Care Continuity, Telehealth Use, and Quality of Diabetes and Hypertension Care in Community Health Centers Before and During the COVID-19 Pandemic: Repeated Cross-sectional Study
Bibliographic record
Abstract
Background Community health centers (CHCs) pivoted to remote chronic care services during the COVID-19 pandemic. While care continuity is associated with improved care quality and patients’ experiences, telehealth’s impact on these relationships is unclear. Objective We aimed to examine the association among care continuity, telehealth use, and quality of diabetes or hypertension care in CHCs before and during the COVID-19 pandemic. Methods We collected electronic health record data from a cohort of 20,792 patients with diabetes or hypertension with ≥2 visits per year from March to December 2019 and 2020 among 166 California CHCs in the OCHIN Accelerating Data Value Across a National Community Health Center Network Collaborative. Logistic regression models estimated the association between care continuity (modified, modified continuity index [MMCI]) and telehealth adoption and blood pressure or hemoglobin A1c (HbA1c) testing. Generalized linear regression models for 2019 and 2020 estimated the association between MMCI and blood pressure or HbA1c, exploring telehealth as a mediator. Results Patients experienced reduced care continuity (2019: MMCI=0.71, SD 0.28; 2020: MMCI=0.63, SD 0.36; P<.001) and more blood pressure (99.99% vs 99.75%) and HbA1c (53.38% vs 48.99%) assessments in 2019 vs 2020. Telehealth accounted for 0.33% of 2019’s visits and 9.55% of 2020’s visits. MMCI scores were associated with higher odds of telehealth use in 2020 (odds ratio [OR] 1.96; P<.001). MMCI (2019: OR 1.72, P<.001; 2020: OR 1.66, P<.001) and telehealth use (2019: OR 2.44, P<.001; 2020: OR 6.82, P<.001) were associated with greater HbA1c testing. MMCI was associated with lower HbA1c values in 2020 (–0.40, P=.01) and lower systolic (2019: –1.64, P=.045; 2020: –2.40, P=.001) and diastolic (2019: –1.24, P=.007; 2020: –1.33, P=.001) blood pressure. MMCI and telehealth were not associated with HbA1c values in 2019. In 2020, telehealth mediated the relationship between MMCI and HbA1c testing (percent mediated 59%), but not between MMCI and other study outcomes. Conclusions Care continuity facilitates telehealth use and enables resilient performance on process measures. Elucidating how care continuity influences telehealth adoption may provide insights about implementing patient-centered innovations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".