Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores.

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 (∼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.

Returning integrated genomic risk and clinical recommendations: The eMERGE study.

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.

Mapping 2 Decades of Research in Health Services Research, Health Policy, and Health Economics Journals.

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.

Factors associated with telemedicine utilization among post-transplant patients at a university kidney and pancreas transplant center.

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.

Advance Directives and Code Status Information Exchange: A Consensus Proposal for a Minimum Set of Attributes.

Documentation of code status and advance directives for end-of-life (EOL) care improves care and quality of life, decreases cost of care, and increases the likelihood of an experience desired by the patient and his/her family. However, the use of advance directives and code status remains low and only a few organizations maintain code status in electronic form. Members of the American Medical Informatics Association's Ethics Committee identified a need for a patient's EOL care wishes to be documented correctly and communicated easily through the electronic health record (EHR) using a minimum data set for the storage and exchange of code status information. After conducting an environmental scan that produced multiple resources, Ethics Committee members used multiple conference calls and a shared document to arrive at consensus on the proposed minimum data set. Ethics Committee members developed a minimum required data set with links to the HL7 C_CDA Advance Directives Module. Data categories include information on the organization obtaining the code status information, the patient, any supporting documentation, and finally the desired code status information including mandatory, optional, and conditional elements. The "minimum set of attributes" to exchange advance directive / code status data described in this manuscript enables communication of patient wishes across multiple providers and health care settings. The data elements described serve as a starting point for a dialog among informatics professionals, physicians experienced in EOL care, and EHR vendors, with the goal of developing standards for incorporating this functionality into the EHR systems.

Hospital budget increase for information technology during phase 1 meaningful use.

Federal policies have a significant effect on how businesses spend money. The 2009 HITECH (Health Information Technology for Economic and Clinical Health Act) authorized incentive payments through Medicare and Medicaid to clinicians and hospitals when they use certified electronic health records privately and securely to achieve specified improvements in care delivery. Federal incentive payments were offered in 2011 for hospitals that had satisfied "meaningful use" criteria. A longitudinal study of nonfederal hospital information technology (IT) budgets (N = 493) during the years 2009 to 2011 found increases in the percentage of hospital annual operating budgets allocated to IT in the years leading up to these federal incentives. This increase was most pronounced among hospitals receiving high proportions of their reimbursements from Medicaid, followed by hospitals receiving high proportions of their reimbursements from Medicare, possibly indicating a budget shift during this period to more IT spending to achieve meaningful-use policy guidelines.

Exploration of an automated approach for receiving patient feedback after outpatient acute care visits.

BACKGROUND: To improve and learn from patient outcomes, particularly under new care models such as Accountable Care Organizations and Patient-Centered Medical Homes, requires establishing systems for follow-up and feedback. OBJECTIVE: To provide post-visit feedback to physicians on patient outcomes following acute care visits. DESIGN: A three-phase cross-sectional study [live follow-up call three weeks after acute care visits (baseline), one week post-visit live call, and one week post-visit interactive voice response system (IVRS) call] with three patient cohorts was conducted. A family medicine clinic and an HIV clinic participated in all three phases, and a cerebral palsy clinic participated in the first two phases. Patients answered questions about symptom improvement, medication problems, and interactions with the healthcare system. PATIENTS: A total of 616 patients were included: 142 from Phase 1, 352 from Phase 2 and 122 from Phase 3. MAIN MEASURES: Primary outcomes included: problem resolution, provider satisfaction with the system, and comparison of IVRS with live calls made by research staff. KEY RESULTS: During both live follow-up phases, at least 96% of patients who were reached completed the call compared to only 48% for the IVRS phase. At baseline, 98 of 113 (88%) patients reported improvement, as well as 167 of 196 (85%) in the live one-week follow-up. In the one-week IVRS phase, 25 of 39 (64%) reported improvement. In all phases, the majority of patients in both the improved and unimproved groups had not contacted their provider or another provider. While 63% of providers stated they wanted to receive patient feedback, they varied in the extent to which they used the feedback reports. CONCLUSIONS: Many patients who do not improve as expected do not take action to further address unresolved problems. Systematic follow-up/feedback mechanisms can potentially identify and connect such patients to needed care.

Federated Aggregate Cohort Estimator (FACE): an easy to deploy, vendor neutral, multi-institutional cohort query architecture.

