Randomized Evaluation of a Remote Management Program to Improve Guideline-Directed Medical Therapy: The DRIVE Trial.

BACKGROUND: Several SGLT2i (sodium-glucose transport protein 2 inhibitors) and GLP1-RA (glucagon-like peptide-1 receptor agonists) reduce cardiovascular events and improve kidney outcomes in patients with type 2 diabetes; however, utilization remains low despite guideline recommendations. METHODS: A randomized, remote implementation trial in the Mass General Brigham network enrolled patients with type 2 diabetes with increased cardiovascular or kidney risk. Patients eligible for, but not prescribed, SGLT2i or GLP1-RA were randomly assigned to simultaneous virtual patient education with concurrent prescription of SGLT2i or GLP1-RA (ie, Simultaneous) or 2 months of virtual education followed by medication prescription (ie, Education-First) delivered by a multidisciplinary team driven by nonlicensed navigators and clinical pharmacists who prescribed SGLT2i or GLP1-RA using a standardized treatment algorithm. The primary outcome was the proportion of patients with prescriptions for either SGLT2i or GLP1-RA by 6 months. RESULTS: Between March 2021 and December 2022, 200 patients were randomized. The mean age was 66.5 years; 36.5% were female, and 22.0% were non-White. Overall, 30.0% had cardiovascular disease, 5.0% had cerebrovascular disease, and 1.5% had both. Mean estimated glomerular filtration rate was 77.9 mL/(min‧1.73 m2), and mean urine/albumin creatinine ratio was 88.6 mg/g. After 2 months, 69 of 200 (34.5%) patients received a new prescription for either SGLT2i or GLP1-RA: 53.4% of patients in the Simultaneous arm and 8.3% of patients in the Education-First arm (P<0.001). After 6 months, 128 of 200 (64.0%) received a new prescription: 69.8% of patients in the Simultaneous arm and 56.0% of patients in Education-First (P<0.001). Patient self-report of taking SGLT2i or GLP1-RA within 6 months of trial entry was similarly greater in the Simultaneous versus Education-First arm (69 of 116 [59.5%] versus 37 of 84 [44.0%]; P<0.001) Median time to first prescription was 24 (interquartile range [IQR], 13-50) versus 85 days (IQR, 65-106), respectively (P<0.001). CONCLUSIONS: In this randomized trial, a remote, team-based program identifies patients with type 2 diabetes and high cardiovascular or kidney risk, provides virtual education, prescribes SGLT2i or GLP1-RA, and improves guideline-directed medical therapy. These findings support greater utilization of virtual team-based approaches to optimize chronic disease management. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06046560.

Remote Cardiovascular Hypertension Program Enhanced Blood Pressure Control During the COVID-19 Pandemic.

Background The COVID-19 pandemic disrupted traditional health care; one fallout was a drastic decrease in blood pressure (BP) assessment. We analyzed the pandemic's impact on our existing remote hypertension management program's effectiveness and adaptability. Methods and Results This retrospective observational analysis evaluated BP control in an entirely remote management program before and during the pandemic. A team of pharmacists, nurse practitioners, physicians, and nonlicensed navigators used an evidence-based clinical algorithm to optimize hypertensive treatment. The algorithm was adapted during the pandemic to simplify BP control. Overall, 1256 patients (605 enrolled in the 6 months before the pandemic shutdown in March 2020 and 651 in the 6 months after) were a median age of 63 years old, 57% female, and 38.2% non-White. Among enrolled patients with sustained hypertension, 51.1% reached BP goals. Within this group, rates of achieving goal BP improved to 94.6% during the pandemic from 75.8% prepandemic (P<0.0001). Mean baseline home BP was 141.7/81.9 mm Hg during the pandemic and 139.8/82.2 prepandemic, and fell ≈16/9 mm Hg in both periods (P<0.0001). Maintenance during the pandemic was achieved earlier (median 11.8 versus 19.6 weeks, P<0.0001), with more frequent monthly calls (8.2 versus 3.1, P<0.0001) and more monthly home BP recordings per patient (32.4 versus 18.9, P<0.0001), compared with the prepandemic period. Conclusions A remote clinical management program was successfully adapted and delivered significant improvements in BP control and increased home BP monitoring despite a nationally observed disruption of traditional hypertension care. Such programs have the potential to transform hypertension management and care delivery.

Results of a Remotely Delivered Hypertension and Lipid Program in More Than 10 000 Patients Across a Diverse Health Care Network.

