Patient Portals Fail to Collect Structured Information About Who Else is Involved in a Person's Care.

The US health care delivery system does not systematically engage or support family or friend care partners. Meanwhile, the uptake and familiarity of portals to personal health information are increasing among patients. Technology innovations, such as shared access to the portal, use separate identity credentials to differentiate between patients and care partners. Although not well-known, or commonly used, shared access allows patients to identify who they do and do not want to be involved in their care. However, the processes for patients to grant shared access to portals are often limited or so onerous that interested patients and care partners often circumvent the process entirely. As a result, the vast majority of care partners resort to accessing portals using a patient's identity credentials-a "do-it-yourself" solution in conflict with a health systems' legal responsibility to protect patient privacy and autonomy. The personal narratives in this viewpoint (shared by permission) elaborate on quantitative studies and provide first-person snapshots of challenges faced by patients and families as they attempt to gain or grant shared access during crucial moments in their lives. As digital modalities increase patient roles in health care interactions, so does the importance of making shared access work for all stakeholders involved-patients, clinicians, and care partners. Electronic health record vendors must recognize that both patients and care partners are important users of their products, and health care organizations must acknowledge and support the critical contributions of care partners as distinct from patients.

Patient perceptions of and experiences with stigma using telehealth for opioid use disorder treatment: a qualitative analysis.

BACKGROUND: Patients with opioid use disorder (OUD) experience various forms of stigma at the individual, public, and structural levels that can affect how they access and engage with healthcare, particularly with medications for OUD treatment. Telehealth is a relatively new form of care delivery for OUD treatment. As reducing stigma surrounding OUD treatment is critical to address ongoing gaps in care, the aim of this study was to explore how telehealth impacts patient experiences of stigma. METHODS: In this qualitative study, we interviewed patients with OUD at a single urban academic medical center consisting of multiple primary care and addiction clinics in Oregon, USA. Participants were eligible if they had (1) at least one virtual visit for OUD between March 2020 and December 2021, and (2) a prescription for buprenorphine not exclusively used for chronic pain. We conducted phone interviews between October and December 2022, then recorded, transcribed, dual-coded, and analyzed using reflexive thematic analysis. RESULTS: The mean age of participants (n = 30) was 40.5 years (range 20-63); 14 were women, 15 were men, and two were transgender, non-binary, or gender-diverse. Participants were 77% white, and 33% had experienced homelessness in the prior six months. We identified four themes regarding how telehealth for OUD treatment shaped patient perceptions of and experiences with stigma at the individual (1), public (2-3), and structural levels (4): (1) Telehealth offers wanted space and improved control over treatment setting; (2) Public stigma and privacy concerns can impact both telehealth and in-person encounters, depending on clinical and personal circumstances; (3) The social distance of telehealth could mitigate or exacerbate perceptions of clinician stigma, depending on both patient and clinician expectations; (4) The increased flexibility of telehealth translated to perceptions of increased clinician trust and respect. CONCLUSIONS: The forms of stigma experienced by individuals with OUD are complex and multifaceted, as are the ways in which those experiences interact with telehealth-based care. The mixed results of this study support policies allowing for a more individualized, patient-centered approach to care delivery that allows patients a choice over how they receive OUD treatment services.

Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors.

