Health Information System Strengthening During Antenatal Care in Haiti: Continuous Quality Improvement Study
Bibliographic record
Abstract
BACKGROUND: Journey to 9 Plus (J9) is an integrated reproductive, maternal, neonatal, and child health approach to care that has at its core the goal of decreasing the rate of maternal and neonatal morbidity and mortality in rural Haiti. For the maximum effectiveness of this program, it is necessary that the data system be of the highest quality. OpenMRS, an electronic medical record (EMR) system, has been in place since 2013 throughout a tertiary referral hospital, the Hôpital Universitaire de Mirebalais, in Haiti and has been expanded for J9 data collection and reporting. The J9 program monthly reports showed that staff had limited time and capacity to perform double charting, which contributed to incomplete and inconsistent reports. Initial evaluation of the quality of EMR data entry showed that only 18% (58/325) of the J9 antenatal visits were being documented electronically at the start of this quality improvement project. OBJECTIVE: This study aimed to improve the electronic documentation of outpatient antenatal care from 18% (58/325) to 85% in the EMR by J9 staff from November 2020 to September 2021. The experiences that this quality improvement project team encountered could help others improve electronic data collection as well as the transition from paper to electronic documentation within a burgeoning health care system. METHODS: A continuous quality improvement strategy was undertaken as the best approach to improve the EMR data collection at Hôpital Universitaire de Mirebalais. The team used several continuous quality improvement tools to conduct this project: (1) a root cause analysis using Ishikawa and Pareto diagrams, (2) baseline evaluation measurements, and (3) Plan-Do-Study-Act improvement cycles to document incremental changes and the results of each change. RESULTS: At the beginning of the quality improvement project in November 2020, the baseline data entry for antenatal visits was 18% (58/325). Ten months of improvement strategies resulted in an average of 89% (272/304) of antenatal visits documented in the EMR at point of care every month. CONCLUSIONS: The experiences that this quality improvement project team encountered can contribute to the transition from paper to electronic documentation within burgeoning health care systems. Essential to success was having a strong and dedicated nursing leadership to transition from paper to electronic data and motivated nursing staff to perform data collection to improve the quality of data and thus, the reports on patient outcomes. Engaging the nursing team closely in the design and implementation of EMR and quality improvement processes ensures long-term success while centering nurses as key change agents in patient care systems.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".