CBL on FHIR: A FHIR-Based Platform for Health Professional Education.

A FHIR based platform for case-based instruction of health professions students has been developed and field tested. The system provides a non-technical case authoring tool; supports individual and team learning using digital virtual patients; and allows integration of SMART Apps into cases via its simulated EMR. Successful trials at the University of Queensland have led to adoption at the University of Melbourne.

Clinical, technical, and implementation characteristics of real-world health applications using FHIR.

Objective: Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps' clinical, technical, and implementation characteristics. Materials and Methods: We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders. Results: Of the 112 apps surveyed, most focused on clinical care (74) or research (45); were implemented across multiple sites (56); and used SMART-on-FHIR (55) and FHIR version R4 (69). Apps were primarily stand-alone web-based (67) or electronic health record (EHR)-embedded (51), although 49 were not listed in an EHR app gallery. Discussion: Though limited in scope, our results show FHIR apps encompass various domains and characteristics. Conclusion: As FHIR use expands, this study-one of the first to characterize FHIR apps at large-highlights the need for systematic, comprehensive methods to assess their characteristics.

Education and Training on Electronic Medical Records (EMRs) for health care professionals and students: A Scoping Review.

BACKGROUND AND OBJECTIVES: The ability of health care providers and students to use EMRs efficiently can lead to achieving improved clinical outcomes. Training policies and strategies play a major role in successful technology implementation and ongoing use of the EMR systems. To provide evidence-based guidance for developing and implementing educational interventions and training, we reviewed and summarized the current literature on EMR training targeting both healthcare professionals (HCP) and students. METHODS: We used the Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA extension of scoping reviews (PRISMA-ScR) checklist for reporting our review. 46 full-text articles that met the eligibility criteria were selected for the review. Narrative synthesis was performed to summarize the evidence using numerical and descriptive analysis. We used inductive content analysis for categorizing the training methods. Also, the modified version of the Kirkpatrick's levels model was used for abstracting the training outcome. RESULTS: Five types of training methods were identified: one-on-one training, peer-coach training, classroom training (CRT), computer-based training (CBT), and blended training. A variety of CBT platforms were used, including a prototype academic electronic medical record system (AEMR), AEMR/simulated EMR (Sim-EMR), mobile based AEMR, eLearning, and electronic educational materials. Each training intervention could have resulted in several outcomes. Most outcomes were related to levels 1-3 of the Kirkpatrick model that involves learners (n = 108), followed by level 4a that involves organizations (n = 7), and lastly level 4b that involves patients (n = 1). The outcomes related to participants' knowledge (level 2b) was the most often measured training outcome (n = 44). CONCLUSIONS: This review presents a comprehensive synthesis of the evidence on EMR training. A variety of training methods, participants, locations, strategies, and outcomes were described in the studies. Training should be aligned with the particular training needs, training objectives, EMR system utilized, and organizational environment. A training plan should include an overall goal and SMART (Specific, Measurable, Achievable, Realistic, Tangible) training objectives, that would allow a more rigorous evaluation of the training outcomes.

Health Care in the Age of Interoperability Part 6: The Future of FHIR.

About this Series This is the sixth and last article in a series on the dramatic transformation taking place in health informatics in large part because of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The first article provided background on health care, electronic health record systems for physicians, and the challenges they both face along with the potential of interoperability to help overcome them. The second introduced the basics of the FHIR standard and some suggested resources for those who are interested in its further exploration. The third introduced SMART on FHIR which, based on its wide adoption, has become the default standard FHIR app platform. The fourth looked at clinical decision support, arguably the single most important provider-facing use case for FHIR. The fifth introduced the personal health record and tools that can utilize the data stored in it as an important use case for FHIR in support of patients. This article looks at the future uses of FHIR with a particular emphasis on those that might impact on research uses of health data. The articles in this series are intended to introduce researchers from other fields to this one and assume no prior knowledge of healthcare or health informatics. They are abstracted from the author's recently published book, Health Informatics on FHIR: How HL7's New API is Transforming Healthcare (Springer International Publishing: https://www.springer.com/us/book/9783319934136).