BACKGROUND: Persons with multimorbidity (≥2 chronic conditions) face an increased risk of poor health outcomes, especially as they age. Psychosocial factors such as social isolation, chronic stress, housing insecurity, and financial insecurity have been shown to exacerbate these outcomes, but are not routinely assessed during the clinical encounter. Our objective was to extract these concepts from chart notes using natural language processing and predict their impact on health care utilization for patients with multimorbidity. METHODS: A cohort study to predict the 1-year likelihood of hospitalizations and emergency department visits for patients 65+ with multimorbidity with and without psychosocial factors. Psychosocial factors were extracted from narrative notes; all other covariates were extracted from electronic health record data from a large academic medical center using validated algorithms and concept sets. Logistic regression was performed to predict the likelihood of hospitalization and emergency department visit in the next year. RESULTS: In all, 76,479 patients were eligible; the majority were White (89%), 54% were female, with mean age 73. Those with psychosocial factors were older, had higher baseline utilization, and more chronic illnesses. The 4 psychosocial factors all independently predicted future utilization (odds ratio=1.27-2.77, C -statistic=0.63). Accounting for demographics, specific conditions, and previous utilization, 3 of 4 of the extracted factors remained predictive (odds ratio=1.13-1.86) for future utilization. Compared with models with no psychosocial factors, they had improved discrimination. Individual predictions were mixed, with social isolation predicting depression and morbidity; stress predicting atherosclerotic cardiovascular disease onset; and housing insecurity predicting substance use disorder morbidity. DISCUSSION: Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to address psychosocial factors to mitigate their health impacts.

The Transforming Outcomes for Patients Through Medical Home Evaluation and reDesign (TOPMED) Cluster Randomized Controlled Trial: Cost and Utilization Results.

BACKGROUND: Primary Care Medical Home (PCMH) redesign efforts are intended to enhance primary care's ability to improve population health and well-being. PCMH transformation that is focused on "high-value elements" (HVEs) for cost and utilization may improve effectiveness. OBJECTIVES: The objective of this study was to determine if a focus on achieving HVEs extracted from successful primary care transformation models would reduce cost and utilization as compared with a focus on achieving PCMH quality improvement goals. RESEARCH DESIGN: A stratified, cluster randomized controlled trial with 2 arms. All practices received equal financial incentives, health information technology support, and in-person practice facilitation. Analyses consisted of multivariable modeling, adjusting for the cluster, with difference-in-difference results. SUBJECTS: Eight primary care clinics that were engaged in PCMH reform. MEASURES: We examined: (1) total claims payments; (2) emergency department (ED) visits; and (3) hospitalizations among patients during baseline and intervention years. RESULTS: In total, 16,099 patients met the inclusion criteria. Intervention clinics had significantly lower baseline ED visits (P=0.02) and claims paid (P=0.01). Difference-in-difference showed a decrease in ED visits greater in control than intervention (ED per 1000 patients: +56; 95% confidence interval: +96, +15) with a trend towards decreased hospitalizations in intervention (-15; 95% confidence interval: -52, +21). Costs were not different. In modeling monthly outcome means, the generalized linear mixed model showed significant differences for hospitalizations during the intervention year (P=0.03). DISCUSSION: The trial had a trend of decreasing hospitalizations, increased ED visits, and no change in costs in the HVE versus quality improvement arms.

Risk Stratification in Primary Care: Value-Based Contributions of Provider Adjudication.

BACKGROUND: In primary care risk stratification, automated algorithms do not consider the same factors as providers. The process of adjudication, in which providers review and adjust algorithm-derived risk scores, may improve the prediction of adverse outcomes. OBJECTIVE: We assessed the patient factors that influenced provider adjudication behavior and evaluated the performance of an adjudicated risk model against a commercial algorithm. DESIGN: (1) Structured interviews with primary care providers (PCP) and multivariable regression analysis and (2) receiver operating characteristic curves (ROC) with sensitivity analyses. PARTICIPANTS: Primary care patients aged 18 years and older with an adjudicated risk score. APPROACH AND MAIN MEASURES: (1) Themes from structured interviews and discrete variables associated with provider adjudication behavior; (2) comparison of concordance statistics and sensitivities between risk models. KEY RESULTS: 47,940 patients were adjudicated by PCPs in 2018. Interviews revealed that, in adjudication, providers consider disease severity, presence of self-management skills, behavioral health, and whether a risk score is actionable. Provider up-scoring from the algorithmic risk score was significantly associated with patient male sex (OR 1.24, CI 1.15-1.34), age > 65 (OR 2.55, CI 2.24-2.91), Black race (1.26, CI 1.02-1.55), polypharmacy >10 medications (OR 4.87, CI 4.27-5.56), a positive depression screen (OR 1.57, CI 1.43-1.72), and hemoglobin A1c >9 (OR 1.89, CI 1.52-2.33). Overall, the adjudicated risk model performed better than the commercial algorithm for all outcomes: ED visits (c-statistic 0.689 vs. 0.684, p < 0.01), hospital admissions (c-statistic 0.663 vs. 0.649, p < 0.01), and death (c-statistic 0.753 vs. 0.721, p < 0.01). When limited to males or seniors, the adjudicated models displayed either improved or non-inferior performance compared to the commercial model. CONCLUSIONS: Provider adjudication of risk stratification improves model performance because providers have a personal understanding of their patients and are able to apply their training to clinical decision-making.

