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.

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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
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article

Clinician Data Needs for Uncontrolled Comorbidities of Metabolic Syndrome: Insights on Assumption Testing

Elena Agapie, Meeshu Agnihotri, Adeline Nyamathi, Vanessa Rodriguez et al.
ACM Transactions on Computing for Healthcare
Chronic Disease Management Strategies
article

Clinician Data Needs for Uncontrolled Comorbidities of Metabolic Syndrome: Insights on Assumption Testing

Elena Agapie, Meeshu Agnihotri, Adeline Nyamathi, Vanessa Rodriguez, Nisa Patel, Yuanhui Huang, Amir Rahmani, Nikil Dutt
article en

Abstract

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.

ACM Transactions on Computing for Healthcare
University of California, Irvine (US), Irvine University (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 20%
Chronic Disease Management Strategies
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Clinician Data Needs for Uncontrolled Comorbidities of Metabolic Syndrome: Insights on Assumption Testing — Elena Agapie, Meeshu Agnihotri, et al. · ACM Transactions on Computing for Healthcare (2026) | TGRS Research Map | TGRS