Objective Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments.Materials and Methods Guided by the Smerecnik framework that theorizes understanding of multifactorial genetic disease through 3 knowledge types, we developed English and Spanish infographics for individuals enrolled in the Electronic Medical Records and Genomics Network. Infographics were developed to explain concepts in lay language and visualizations. We conducted iterative sessions using a modified "think-aloud" process with 10 participants (6 English, 4 Spanish-speaking) to explore comprehension of and attitudes towards the infographics.Results We found that all but one participant had "awareness knowledge" of genetic disease risk factors upon viewing the infographics. Many participants had difficulty with "how-to" knowledge of applying genetic risk factors to specific monogenic and polygenic risks. Participant attitudes towards the iteratively-refined infographics indicated that design saturation was reached.Discussion There were several elements that contributed to the participants' comprehension (or misunderstanding) of the infographics. Visualization and iconography techniques best resonated with those who could draw on prior experiences or knowledge and were absent in those without. Limited graphicacy interfered with the understanding of absolute and relative risks when presented in graph format. Notably, narrative and storytelling theory that informed the creation of a vignette infographic was most accessible to all participants.Conclusion Engagement with the intended audience who can identify strengths and points for improvement of the intervention is necessary to the development of effective infographics.

Objective: Apply natural language processing (NLP) to Amazon consumer reviews to identify adverse events (AEs) associated with unapproved over the counter (OTC) homeopathic drugs and compare findings with reports to the US Food and Drug Administration Adverse Event Reporting System (FAERS).Materials and methods: Data were extracted from publicly available Amazon reviews and analyzed using JMP 16 Pro Text Explorer. Topic modeling identified themes. Sentiment analysis (SA) explored consumer perceptions. A machine learning model optimized prediction of AEs in reviews. Reports for the same time interval and product class were obtained from the FAERS public dashboard and analyzed.Results: Homeopathic cough/cold products were the largest category common to both data sources (Amazon = 616, FAERS = 445) and were analyzed further. Oral symptoms and unpleasant taste were described in both datasets. Amazon reviews describing an AE had lower Amazon ratings (X-2 = 224.28, P < .0001). The optimal model for predicting AEs was Neural Boosted 5-fold combining topic modeling and Amazon ratings as predictors (mean AUC = 0.927).Discussion: Topic modeling and SA of Amazon reviews provided information about consumers' perceptions and opinions of homeopathic OTC cough and cold products. Amazon ratings appear to be a good indicator of the presence or absence of AEs, and identified events were similar to FAERS.Conclusion: Amazon reviews may complement traditional data sources to identify AEs associated with unapproved OTC homeopathic products. This study is the first to use NLP in this context and lays the groundwork for future larger scale efforts.

BackgroundThe Health Information Technology for Economic and Clinical Health Act of 2009 was legislated to reduce health care costs, improve quality, and increase patient safety. Providers and organizations were incentivized to exhibit meaningful use of certified electronic health record (EHR) systems in order to achieve this objective. EHR adoption is an expensive investment, given the resources and capital that are invested. Due to the cost of the investment, a return on the EHR adoption investment is expected. ObjectiveThis study performed a value analysis of EHRs. The objective of this study was to investigate the relationship between EHR adoption levels and financial and clinical outcomes by combining both financial and clinical outcomes into one conceptual model. MethodsWe examined the multivariate relationships between different levels of EHR adoption and financial and clinical outcomes, along with the time variant control variables, using moderation analysis with a longitudinal fixed effects model. Since it is unknown as to when hospitals begin experiencing improvements in financial outcomes, additional analysis was conducted using a 1- or 2-year lag for profit margin ratios. ResultsA total of 5768 hospital-year observations were analyzed over the course of 4 years. According to the results of the moderation analysis, as the readmission rate increases by 1 unit, the effect of a 1-unit increase in EHR adoption level on the operating margin decreases by 5.38%. Hospitals with higher readmission payment adjustment factors have lower penalties. ConclusionsThis study fills the gap in the literature by evaluating individual relationships between EHR adoption levels and financial and clinical outcomes, in addition to evaluating the relationship between EHR adoption level and financial outcomes, with clinical outcomes as moderators. This study provided statistically significant evidence (P<.05), indicating that there is a relationship between EHR adoption level and operating margins when this relationship is moderated by readmission rates, meaning hospitals that have adopted EHRs could see a reduction in their readmission rates and an increase in operating margins. This finding could further be supported by evaluating more recent data to analyze whether hospitals increasing their level of EHR adoption would decrease readmission rates, resulting in an increase in operating margins. Hospitals would incur lower penalties as a result of improved readmission rates, which would contribute toward improved operating margins.