Racial and Ethnic Differences in Multimorbidity Changes Over Time.

BACKGROUND: Our understanding of how multimorbidity progresses and changes is nascent. OBJECTIVES: Assess multimorbidity changes among racially/ethnically diverse middle-aged and older adults. DESIGN, SETTING, AND PARTICIPANTS: Prospective cohort study using latent class analysis to identify multimorbidity combinations over 16 years, and multinomial logistic models to assess change relative to baseline class membership. Health and Retirement Study respondents (age 51 y and above) in 1998 and followed through 2014 (N=17,297). MEASURES: Multimorbidity latent classes of: hypertension, heart disease, lung disease, diabetes, cancer, arthritis, stroke, high depressive symptoms. RESULTS: Three latent classes were identified in 1998: minimal disease (45.8% of participants), cardiovascular-musculoskeletal (34.6%), cardiovascular-musculoskeletal-mental (19.6%); and 3 in 2014: cardiovascular-musculoskeletal (13%), cardiovascular-musculoskeletal-metabolic (12%), multisystem multimorbidity (15%). Remaining participants were deceased (48%) or lost to follow-up (12%) by 2014. Compared with minimal disease, individuals in cardiovascular-musculoskeletal in 1998 were more likely to be in multisystem multimorbidity in 2014 [odds ratio (OR)=1.78, P<0.001], and individuals in cardiovascular-musculoskeletal-mental in 1998 were more likely to be deceased (OR=2.45, P<0.001) or lost to follow-up (OR=3.08, P<0.001). Hispanic and Black Americans were more likely than White Americans to be in multisystem multimorbidity in 2014 (OR=1.67, P=0.042; OR=2.60, P<0.001, respectively). Black compared with White Americans were more likely to be deceased (OR=1.62, P=0.01) or lost to follow-up (OR=2.11, P<0.001) by 2014. CONCLUSIONS AND RELEVANCE: Racial/ethnic older adults are more likely to accumulate morbidity and die compared with White peers, and should be the focus of targeted and enhanced efforts to prevent and/or delay progression to more complex multimorbidity patterns.

Improving Quality Improvement Capacity and Clinical Performance in Small Primary Care Practices.

PURPOSE: We undertook a study to assess whether implementing 7 evidence-based strategies to build improvement capacity within smaller primary care practices was associated with changes in performance on clinical quality measures (CQMs) for cardiovascular disease. METHODS: A total of 209 practices across Washington, Oregon, and Idaho participated in a pragmatic clinical trial that focused on building quality improvement capacity as measured by a validated questionnaire, the 12-point Quality Improvement Capacity Assessment (QICA). Clinics reported performance on 3 cardiovascular CQMs-appropriate aspirin use, blood pressure (BP) control (<140/90 mm Hg), and smoking screening/cessation counseling-at baseline (2015) and follow-up (2017). Regression analyses with change in CQM as the dependent variable allowed for clustering by practice facilitator and adjusted for baseline CQM performance. RESULTS: Practices improved QICA scores by 1.44 points (95% CI, 1.20-1.68; P <.001) from an average baseline of 6.45. All 3 CQMs also improved: aspirin use by 3.98% (average baseline = 66.8%; 95% CI for change, 1.17%-6.79%; P = .006); BP control by 3.36% (average baseline = 61.5%; 95% CI for change, 1.44%-5.27%; P = .001); and tobacco screening/cessation counseling by 7.49% (average baseline = 73.8%; 95% CI for change, 4.21%-10.77%; P <.001). Each 1-point increase in QICA score was associated with a 1.25% (95% CI, 0.41%-2.09%, P = .003) improvement in BP control; the estimated likelihood of reaching a 70% BP control performance goal was 1.24 times higher (95% CI, 1.09-1.40; P <.001) for each 1-point increase in QICA. CONCLUSION: Improvements in clinic-level performance on BP control may be attributed to implementation of 7 evidence-based strategies to build quality improvement capacity. These strategies were feasible to implement in small practices over 15 months.

