Improving follow-up visits among individuals with hypertension: Quality Improvement project in the District Hospital, Seoni, Madhya Pradesh, India, 2021–2022
Bibliographic record
Abstract
BACKGROUND: In India, to achieve a 25% relative reduction in the prevalence of raised blood pressure (BP) by 2025, approximately 4.5 crore additional people with hypertension will need to have their BP effectively treated. We conducted a Quality Improvement (QI) initiative to improve follow-up and reduce missed visits among individuals with hypertension registered under India Hypertension Control Initiative, District Hospital, Seoni, Madhya Pradesh, India, in 2022. METHODS: We conducted a quasiexperimental study from January to September 2022 in the District Hospital in Seoni, Madhya Pradesh. Following the Ishikawa diagram, the major root causes for missed visits were identified, and countermeasures were developed. The packages under Plan-Do-Study-Act (PDSA) included (i) training urban Accredited Social Health Activists to conduct house visits for individuals with missed visits and (ii) triangulating the follow-up records from various information systems. The review meetings for QI initiatives were conducted fortnightly to follow-up PDSAs. We calculated the proportion of individuals who were followed-up monthly, and the proportion of missed visits among those registered quarterly. RESULTS: Cumulatively, 2850 individuals were registered with hypertension till September 2022. Following the intervention, the monthly follow-up proportion increased from 21% in January to 37% in September 2022. Missed visit proportion decreased from 66% (228/345) in quarter four, 2021, to 22% (40/180) in quarter three, 2022. Of the 1438 individuals counselled by ASHA home visits, 74.9% returned for follow-up. CONCLUSION: In our setting, QI initiatives suggested that missed visits decreased during the intervention period. However, the interventions must be implemented continuously for better monitoring and use in similar settings.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".