CIC 2022: The ACTIVATE Digital Health Pilot Program for Diabetes and Hypertension in an Underserved and Rural Community.

BACKGROUND: Community health centers and patients in rural and agricultural communities struggle to address diabetes and hypertension in the face of health disparities and technology barriers. The stark reality of these digital health disparities were highlighted during the COVID-19 pandemic. OBJECTIVES: The objective of the ACTIVATE project was to co-design a platform for remote patient monitoring and program for chronic illness management that would address these disparities and offer a solution that fit the needs and context of the community. METHODS: ACTIVATE was a digital health intervention implemented in three phases-community codesign, feasibility assessment, and a pilot phase. Pre- and post-intervention outcomes included regularly-collected hemoglobin A1c (A1c) for participants with diabetes and blood pressure for those with hypertension. RESULTS: Participants were adult patients with uncontrolled diabetes and/or hypertension (n=50). Most were White and Hispanic or Latino (84%) with Spanish as a primary language (69%), and the mean age was 55. There was substantial adoption and use of the technology: over 10,000 glucose and blood pressure measures were transmitted using connected remote monitoring devices over a six-month period. Participants with diabetes achieved a mean reduction in A1c of 3.28 percentage points (SD 2.81) at three months, and 4.19 (SD 2.69) at six months. The vast majority of patients achieved an A1c in the target range for control (7.0 %to 8.0%). Participants with hypertension achieved reductions in systolic blood pressure of 14.81 mmHG (SD 21.40) at three months and 13.55 (SD 23.31) at six months, with smaller reductions in diastolic blood pressure. The majority of participants also reached target blood pressure (less than 130/80). CONCLUSIONS: The ACTIVATE pilot demonstrated that a co-designed solution for remote patient monitoring and chronic illness management delivered by community health centers can overcome digital divide barriers and show positive health outcomes for rural and agricultural residents.

Informatics-enabled citizen science to advance health equity.

The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled citizen science, challenging the types of questions being asked, prioritized, and acted upon. We envision this democratization of informatics that builds upon the inclusive tradition of community-based participatory research (CBPR) as a logical and transformative step toward improving individual, community, and population health in a way that deeply reflects the needs of historically marginalized populations.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations.

There is an urgent need to answer questions related to COVID-19's clinical course and associations with underlying conditions and health outcomes. Multi-center data are necessary to generate reliable answers, but centralizing data in a single repository is not always possible. Using a privacy-protecting strategy, we launched a public Questions & Answers web portal (https://covid19questions.org) with analyses of comorbidities, medications and laboratory tests using data from 202 hospitals (59,074 COVID-19 patients) in the USA and Germany. We find, for example, that 8.6% of hospitalizations in which the patient was not admitted to the ICU resulted in the patient returning to the hospital within seven days from discharge and that, when adjusted for age, mortality for hospitalized patients was not significantly different by gender or ethnicity.