Faculty Bibliography

OBJECTIVE:Using the newly created University of California (UC) Health Data Warehouse, we present the first study to analyze antihyperglycemic treatment utilization across the five large UC academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco). RESEARCH DESIGN AND METHODS:This retrospective analysis used deidentified electronic health records (EHRs; 2014-2019) including 97,231 patients with type 2 diabetes from 1,003 UC-affiliated clinical settings. Significant differences between health systems and individual providers were identified using binomial probabilities with cohort matching. RESULTS:Our analysis reveals statistically different treatment utilization patterns not only between health systems but also among individual providers within health systems. We identified 21 differences among health systems and 29 differences among individual providers within these health systems, with respect to treatment intensifications within existing guidelines on top of either metformin monotherapy or dual therapy with metformin and a sulfonylurea. Next, we identified variation for medications within the same class (e.g., glipizide vs. glyburide among sulfonylureas), with 33 differences among health systems and 86 among individual providers. Finally, we identified 2 health systems and 55 individual providers who more frequently used medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also 1 health system and 8 providers who prescribed such medications less frequently for these patients. CONCLUSIONS:Our study used cohort-matching techniques to highlight real-world variation in care between health systems and individual providers. This demonstrates the power of EHRs to quantify differences in treatment utilization, a necessary step toward standardizing precision care for large populations.

OBJECTIVE:The study sought to evaluate if peer input on outpatient cases impacted diagnostic confidence. MATERIALS AND METHODS:This randomized trial of a peer input intervention occurred among 28 clinicians with case-level randomization. Encounters with diagnostic uncertainty were entered onto a digital platform to collect input from ≥5 clinicians. The primary outcome was diagnostic confidence. We used mixed-effects logistic regression analyses to assess for intervention impact on diagnostic confidence. RESULTS:Among the 509 cases (255 control; 254 intervention), the intervention did not impact confidence (odds ratio [OR], 1.46; 95% confidence interval [CI], 0.999-2.12), but after adjusting for clinician and case traits, the intervention was associated with higher confidence (OR, 1.53; 95% CI, 1.01-2.32). The intervention impact was greater in cases with high uncertainty (OR, 3.23; 95% CI, 1.09- 9.52). CONCLUSIONS:Peer input increased diagnostic confidence primarily in high-uncertainty cases, consistent with findings that clinicians desire input primarily in cases with continued uncertainty.

Background Self-monitoring of blood pressure (SMBP) improves blood pressure (BP) outcomes at 12-months, but information is lacking on how SMBP affects hypertension care processes and longer-term BP outcomes. Methods and Results We pooled individual participant data from 4 randomized clinical trials of SMBP in the United Kingdom (combined n=2590) with varying intensities of support. Multivariable random effects regression was used to estimate the probability of antihypertensive intensification at 12 months for usual care versus SMBP. Using these data, we simulated 5-year BP control rates using a validated mathematical model. Trial participants were mostly older adults (mean age 66.6 years, SD 9.5), male (53.9%), and predominantly white (95.6%); mean baseline BP was 151.8/85.0 mm Hg. Compared with usual care, the likelihood of antihypertensive intensification increased with both SMBP with feedback to patient or provider alone (odds ratio 1.8, 95% CI 1.2-2.6) and with telemonitoring or self-management (3.3, 2.5-4.2). Over 5 years, we estimated 33.4% BP control (<140/90 mm Hg) with usual care (95% uncertainty interval 27.7%-39.4%). One year of SMBP with feedback to patient or provider alone achieved 33.9% (28.3%-40.3%) BP control and SMBP with telemonitoring or self-management 39.0% (33.1%-45.2%) over 5 years. If SMBP interventions and associated BP control processes were extended to 5 years, BP control increased to 52.4% (45.4%-59.8 %) and 72.1% (66.5%-77.6%), respectively. Conclusions One year of SMBP plus telemonitoring or self-management increases the likelihood of antihypertensive intensification and could improve BP control rates at 5 years; continuing SMBP for 5 years could further improve BP control.

