize and record transactions among participants.

Federated (Decentralized): Interconnected but independent databases allow for data sharing and exchange, and grant users access to the information only when needed.

Hybrid: Incorporates variations of federated and centralized architectures to harness the advantages of both. These are becoming common as various combinations of available services are implemented.

An important technical development that has expanded exchange partners and data sources is the emergence of healthcare APIs. APIs outline a set of clearly defined specifications to allow for one software application to build on the data and functionality of another application, without needing to understand its system design. APIs are already ubiquitous in today's web economy, and will play an essential role with respect to fueling healthcare interoperability for person centered care.

Types of HIE Organizations

An HIE organization oversees and governs the exchange of health-related information among organizations according to nationally recognized standards. The purpose of a health information exchange organization is to perform oversight and governance functions for information exchange. There are several different types of HIEs currently operating across the U.S. and its territories:

1. Hybrid HIEs are often collaborations between organizations, such as an ACO and a vendor network, within a state or region. The Kentucky Health Information Exchange is an example of a hybrid model.

2. Private/Proprietary HIEs concentrate on a single community or network, often based within a single organization, and include overall management, finance and governance. Examples may include hospital/integrated delivery system networks, payer-based HIEs and disease-specific HIEs. Some software vendors have also established an HIE network for their clients across the U.S. Additionally, the industry may see other evolving entities such as Accountable Care Organizations (ACOs) supporting information exchange.

3. Regional/Community HIEs are inter-organizational and depend on a variety of funding sources. Most are not-for-profit. Indiana Health Information Exchange and Chesapeake Regional Information System for Our Patients are examples of a regional HIEs.

4. State-wide HIEs are run by the governments of their respective states or maybe the state's designated entity. Some state-wide (and regional) HIEs use an umbrella approach and serve as the aggregator for disparate private health information exchanges. Statewide Health Information Network for New York and Arizona's Health Current are examples of a state-wide HIEs.

U.S. Exchange Initiatives: An Environmental Scan

Many organizations are actively working to achieve ubiquitous exchange. This environmental scan provides a curated review of the current landscape of the U.S.-based networks and frameworks which enable interoperable, nationwide health information exchange via a variety of methods and collaborations.

International Networks Enabling Exchange

Beyond the examples outlined above, various exchange networks and infrastructure have been established globally.

The eHealth Digital Service Infrastructure (EHDSI) facilitates the two primary building blocks of cross-border digital health services in Europe: ePrescription and Patient Summary. ePrescription (and eDispensation) allows EU citizens to obtain their medication in a pharmacy located in another EU country. Patient Summary provides information on important health related aspects such as allergies, current medication, previous illness, surgeries, etc. The digital Patient Summary is meant to provide doctors with essential information in their own language concerning the patient, when the patient comes from another EU country and there may be a linguistic barrier.

An important precursor of major exchange initiatives in Europe have been the European Reference Networks. These are virtual networks involving healthcare providers across Europe. They aim to facilitate discussion on complex diseases and conditions that require highly specialized treatment, providing concentrated knowledge and resources. Coordinators convene virtual advisory panels of medical specialists across different disciplines, using a dedicated IT platform and telemedicine tools.

Argentina's National Interoperability Network uses standards to enable communication between participating health information systems, providing a bridge for identity management across systems, sharing documents and patient summaries, managing ePrescriptions and supporting national registries.

Australia's My Health Record is an opt-out, CDA-based document repository used to store shared health summaries, eReferrals, specialist letters, discharge summaries, event summaries, prescription records, diagnostic imaging and pathology reports. Once patients have their record, they are able to manage access and permissions.

Canada's Health Infoway manages an ePrescribing service, PrescribeIT, electronically shares prescription information from clinicians with pharmacies for dispensing. Another Infoway initiative, ACCESS Health, aims to accelerate citizen access to personal health information and digital health services. The exchange initiative is expected to use a cloud-based infrastructure, a FHIR-based API service and a blockchain-enabled consent service.

Hong Kong's Electronic Health Record Sharing System is the country's equivalent to a regional HIE, managing patient data on care episodes, lab results, radiology studies and drug items. The Hong Kong Hospital Authority launched eHRSS to public and private health sectors using an opt-in patient consent model.

Uses of Information Exchange
Watch the HIMSS TV deep-dive on interoperability.
Ultimately, the goal is to be able to share and access information that informs an individual's full, longitudinal health story. By having and understanding the complete and accurate picture of an individual's health-including their preferences and other determinants of health-includes a number of benefits. Clinicians can better inform care and decision making, patients can become active participants in their care plans, and health IT developers and implementers can leverage evidence to create and adopt systems that support clinical processes and improve care delivery.