Health Care in the Age of Interoperability Part 5: The Personal Health Record.

About this series This is the fifth in a series of articles on the dramatic transformation taking place in health informatics in large part because of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The first article provided background on health care, electronic health record systems for physicians, and the challenges they both face along with the potential of interoperability to help overcome them. The second introduced the basics of the FHIR standard and some suggested resources for those who are interested in its further exploration. The third introduced SMART on FHIR which, based on its wide adoption, has become the default standard FHIR app platform. The fourth looked at clinical decision support, arguably the single most important provider-facing use case for FHIR. This article introduces the personal health record and tools that can utilize the data stored in it as an important use case for FHIR in support of patients. The articles in this series are intended to introduce researchers from other fields to this one and assume no prior knowledge of health care or health informatics. They are abstracted from the author's recently published book, Health Informatics on FHIR: How HL7's New API is Transforming Healthcare (Springer International Publishing: https://www.springer.com/us/book/9783319934136).

Health Care in the Age of Interoperability: Part 4.

About this series This is the fourth in a series of articles on the dramatic transformation taking place in health informatics in large part because of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The first article provided background on healthcare, electronic health record systems for physicians, and the challenges they both face along with the potential of interoperability to help overcome them. The second introduced the basics of the FHIR standard and some suggested resources for those who are interested in its further exploration. The third introduced SMART on FHIR, which, based on its wide adoption, has become the default standard FHIR app platform. This article introduces clinical decision support as an important use case for FHIR in support of health care providers. The articles in this series are intended to introduce researchers from other fields to this one and assume no prior knowledge of health care or health informatics. They are abstracted from the author's recently published book, Health Informatics on FHIR: How HL7's New API is Transforming Healthcare (Springer International Publishing: https://www.springer.com/us/book/9783319934136).

Healthcare in the Age of Interoperability: Part 3.

About this series This is the third in a series of articles on the dramatic transformation taking place in health informatics in large part because of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The first article provided the background on healthcare, electronic health record systems for physicians, and the challenges they both face along with the potential of interoperability to help overcome them. The second introduced the basics of the FHIR standard and some suggested resources for those who are interested in its further exploration. In this article, we explore SMART on FHIR which has become the default standard FHIR app platform based on its wide adoption. The articles in this series are intended to introduce researchers from other fields to this one and assume no prior knowledge of healthcare or health informatics. They are abstracted from the author's recently published book, Health Informatics on FHIR: How HL7's New API Is Transforming Healthcare (Springer International Publishing: https://www.springer.com/us/book/9783319934136).

The development and electronic delivery of case-based learning using a fast healthcare interoperability resource system.

HL7 International's Fast Healthcare Interoperability Resources (FHIR) standard provides a common format for sharing health data (eg, FHIR resources) and a RESTful Application Programming Interface (eg, FHIR API) for accessing those resources via a FHIR server connected to an electronic health record system or any other system storing clinical data. Substitutable Medical Applications and Reusable Technologies (SMART) leverages FHIR to create an electronic health record (EHR) agnostic app platform. It utilizes the OAuth standard to provide for authorization and authentication. This paper describes the development and informal evaluation of Case Based Learning on FHIR (CBL on FHIR), a prototype EHR-connected FHIR/SMART platform to provide interactive digital cases for use in medical education. The project goals were to provide a more interactive form of CBL than is possible on paper to more realistically simulate clinical decision making and to expose medical students to modern informatics systems and tools for use in patient care.

Healthcare in the Age of Interoperability: The Promise of Fast Healthcare Interoperability Resources.

