Objective:Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities. Methods:We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels. Results:The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning. Conclusion:As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Introduction Efforts to improve population health by being responsive to patients’ social and economic conditions will benefit from care models and technologies that assess and address unmet social needs. In 2019, NCCARE360 launched in North Carolina as the first state­wide digital care coordination network to “close the loop” on referrals between community-based organizations (CBOs), health service providers, and social service agencies. The platform creates a shared network for sending and receiving electronic referrals and track client outcomes. Methods As a case study, we compare NCCARE360 resolution rates for community resource referrals originating from a large integrated health system primarily in Durham County from September 1, 2020, through February 28, 2021. In the first year, COVID-19 Support Ser­vices Program (COVID-SSP) funding was available to reimburse associated CBOs for providing food assistance and case management services. We compared this with the same period the following year after funds had been exhausted. We present frontline implementation experiences and highlight opportunities, challenges, and recommendations for NCCARE360 implementation. Results Multi-level considerations for individual end users, organizations adopting the platform, and policymakers are presented. Addi­tionally, we find that when COVID-SSP funding was available, more referrals were placed (3,220 cases) and referrals were more likely to be resolved (88% resolution rate) when compared to the same time frame when funds were no longer available (860 cases; 30% resolution rate). These results underscore the importance of reimbursement mechanisms and funding. Limitations The examination of referral rates is observational and may not generalize to other contexts. Conclusion The shift to value-based care is an opportunity to embrace structural solutions to health and social care fragmentation. There is also an opportunity to realize the potential of NCCARE360 and efforts like it to contain costs and improve health outcomes and equity.

BACKGROUND:Patients with diabetes at risk of food insecurity face cost barriers to healthy eating and, as a result, poor health outcomes. Population health management strategies are needed to improve food security in real-world health system settings. We seek to test the effect of a prescription produce program, 'Eat Well' on cardiometabolic health and healthcare utilization. We will also assess the implementation of an automated, affirmative outreach strategy. METHODS:We will recruit approximately 2400 patients from an integrated academic health system in the southeastern United States as part of a two-arm parallel hybrid type 1 pragmatic randomized controlled trial. Patients with diabetes, at risk for food insecurity, and a recent hemoglobin A1c reading will be eligible to participate. The intervention arm receives, 'Eat Well', which provides a debit card with $80 (added monthly) for 12 months valid for fresh, frozen, or canned fruits and vegetables across grocery retailers. The control arm does not. Both arms receive educational resources with diabetes nutrition and self-management materials, and information on existing care management resources. Using an intent-to-treat analysis, primary outcomes include hemoglobin A1C levels and emergency department visits in the 12 months following enrollment. Reach and fidelity data will be collected to assess implementation. DISCUSSION:Addressing food insecurity, particularly among those at heightened cardiometabolic risk, is critical to equitable and effective population health management. Pragmatic trials provide important insights into the effectiveness and implementation of 'Eat Well' and approaches like it in real-world settings. REGISTRATION:ClinicalTrials.gov Identifier: NCT05896644; Clinical Trial Registration Date: 2023-06-09.

Diabetes Self-Management Education and Support (DSMES) programs are an effective, yet underutilized, resource to improve health outcomes and behaviors for people with diabetes. We examined the attendance and referral rates for people with diabetes to DSMES classes at an academic medical center, noting a 10% referral rate and 37% completion rate for those referred. We identified barriers to DSMES care at patient, provider, and health system levels. Current technology platforms and training fail to prioritize referrals to diabetes education; providers and people with diabetes are often unfamiliar with program content and benefits. Scheduling mechanisms often delay or lose interested patients in receiving vital education. Existing Medicare reimbursement strategies limit expansion of DSMES programs, generating significant wait times and limit capabilities for Diabetes Care and Education Specialists. We identify potential policy solutions and recommend alterations to existing referral and scheduling systems to expand existing technology platforms for DSMES programs and shift reimbursement policies to individualize and better support care for persons with diabetes.