A number of benefits can be realized for exchange stakeholders, including:

1. Care coordination: Care coordination often involves a variety of stakeholders, including patients, caregivers and care teams across settings in the management of a patient's health. This is a dynamic process that requires data movement across platforms and among service providers in real time to successfully manage care.

2. Improving business and administrative processes: Having access to health information can eliminate time-consuming tasks within a health system, including processing of intake information, coordination across care teams and reporting needs associated with various regulatory requirements.

3. Increased patient safety and satisfaction: Having a full picture of a patient's health through access to disparate data can better inform clinicians on the patient's medical history, preferences and past encounters. This can help avoid duplicate testing, reduce adverse events, inform care decisions and conduct appropriate follow-up to ensure adherence with care management.

4. Value-based care: Population-level data analysis is an essential component of health systems managing high-risk patient populations, public health systems and other stakeholders working to coordinate care needs for a community. Understanding population data can enable better risk analysis and more cost-effective care. At an individual level, having access to longitudinal health data on a patient can reduce duplicative testing and close gaps in information. A key consideration in the shift to value-based care is the incorporation of non-traditional health data, such as an individual's social determinants of health data (e.g. food security status, housing stability and access to reliable transportation) into patient records. This data can be leveraged by health systems to proactively identify a health risk within a target population and intervene to work toward positive outcomes.

With these benefits in mind, taking a use-case driven approach can frame how information sharing may inform care or business practices and can help frame the potential benefits and drive interoperability forward. The following examples provide a snapshot at how interoperable exchange may occur.

Care delivery data: A physician can query a network or external health system for the appropriate patient data from other sources, such as other clinicians and hospitals. The physician can then select certain patient documents or resources (such as medication lists and reconciliations, allergy lists or lab reports) that they want to access during a patient visit.

Consent management: Where consent is not governed by other laws or regulations, a centralized consent management process can enable consensus among the community's stakeholders for a patient consent model, and provide awareness of a patient's willingness to participate in data sharing activities.

Lab results: HIE services provide physicians with the ability to get results directly via their exchange networks.

Medication reconciliation: Having a longitudinal view of all of a patient's encounters can raise awareness of the potential medications a patient was previously or is currently using. This can allow for appropriate follow up with the patient to reconcile differences in records to produce an accurate picture of their medication lists for future encounters.

Patient intake: Using a query and response process, the patient's health record can be acquired ahead of an appointment for clinician review.

Patient preferences: Exchange networks can enable access to information about a patient's care preferences, such as advance directives, empowering patients to make decisions in their care.

Provider alerts: HIE organizations are able to provide alerts to a clinician on their patients when one presents to the emergency department or is admitted to or discharged from a hospital, to ensure appropriate follow-up can be conducted.

Query services: Physicians may want to obtain more information about a patient that is referred to their office. They can query an exchange network which can in turn query other networks or health systems to assemble a complete, longitudinal health record.

Record locator services/master patient indexes: Many exchange networks enable creation of one continuous community record or search capabilities for patient records, facilitating patient identification across multiple provider settings.

Referrals: Organizations have the ability to send referrals directly through HIE services, along with all appropriate documentation.

Secure messaging: Many use cases require the exchange of patient records or related health information between clinicians in different healthcare settings with different EHRs or IT systems. For instance, providers needing to send referrals can electronically share data, eliminating paper documents and expediting the patient's treatment.

Transitions of care: As patients make the transition from one care setting to another (such as from hospital to a primary care provider or skilled nursing facility), interoperable exchange of relevant documentation from one care setting can be easily accessed at the next.

Workflow Considerations and Guidance

As the HIMSS definition suggests, interoperability goes beyond its technical requirements. Often there are policy changes influencing the value proposition and therefore willingness to exchange. Organizational considerations must align with external organizations and work toward achieving trust across stakeholders to enable meaningful data sharing. The following outlines some of these considerations and the approaches that may be leveraged.

Obtaining Patient Consent

Ensuring and communicating patient consent for data sharing activities is critical to abide by regulatory requirements and maintain the trust of the patient. There are several consent models used in information exchange, dependent on regulatory requirements within a jurisdiction.

No consent: Patient health information at a participating healthcare organization is automatically included in and available for exchange.

Opt-in: No patient data sets are made available for electronic exchange until patients actively express whether they would like to make all, or a pre-defined set, of their information available.

Opt-in with restrictions: No patient data sets are made available for electronic exchange until patients actively give their consent to participate. Patients have the option to:

Allow information to flow only to specific providers/li>

Include only specific categories of data or data elements

Make all of their information available for exchange

Opt-out: All or some predefined data sets are qualified to be included and available for exchange, after patients are given the opportunity to opt out in full.