Cross-institutional data sharing for cohort discovery is critical to enabling future research. While particularly useful in rare diseases, the ability to target enrollment and to determine if an institution has a sufficient number of patients is valuable in all research, particularly in the initiation of projects and collaborations. An optimal technology solution would work with any source database with minimal resource investment for deployment and would meet all necessary security and confidentiality requirements of participating organizations. We describe a platform-neutral reference implementation to meet these requirements: the Federated Aggregate Cohort Estimator (FACE). FACE was developed and implemented through a collaboration of The University of Alabama at Birmingham (UAB), The Ohio State University (OSU), the University of Massachusetts Medical School (UMMS), and the Denver Health and Hospital Authority (DHHA) a clinical affiliate of the Colorado Clinical and Translational Sciences Institute. The reference implementation of FACE federated diverse SQL data sources and an i2b2 instance to estimate combined research subject availability from three institutions. It used easily-deployed virtual machines and addressed privacy and security concerns for data sharing.

Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative Study.

BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: A 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. CONCLUSIONS: This 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.

Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study.

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.

Recommendations for the design, implementation and evaluation of social support in online communities, networks, and groups.

A new model of health care is emerging in which individuals can take charge of their health by connecting to online communities and social networks for personalized support and collective knowledge. Web 2.0 technologies expand the traditional notion of online support groups into a broad and evolving range of informational, emotional, as well as community-based concepts of support. In order to apply these technologies to patient-centered care, it is necessary to incorporate more inclusive conceptual frameworks of social support and community-based research methodologies. This paper introduces a conceptualization of online social support, reviews current challenges in online support research, and outlines six recommendations for the design, evaluation, and implementation of social support in online communities, networks, and groups. The six recommendations are illustrated by CanConnect, an online community for cancer survivors in middle Tennessee. These recommendations address the interdependencies between online and real-world support and emphasize an inclusive framework of interpersonal and community-based support. The applications of these six recommendations are illustrated through a discussion of online support for cancer survivors.

An observational study of the accuracy and completeness of an anesthesia information management system: recommendations for documentation system changes.

Anesthesia information management systems must often be tailored to fit the environment in which they are implemented. Extensive customization necessitates that systems be analyzed for both accuracy and completeness of documentation design to ensure that the final record is a true representation of practice. The purpose of this study was to determine the accuracy of a recently installed system in the capture of key perianesthesia data. This study used an observational design and was conducted using a convenience sample of nurse anesthetists. Observational data of the nurse anesthetists'delivery of anesthesia care were collected using a touch-screen tablet computer utilizing an Access database customized observational data collection tool. A questionnaire was also administered to these nurse anesthetists to assess perceived accuracy, completeness, and satisfaction with the electronic documentation system. The major sources of data not documented in the system were anesthesiologist presence (20%) and placement of intravenous lines (20%). The major sources of inaccuracies in documentation were gas flow rates (45%), medication administration times (30%), and documentation of neuromuscular function testing (20%)-all of the sources of inaccuracies were related to the use of charting templates that were not altered to reflect the actual interventions performed.

Closing the feedback loop: an interactive voice response system to provide follow-up and feedback in primary care settings.

In primary care settings, follow-up regarding the outcome of acute outpatient visits is largely absent. We sought to develop an automated interactive voice response system (IVRS) for patient follow-up with feedback to providers capable of interfacing with multiple pre-existing electronic medical records (EMRs). A system was designed to extract data from EMRs, integrate with the IVRS, call patients for follow-up, and provide a feedback report to providers. Challenges during the development process were analyzed and summarized. The components of the technological solution and details of its implementation are reported. Lessons learned include: (1) Modular utilization of system components is often needed to adapt to specific clinic workflow and patient population needs (2) Understanding the local telephony environment greatly impacts development and is critical to success, and (3) Ample time for development of the IVRS questionnaire (mapping all branching paths) and speech recognition tuning (sensitivity, use of barge-in tuning, use of "known voice") is needed. With proper attention to design and development, modular follow-up and feedback systems can be integrated into existing EMR systems providing the benefits of IVRS follow-up to patients and providers across diverse practice settings.

Findings from the 2023 Yearbook Section on Health Information Exchange.

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.

How suitable are clinical vignettes for the evaluation of symptom checker apps? A test theoretical perspective.

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.

Impact of clinical note format on diagnostic accuracy and efficiency.

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.

Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).

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 (X2 = 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.

Mapping the delineation of practice to the AMIA foundational domains for applied health informatics.

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.