Introduction: Although unmet social needs can impact health outcomes, health systems often lack the capacity to fully address these needs. Our study describes a model that organized student volunteers as a community-based organisation (CBO) to serve as a social referral hub on a coordinated social care platform, NCCARE360. Description: Patients at two endocrinology clinics were systematically screened for social needs. Patients who screened positive and agreed to receive help were referred via NCCARE360 to student ‘Help Desk’ volunteers, who organised as a CBO. Trained student volunteers called patients to place referrals to resources and document them on the platform. The platform includes documentation at several levels, acting as a shared information source between healthcare providers, volunteer student patient navigators, and community resources. Navigators followed up with patients to problem-solve barriers and track referral outcomes on the platform, visible to all parties working with the patient. Discussion: Of the 44 patients who screened positive for social needs and were given referrals by Help Desk, 41 (93%) were reached for follow-up. Thirty-six patients (82%) connected to at least one resource. These results speak to the feasibility and utility of organising undergraduate student volunteers into a social referral hub to connect patients to resources on a coordinated care platform. Conclusion: Organising students as a CBO on a centralized social care platform can help bridge a critical gap between healthcare and social services, addressing health system capacity and ultimately improving patients’ connections with resources.

Background:The problem list (PL) is a repository of diagnoses for patients' medical conditions and health-related issues. Unfortunately, over time, our PLs have become overloaded with duplications, conflicting entries, and no-longer-valid diagnoses. The lack of a standardized structure for review adds to the challenges of clinical use. Previously, our default electronic health record (EHR) organized the PL primarily via alphabetization, with other options available, for example, organization by clinical systems or priority settings. The system's PL was built with limited groupers, resulting in many diagnoses that were inconsistent with the expected clinical systems or not associated with any clinical systems at all. As a consequence of these limited EHR configuration options, our PL organization has poorly supported clinical use over time, particularly as the number of diagnoses on the PL has increased.Objective:We aimed to measure the accuracy of sorting PL diagnoses into PL system groupers based on Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept groupers implemented in our EHR.Methods:We transformed and developed 21 system- or condition-based groupers, using 1211 SNOMED CT hierarchal concepts refined with Boolean logic, to reorganize the PL in our EHR. To evaluate the clinical utility of our new groupers, we extracted all diagnoses on the PLs from a convenience sample of 50 patients with 3 or more encounters in the previous year. To provide a spectrum of clinical diagnoses, we included patients from all ages and divided them by sex in a deidentified format. Two physicians independently determined whether each diagnosis was correctly attributed to the expected clinical system grouper. Discrepancies were discussed, and if no consensus was reached, they were adjudicated by a third physician. Descriptive statistics and Cohen κ statistics for interrater reliability were calculated.Results:Our 50-patient sample had a total of 869 diagnoses (range 4-59; median 12, IQR 9-24). The reviewers initially agreed on 821 system attributions. Of the remaining 48 items, 16 required adjudication with the tie-breaking third physician. The calculated κ statistic was 0.7. The PL groupers appropriately associated diagnoses to the expected clinical system with a sensitivity of 97.6%, a specificity of 58.7%, a positive predictive value of 96.8%, and an F1-score of 0.972.Conclusions:We found that PL organization by clinical specialty or condition using SNOMED CT concept groupers accurately reflects clinical systems. Our system groupers were subsequently adopted by our vendor EHR in their foundation system for PL organization.

This cohort study compares measures of referral vs receipt in evaluating social resource platform outcomes among patients with health-related social needs.

Duke Health successfully implemented systematic screening for health-related social needs (HRSNs) and NCCARE360, a social care referral network and technology platform, within its electronic health record (EHR). Using a tandem strategy of discrete HRSN identification (screening) and then referral placement, Duke Health increased overall identification of patient HRSNs and the ability to track outcomes of referrals. Using flexible point-of-care screening workflows led to effective and sustained HRSN screening, averaging 8,500 patients per month across 32 ambulatory clinics and inpatient encounters over 18 months. Duke Health negotiated an enterprise-wide license of NCCARE360 to permit all EHR users to place and view referrals to community and government resources rather than limiting the referral function to its social workers. Distributing the work across clinical roles and leveraging the EHR integration to screen and refer, Duke Health attained a tenfold higher referral rate than peer North Carolina institutions that were also using NCCARE360. Clinical locations with dedicated support staff, such as a population health nurse trained in social resource allocation, were more successful in placing initial referrals. Duke Health recognized a need for a human connector to facilitate follow-up for service navigation and verification of referral completion.