Opt-out with exceptions: All or some predefined data sets are qualified to be included and available for exchange after patients are given the opportunity to:

Allow the exchange of their information only for specific purposes

Limit exchange of their information only for specific purposes

Limit exchange of their information to specific providers or provider organizations

Opt out in full

Selectively exclude categories of data or specific data elements from the exchange

Regardless of the model leveraged, a patient has the right to revoke their consent at any time, and processes to ensure that preference is communicated and enforced is important for data sharing processes.

Ensuring Privacy and Security

Regulations such as GDPR in the EU, HIPAA in the U.S., and various other national and state laws and regulations set forth privacy and security requirements to safeguard health information when it is created, received, transmitted, or maintained by health systems and other organizations. Privacy and security considerations may include the following:

Executing and implementing data use and reciprocal support agreements (DURSA)-also known as trust agreements-with the health systems and other stakeholders. These agreements set forth who may access and exchange the data. Additionally, such agreements set forth standard privacy and security policies and procedures that govern data protection and use. Further, data is typically encrypted when data is exchanged from one organization to another.

While specific security requirements may differ depending upon the DURSA, a typical DURSA may call for each participant to maintain appropriate administrative, physical and technical safeguards to ensure the confidentiality, integrity and availability of the data, aligned with the HIPAA requirements and any other applicable laws and/or regulations.

Producing High-Quality Data

In order to ensure that the information received from disparate sources can be trusted that it is accurate and complete, there needs to be efforts by organizations to maintain high-quality data. This can be achieved by ensuring that:

1. Data are structured and coded with standardized terminology and styles where possible to be leveraged in a machine-readable format for analysis and interpretation

2. Documentation is robust and complete to communicate the full patient story in a way that provides meaning to the human end user of the information.

Public Policy and Government Efforts

Achieving interoperability to date has required government involvement, guidance and regulation. Public policy efforts have the potential to drive forward frameworks for trusted exchange, align and educate stakeholders on existing and emerging standards and broaden stakeholder participation to ensure an inclusive exchange ecosystem for care coordination and continuity. The following outlines examples of how governments have worked to facilitate exchange both within the U.S. and in countries around the world.

United States Policies and Initiatives

With the goal of achieving ubiquitous, interoperable, nationwide exchange, the U.S. Department of Health and Human Services (HHS) has enacted and implemented a variety of legislation, regulations and guidance to further the adoption of standards-based approaches to interoperability.

21st Century Cures: Key Provisions

Among the health IT provisions outlined in 21st Century Cures, many sections provide directives to the ONC, Centers for Medicare and Medicaid Services (CMS) and other agencies related to improving interoperability.

The draft Trusted Exchange Framework and Common Agreement includes the policies, procedures and technical standards that build from existing health information network capabilities and enables them to work together to provide that single on-ramp to electronic health information regardless of the developer, health information exchange, or where a patient's records are located.

The U.S. Core Data for Interoperability is a "standardized set of health data classes and constituent data elements for nationwide, interoperable health information exchange." A "Data Class" is an "aggregation of various Data Elements by a common theme or use case."

The ONC Interoperability and Information Blocking Final Regulation implements key provisions of the 21st Century Cures Act focused on advancing interoperable exchange; supporting the access, exchange and use of electronic health information; and addressing occurrences of information blocking. The rule calls for accelerated use of healthcare APIs. ONC Certified Health IT must have standardized APIs for patient and population services, and meet specific requirements called out in the API Conditions and Maintenance of Certification. Most importantly, the regulation requires that United States Core Data for Interoperability is available for healthcare providers and patients "without special effort."

The Cures Act tasks ONC with the creation of an EHR Reporting Program as a condition of their certification and maintenance of certification. ONC has a process to solicit feedback from the industry on what criteria and methods of collection should be explored for this program. The program's reporting criteria aims to address the following five categories: security, interoperability, usability and user-centered design, conformance to certification testing, and other categories as appropriate to measure the performance of certified EHR technology.

In parallel with the release of the ONC Interoperability and Information Blocking Final Regulation, CMS also outlined an Interoperability and Patient Access Final Regulation, as part of their MyHealthEData Initiative, focused on driving exchange of and patient access to health information by liberating patient data using CMS authority to regulate Medicare Advantage, Medicaid, Children's Health Insurance Plan, and Qualified Health Plan issuers on the federally facilitated exchanges.