Objective To evaluate the ability of case vignettes to assess the performance of symptom checker applications and to suggest refinements to the methodology used in case vignette-based audit studies. Methods We re-analyzed the publicly available data of two prominent case vignette-based symptom checker audit studies by calculating common metrics of test theory. Furthermore, we developed a new metric, the Capability Comparison Score (CCS), which compares symptom checker capability while controlling for the difficulty of the set of cases each symptom checker evaluated. We then scrutinized whether applying test theory and the CCS altered the performance ranking of the investigated symptom checkers. Results In both studies, most symptom checkers changed their rank order when adjusting the triage capability for item difficulty (ID) with the CCS. The previously reported triage accuracies commonly overestimated the capability of symptom checkers because they did not account for the fact that symptom checkers tend to selectively appraise easier cases (i.e., with high ID values). Also, many case vignettes in both studies showed insufficient (very low and even negative) values of item-total correlation (ITC), suggesting that individual items or the composition of item sets are of low quality. Conclusions A test–theoretic perspective helps identify previously undetected threats to the validity of case vignette-based symptom checker assessments and provides guidance and specific metrics to improve the quality of case vignettes, in particular by controlling for the difficulty of the vignettes an app was (not) able to evaluate correctly. Such measures might prove more meaningful than accuracy alone for the competitive assessment of symptom checkers. Our approach helps elaborate and standardize the methodology used for appraising symptom checker capability, which, ultimately, may yield more reliable results.

OBJECTIVES:To summarize the recent literature and research and present a selection of the best papers published in 2022 related to Health Information Exchange (HIE).METHODS:A systematic review of the literature was performed by the two section editors with the help of a medical librarian. We searched bibliographic databases for HIE-related papers using both MeSH headings and keywords in titles and abstracts. A shortlist of ten candidate best papers was first selected by section editors before being peer-reviewed by Yearbook editors and independent external reviewers.RESULTS:Major themes of the set of ten articles included factors influencing the organizational adoption of HIE and clinicians' use of the information, use of HIE in non-traditional settings, patients' perspectives on HIE, and outcomes of using HIE.CONCLUSIONS:These studies provide suggestions for the research questions, theories, settings, methods, and outcomes that can be fruitfully used for further research on HIE.

OBJECTIVE:This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the discipline of health informatics. The purpose of this article is to determine whether the foundational domains need modification to better reflect applied practice. MATERIALS AND METHODS:Using an iterative process and through individual and collective approaches, the foundational domains and the DoP statements were analyzed for alignment and eventual harmonization. Tables and Sankey plot diagrams were used to detail and illustrate the resulting alignment. RESULTS:We were able to map all the individual DoP knowledge statements and tasks to the AMIA foundational domains, but the statements within a single DoP domain did not all map to the same foundational domain. Even though the AMIA foundational domains and DoP domains are not in perfect alignment, the DoP provides good examples of specific health informatics competencies for most of the foundational domains. There are, however, limited DoP knowledge statements and tasks mapping to foundational domain 6-Social and Behavioral Aspects of Health. DISCUSSION:Both the foundational domains and the DoP were developed independently, several years apart, and for different purposes. The mapping analyses reveal similarities and differences between the practice experience and the curricular needs of health informaticians. CONCLUSIONS:The overall alignment of both domains may be explained by the fact that both describe the current and/or future health informatics professional. One can think of the foundational domains as representing the broad foci for educational programs for health informaticians and, hence, they are appropriately the focus of organizations that accredit these programs.