The COVID-19 pandemic challenged how healthcare systems provided care in socially distanced formats. We hypothesized that the COVID-19 era changes in clinical care delivery models contributed to increased Electronic Health Record (EHR) related work. To evaluate the changes in time and volume metrics of EHR usage, we segregated EHR audit log metric data into PreCOVID2019 March/April/May, initial COVID2020 March/April/May, and late COVID2021 March/April/May for 1262 physician providers. We discovered significant and pragmatically meaningful increases in total average time providers spent in the EHR in minutes mean(SD) PreCOVID2019=1958(1576), Mid-COVID2020=1709(1473), Late-COVID2021=2007(1563). Differences in total time in the EHR were significant Pre-mid:p-value=<0.001, but not Pre-Late:p=0.439. Total number of messages received across all specialties increased significantly mean(SD) PreCOVID=459(389), MidCOVID=400(362), LateCOVID 521(423) Pre-Mid p-value=<0.001 and Pre-Late p-value=<0.001. We additionally found changes in total time to differ significantly across select specialties. Based on these findings we recommend further assessment of physician workload and how new factors such as telehealth are contributing to EHR usage.

To the Editor: In 2018, the Centers for Medicare and Medicaid Services (CMS) revised their Medicare Claims Processing Manual to permit teaching providers to fully bill for medical student notes. 1 This change was met with initial concern that documentation burdens would be shifted onto students at the expense of time spent with patients. 2 Gagliardi and colleagues examined the effect of the 2018 CMS changes on student documentation patterns and attitudes at their institution. 3 Following the implementation of the CMS guidelines, the vast majority of students engaged in clinical documentation and felt their notes positively impacted patient care. The authors did not assess how the CMS changes affected students’ time spent in the electronic health record (EHR), and there was no control group when assessing trainee attitudes toward documentation. Nonetheless, Gagliardi and colleagues highlight the benefits of students participating in more substantial documentation roles, including enhanced feedback and authentic engagement in the expected duties of practitioners. 2,3 At our institution, only 1 of 4 clerkship sites expanded student notes for billing following the 2018 CMS guidelines. This permitted us to directly compare trainee attitudes toward documentation across differing levels of note utilization. Similar to the design of Gagliardi and colleagues’ study, 107 students were assessed at the end of their core clinical rotations between September 2019 and February 2020. When compared with sites that did not enact the CMS guidelines, students whose notes were used for billing (n = 70) more often agreed their documentation positively contributed to patient care (50/70, 71% vs 11/37, 30%; P < .001) and enhanced their understanding of their patients’ conditions (63/70, 90% vs 21/37, 57%; P < .001). Notably, students at sites that did not expand billing (n = 37) felt their documentation was redundant (23/37, 62% vs 12/70, 17%; P < .001). Time spent in the EHR was not compared across these sites, which is a limitation of our sample. Contrary to initial concerns of passing the burden of documentation onto our most early-career trainees, the inclusion of student documentation for billing may promote positive student attitudes toward documentation and enhanced understanding of their patients. 2,4 We applaud Gagliardi and colleagues for sharing their experiences and the benefits of expanding student documentation. We hope there are ongoing studies investigating the impact of the CMS guidelines on trainee time spent in the EHR, especially if the responsibility of documentation is being shifted toward trainees with less experience navigating the EHR. 4,5

This cohort study examines the association of sex with electronic health record use among physicians and patient satisfaction.