Interoperability Standards Advisory

The Interoperability Standards Advisory (ISA) process represents a single, public list of standards and implementation specifications published by ONC. ONC coordinates the identification, assessment and determination of these recommended standards and implementation specifications for industry use to fulfill specific clinical health IT interoperability needs. Stakeholders are encouraged to implement and use these recommended standards as applicable to their needs.

The ISA is organized into sections with related interoperability needs, each including one or more standards or implementation specifications to accomplish the listed need. Additional characteristics on these standards and implementation specifications are listed to inform stakeholders on maturity and adoption. These include:

1. Adoption Level: Approximate, average adoption level for that specific use case in healthcare within the United States.

2. Cost: Conveys whether a fee is involved to purchase, license, or obtain membership for access or use of the recommended standard or implementation specification.

3. Federally Required Status: Conveys whether a standard or implementation specification has been adopted in regulation, referenced as a federal program requirement, or referenced in a federal procurement (i.e., contract or grant).

4. Implementation Maturity: Conveys maturity based upon its implementation state.

5. Standards Process Maturity: Conveys maturity in terms of its stage within a particular organization's approval/voting process.

6. Test Tool Availability: Conveys whether a test tool is available to evaluate health IT's conformance to the standard or implementation specification.

Advancing Standards for Precision Medicine

This ONC project aims to standardize data found outside of traditional care delivery settings. As part of the precision medicine initiative, it focuses on standards in two areas:

1. Mobile health, sensor and wearable data

2. Social determinants of health data

Interoperability Proving Ground

ONC hosts an open, community platform where individuals and organizations can share and learn from projects occurring in the United States and internationally.

Data: Elemental to Health Campaign

HIMSS and PCHAlliance strongly urge prioritization of necessary funding and technical assistance to implement modern health information and technology strategies to ensure timely interoperability and sharing of public health surveillance data. An important component is the work in partnership with the Association of Public Health Laboratories, Council of State and Territorial Epidemiologists and the National Association for Public Health Statistics and Information Systems that support this multi-year effort to modernize public health data systems, surveillance and analytics at the CDC, and state, local and tribal health departments. Funding included in the Fiscal Year 2020 Appropriations Package and the Coronavirus Aid, Relief and Economic Security (CARES) Act, will help address some of the challenges, but in the long term, we need a predictable and sustained funding source to fully tackle this issue.

State-Level Interoperability Initiatives

In the U.S., state public health agencies manage and track information for immunizations, infectious diseases and vital statistics, however, they often lack adequate funding to ensure their information systems are updated and conform to national standards and profiles. Health information exchanges -which are public or private entities, depending on the state and region-aim to ensure there is robust and standardized data exchange between and among public health entities and state and local health departments.

Global Governmental Policies and Initiatives

At an international level, there are a variety of initiatives being led by health ministries and governments to further country-level and cross-border interoperability efforts.

The Global Digital Health Partnership (GDHP) is a collaboration of over 40 governments and territories, government agencies and the World Health Organization, formed to support the effective implementation of digital health services. Interoperability is one of the various work streams explored by GDHP. The proposed work stream activities focus on the evolving challenges of sharing patient data between care providers, organizations, caregivers and patients. These challenges are partly a technical problem for health systems, clinicians and patients, however they also pose significant risks for patient safety, and detract from high-quality coordinated care and the efficient delivery of services. A GDHP report outlines a variety of government efforts from 15 participating countries.

In Europe, the EU EHR Exchange Format developed the EU's eHealth Network to support the digital transformation of health and care in the EU by seeking to unlock the flow of health data across borders, and support implementation of the Cross-Border Health Care Directive. The recommendation seeks to facilitate the cross-border exchange of EHRs in the EU by supporting member states in their efforts to ensure that citizens can securely access and exchange their health data wherever they are in the EU. It will help citizens to quickly access and share their health data with healthcare professionals, for example, when consulting a specialist or receiving emergency treatment in another EU country.

The backbone infrastructure for the EHR exchange is the EHDSI. The EHDSI consists of services and infrastructure that use information and communication technologies to enable cross-border healthcare services. Initial focus is on enabling the exchange of baseline health information, including:

ePatient Summary to provide access to verified key health data of a patient during an unplanned care encounter while abroad

ePrescriptions to enables patients to receive equivalent medication treatment while abroad to what they would receive in their home country

A number of EU countries are already exchanging the ePatient summaries and ePrescriptions. As part of the EHDSI, Finland and Estonia were among the first to implement infrastructures to support the exchange of patient summaries, leveraging the X-Road platform, a free and open-source data exchange layer solution that enables organizations to exchange information securely over the internet. Future phases will include lab results, medical imaging and reports, and hospital discharge letters.