Polygenic risk scores (PRS) have potential to improve health care by identifying individuals that have elevated risk for common complex conditions. Use of PRS in clinical practice, however, requires careful assessment of the needs and capabilities of patients, providers, and health care systems. The electronic Medical Records and Genomics (eMERGE) network is conducting a collaborative study which will return PRS to 25,000 pediatric and adult participants. All participants will receive a risk report, potentially classifying them as high risk (similar to 2-10% per condition) for 1 or more of 10 conditions based on PRS. The study population is enriched by participants from racial and ethnic minority populations, underserved populations, and populations who experience poorer medical outcomes. All 10 eMERGE clinical sites conducted focus groups, interviews, and/or surveys to understand educational needs among key stakeholders-participants, providers, and/or study staff. Together, these studies highlighted the need for tools that address the perceived benefit/value of PRS, types of education/support needed, accessibility, and PRS-related knowledge and understanding. Based on findings from these preliminary studies, the network harmonized training initiatives and formal/informal educational resources. This paper summarizes eMERGE's collective approach to assessing educational needs and developing educational approaches for primary stakeholders. It discusses challenges encountered and solutions provided. (c) 2023 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.

PURPOSE:Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk. METHODS:To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results. RESULTS:GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022. CONCLUSION:Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

Background Clinician notes are structured in a variety of ways. This research pilot tested an innovative study design and explored the impact of note formats on diagnostic accuracy and documentation review time. Objective To compare two formats for clinical documentation (narrative format vs. list of findings) on clinician diagnostic accuracy and documentation review time. Method Participants diagnosed written clinical cases, half in narrative format, and half in list format. Diagnostic accuracy (defined as including correct case diagnosis among top three diagnoses) and time spent processing the case scenario were measured for each format. Generalised linear mixed regression models and bias-corrected bootstrap percentile confidence intervals for mean paired differences were used to analyse the primary research questions. Results Odds of correctly diagnosing list format notes were 26% greater than with narrative notes. However, there is insufficient evidence that this difference is significant (75% CI 0.8-1.99). On average the list format notes required 85.6 more seconds to process and arrive at a diagnosis compared to narrative notes (95% CI -162.3, -2.77). Of cases where participants included the correct diagnosis, on average the list format notes required 94.17 more seconds compared to narrative notes (75% CI -195.9, -8.83). Conclusion This study offers note format considerations for those interested in improving clinical documentation and suggests directions for future research. Balancing the priority of clinician preference with value of structured data may be necessary. Implications This study provides a method and suggestive results for further investigation in usability of electronic documentation formats.

Objective:To identify the informatics educational needs of clinical and translational research professionals whose primary focus is not informatics.Introduction:Informatics and data science skills are essential for the full spectrum of translational research, and an increased understanding of informatics issues on the part of translational researchers can alleviate the demand for informaticians and enable more productive collaborations when informaticians are involved. Identifying the level of interest in different topics among various types of of translational researchers will help set priorities for development and dissemination of informatics education.Methods:We surveyed clinical and translational science researchers in Clinical and Translational Science Award (CTSA) programs about their educational needs and preferences.Results:Researchers from 23 out of the 62 CTSA hubs responded to the survey. 67% of respondents across roles and topics expressed interest in learning about informatics topics. There was high interest in all 30 topics included in the survey, with some variation in interest depending on the role of the respondents.Discussion:Our data support the need to advance training in clinical and biomedical informatics. As the complexity and use of information technology and data science in research studies grows, informaticians will continue to be a limited resource for research collaboration, education, and training. An increased understanding of informatics issues across translational research teams can alleviate this burden and allow for more productive collaborations. To inform a roadmap for informatics education for research professionals, we suggest strategies to use the results of this needs assessment to develop future informatics education.