Importance Electronic health records (EHRs) are considered a potentially significant contributor to clinician burnout. Objective To describe the association of EHR usage, sex, and work culture with burnout for 3 types of clinicians at an academic medical institution. Design, Setting, and Participants This cross-sectional study of 1310 clinicians at a large tertiary care academic medical center analyzed EHR usage metrics for the month of April 2019 with results from a well-being survey from May 2019. Participants included attending physicians, advanced practice providers (APPs), and house staff from various specialties. Data were analyzed between March 2020 and February 2021. Exposures Clinician demographic characteristics, EHR metadata, and an institution-wide survey. Main Outcomes and Measures Study metrics included clinician demographic data, burnout score, well-being measures, and EHR usage metadata. Results Of the 1310 clinicians analyzed, 542 (41.4%) were men (mean [SD] age, 47.3 [11.6] years; 448 [82.7%] White clinicians, 52 [9.6%] Asian clinicians, and 21 [3.9%] Black clinicians) and 768 (58.6%) were women (mean [SD] age, 42.6 [10.3] years; 573 [74.6%] White clinicians, 105 [13.7%] Asian clinicians, and 50 [6.5%] Black clinicians). Women reported more burnout (survey score >= 50: women, 423 [52.0%] vs men, 258 [47.6%]; P = .008) overall. No significant differences in EHR usage were found by sex for multiple metrics of time in the EHR, metrics of volume of clinical encounters, or differences in products of clinical care. Multivariate analysis of burnout revealed that work culture domains were significantly associated with self-reported results for commitment (odds ratio [OR], 0.542; 95% CI, 0.427-0.688; P < .001) and work-life balance (OR, 0.643; 95% CI, 0.559-0.739; P < .001). Clinician sex significantly contributed to burnout, with women having a greater likelihood of burnout compared with men (OR, 1.33; 95% CI, 1.01-1.75; P = .04). An increased number of days spent using the EHR system was associated with less likelihood of burnout (OR, 0.966; 95% CI, 0.937-0.996; P = .03). Overall, EHR metrics accounted for 1.3% of model variance (P = .001) compared with work culture accounting for 17.6% of variance (P < .001). Conclusions and Relevance In this cross-sectional study, sex-based differences in EHR usage and burnout were found in clinicians. These results also suggest that local work culture factors may contribute more to burnout than metrics of EHR usage. Question What is the association of clinician sex, use of the electronic health record (EHR), and work culture with clinician burnout? Findings This cross-sectional study of 1310 clinicians found burnout to be more prevalent in women, attending physicians, and advanced practice providers. Multivariate modeling of burnout identified local work culture accounting for 17.6% variance compared with only 1.3% variance for EHR metrics. Female sex independently contributed more to likelihood of clinician burnout and significantly interacted with work culture domains of commitment and work-life balance. Meaning These findings suggest that clinician sex and local work culture may contribute more to burnout than the EHR. This cross-sectional study of clinicians at a large tertiary care academic medical center examines the association of clinician burnout with sex, clinician type, work culture, and use of electronic medical records.

Purpose When the Centers for Medicare and Medicaid Services (CMS) changed policies about medical student documentation, students with proper supervision may now document their history, physical exam, and medical decision making in the electronic health record (EHR) for billable encounters. Since documentation is a core entrustable professional activity for medical students, the authors sought to evaluate student opportunities for documentation and feedback across and between clerkships. Method In February 2018, a multidisciplinary workgroup was formed to implement student documentation at Duke University Health System, including educating trainees and supervisors, tracking EHR usage, and enforcing CMS compliance. From August 2018 to August 2019, locations and types of student-involved services (student-faculty or student-resident-faculty) were tracked using billing data from attestation statements. Student end-of-clerkship evaluations included opportunity for documentation and receipt of feedback. Since documentation was not allowed before August 2018, it was not possible to compare with prior student experiences. Results In the first half of the academic year, 6,972 patient encounters were billed as student-involved services, 52% (n = 3,612) in the inpatient setting and 47% (n = 3,257) in the outpatient setting. Most (74%) of the inpatient encounters also involved residents, and most (92%) of outpatient encounters were student-teaching physician only. Approximately 90% of students indicated having had opportunity to document in the EHR across clerkships, except for procedure-based clerkships such as surgery and obstetrics. Receipt of feedback was present along with opportunity for documentation more than 85% of the time on services using evaluation and management coding. Most students (> 90%) viewed their documentation as having a moderate or high impact on patient care. Conclusions Changes to student documentation were successfully implemented and adopted; changes met both compliance and education needs within the health system without resulting in potential abuses of student work for service.

Objective: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts.Materials and Methods: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample.Results: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements.Discussion and Conclusions: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.

OBJECTIVES:Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.METHODS:We integrated an uncertainty sampling AL approach with support vector machines-based phenotyping algorithms and evaluated its performance using three annotated disease cohorts including rheumatoid arthritis (RA), colorectal cancer (CRC), and venous thromboembolism (VTE). We investigated performance using two types of feature sets: unrefined features, which contained at least all clinical concepts extracted from notes and billing codes; and a smaller set of refined features selected by domain experts. The performance of the AL was compared with a passive learning (PL) approach based on random sampling.RESULTS:Our evaluation showed that AL outperformed PL on three phenotyping tasks. When unrefined features were used in the RA and CRC tasks, AL reduced the number of annotated samples required to achieve an area under the curve (AUC) score of 0.95 by 68% and 23%, respectively. AL also achieved a reduction of 68% for VTE with an optimal AUC of 0.70 using refined features. As expected, refined features improved the performance of phenotyping classifiers and required fewer annotated samples.CONCLUSIONS:This study demonstrated that AL can be useful in ML-based phenotyping methods. Moreover, AL and feature engineering based on domain knowledge could be combined to develop efficient and generalizable phenotyping methods.