The European Commission also outlined priorities for 2019-2024 EU Digital Strategy, which included the creation of a European Health Data Space (EHDS) to foster the exchange and sharing of different kinds of health data (electronic health records, genomics, registries, etc.) in Europe. It aims to not only support exchange for the delivery of primary care, as well as, the development of new treatments, medicines, medical devices and services. Beyond the citizens, this would help meet the needs of different users and actors in the health system, while simultaneously protecting citizens' data. The commission is currently working with the member states and stakeholders to define the best governance structure and set up the appropriate infrastructure for the EHDS. Additionally, some existing regulatory gaps regarding member states implementation of the GDPR for the secondary use of data are being addressed.

The European Interoperability Framework (EIF) gives specific guidance on how to set up interoperable digital public services. It offers public administrations 47 concrete recommendations on how to improve governance of their interoperability activities, establish cross-organizational relationships, streamline processes supporting end-to-end digital services, and ensure that both existing and new legislation do not compromise efforts.

In an effort to ensure secure and federated access to genomic data, the European 1+ Million Genomes Initiative aims to create a technical infrastructure to improve disease prevention, allow for more personalized treatments and provide a sufficient scale for new clinically impactful research. In addition to the technical foundation for this effort, the signatory countries will also assess ethical and legal implications of genomics, and data storage, security and ethical uses.

Beyond these broader initiatives occurring across Europe, many countries are also leading specific regional and national efforts to expand exchange abilities./p>

The Nordic Interoperability Project is a consolidated grassroots effort to demonstrate the value for the patient from a seamless cross-border exchange of health data. The consortium includes organizations from all five Nordic countries-Denmark, Finland, Iceland, Norway, Sweden-and works to enable patients to live and act in an open, seamless, cross-border healthcare ecosystem, by showcasing and implementing solutions and innovations from the Nordics. The goal is to enable the Nordics by 2030 to be the most sustainable and integrated health region in the world, providing personalized healthcare for all its citizens.

In Portugal, the Shared Services of the Ministry of Health (SPMS) is a state-owned enterprise that manages the Portuguese Health Ministry Central Purchasing and serves as the national IT authority and the Portuguese representative for eHealth cross-border affairs. SPMS provides shared services in the areas of purchasing and logistics, financial services, human resources, and information and communication technologies to health and care entities to centralize and optimize the procurement of goods and services within the Portuguese National Health System.

In Asia and the South Pacific, many governments are also adopting digital health strategies to encourage interoperable exchange. For example, India's The Ministry of Health and Family Welfare (MoHFW) publishes EHR standards and created a Centre for Health Informatics under the eHealth Division of MoHFW as part of their Digital Health Strategy to initiate several digital health initiatives in the country, and started the National Health Portal as a citizen portal with the objective of improving the health literacy of the masses in India. New Zealand's health agencies, organizations and individuals signed a Commitment to New Zealand Health Interoperability to outline principles to drive the adoption of exchange infrastructures and processes in their health sector.

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Discover key insights about interoperability and better understand how data sharing can make a difference in healthcare.

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Health informatics in the healthcare industry uses information technology to organize and analyze health records to improve healthcare outcomes. It also provides electronic access to medical records for patients, doctors, nurses, hospital administrators, insurance companies, and so on. Most importantly, it allows patients to provide their providers with vital information and share it with family, friends, and other patients. Hence, the patient exerts greater control over their care with the help of informatics.

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This week, we expanded our knowledge of health informatics by learning about informatics competencies, which focused on TIGER's initiative to educate healthcare professionals about the use of informatics in their practice. I learned that this initiative was designed to address skills that all nurses will need in the 21st century. Upon my research, I found this article explaining how informatics competencies for nursing leaders were used to address computer skills, informatics knowledge, and informatics skills. The study also identified areas related to nursing leadership, including front-line managers and nursing executives. Moreover, participants of this study determined that differences in competencies for nursing leaders were due to different roles or positions they play in the field.

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Abstract

Historically, educational preparation did not address informatics competencies; thus managers, administrators, or executives may not be prepared to use or lead change in the use of health information technologies. A number of resources for informatics competencies exist, however, a comprehensive list addressing the unique knowledge and skills required in the role of a manager or administrator was not found. The purpose of this study was to develop informatics competencies for nursing leaders. A synthesis of the literature and a Delphi approach using three rounds of surveys with an expert panel resulted in identification of informatics competencies for nursing leaders that address computer skills, informatics knowledge, and informatics skills.

