SIMCard: Toward better connected electronic health record visualization

e18074 Background: Physician reimbursement for care delivered to Medicare beneficiaries fundamentally changed with the 2015 MACRA legislation, requiring eligible clinicians to report quality measures in the Merit-Based Incentive Payment System (MIPS). To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS results, EHR data ingested by ASCO’s CancerLinQ (CLQ) was analyzed. Methods: Nineteen MIPS measures specified for medical oncology, including 8 shared by other specialties, were retrieved from qpp.cms.gov and systematically evaluated to determine data elements necessary to satisfy each measure. The existence of corresponding data fields and completion of these fields with clinical data was analyzed according to EHR implementation in de-identified and aggregated CLQ data. Results: Five clinician informaticists reviewed the 19 oncology MIPS measures, and identified a consensus list of 52 discrete EHR data elements (DEs) that would be needed. CLQ-processed data from 4 commercial EHR systems implemented at 47 CLQ practices found structured data fields for 84% (43 of 52) of the DE, but fewer than half (46%) of these fields were ever populated and only 32% (17 of 52) of DE were recorded for > 20% of cases. Only 3-5 of 19 MIPS measures could be reliably reported based on data element availability by most practices in this sample set. There were minimal differences between the EHRs ability to encode MIPS DE. Elements most likely to be encoded were those for registration (birthdate, gender), billing (diagnosis, meds), vital signs and smoking status, while those seldom or never encoded related to care plans (tobacco, alcohol, pain management). Other DE rarely encoded were patient events occurring outside the oncology practice (receipt/completion of consultations, dates of hospice enrollment and death), which would be dependent on data exchange between work units and practice entities or, more likely, re-entry by oncology practices. Conclusions: Only a minority of DE required to satisfy MIPS criteria are available as discrete data fields in current EHRs, limiting automated reporting efforts. Improved data quality and completeness is needed to satisfy mandated reporting.

IntroductionThe ability to utilize the electronic health record (EHR) without compromising the doctor-patient relationship (DPR) is an essential skill of all physicians and trainees, yet little time is spent on educating or assessing learners on needed techniques. To address this gap, we developed a conventional OSCE station coupled with a simulated patient chart within the Epic program in order to assess our students' skills utilizing the EHR during a patient encounter.MethodsOf third-year medical students, 119 were given full access to the patient's simulated chart 24 hours in advance of their OSCE to review clinical data. During an in-person OSCE with a standardized patient (SP), students performed a focused history and physical, using the EHR to verify allergies and medications. Students completed an electronic patient note graded by faculty. SPs evaluated the students on communication and interpersonal skills with specific rubric elements. Faculty graded the students' notes to evaluate their expression of clinical reasoning in the assessment and plan.ResultsTraining SPs and faculty to assess students on EHR skills was feasible. After implementation of a comprehensive curriculum focused on EHR and DPR, there was a significant difference on EHR-related communication skills (M = 76.4, SD = 17.6) versus (M = 37, SD = 28.9) before curriculum enhancement t (117.9) = −12.4, p <.001.DiscussionThe EHR OSCE station provided a standardized method of assessing students' EHR skills during a patient encounter. Challenges still exist in the technological requirements to develop and deliver cases in today's EHR platform.

Hospital adoption of API-enabled patient data access

Objective: To evaluate the continuity and completeness of electronic health record (EHR) data, and the concordance of select clinical outcomes and baseline comorbidities between EHR and linked claims data, from three healthcare delivery systems in Taiwan. Methods: We identified oral hypoglycemic agent (OHA) users from the Integrated Medical Database of National Taiwan University Hospital (NTUH-iMD), which was linked to the National Health Insurance Research Database (NHIRD), from June 2011 to December 2016. A secondary evaluation involved two additional EHR databases. We created consecutive 90-day periods before and after the first recorded OHA prescription and defined patients as having continuous EHR data if there was at least one encounter or prescription in a 90-day interval. EHR data completeness was measured by dividing the number of encounters in the NTUH-iMD by the number of encounters in the NHIRD. We assessed the concordance between EHR and claims data on three clinical outcomes (cardiovascular events, nephropathy-related events, and heart failure admission). We used individual comorbidities that comprised the Charlson comorbidity index to examine the concordance of select baseline comorbidities between EHRs and claims. Results: We identified 39,268 OHA users in the NTUH-iMD. Thirty-one percent (n = 12,296) of these users contributed to the analysis that examined data continuity during the 6-month baseline and 24-month follow-up period; 31% (n = 3,845) of the 12,296 users had continuous data during this 30-month period and EHR data completeness was 52%. The concordance of major cardiovascular events, nephropathy-related events, and heart failure admission was moderate, with the NTU-iMD capturing 49–55% of the outcome events recorded in the NHIRD. The concordance of comorbidities was considerably different between the NTUH-iMD and NHIRD, with an absolute standardized difference >0.1 for most comorbidities examined. Across the three EHR databases studied, 29–55% of the OHA users had continuous records during the 6-month baseline and 24-month follow-up period. Conclusion: EHR data continuity and data completeness may be suboptimal. A thorough evaluation of data continuity and completeness is recommended before conducting clinical and translational research using EHR data in Taiwan.

Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved. Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review. By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment. The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

Electronic health record (EHR) data are widely used in public health research, including in HIV-related studies, but are limited by potential bias due to incomplete and inaccurate information, lack of generalizability, and lack of representativeness. This study explores how workflow processes among HIV health care providers (HCPs), data scientists, and state health department professionals may potentially introduce or minimize bias within EHR data. One focus group with 3 health department professionals working in HIV surveillance and 16 in-depth interviews (ie, 5 people with HIV, 5 HCPs, 5 data scientists, and 1 health department professional providing retention-in-care services) were conducted with participants purposively sampled in South Carolina from August 2023 to April 2024. All interviews were transcribed verbatim and analyzed using a constructivist grounded theory approach, where transcripts were first coded and then focused, axial, and theoretically coded. The EHR data lifecycle originates with people with HIV and HCPs in the clinical setting. Data scientists then curate EHR data and health department professionals manage and use the data for surveillance and policy decision-making. Throughout this lifecycle, the three primary stakeholders (ie, HCPs, data scientists, and health department professionals) identified challenges with EHR processes and provided their recommendations and accommodations in addressing the related challenges. HCPs reported the influence of socio-structural biases on their inquiry, interpretation, and documentation of social determinants of health (SDOH) information of people living with HIV, the influence of which is proposed to be mitigated through people living with HIV access to their EHRs. Data scientists identified limited data availability and representativeness as biasing the data they manage. Health department professionals face challenges with delayed and incomplete data, which may be addressed statistically but require consideration of the data's limitations. Overall, bias within the EHR data lifecycle persists because workflows are not intentionally structured to minimize bias and there is a diffusion of responsibility for data quality between the various stakeholders. From the perspective of various stakeholders, this study describes the EHR data lifecycle and its associated challenges as well as stakeholders' accommodations and recommendations for mitigating and eliminating bias in EHR data. Based upon these findings, studies reliant on EHR data should adequately consider its challenges and limitations. Throughout the EHR data lifecycle, bias could be reduced through an inclusive, supportive health care environment, people living with HIV verification of SDOH information, the customization of data collection systems, and EHR data inspection for completeness, accuracy, and timeliness. Future research is needed to further identify instances where bias is introduced and how it can best be mitigated and eliminated across the EHR data lifecycle. Systematic changes are necessary to reduce instances of bias between data workflows and stakeholders.

The increase in hospitalizations of patients with noncommunicable diseases remains a key challenge for healthcare systems. A significant portion of these hospitalizations is potentially preventable with timely outpatient monitoring and effective primary care. Predictive models based on data from health information systems (HIS) and electronic health records (EHRs) make it possible to identify patients at increased risk of hospitalization and improve their management strategies. The article systematizes and summarizes international experience in using hospitalization risk prognostic models, developed on the basis of HIS and EHR data. We made a systematic review of publications presented in Russian and foreign databases (Scopus, PubMed, BMC Health Services Research, BMJ Research, Google Scholar, Elibrary, Oxford Academic, Wiley Online Library) for the period 1993-2023. The analysis included studies that used EHR and HIS data to develop and/or validate prognostic models of hospitalization risk. The information is systematized by following predictor categories: diseases and comorbidities, prescriptions, interaction with healthcare organizations, sociodemographic and laboratory parameters. The final analysis included 14 studies describing 28 models for predicting the hospitalization risk. Most studies used logistic regression. Machine learning methods (gradient boosting, random forest, and Bayesian ensemble models) were used in some studies. The categories with the highest prognostic value were "diseases and comorbidities" (42,3%), "medication prescriptions" (23,6%), and "interaction with health organizations" (19,8%). Including temporal characteristics (frequency and recency of hospitalizations, visits to primary care facilities, and laboratory tests) increased the model accuracy. Predictive models based on EHR and HIS data demonstrate high discriminatory power and enable the assessment of hospitalization risk at the primary care level. Machine learning methods in some studies demonstrated the possibility of a more in-depth analysis of the relationships between predictors and improved prediction accuracy. A promising area of research is the further development, external validation, and adaptation of models using expanded EHR and HIS data sets for its use in outpatient monitoring and preventing noncommunicable diseases.