Deep venous thrombosis and pulmonary embolism are diseases associated with significant morbidity and mortality. Known risk factors are attributed for only slight majority of venous thromboembolic disease (VTE) with the remainder of risk presumably related to unidentified genetic factors. We designed a general purpose Natural Language (NLP) algorithm to retrospectively capture both acute and historical cases of thromboembolic disease in a de-identified electronic health record. Applying the NLP algorithm to a separate evaluation set found a positive predictive value of 84.7% and sensitivity of 95.3% for an F-measure of 0.897, which was similar to the training set of 0.925. Use of the same algorithm on problem lists only in patients without VTE ICD-9s was found to be the best means of capturing historical cases with a PPV of 83%. NLP of VTE ICD-9 positive cases and non-ICD-9 positive problem lists provides an effective means for capture of both acute and historical cases of venous thromboembolic disease.

Introduction: Medication safety requires monitoring throughout a drug's market life. Early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results in the EMR to identify ADRs. Methods: Using 12 years of EMR data, we designed a study to correlate abnormal laboratory results with specific drug orders by comparing outcomes of a drug-exposed group and a matched unexposed group. We assessed the relative merits of six pharmacovigilance methods used in spontaneous reporting systems (SRS), including proportional reporting ratio (PRR), reporting odds ratio (ROR), Yule's Q, the Chi-square test, Bayesian confidence propagation neural networks (BCPNN) and a gamma Poisson shrinker (GPS). The time of admission was set as "day zero" and all drug orders and laboratory results timings were represented as days elapsed since that time until discharge. Each patient in the exposed group was randomly matched to four unexposed patients by age group, gender, race, and major diagnoses based on ICD9 codes.

Purpose: Return of individual genetic results to research participants, including participants in archives and biorepositories, is receiving increased attention. However, few groups have deliberated on specific results or weighed deliberations against relevant local contextual factors. Methods: The Electronic Medical Records and Genomics (eMERGE) Network, which includes five biorepositories conducting genome-wide association studies, convened a return of results oversight committee to identify potentially returnable results. Network-wide deliberations were then brought to local constituencies for final decision making. Results: Defining results that should be considered for return required input from clinicians with relevant expertise and much deliberation. The return of results oversight committee identified two sex chromosomal anomalies, Klinefelter syndrome and Turner syndrome, as well as homozygosity for factor V Leiden, as findings that could warrant reporting. Views about returning findings of HFE gene mutations associated with hemochromatosis were mixed due to low penetrance. Review of electronic medical records suggested that most participants with detected abnormalities were unaware of these findings. Local considerations relevant to return varied and, to date, four sites have elected not to return findings (return was not possible at one site). Conclusion: The eMERGE experience reveals the complexity of return of results decision making and provides a potential deliberative model for adoption in other collaborative contexts. Genet Med 2012:14(4):424–431

OBJECTIVE Medication  safety requires that each drug be monitored throughout its market life as early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results documented in the EMR to identify ADRs. METHODS Using 12 years of EMR data from Vanderbilt University Medical Center (VUMC), we designed a study to correlate abnormal laboratory results with specific drug administrations by comparing the outcomes of a drug-exposed group and a matched unexposed group. We assessed the relative merits of six pharmacovigilance measures used in spontaneous reporting systems (SRSs): proportional reporting ratio (PRR), reporting OR (ROR), Yule's Q (YULE), the χ(2) test (CHI), Bayesian confidence propagation neural networks (BCPNN), and a gamma Poisson shrinker (GPS). RESULTS We systematically evaluated the methods on two independently constructed reference standard datasets of drug-event pairs. The dataset of Yoon et al contained 470 drug-event pairs (10 drugs and 47 laboratory abnormalities). Using VUMC's EMR, we created another dataset of 378 drug-event pairs (nine drugs and 42 laboratory abnormalities). Evaluation on our reference standard showed that CHI, ROR, PRR, and YULE all had the same F score (62%). When the reference standard of Yoon et al was used, ROR had the best F score of 68%, with 77% precision and 61% recall. CONCLUSIONS Results suggest that EMR-derived laboratory measurements and medication orders can help to validate previously reported ADRs, and detect new ADRs.