BACKGROUND:Uncontrolled blood pressure (BP) is a leading preventable cause of death that remains common in the US population despite the availability of effective medications. New technology and program innovation has high potential to improve BP but may be expensive and burdensome for patients, clinicians, health systems, and payers and may not produce desired results or reduce existing disparities in BP control. METHODS AND RESULTS:The PCORnet Blood Pressure Control Laboratory is a platform designed to enable national surveillance and facilitate quality improvement and comparative effectiveness research. The platform uses PCORnet, the National Patient-Centered Clinical Research Network, for engagement of health systems and collection of electronic health record data, and the Eureka Research Platform for eConsent and collection of patient-reported outcomes and mHealth data from wearable devices and smartphones. Three demonstration projects are underway: BP track will conduct national surveillance of BP control and related clinical processes by measuring theory-derived pragmatic BP control metrics using electronic health record data, with a focus on tracking disparities over time; BP MAP will conduct a cluster-randomized trial comparing effectiveness of 2 versions of a BP control quality improvement program; BP Home will conduct an individual patient-level randomized trial comparing effectiveness of smartphone-linked versus standard home BP monitoring. Thus far, BP Track has collected electronic health record data from over 826 000 eligible patients with hypertension who completed ≈3.1 million ambulatory visits. Preliminary results demonstrate substantial room for improvement in BP control (<140/90 mm Hg), which was 58% overall, and in the clinical processes relevant for BP control. For example, only 12% of patients with hypertension with a high BP measurement during an ambulatory visit received an order for a new antihypertensive medication. CONCLUSIONS:The PCORnet Blood Pressure Control Laboratory is designed to be a reusable platform for efficient surveillance and comparative effectiveness research; results from demonstration projects are forthcoming.

BACKGROUND:Warfarin is one of the most commonly prescribed medications in the United States, and it causes a significant proportion of adverse drug events. Patients taking warfarin fall outside of the recommended therapeutic range 30% of the time, largely because of inadequate laboratory monitoring and dose adjustment. This leads to an increased risk of blood clots or bleeding events. We propose a comparative effectiveness study to examine whether a technology-enabled anticoagulation management program can improve long-term clinical outcomes compared with usual care. OBJECTIVE:Our proposed intervention is the implementation of an electronic dashboard (integrated into a preexisting electronic health record) and standardized workflow to track patients' laboratory results, identify patients requiring follow-up, and facilitate the use of a validated nomogram for dose adjustment. The primary outcome of this study is the time in therapeutic range (TTR) at 6 months post intervention (a validated metric of anticoagulation quality among patients receiving warfarin). METHODS:We will employ a pre-post quasi-experimental design with a nonequivalent usual-care comparison site and a difference-in-differences approach to compare the effectiveness of a technology-enabled anticoagulation management program compared with usual care at a large university-affiliated safety-net clinic. RESULTS:We used a commercially available health information technology (HIT) platform to host a registry of patients on warfarin therapy and create the electronic dashboard for panel management. We developed the intervention with, and for, frontline clinician users, using principles of human-centered design. This study is funded until September 2020 and is approved by the University of California, San Francisco Institutional Review Board until June 22, 2020. We completed data collection in September 2019 and expect to complete our proposed analyses by February 2020. CONCLUSIONS:We anticipate that the intervention will increase TTR among patients taking warfarin and that the use of this HIT platform will facilitate tracking and monitoring of patients on warfarin, which could enable outreach to those overdue for visits or laboratory monitoring. We will use these findings to iteratively improve the platform in preparation for a larger, multiple-site, pragmatic clinical trial. If successful, our study will demonstrate the integration of HIT platforms into existing electronic health records to improve patient care in real-world clinical settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/13835.