BACKGROUND Inflammatory Bowel Disease (IBD) is an increasingly prevalent condition that results in weight loss, bloody diarrhea, and poor quality of life. Traditional population-based research on patients with IBD has involved use of relatively shallow claims data that is deficient in precise demographics. IBD research using richly detailed electronic health record (EHR) data has been difficult due to the need for laborious manual chart review to extract information, but new methods and technology allow for rapid EHR data extraction. We present here the results of the extraction of IBD patient data from our EHR into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). METHODS This study is an observational cohort study using patient health care data from our EHR extracted into the OMOP CDM. Transforming data into the standard CDM format allows for information from different databases and coding schema to be compared and aggregated. We used a de-identified dataset of all patients seen at Johns Hopkins Medicine since 2016. Our cohort of IBD, Crohn’s disease (CD), and Ulcerative Colitis (UC) patients were stratified by age, sex, race, weight, and pregnancy status. Within the cohorts, we obtained demographics, comorbidities, symptoms, labs, medications, and other data. We validated the extraction by comparison with EHR queries using Slicer/Dicer (SD). RESULTS There were 11,357 IBD patients including 6,512 CD patients and 4,224 UC patients. There were more female patients for both CD and UC (56.4% v 57.5%) and more white patients for both CD and UC (55.4% v 56.7%) compared to black (23.5% v 22.6%) and Asian patients (5.6% v 4.9%). CD and UC patients were treated with systemic steroids (49.1% v 51.5%), 5-ASA (24.9% v 47.2%) anti-TNF inhibitors (24.0% v 12%), IL12/23 inhibitors (8.4% v 2.7%), and JAK inhibitors (0.3% v 1.8%). Using SD queries, the percent differences were determined between the OMOP and EHR data, revealing a 0.54% difference in the number of CD patients and 10.36% difference in the number of UC patients (Table 1). CONCLUSION Using OMOP CDM to analyze clinical characteristics and outcomes for patients with IBD is a valuable method for obtaining de-identified patient data, reducing patient risk, and facilitating rapid creation of on-demand cohorts for multi-institutional research studies. This advantage is balanced by the lack of access to row-level data to validate cohorts. Local validation of data extraction through SD queries can allow for rapid creation of cohorts for big data research. The most significant differences between SD queries and OMOP CDM was in the calculation of prevalence of medications and race, suggesting that differing data extraction coding methods could result in queries that underestimate rates of treatment due to proprietary local coding schema.

AB1206 SELF-REPORT OF FRACTURE HISTORY COMPARED TO FRACTURE CODES FROM AN ELECTRONIC HEALTH RECORD DATASET

Background Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or “grouper.” For constructing value sets, Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. Objective This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. Methods We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. Results A total of 125 condition-defining “standard” SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th–75th percentiles: 1–5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th–75th percentiles: 8–85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th–75th percentiles: 63–976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. Conclusion SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, “smarter” EHR feature configuration, and clinical–translational research employing EHR-derived data.

We assessed the suitability of pooled electronic health record (EHR) data from clinical research networks (CRNs) of the patient-centered outcomes research network to conduct studies of the association between tumor necrosis factor inhibitors (TNFi) and infections. EHR data from patients with one of seven autoimmune diseases were obtained from three CRNs and pooled. Person-level linkage of CRN data and Centers for Medicare and Medicaid Services (CMS) fee-for-service claims data was performed where possible. Using filled prescriptions from CMS claims data as the gold standard, we assessed the misclassification of EHR-based new (incident) user definitions. Among new users of TNFi, we assessed subsequent rates of hospitalized infection in EHR and CMS data. The study included 45 483 new users of TNFi, of whom 1416 were successfully linked to their CMS claims. Overall, 44% of new EHR TNFi prescriptions were not associated with medication claims. Our most specific new user definition had a misclassification rate of 3.5%-16.4% for prevalent use, depending on the medication. Greater than 80% of CRN prescriptions had either zero refills or missing refill data. Compared to using EHR data alone, there was a 2- to 8-fold increase in hospitalized infection rates when CMS claims data were added to the analysis. EHR data substantially misclassified TNFi exposure and underestimated the incidence of hospitalized infections compared to claims data. EHR-based new user definitions were reasonably accurate. Overall, using CRN data for pharmacoepidemiology studies is challenging, especially for biologics, and would benefit from supplementation by other sources.

Comparison of Lactation Information from Electronic Health Records with Survey Data Across Five US Health Systems.

We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques.

