Clinician Data Needs for Uncontrolled Comorbidities of Metabolic Syndrome: Insights on Assumption Testing
To support patient care, providers need insights from patient-generated data (PGD) to help them make sense of a patient’s health condition and inform future decisions. Provider needs for PGD use vary based on the health condition being treated. Research has identified the types of insights that providers need for treating individual health conditions. However, we need further understanding of clinicians’ PGD needs for collection, interpretation, and decision-making for treating patients with comorbidities, particularly when some comorbidities are uncontrolled. We interviewed 11 providers from Federally Qualified Health Centers (FQHCs) treating patients with uncontrolled metabolic syndrome. We identify the types of clinical assessments that providers need to make based on PGD in order to treat patients, including assessing changes in the level of control of comorbidities, negative events related to control level and comorbidity type, and interference of factors across comorbidities. These needs influenced the types and frequency of PGD collection and the interpretation of trends or relations between specific types of data streams. Providers also interpreted data in light of the patient’s social determinants of health. We discuss how clinical dashboards can support providers in checking assumptions, data collection customization, and contextualization of data based on social determinants of health.
Authors
- Elena Agapie (ORCID: https://orcid.org/0000-0001-8292-9232)
- Meeshu Agnihotri (ORCID: https://orcid.org/0009-0005-2193-6021)
- Adeline Nyamathi (ORCID: https://orcid.org/0000-0003-4979-6620)
- Vanessa Rodriguez
- Nisa Patel
- Yuanhui Huang
- Amir Rahmani
- Nikil Dutt
Institutions
- University of California, Irvine (US)
- Irvine University (US)
Publication Details
- Journal
- ACM Transactions on Computing for Healthcare
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1145/3806831
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 0.00