BACKGROUND:Diagnostic error in ambulatory care, a frequent cause of preventable harm, may be mitigated using the collective intelligence of multiple clinicians. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process. OBJECTIVE:This study aims to assess the efficacy of a collective intelligence output to improve diagnostic confidence and accuracy in ambulatory care cases (from primary care and urgent care clinic visits) with diagnostic uncertainty. METHODS:This is a pragmatic randomized controlled trial of using collective intelligence in cases with diagnostic uncertainty from clinicians at primary care and urgent care clinics in 2 health care systems in San Francisco. Real-life cases, identified for having an element of diagnostic uncertainty, will be entered into a collective intelligence digital platform to acquire collective intelligence from at least 5 clinician contributors on the platform. Cases will be randomized to an intervention group (where clinicians will view the collective intelligence output) or control (where clinicians will not view the collective intelligence output). Clinicians will complete a postvisit questionnaire that assesses their diagnostic confidence for each case; in the intervention cases, clinicians will complete the questionnaire after reviewing the collective intelligence output for the case. Using logistic regression accounting for clinician clustering, we will compare the primary outcome of diagnostic confidence and the secondary outcome of time with diagnosis (the time it takes for a clinician to reach a diagnosis), for intervention versus control cases. We will also assess the usability and satisfaction with the digital tool using measures adapted from the Technology Acceptance Model and Net Promoter Score. RESULTS:We have recruited 32 out of our recruitment goal of 33 participants. This study is funded until May 2020 and is approved by the University of California San Francisco Institutional Review Board until January 2020. We have completed data collection as of June 2019 and will complete our proposed analysis by December 2019. CONCLUSIONS:This study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty. If shown to be valuable in improving clinicians' diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care. The findings of this study may provide a path forward for improving the diagnostic process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/13151.

BACKGROUND:The Centers for Disease Control and Prevention's Million Hearts initiative includes an ambitious ≥80% blood pressure control goal in US adults with hypertension by 2022. We used the validated Blood Pressure Control Model to quantify changes in clinic-based hypertension management processes needed to attain ≥80% blood pressure control. METHODS AND RESULTS:The Blood Pressure Control Model simulates patient blood pressures weekly using 3 key modifiable hypertension management processes: office visit frequency, clinician treatment intensification given uncontrolled blood pressure, and continued antihypertensive medication use (medication adherence rate). We compared blood pressure control rates (using the Seventh Joint National Committee on hypertension targets) achieved over 4 years between usual care and the best-observed values for management processes identified from the literature (1-week return visit interval, 20%-44% intensification rate, and 76% adherence rate). We determined the management process values needed to achieve ≥80% blood pressure control in US adults. In adults with uncontrolled blood pressure, usual care achieved 45.6% control (95% uncertainty interval, 39.6%-52.5%) and literature-based best-observed values achieved 79.7% control (95% uncertainty interval, 79.3%-80.1%) over 4 years. Increasing treatment intensification rates to 62% of office visits with an uncontrolled blood pressure resulted in ≥80% blood pressure control, even when the return visit interval and adherence remained at usual care values. Improving to best-observed values for all 3 management processes would achieve 78.1% blood pressure control in the overall US population with hypertension, approaching the ≥80% Million Hearts 2022 goal. CONCLUSIONS:Achieving the Million Hearts blood pressure control goal by 2022 will require simultaneously increasing visit frequency, overcoming therapeutic inertia, and improving patient medication adherence. As the relative importance of each of these 3 processes will depend on local characteristics, simulation models like the Blood Pressure Control Model can help local healthcare systems tailor strategies to reach local and national benchmarks.

OBJECTIVES/OBJECTIVE:Usable tools to support individual primary care clinicians in their diagnostic processes could help to reduce preventable harm from diagnostic errors. We conducted a formative study with primary care providers to identify key requisites to optimize the acceptability of 1 online collective intelligence platform (Human Diagnosis Project; Human Dx). MATERIALS AND METHODS/METHODS:We conducted semistructured interviews with practicing primary care clinicians in a sample of the US community-based clinics to examine the acceptability and early usability of the collective intelligence online platform using standardized clinical cases and real-world clinical cases from the participants' own practice. We used an integrated inductive-deductive qualitative analysis approach to analyze the interview transcripts. RESULTS AND DISCUSSION/CONCLUSIONS:Perceived usefulness, perceived accuracy, quality assurance, trust, and ease of use emerged as essential domains of acceptability required for providers to use a collective intelligence tool in clinical practice. Participants conveyed that the collective opinion should: (1) contribute to their clinical reasoning, (2) boost their confidence, (3) be generated in a timely manner, and (4) be relevant to their clinical settings and use cases. Trust in the technology platform and the clinical accuracy of its collective intelligence output emerged as an incontrovertible requirement for user acceptance and engagement. CONCLUSION/CONCLUSIONS:We documented key requisites to building a collective intelligence technology platform that is trustworthy, useful, and acceptable to target end users for assistance in the diagnostic process. These key lessons may be applicable to other provider-facing decision support platforms.