Introduction

With the goal that all Americans have an electronic health record (EHR) by 2014, health care leaders must be prepared to select, adopt, and implement EHRs as well as other health information technologies (HITs). Historically, nursing education did not include informatics competencies; thus, current managers, administrators, or nurse executives may not be adequately prepared to use or lead change in the use of HITs. Healthcare organizations are beginning to emphasize the need for informatics competencies appropriate for leadership roles. In fact the American Organization of Nurse Executives (AONE) included informatics in it's recent update of competencies for nursing executives, t, but most competencies were stated broadly and needed further specifications to translate them for implementation.1

More recently, the TIGER Initiative (Technology Informatics Guiding Education) identified that all nurses in every role must be prepared to make HIT the stethoscope of the 21 st century. TIER is a collaborative of 120 diverse organizations working together to educate nurses "to use informatics tools, principles, theories, and practices to make health care safer, more effective, efficient, patient-centered, timely, and equitable by interweaving enabling technologies transparently into nursing practice and education."2 Management's role is pivotal in organizational transformation using EHRs and other HITs. In fact, one of the seven pillars of the Tiger Initiative focuses on management and leadership; however, competencies in this area are yet to be defined. A number of resources for informatics competencies exist, however, a comprehensive list addressing the unique knowledge and skills required in the role of a manager or administrator was not found. 3⁻7 Therefore, the purpose of this study was to develop informatics competencies for nursing leaders.

Method

Informatics competencies for nursing leaders were developed through review of the literature and validated through a Delphi approach. The investigators used the AONE Nursing Executive Competencies 1 to identify the roles and functions of the nursing executive or nursing leaders aspiring to be executives. Informatics competencies from the literature were identified.3⁻7 Competencies from each of these sources were categorized as computer skills, informatics knowledge and informatics skills, using the framework developed by Staggers, Gassert, and Curran.7 The combined competencies were then evaluated for redundancy and reworded for consistency. The initial list of competencies was again compared with AONE's Nursing Executive Competencies. Some items were deleted, others reworded, and additional competencies added to reflect the role and functions of nursing leaders. Further subcategories were identified for categorizing competencies.

The first survey resulting from this process included a total of 119 items with 38 competencies addressing computer skills, 37 addressing informatics knowledge, and 44 addressing informatics skills. Within each of these three major categories were subdivisions for easier comprehension of content areas. Computer skills included: basic software applications, administration applications, electronic communications, data access and decision support, patient documentation and monitoring, education, systems, and research. Informatics knowledge included: data, impact, ethical and legal issues, systems, education, research, and usability and ergonomics. Informatics skills were categorized as: management, system requirements and selection, design and development, fiscal management, implementation, analysis and evaluation, and system maintenance.

A Delphi approach was used for obtaining consensus on the competencies. A purposive sample of 13 nursing leaders, informaticians, and researchers was selected from the American Medical Informatics Association Nursing Informatics Working Group, the Minnesota Organization of Nursing Leaders, and the University of Minnesota School Of Nursing. The Expert Panel consisted of registered nurses with a master's degree or higher and a minimum of five years of experience in nursing informatics, leadership in a practice setting, or research. Three rounds of surveys were completed for validating the competencies, with each successive round including refinement from results of the previous survey. Experts were given a copy of the AONE Nursing Executive Competencies and a list of the Informatics Competencies for Nursing Leaders developed by the investigators. The Panel was asked to rate the relevance of each item for nursing leaders on a 4-point Likert-type scale: 1) not relevant, 2) somewhat relevant, 3) quite relevant, and 4) very relevant. Experts also were asked to reword any items that were not clear. Open ended questions were included for additional recommendations.

Analysis

A content validity index (CVI) was calculated,8 which is the proportion of ratings for a competency that were very or quite relevant. A threshold of 80% agreement was established for determining consensus on competencies. All responses were entered into Excel and a percent agreement calculated for each competency. All competencies with 80% agreement were retained, but wording changes or reorganization of competencies recommended by respondents were made. The investigators examined competencies with ratings less than 80% for relevance based on the AONE Nursing Executive Competencies and reworded items for additional testing if they addressed an area in the AONE competencies. All other competencies with less than 80% agreements were deleted.

Results

In the first of three rounds of surveys, the return rate was 92%. Based on the responses to the survey, rewording of 34 competencies was recommended, 28 competencies were deleted, two competencies added, and four competencies were moved to another category. Participants identified the following reasons for deleted competencies: the competency was relevant for the role of the nursing informatician, end user, or researcher rather than the role of the nurse leader; or, it was not an informatics competency. Examples of competencies for nurse informaticians rather than the nurse leader included: "extracts data from clinical data sets;" or, "prepares process flow charts to describe current and proposed information flows for all aspects of clinical systems." An example of a competency rated less than 80% that was reworded included: uses project management software to plan and track progress of projects. This competency was reworded as "uses project management software." Based on the results of round one, there were a total of 93 competencies: 24 competencies addressed computer skills, 36 addressed informatics knowledge, and 33 informatics skills.

