Implementation Status and Usability of Digital Health Interventions Among Health Care Workers and End Users at the Primary Health Care Level in Chandigarh, North India: Cross-Sectional Study
Bibliographic record
Abstract
Background: Digital health interventions (DHIs) refer to the use of information and communication technologies to support or facilitate the achievement of health objectives. The Government of India has launched various DHIs at the primary health care level to improve health services and health-seeking behaviors. However, there is a paucity of evidence on the effectiveness of implementing these interventions and the user response from target end-users within the government health system setting. Objective: This study aimed to assess the implementation status of DHIs and the user response of target end users, that is, the general population and health care workers (HCWs), in health and wellness centers (HWCs) in Chandigarh, India. Methods: A cross-sectional study was conducted to assess the implementation status of 9 DHIs: the Electronic Vaccine Intelligence Network (eVIN), Reproductive and Child Health (RCH), Health Management Information System (HMIS), HWC portal, Comprehensive Primary Health Care-Noncommunicable Disease (CPHC-NCD), Family Planning-Logistics Management Information System (FP-LMIS), eSanjeevani, Integrated Disease Surveillance Program-Integrated Health Information Program (IDSP-IHIP) portal, Aarogya Setu, and the COVID-19 Vaccine Intelligence Network (CoWIN) app. Data were collected from 4 purposively selected HWCs using a pretested data extraction form and observation checklist from June to September 2022. The implementation status of these DHIs was evaluated by categorizing indicators into input, process, and output components and estimating cumulative percentage scores using a score-based logic model framework. Pretested interview schedules were used to assess awareness and user response of DHIs among 120 target end users (clients visiting HWCs) and 120 HCWs (auxiliary nurse midwives, data entry operators, and medical officers). The prevalence of user response was then estimated. Results: The implementation status scores of the eVIN and RCH portals ranged from 70% to 90%. The HMIS portal, HWC portal, CPHC-NCD portal, and FP-LMIS scored between 25% and 50%, while eSanjeevani and the IDSP-IHIP portal scored between 51% and 70%. Community awareness of DHIs was poor, ranging from 1% to 18.3%, except for Aarogya Setu (94/120, 78.3%) and the CoWIN app (43/120, 35.8%), despite 86.7% (104/120) of participants having access to a mobile phone. Low awareness of DHIs was significantly associated with lower socioeconomic status (P=.02) and lower education levels (P=.04). In total, 66% (80/120) of HCWs reported that working with DHIs was easy; however, 89.2% (107/120) stated that dual data entry increased their workload. Frequent technical glitches were most commonly reported for the Auxiliary Nurse Midwife OnLine app (78/80, 97%) by HCWs. Help desk or feedback options in DHIs were rarely used by auxiliary nurse midwives/multipurpose workers (0%-3.8%). Conclusions: The RCH and eVIN portals were effectively implemented, eSanjeevani was moderately implemented, while the HMIS, HWC portal, CPHC, and FP-LMIS were poorly implemented. Community awareness of DHIs was low, except for the Aarogya Setu and CoWIN apps. Although HCWs found DHIs easy to use, increased workload due to dual data entry and frequent technical issues was a key concern.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".