BACKGROUND:Detection of chronic kidney disease (CKD) with urine albumin-to-creatinine ratio (UACR) among patients with hypertension (HTN) provides an opportunity for early treatment, potentially mitigating risk of CKD progression and cardiovascular complications. Differences in UACR testing patterns among racial/ethnic populations at risk for CKD could contribute to known disparities in CKD complications. METHODS:We examined the prevalence of UACR testing among low-income adult primary care patients with HTN, defined by a new administrative code for HTN or 2 clinic blood pressures >140/90 mm Hg between January 1, 2014, and January 1, 2017, in one public health-care delivery system with a high prevalence of end-stage kidney disease among race/ethnic minorities. Logistic regression was used to identify odds of UACR testing within 1 year of a HTN diagnosis, overall, and by racial/ethnic subgroup, adjusted for demographic factors, estimated glomerular filtration rate, and HTN severity. Models were also stratified by diabetes status. RESULTS:The cohort (n = 16,414) was racially/ethnically diverse (16% White, 21% Black, 34% Asian, 19% Hispanic, and 10% other) and 51% female. Only 35% of patients had UACR testing within 1 year of a HTN diagnosis. Among individuals without diabetes, odds of UACR testing were higher among Asians, Blacks, and Other subgroups compared to Whites (adjusted OR [aOR] 1.19; 95% CI 1.00-1.42 for Blacks; aOR 1.33; 1.13-1.56 for Asians; aOR 1.30; 1.04-1.60 for Other) but were not significantly different between Hispanics and Whites (aOR 1.17; 0.97-1.39). Among individuals with diabetes, only Asians had higher odds of UACR testing compared to Whites (aOR 1.35; 1.12-1.63). CONCLUSIONS:Prevalence of UACR testing among low-income patients with HTN is low in one public health-care delivery system, with higher odds of UACR testing among racial/ethnic minority subgroups compared to Whites without diabetes and similar odds among those with diabetes. If generalizable, less albuminuria testing may not explain higher prevalence of kidney failure in racial/ethnic minorities.

INTRODUCTION:We aimed to standardize data collection from 3 health systems (HS1, HS2, HS3) participating in the San Francisco Bay Collaborative Research Network, and compare rates and predictors of uncontrolled blood pressure among hypertensive adults to identify opportunities for regional collaboration in quality improvement. METHODS:Retrospective cohort study using deidentified electronic health record data from all primary care patients with at least 1 visit in a 2-year period, using standard data definitions in a common data repository. Primary outcome was uncontrolled blood pressure at the most recent primary care visit. RESULTS:Of 169,793 adults aged 18 to 85 years, 53,133 (31.3%) had a diagnosis of hypertension. Of these, 18,751 (35%) had uncontrolled blood pressure at their last visit, with the proportion varying by system (29%, HS1; 31%, HS2; and 44%, HS3) and by clinical site within each system. In multivariate analyses, differences between health systems persisted, with HS2 and HS3 patients having a 1.15 times (95% CI, 1.11 to 1.19) and 1.46 times (95% CI, 1.42 to 1.50) greater relative risk of uncontrolled blood pressure compared with HS1. Across health systems, hypertensive patients were more likely to have uncontrolled blood pressure if they were uninsured, African Americans, current smokers, obese, or had fewer than 2 primary care visits during the 2-year measurement period. CONCLUSIONS:After controlling for standard individual predictors of hypertension control, significant and substantial differences in hypertension control persisted between health systems, possibly due to local quality improvement programs among other factors. There may be opportunities to share best practices and address common disparities across health systems.