Primary care practices' ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation.

BACKGROUND: Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. METHODS: Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. RESULTS: In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71-0.88) but sensitivity and PPV were low (0.16-0.40). Practice-created scores had 0.02-0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. CONCLUSIONS: Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

A Randomized Trial of External Practice Support to Improve Cardiovascular Risk Factors in Primary Care.

PURPOSE: We conducted a randomized controlled trial to compare the effectiveness of adding various forms of enhanced external support to practice facilitation on primary care practices' clinical quality measure (CQM) performance. METHODS: Primary care practices across Washington, Oregon, and Idaho were eligible if they had fewer than 10 full-time clinicians. Practices were randomized to practice facilitation only, practice facilitation and shared learning, practice facilitation and educational outreach visits, or practice facilitation and both shared learning and educational outreach visits. All practices received up to 15 months of support. The primary outcome was the CQM for blood pressure control. Secondary outcomes were CQMs for appropriate aspirin therapy and smoking screening and cessation. Analyses followed an intention-to-treat approach. RESULTS: Of 259 practices recruited, 209 agreed to be randomized. Only 42% of those offered educational outreach visits and 27% offered shared learning participated in these enhanced supports. CQM performance improved within each study arm for all 3 cardiovascular disease CQMs. After adjusting for differences between study arms, CQM improvements in the 3 enhanced practice support arms of the study did not differ significantly from those seen in practices that received practice facilitation alone (omnibus P = .40 for blood pressure CQM). Practices randomized to receive both educational outreach visits and shared learning, however, were more likely to achieve a blood pressure performance goal in 70% of patients compared with those randomized to practice facilitation alone (relative risk = 2.09; 95% CI, 1.16-3.76). CONCLUSIONS: Although we found no significant differences in CQM performance across study arms, the ability of a practice to reach a target level of performance may be enhanced by adding both educational outreach visits and shared learning to practice facilitation.

Harnessing the Promise of Artificial Intelligence Responsibly.

This Viewpoint discusses the benefits and potential harms of using artificial intelligence (AI) algorithms in medicine and proposes the collaborative creation of a Code of Conduct for AI in Health Care.

Effect of a Pragmatic, Cluster-randomized Controlled Trial on Patient Experience With Care: The Transforming Outcomes for Patients Through Medical Home Evaluation and reDesign (TOPMED) Study.

BACKGROUND: Health reform programs like the patient-centered medical home are intended to improve the triple aim. Previous studies on patient-centered medical homes have shown mixed effects, but high value elements (HVEs) are expected to improve the triple aim. OBJECTIVE: The aim of this study is to understand whether focusing on HVEs would improve patient experience with care. METHODS: Eight clinics were cluster-randomized in a year-long trial. Both arms received practice facilitation, IT-based reporting, and financial incentives. Intervention practices were encouraged to choose HVEs for quality improvement goals. To assess patient experience, 1597 Consumer Assessment of Healthcare Providers and Systems surveys were sent pretrial and posttrial to a stratified random sample of patients. Difference-in-difference multivariate analysis was used to compare patient responses from intervention and control practices, adjusting for confounders. RESULTS: The response rate was 43% (n=686). Nonrespondent analysis showed no difference between arms, although differences were seen by risk status and age. The overall difference in difference was 2.8%, favoring the intervention. The intervention performed better in 9 of 11 composites. The intervention performed significantly better in follow-up on test results (P=0.091) and patients' rating of the provider (P=0.091), whereas the control performed better in access to care (P=0.093). Both arms also had decreases, including 4 of 11 composites for the intervention, and 8 of 11 for the control. DISCUSSION: Practices that targeted HVEs showed significantly more improvement in patient experience of care. However, contemporaneous trends may have affected results, leading to declines in patient experience in both arms.

