Software-driven chronic disease management: Algorithm design and implementation in a community-based blood pressure control pilot

Rahul C Deo; Rebecca Smith; Calum A MacRae; Esha Price; Horace Sheffield; Rahul Patel

doi:10.1177/20503121241284025

Software-driven chronic disease management: Algorithm design and implementation in a community-based blood pressure control pilot

SAGE Open Med. 2024 Oct 31:12:20503121241284025. doi: 10.1177/20503121241284025. eCollection 2024.

Authors

Rahul C Deo^{1

2}, Rebecca Smith¹, Calum A MacRae^{1

2}, Esha Price³, Horace Sheffield³, Rahul Patel¹

Affiliations

¹ Atman Health, Needham, MA, USA.
² Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
³ Detroit Association of Black Organizations, Detroit, MI, USA.

Abstract

Background: Optimal guideline-directed medical therapy is rarely attained in practice, resulting in inadequate control of diseases such as hypertension, with poorer results in under-resourced communities. Technology, including artificial intelligence-driven decision support and software-driven workflow transformation, can potentially improve disease outcomes at a reduced cost, although it must be integrated with a holistic approach.

Methods: We describe the design of a software platform that enables rapid iterative remote management of >20 conditions across cardiac-kidney-metabolic disease. The platform distributes work across a care team of providers and care navigators, automates decision-making, ordering, and documentation, supports rapid incorporation of new evidence, and launches pragmatic trials. We describe software used in a 500-person community-based blood pressure control implemented as a single-arm quality improvement program. The primary endpoint was the proportion of patients meeting the Healthcare Effectiveness Data and Information Set quality measure blood pressure goal (<140/90) at 12 weeks.

Results: A total of 1609 patients were screened, 945 (59%) were found to have uncontrolled hypertension, and 512 patients consented to join the program. The average age was 61 ± 11 years; 59% were female, and 99% self-identified as Black. Blood pressure distribution was: 10% Stage 1 (SBP 130-139 mmHg or DBP 80-89 mmHg), 69% Stage 2 (SBP 140-179 mmHg or DBP 90-119 mmHg), and 21% Stage 3 (SBP >180 mmHg or DBP >120 mmHg). Two hundred four patients (39%) proceeded to a provider encounter, and 160 of these (78%) completed the program. The Healthcare Effectiveness Data and Information Set blood pressure goal was achieved in <12 weeks of enrollment for 141 participants (69% of those enrolled, 88% of those who completed the program).

Conclusion: Software-driven remote blood pressure is feasible, although strategies to improve patient enrollment will be needed to achieve maximum impact. Future work will be required to compare outcomes to usual care and evaluate concurrent management of multiple cardiac-kidney-metabolic conditions.

Keywords: Health equity; artificial intelligence; automation; guideline-directed medical therapy; hypertension; quality measures; software; value-based care.