A discussion and debate on the American Medical Informatics Association's (AMIA) Ethical, Legal, and Social Issues (ELSI) Working Group listserv in 2021 raised important issues related to a forthcoming conference in Texas. Texas had recently enacted a restrictive abortion law and restricted voting rights. Several AMIA members advocated for a boycott of the state and the scheduled conference. The discussion led the AMIA Board of Directors to request that the organization's Ethics Committee provide general guidance for principle-based venue selection. This document recommends overarching principles for the venue selection for future AMIA events and conferences. Discussions by the AMIA Board, the Ethics Committee, and the ELSI Working Group informed these recommendations, and this document on guiding principles was approved by the AMIA Board of Directors in April 2022.

Objective: To identify major research topics and exhibit trends in these topics in 15 health services research, health policy, and health economics journals over 2 decades. Data Sources: The study sample of 35,159 abstracts (1999–2020) were collected from PubMed for 15 journals. Study Design: The study used a 3-phase approach for text analyses: (1) developing the corpus of 40,618 references from PubMed (excluding 5459 of those without abstract or author information); (2) preprocessing and generating the term list using natural language processing to eliminate irrelevant textual data and identify important terms and phrases; (3) analyzing the preprocessed text data using latent semantic analysis, topic analyses, and multiple correspondence analysis. Principal Findings: Application of analyses generated 16 major research topics: (1) implementation/intervention science; (2) HIV and women’s health; (3) outcomes research and quality; (4) veterans/military studies; (5) provider/primary-care interventions; (6) geriatrics and formal/informal care; (7) policies and health outcomes; (8) medication treatment/therapy; (9) patient interventions; (10) health insurance legislation and policies; (11) public health policies; (12) literature reviews; (13) cost-effectiveness and economic evaluation; (14) cancer care; (15) workforce issues; and (16) socioeconomic status and disparities. The 2-dimensional map revealed that some journals have stronger associations with specific topics. Findings were not consistent with previous studies based on user perceptions. Conclusion: Findings of this study can be used by the stakeholders of health services research, policy, and economics to develop future research agendas, target journal submissions, and generate interdisciplinary solutions by examining overlapping journals for particular topics.

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OBJECTIVE:This study aimed to understand the association between primary care physician (PCP) proficiency with the electronic health record (EHR) system and time spent interacting with the EHR.MATERIALS AND METHODS:We examined the use of EHR proficiency tools among PCPs at one large academic health system using EHR-derived measures of clinician EHR proficiency and efficiency. Our main predictors were the use of EHR proficiency tools and our outcomes focused on 4 measures assessing time spent in the EHR: (1) total time spent interacting with the EHR, (2) time spent outside scheduled clinical hours, (3) time spent documenting, and (4) time spent on inbox management. We conducted multivariable quantile regression models with fixed effects for physician-level factors and time in order to identify factors that were independently associated with time spent in the EHR.RESULTS:Across 441 primary care physicians, we found mixed associations between certain EHR proficiency behaviors and time spent in the EHR. Across EHR activities studied, QuickActions, SmartPhrases, and documentation length were positively associated with increased time spent in the EHR. Models also showed a greater amount of help from team members in note writing was associated with less time spent in the EHR and documenting.DISCUSSION:Examining the prevalence of EHR proficiency behaviors may suggest targeted areas for initial and ongoing EHR training. Although documentation behaviors are key areas for training, team-based models for documentation and inbox management require further study.CONCLUSIONS:A nuanced association exists between physician EHR proficiency and time spent in the EHR.