Developing a model for understanding patient collection of observations of daily living: A qualitative meta-synthesis of the Project HealthDesign Program.

We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health.

Racial and ethnic differences in clinical outcome trajectories for care managed patients.

BACKGROUND: Care management has demonstrated improvements in quality of care for patients with complex care needs. The extent to which these interventions benefit race/ethnic minority populations is unclear. OBJECTIVES: To characterize race/ethnic differences in the longitudinal control of clinical outcomes for patients with complex care needs enrolled in Care Management Plus, a health information technology-enabled care coordination intervention. RESEARCH DESIGN: Multilevel models of repeated observations from clinical encounters before and after program enrollment for 6 Oregon and California primary care clinics. SUBJECTS: A total of 18,675 clinic patients were examined. We estimated multilevel models for 1481 and 5320 care-managed individuals with repeated hemoglobin A1c and blood pressure measurements, respectively. MEASURES: Primary outcomes were changes over time for 2 clinical markers of health status for complex care patients: (1) hemoglobin A1c for patients with diabetes; and (2) mid-blood pressure (BP) (average systolic and diastolic blood pressure). RESULTS: We found significant reductions in A1c for patients with previously uncontrolled A1c (preperiod slope, b=1.03 [0.83, 1.24]; postperiod slope, b=-0.63 [-0.91, -0.35]). For mid-BP we found increasing unconditional preperiod trajectories (b=3.52 [2.39, 4.64]) and decreasing postperiod trajectories (b=-5.21 [-5.70, -4.72]). We also found the trajectories of A1c and mid-BP were not statistically different for black, Latino, and white patients. CONCLUSIONS: These analyses demonstrate some promising results for intermediate clinical outcomes for underrepresented patients with complex chronic care needs. It remains to be seen whether these health care system delivery redesigns yield long-term benefits for patients, such as improvements in function and quality of life.

Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.

OBJECTIVES: Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS: We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS: EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION: As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION: This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose.

BACKGROUND: The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. METHODS: We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. RESULTS: We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains. CONCLUSIONS: This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs.

Maturity in enterprise data warehouses for research operations: Analysis of a pilot study.

Enterprise data warehouses for research (EDW4R) is a critical component of National Institutes of Health Clinical and Translational Science Award (CTSA) hubs. EDW4R operations have unique needs that require specialized skills and collaborations across multiple domains which limit the ability to apply existing models of information technology (IT) performance. Because of this uniqueness, we developed a new EDW4R maturity model based on prior qualitative study of operational practices for supporting EDW4Rs at CTSA hubs. In a pilot study, respondents from fifteen CTSA hubs completed the novel EDW4R maturity index survey by rating 33 maturity statements across 6 categories using a 5-point Likert scale. Of the six categories, respondents rated workforce as most mature (4.17 [3.67-4.42]) and relationship with enterprise IT as the least mature (3.00 [2.80-3.80]). Our pilot of a novel maturity index shows a baseline quantitative measure of EDW4R functions across fifteen CTSA hubs. The maturity index may be useful to faculty and staff currently leading an EDW4R by creating opportunities to explore the index in local context and comparison to other institutions.

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.

OBJECTIVES: Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. TARGET AUDIENCE: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. SCOPE: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.