The response rate for the second survey was 83%. All competencies were rated at or above 80% agreement except five. In the category of computer skills, 2 competencies addressed using project management software and using up-to-date information science and technology as a strategic tool to promote self-health. The first competency was changed and the second deleted. "Uses project management software" competency was modified for the final survey with an emphasis on the role of the leader to include "develops the project scope, objectives, and resources for each proposed application, system, or enhancement" and "assure principles and concepts of project management are used for the implementation of information systems." Under the category of informatics knowledge, two competencies were rated at 70%: "describes how various hardware or network issues affect decisions about information systems," and "summarizes limitations of computer programs due to design and computer or network capacity at a general level." It was recommended to simplify these competencies by removing the verb and using simple concepts that address only one idea per competency where possible. The competency "collaborates on oversight to assure that testing plans are developed, implemented and evaluated at every phase of system implementations" was rated at 78% and the investigators decided to retain this competency for round three of the survey. Additional changes included recommended wording, regrouping of competencies, and removing additional redundancies identified by participants. Based on the results of round two, there were a total of 91 competencies: 24 competencies addressed computer skills, 38 addressed informatics knowledge, and 29 informatics skills.

The response rate for round three of the survey was 83%. All competencies were rated as 80% or higher in this round except two, which were deleted. One was identified as a research competency and the second was a duplicate of another competency. The research competency that was removed was "using software for qualitative data analysis or text mining." Participants identified that one competency, "[knowledge of] ergonomics of work station, bed side and portable technology apparatus in practice" included multiple concepts and this competency was split into separate competencies. Rewording recommendations were made and recommendations for reorganizing competencies. Based on the results of round three, there were a total of 92 competencies: 24 competencies addressed computer skills, 40 addressed informatics knowledge, and 28 informatics skills. The results of the final survey included the following categories and subcategories as shown below in Tables 1 - 3 .

Table 1

Basic Software Applications

Word processing software

Spreadsheet software (e.g.. Excel)

Presentation software (e.g. PowerPoint)

Internet browsers

Advances Software Applications

Forecasting

Budgeting

Human resources

Quality assurance

Staffing/Determining patient acuity

Statistical analysis

Synthesizing the whole patient picture from a multidisciplinary perspective

Planning and making decisions

Electronic Communications

Composing e-mails

Sending confidential documents

Participating in group communication

Access Data/ Information

Navigating systems (e.g., file servers)

Searching information retrieval systems

Distance-learning

Patient Related Applications

Documenting patient assessments

Creating care plans

Documenting nursing interventions

Documenting outcomes of care

Monitoring trends in patient outcomes

Supporting patient education

Table 3

Requirements And System Selection

Develop project scope, objectives, and resources

Integrate patient care processes and nursing administrative functions in system requirements

Involve front-line staff in the development of system requirements

Involve front-line staff in the development of system selection

Specify system requirements based on the needs of the organization

Collaborate with interprofessional team in system selection process

Evaluate information systems in practice settings

Advocate for the development (or purchase) and use of integrated, cost-effective health information systems within the organization

Advocate new applications meet standards for interoperability

Financial

Priorities are within budget constraints and organizational priorities

Alternatives for funding information systems

Costs and benefits analysis applied for in practice, education, administration, and/or research

Financial and staffing implications of ongoing updates to information systems

Actual vs budgeted costs for implementing nursing-related information systems

Collaborate with interprofessional team around financial issues

Implementation/ Management

Implementation of systems consistent with the vision, mission, strategic and tactical plans

Use project management for implementation of IS

Manage the impact of change due to IS implementation

Front-line staff are involved in appropriate aspects of design, implementation, and testing related to their practice

Improve the use of informatics within nursing practice

Collaborate with interprofessional team to manage information systems

Ethical/ Legal Concepts

Access to system information

Use of data (obtaining, storing, and disseminating text, data, images, or sounds)

Access to personal health information (PHI - HIPAA language)

Analysis/ Evaluation

Consistency with organizational policies, external licensing, accreditation, and regulatory agency requirements

Ensure testing plans are evaluated at every phase of system implementation

Ensure that front-line staff (users) are involved in the evaluation of information systems

Collaborate with interprofessional team to evaluate information systems

Discussion

Informatics competencies for nursing leaders were developed through a review of the literature and a Delphi process that included three rounds of surveys using an expert panel of experienced nursing leaders, informaticians, and researchers. The first round resulted in significant changes, while the second and third rounds were more editorial. There was considerable feedback from participants about informatics competencies for the nursing leader versus competencies for the nurse informatician or other nursing roles. Participants commented that informatics competencies for nursing leaders should emphasize knowledge and skills for planning, coordinating, supporting, directing, and evaluating informatics initiatives rather than "doing." For instance, nursing leaders do not need to actually perform specific informatics skills such as use project management software. The one exception was the need for leaders to be competent in computer skills.