C ollecting data on patient race, ethnicity, and language (REL) is an important step in reducing health care disparities, as clinical performance measures can then be stratified by REL to guide quality improvement efforts. In this month’s issue of JGIM, Klinger et al. report important inaccuracies in REL data in electronic health records (EHRs) in 13 primary care clinics. For example, 3 % and 6.6 % of patients who self-identified as Hispanic and African American, respectively, were not recorded as such in the EHR, and 20 % of persons documented in the EHRs as English-speaking elected to take a survey in Spanish. Inaccuracies in REL data have clustered around the distinction between Latino and black, American Indians, and multiracial categories, and lack of granularity among Asian American and Latino subgroups. Klinger et al.’s study is the first to report inaccuracies in REL data in EHRs. Because meaningful use EHR regulations incentivize the collection of REL data, these data are more likely to be collected and available for electronic use. In this editorial, I will discuss how to accurately collect REL data, where REL data fit within comprehensive efforts to achieve health equity, the powerful incentives that are driving the collection of REL data, and emerging innovative uses of REL data. The Health Research and Education Trust (HRET) has published best practices and training materials for REL data collection, and many of its recommendations are consistent with those of the Institute of Medicine. Patient self-reporting is the gold standard; thus, clerical staff must directly ask patients about their REL. Patients generally understand the importance of collecting language data for improving communication and to determine the need for interpreter services. The two key questions to ask are BHow would you rate your ability to speak and understand English?^ and BWhat language do you feel most comfortable speaking with your doctor or nurse?^ However, patients sometimes question why race and ethnicity information are collected, and health care organizations must ensure that patients understand why REL data are important for improving the quality of care. For example, HRET recommends saying to patients, BNow I would like you to tell me your race and ethnic background. We use this information to review the treatment patients receive and make sure everyone gets the highest quality of care.^ BDo you consider yourself Hispanic/ Latino?^ BWhich category best describes your race?^ HRET recommends following these two questions with a granular ethnicity screening question such as BI would like you to describe your race or ethnic background. You can use specific terms such as Korean, Mexican, Haitian, Somali.^ Even with this explanation of why REL data are collected, some patients remain suspicious. Thus, organizations must train their staff in how to respond to challenging patient questions, such as: BDo you discriminate against African-American patients like myself?^, BDo you provide worse care for minority patients?^, and BAre you trying to find out if I am a U.S. citizen?^ However, it is not enough to tell the intake clerical staff to ask patients REL questions as recommended above. Multiple departments must take action to collect and use REL data appropriately. The University of Chicago is currently working to improve the process of REL data collection, involving 15 different departments (e.g., information technology, patient care services, center for quality, managed care, population health, ambulatory, finance, etc.). Achieving accuracy in REL data collection is often an uphill battle unless leadership and staff of health care organizations make reducing disparities a priority. The ultimate goal of REL data collection is improving the quality of care and outcomes for all patients and reducing health care disparities. Thus, REL data collection is not an end in itself. REL data alone are not a magic bullet that will end health care disparities, just as universal health insurance or cultural competency training for clinicians in isolation are not complete solutions for achieving equity. REL data are useful for stratifying clinical performance measures to identify disparities and thus increase From the Section of General Internal Medicine, the Department of Medicine, the Chicago Center for Diabetes Translation Research, the MacLean Center for Clinical Medical Ethics, the Robert Wood Johnson Foundation (RWJF) Finding Answers: Disparities Research for Change National Program Office, and the RWJF Reducing Health Care Disparities Through Payment and Delivery System Reform Program Office, University of Chicago, Chicago, Illinois, USA.

Canada has achieved near-universal adoption of electronic health records (EHRs) and yet interoperability, the secure exchange and use of health data across different systems and settings, remains limited. We aimed to describe the current state of EHRs in 10 provincial and 3 territorial jurisdictions in Canada and evaluate the maturity of their interoperability using a structured interoperability assessment model. We conducted an environmental scan of EHR use and interoperability across all provinces and territories using Canada Health Infoway documents and structured interviews with 23 subject matter experts. Using a rigorously designed interoperability maturity model, we evaluated jurisdictions across 4 enabler dimensions (governance, legislation and standards, incentives and capacity-building, and technical infrastructure) and 4 interoperability status dimensions (community EHRs, hospital EHRs, patient portals, and system analytics). We found that, although EHR adoption was high, maturity of EHR interoperability was low and uneven across Canada. Integrated EHR health data exchange was limited, and nearly all jurisdictions lacked EHR interoperability between hospitals, community specialists, and primary care. Data exchange between primary care and specialists, and between hospitals and community settings, was heavily dependent on fax (traditional or online) or mailed letters in every jurisdiction. Patient portal contents and system-level analytics using EHR data were underdeveloped nationally. No jurisdiction was advanced in all dimensions. Although most jurisdictions showed strength in at least 1 area, they also exhibited many areas for growth. We identified 8 key barriers to interoperability, each of which can be overcome. Canada has widespread EHR adoption, but maturity of EHR interoperability and the enabling conditions required for true interoperability are low and inconsistent across jurisdictions. Strengthening governance, legislation, standards, incentives, and technical infrastructure - supported by national legislation to mandate interoperability across different EHRs - will be essential to advancing connected care across Canada and realizing widespread benefits for patients, clinicians, and health systems.