Importance: Blood pressure (BP) and cholesterol control remain challenging. Remote care can deliver more effective care outside of traditional clinician-patient settings but scaling and ensuring access to care among diverse populations remains elusive. Objective: To implement and evaluate a remote hypertension and cholesterol management program across a diverse health care network. Design, Setting, and Participants: Between January 2018 and July 2021, 20 454 patients in a large integrated health network were screened; 18 444 were approached, and 10 803 were enrolled in a comprehensive remote hypertension and cholesterol program (3658 patients with hypertension, 8103 patients with cholesterol, and 958 patients with both). A total of 1266 patients requested education only without medication titration. Enrolled patients received education, home BP device integration, and medication titration. Nonlicensed navigators and pharmacists, supported by cardiovascular clinicians, coordinated care using standardized algorithms, task management and automation software, and omnichannel communication. BP and laboratory test results were actively monitored. Main Outcomes and Measures: Changes in BP and low-density lipoprotein cholesterol (LDL-C). Results: The mean (SD) age among 10 803 patients was 65 (11.4) years; 6009 participants (56%) were female; 1321 (12%) identified as Black, 1190 (11%) as Hispanic, 7758 (72%) as White, and 1727 (16%) as another or multiple races (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown, other, and declined to respond; consolidated owing to small numbers); and 142 (11%) reported a preferred language other than English. A total of 424 482 BP readings and 139 263 laboratory reports were collected. In the hypertension program, the mean (SD) office BP prior to enrollment was 150/83 (18/10) mm Hg, and the mean (SD) home BP was 145/83 (20/12) mm Hg. For those engaged in remote medication management, the mean (SD) clinic BP 6 and 12 months after enrollment decreased by 8.7/3.8 (21.4/12.4) and 9.7/5.2 (22.2/12.6) mm Hg, respectively. In the education-only cohort, BP changed by a mean (SD) -1.5/-0.7 (23.0/11.1) and by +0.2/-1.9 (30.3/11.2) mm Hg, respectively (P < .001 for between cohort difference). In the lipids program, patients in remote medication management experienced a reduction in LDL-C by a mean (SD) 35.4 (43.1) and 37.5 (43.9) mg/dL at 6 and 12 months, respectively, while the education-only cohort experienced a mean (SD) reduction in LDL-C of 9.3 (34.3) and 10.2 (35.5) mg/dL at 6 and 12 months, respectively (P < .001). Similar rates of enrollment and reductions in BP and lipids were observed across different racial, ethnic, and primary language groups. Conclusions and Relevance: The results of this study indicate that a standardized remote BP and cholesterol management program may help optimize guideline-directed therapy at scale, reduce cardiovascular risk, and minimize the need for in-person visits among diverse populations.

I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis.

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.

Evaluation of the Usage and Dosing of Guideline-Directed Medical Therapy for Heart Failure With Reduced Ejection Fraction Patients in Clinical Practice.

BACKGROUND: Although strategies for optimization of pharmacologic therapy in patients with heart failure with reduced ejection fraction (HFrEF) are scripted by guidelines, data from HF registries suggests that guideline-directed medical therapies (GDMT) are underutilized among eligible patients. Whether this discrepancy reflects medication intolerance, contraindications, or a quality of care issue remains unclear. OBJECTIVE: The objective of this initiative was to identify reasons for underutilization and under-dosing of HFrEF therapy in patients at a large, academic medical center. METHODS: Among 500 patients with HFrEF enrolled in a quality improvement project at a tertiary center, we evaluated usage and dosing of 4 categories of GDMT: ACE inhibitors/Angiotensin Receptor Blockers (ACE-i/ARB), Angiotensin Receptor-Neprilysin Inhibitors (ARNi), beta blockers, and Mineralocorticoid Receptor Antagonists (MRA). Reasons for nonprescription and usage of suboptimal doses were abstracted from notes in the chart and from telephone review of previous medication trials with the patient. RESULTS: Of 500 patients identified, 472 subjects had complete data for analysis. Among eligible patients, ACE-i/ARB were prescribed in 81.4% (293 of 360) and beta blockers in 94.4% (442 of 468). Of these patients, 10.6% were prescribed target doses of ACE-i/ARB and 12.4% were prescribed target doses of beta blockers. Utilization of other categories of GDMT was lower, with 54% of eligible patients prescribed MRAs and 27% prescribed an ARNi. In most cases, the reasons for nonprescription or under-dosing of GDMT were not apparent on review of the health record or discussion with the patient. CONCLUSION: Clear rationale for nonprescription and under-dosing of GDMT often cannot be ascertained from detailed review and is only rarely related to documented medication intolerance or contraindications, suggesting an opportunity for quality improvement.

Population health management of low-density lipoprotein cholesterol via a remote, algorithmic, navigator-executed program.