The purpose of developing informatics competencies was to identify those areas related to nursing leadership, which included front line managers to the nursing executive. Similar to the levels of public health users,6 informatics competencies for nursing leaders varies based on their level of management.

For instance, the front line manager's role requires documenting patient care in EHRs while the role of the nursing executive is focused on procuring financial support for the EHR.

Participants who were nursing informaticians also identified that there may be differences in competencies for nursing leaders based on the reporting relationship of the informatician. Some nurse informaticians report to the chief informatics officer (CIO) while others report to the chief nursing officer. A CIO may require greater technology knowledge and skills while a nurse executive may require a greater emphasis on use of informatics in nursing.

Another key message from participants was an emphasis on interdisciplinary coordination. Nursing leaders must have computer and informatics knowledge and skills to work with other disciplines, but clearly can not operate in a silo. This influenced the development of the competencies to emphasize the interdisciplinary nature of informatics.

Conclusion

This study contributes to the identification of competencies that are unique to the role of the nursing leader. Informatics competencies that address computer skills are similar to previously identified competencies.3⁻7 However, informatics knowledge and skills are unique in this study. In future research, competencies need to be leveled by nursing leadership roles and responsibilities. Additionally validation also is needed to determine the applicability across health care settings.

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Table 2

Management Concepts

Management of system implementations

Avoidance of potential negative impacts

Allocating financial resources

Inclusion of nursing information within systems

Communicating a vision about the benefits

Anticipating changes in economic and business processes

Analysis of interprofessional workflow processes

Revising processes from workflow analysis

Decisions impacting clinical IS implementation, use, and maintenance

Change management

Data Issues

Health care data standards

Importance of integrating nursing data elements in systems

Importance of integrating standardized nursing languages in systems

Data quality issues

Data reporting issues

Information Systems Concepts

Value of clinicians' involvement in at all appropriate phases

Strengths and limitations of applications related to software programming language or design

Strengths and limitations of applications related to hardware/ networks

Technological trends, issues and new developments as they apply to nursing

"Work arounds" and the consequences of these

Human - computer interface interactions

Ergonomics of work station, bed side

Ergonomics of adjunct technologies

Ergonomics of portable technology

Application of IS technologies to clinical practice

Application of IS technologies to administration

Application of IS technologies to clinical research situations

Staff Education

Levels of informatics knowledge by roles

Methods for education

Clinical Research

Evaluating internet resources

Methods for evaluation of IS implementation and use

Reuse of patient/ administrative data for research

Application of informatics research for practice

Ethical/ Legal Concepts

Patients' rights r/t computerized information management

Principles of data integrity

Ethical principles for collection, maintenance, use, and dissemination of data and information

Application of HIPAA to information systems

Compliance with IRB for research with data from information systems

Policies and procedures consistent with regulatory and accrediting requirements for IS

Intellectual property, copyright, and fair use of copyrighted material

References

4. Curran CR. Informatics competencies for nurse practitioners. AACN Clinical Issues: Advanced Practice in Acute and Critical Care. 2003;14:320-30. [[ PubMed] Google Scholar]

7. Staggers N, Gassert CA, Curran C. A Delphi study to determine informatics competencies for nurses at four levels of practice. Nursing Research. 2002;51:383-390. [[ PubMed] Google Scholar]

8. Waltz CF, Strickland OL, Lenz ER. Philadelphia: F.A. Davis Company; 1984. Measurement in Nursing Research. [ Google Scholar]

Articles from AMIA Annual Symposium Proceedings are provided here courtesy of American Medical Informatics Association

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Informatics Competencies for Nursing and Healthcare Leaders

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Historically, educational preparation did not address informatics competencies; thus managers, administrators, or executives may not be prepared to use or lead change in the use of health information technologies. A number of resources for informatics ...

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The Evolution Of Data Visualization | Dustin Cabral | TEDxBryantU

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This video details how data visualization has evolved from 30,000 BC to the present. He categorized the progress of data visualization into four areas, speed, data storage, automation, and accessibility. It is amazing how each category has improved over time due to advanced digital technology.

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The Evolution Of Data Visualization | Dustin Cabral | TEDxBryantU