The first article of this series (see "About This Series") mentioned that, after the success of its new messaging standard for electronic health record (EHR) systems, Health Level 7 (HL7) found it difficult to develop and widely deploy a standard for the rich representation of clinical data for use in patient care. This was due, in large part, to the complexity of medicine and the resulting complexity of the clinical terminologies developed to represent it.

Health Care in the Age of Interoperability: The Potential and Challenges.

It is hard to conceive of a better rationale for healthcare interoperability than the management of chronic disease. People in advanced, industrialized countries are living longer, and chronic disease rates among the elderly are on the rise in part because of lifestyle issues, such as obesity and inadequate exercise. As a result, the care of chronic diseases (such as hypertension, heart disease, diabetes, chronic lung disease, and chronic kidney disease) accounts for well over 90% of spending by Medicare, the U.S. health insurance program for people age 65 and over. The Agency for Healthcare Research and Quality has found that the top 5% of patients with four or more chronic diseases are responsible for 30% of all Medicare chronic disease spending. While just 17% of Medicare patients live with more than six chronic conditions, they account for half of all spending on beneficiaries with chronic disease.

Understanding variations in pediatric asthma care processes in the emergency department using visual analytics.

Health care delivery processes consist of complex activity sequences spanning organizational, spatial, and temporal boundaries. Care is human-directed so these processes can have wide variations in cost, quality, and outcome making systemic care process analysis, conformance testing, and improvement challenging. We designed and developed an interactive visual analytic process exploration and discovery tool and used it to explore clinical data from 5784 pediatric asthma emergency department patients.

Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

A Visual Analytics Approach to Understanding Care Process Variation and Conformance.

With greater pressures of providing high-quality care at lower cost due to a changing financial and policy environment, the ability to understand variations in care delivery and associated outcomes and act upon this understanding is of critical importance. Building on prior work in visualizing health-care event sequences and in collaboration with our clinical partner, we describe our process in developing a multiple, coordinated visualization system that helps identify and analyze care processes and their conformance to existing care guidelines. We demonstrate our system using data of 5,784 pediatric emergency department visits over a 13-month period for which asthma was the primary diagnosis.

Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology.

OBJECTIVE: Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time. MATERIALS AND METHODS: We present a supervised Laplacian Eigenmap method to enhance predictive models by embedding textual predictors into a low-dimensional latent space, which preserves the local similarities among textual data in high-dimensional space. The proposed implementation performs alternating optimization using gradient descent. For the evaluation, we applied our method to over 2000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. In our experiments, we consider relatively short textual descriptions because of data availability. We compared our method with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using four metrics: the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), specificity, and sensitivity. RESULTS AND DISCUSSION: The results indicate that supervised Laplacian Eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC, respectively. Supervised Laplacian Eigenmaps showed an increase of 8.16% in AUC and 20.6% in MCC over the baseline that excluded textual data and a 2.69% and 5.35% increase in AUC and MCC, respectively, over unsupervised Laplacian Eigenmaps. CONCLUSIONS: As a solution, we present a supervised Laplacian Eigenmap method to embed textual predictors into a low-dimensional Euclidean space. This method allows many existing machine learning predictors to effectively and efficiently capture the potential of textual predictors, especially those based on short texts.

Utility of a clinical support tool for outpatient evaluation of pediatric chest pain.

This study evaluates a clinical pathway currently being employed at a large single-center pediatric cardiology practice. The dataset includes 1,997 pediatric patients with the primary complaint of chest pain. A logistic regression model was developed to predict cardiac disease and identify strong indicators of cardiac pathology. The area under the ROC curve was 0.73 and the Matthews correlation coefficient was 0.23. Given the low incidence of pathology disease, this study was unable to identify strong predictors of major cardiac pathology. The analysis did support syncope, palpitations and the onset of chest pain in the past 2-7 days as predictors of minor cardiac disease. However, the model indicated exertional chest pain is negatively associated with cardiac disease. This data should be evaluated with caution as some of the results are contrary to most clinical cardiologists' views. The majority of the results support the cardiac disease predictors in the clinical pathway.