BACKGROUND: Implementation of guideline-directed cholesterol management remains low despite definitive evidence establishing such measures reduce cardiovascular (CV) events, especially in high atherosclerotic CV disease (ASCVD) risk patients. Modern electronic resources now exist that may help improve health care delivery. While electronic medical records (EMR) allow for population health screening, the potential for coupling EMR screening to remotely delivered algorithmic population-based management has been less studied as a way of overcoming barriers to optimal cholesterol management. METHODS: In an academically affiliated healthcare system, using EMR screening, we sought to identify 1,000 high ASCVD risk patients not meeting guideline-directed low-density lipoprotein-cholesterol (LDL-C) goals within specific system-affiliated primary care practices. Contacted patients received cholesterol education and were offered a remote, guideline-directed, algorithmic cholesterol management program executed by trained but non-licensed "navigators" under professional supervision. Navigators used telephone, proprietary software and internet resources to facilitate algorithm-driven, guideline-based medication initiation/titration, and laboratory testing until patients achieved LDL-C goals or exited the program. As a clinical effectiveness program for cholesterol guideline implementation, comparison was made to those contacted patients who declined program-based medication management, and received education only, along with their usual care. RESULTS: 1021 patients falling into guideline-defined high ASCVD risk groups warranting statin therapy (ASCVD, type 2 diabetes, LDL ≥ 190 mg/dL, calculated 10-year ASCVD risk ≥7.5%) and not achieving guideline-defined target LDL-C levels and/or therapy were identified and contacted. Among the 698 such patients who opted for program medication management, significant LDL-C reductions occurred in the total cohort (mean -65.4 mg/dL, 45% decrease), and each high ASCVD risk subgroup: ASCVD (-57.2 mg/dL, -48.0%); diabetes mellitus (-53.1 mg/dL, -40.0%); severe hypercholesterolemia (-76.3 mg/dL, -45.7%); elevated ASCVD 10-year risk (-62.8 mg/dL, -41.1%) (P<0.001 for all), without any significant complications. Among 20% of participants with reported statin intolerance, average LDL-C decreased from baseline 143 mg/dL to 85 mg/dL using mainly statins and ezetimibe, with limited PCSK9 inhibitor use. In comparison, eligible high ASCVD risk patients who were contacted but opted for education only, a 17% LDL-C decrease occurred over a similar timeframe, with 80% remaining with an LDL-C over 100 mg/dL. CONCLUSIONS: A remote, algorithm-driven, navigator-executed cholesterol management program successfully identified high ASCVD risk undertreated patients using EMR screening and was associated with significantly improved guideline-directed LDL-C control, supporting this approach as a novel strategy for improving health care access and delivery.

Remote Optimization of Guideline-Directed Medical Therapy in Patients With Heart Failure With Reduced Ejection Fraction.

Importance: Optimal treatment of heart failure with reduced ejection fraction (HFrEF) is scripted by treatment guidelines, but many eligible patients do not receive guideline-directed medical therapy (GDMT) in clinical practice. Objective: To determine whether a remote, algorithm-driven, navigator-administered medication optimization program could enhance implementation of GDMT in HFrEF. Design, Setting, and Participants: In this case-control study, a population-based sample of patients with HFrEF was offered participation in a quality improvement program directed at GDMT optimization. Treating clinicians in a tertiary academic medical center who were caring for patients with heart failure and an ejection fraction of 40% or less (identified through an electronic health record-based search) were approached for permission to adjust medical therapy according to a sequential titration algorithm modeled on the current American College of Cardiology/American Heart Association heart failure guidelines. Navigators contacted participants by telephone to direct medication adjustment and conduct longitudinal surveillance of laboratory tests, blood pressure, and symptoms under supervision of a pharmacist, nurse practitioner, and heart failure cardiologist. Patients and clinicians declining to participate served as a control group. Exposures: Navigator-led remote optimization of GDMT compared with usual care. Main Outcomes and Measures: Proportion of patients receiving GDMT in the intervention and control groups at 3 months. Results: Of 1028 eligible patients (mean [SD] values: age, 68 [14] years; ejection fraction, 32% [8%]; and systolic blood pressure, 122 [18] mm Hg; 305 women (30.0%); 892 individuals [86.8%] in New York Heart Association class I and II), 197 (19.2%) participated in the medication optimization program, and 831 (80.8%) continued with usual care as directed by their treating clinicians (585 [56.9%] general cardiologists; 443 [43.1%] heart failure specialists). At 3 months, patients participating in the remote intervention experienced significant increases from baseline in use of renin-angiotensin system antagonists (138 [70.1%] to 170 [86.3%]; P < .001) and ß-blockers (152 [77.2%] to 181 [91.9%]; P < .001) but not mineralocorticoid receptor antagonists (51 [25.9%] to 60 [30.5%]; P = .14). Doses for each category of GDMT also increased from baseline in the intervention group. Among the usual-care group, there were no changes from baseline in the proportion of patients receiving GDMT or the dose of GDMT in any category. Conclusions and Relevance: Remote titration of GDMT by navigators using encoded algorithms may represent an efficient, population-level strategy for rapidly closing the gap between guidelines and clinical practice in patients with HFrEF.

Extending i2b2 into a framework for semantic abstraction of EHR to facilitate rapid development and portability of Health IT applications.

The wide gap between a care provider's conceptualization of electronic health record (EHR) and the structures for electronic health record (EHR) data storage and transmission, presents a multitude of obstacles for development of innovative Health IT applications. While developers model the EHR view of the clinicians at one end, they work with a different data view to construct health IT applications. Although there has been considerable progress to bridge this gap by evolution of developer friendly standards and tools for terminology mapping and data warehousing, there is a need for a simplified framework to facilitate development of interoperable applications. To this end, we propose a framework for creating a layer of semantic abstraction on the EHR and describe preliminary work on the implementation of this framework for management of hyperlipidemia and hypertension. Our goal is to facilitate the rapid development and portability of Health IT applications.