Learning Health Systems (LHS) require a workforce with specific knowledge and skills to identify and address healthcare quality issues, develop solutions to address those issues, and sustain and spread improvements within and outside the organization. Educational programs are tasked with designing learning opportunities that can meet these organizational needs. This manuscript explores different mechanisms for addressing challenges to creating educational programs to prepare individuals who can work in and lead LHS. Strategies and recommendations for educational programs to support the LHS include the creation of a new program, collaborating across existing programs, and producing a set of instructional materials.

Introduction Telemedicine has become prevalent during the novel-coronavirus disease-2019 (COVID-19) pandemic. The study explored patient factors associated with telemedicine utilization among post-kidney and pancreas transplant patients at a university center. Methods and Results After analyzing 2801 patients and their visits using chi-square test and logistic regression, we found that government-insured (P < .0001) post-kidney and pancreas transplant patients were less likely to use telemedicine. Sex (P = .748), patient race (P = .920), age groups (P = .812), and traveling distance (P = .837) were not associated with telemedicine use. Conclusion Centers should consider focusing on the subgroup of government-insured patients to improve telemedicine use and future studies should consider exploring barriers for underutilization of telemedicine in this population.

Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2021 related to health information exchange (HIE). Methods: A systematic review of the literature was performed by the two section editors with the help of a medical librarian. We searched bibliographic databases for HIE-related papers using both MeSH headings and keywords in titles and abstracts. A shortlist of candidate 15 best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: Major themes of the set of 15 articles included the issues to be addressed in building and maintaining HIEs, HIE implementation barriers and facilitators, and the outcomes of using HIEs. The outcomes of using HIE encompassed the impact on patient care and the ability of HIEs to provide a repository of data for further research. Conclusions: The growth of HIE has followed a course very similar to the growth of electronic health records (EHRs). Initial foci of research included technical issues in the deployment, followed by research on barriers to use. Now that EHRs are more widely implemented and used, the newer research involves the use of the electronic data contained in them. Although HIEs are currently at an earlier stage of maturity and development than EHRs and most of the articles in this review focused on implementation barriers, we have seen the beginning of research on the large amount of longitudinal and diverse data that HIEs can make available. As the implementation and use of HIEs continue to increase and become more widely deployed, we can expect that research about HIE and leveraging HIEs and the data they collect, will continue to increase.

OBJECTIVES:To summarize the recent literature and research and present a selection of the best papers published in 2020 in the field of Health Information Management (HIM) and Health Informatics. METHODS:A systematic review of the literature for the IMIA Yearbook HIM section was performed by the two section editors with the help of a medical librarian. We searched bibliographic databases for HIM-related papers using both MeSH headings and keywords in titles and abstracts. A shortlist of the fifteen best candidate papers was first selected by section editors before being peer-reviewed by independent external reviewers. RESULTS:The three major themes of Health Information Exchange (transmitting, sharing, and accessing patient health-related data and information) (HIE), Data Quality, and Privacy and Security make up 80% of the fifteen papers, with individual papers on personal health records, information governance and the professionalism of the HIM field. CONCLUSIONS:Traditional HIM concerns about HIM practice and workforce as well as issues about the data in electronic health records (EHRs) including data quality, coding, health information exchange among entities within the healthcare systems and privacy and confidentiality continue to be a large part of the HIM research literature. Although there was little research applying these themes to pandemic concerns, HIM professionals have the expertise to make ccontributions to public health informatics research and this research would benefit from their involvement.

Machine learning methods, a subdomain of artificial intelligence, in healthcare have been experiencing rapid growth and development but these methods have also been criticized. This article explores some of the more frequent criticisms leveled at machine learning approaches and offers suggestions on how to address problems of bias, "black box" systems, appropriate training for users, and monitoring model performance. Some of these problems can potentially be addressed using machine learning methods. We advocate for more involvement of clinicians in the development and evaluation of machine learning systems in healthcare and more regular monitoring of performance to take advantage of the strengths and minimize the weaknesses of these systems.