Learnings From the Implementation of an Electronic Human Resource Management System for the Health Workforce in Uttar Pradesh, India
Bibliographic record
Abstract
The state of Uttar Pradesh (UP), India, has one of the largest single public health systems globally, serving about 235 million people through more than 30,000 public health facilities with approximately 160,000 health personnel. Yet, the UP health system has a shortfall of public health facilities to meet the population's needs, a shortage of clinical and nonclinical health personnel, inequitable distribution of existing health personnel, and low utilization of public health facilities. A robust and effective electronic human resource management system (eHRMS) that provides real-time information about the lifecycle of all health professionals in UP may aid in improving the health workforce, resulting in better health services and improved health outcomes. The Government of UP rolled out Manav Sampada, a comprehensive eHRMS that complied with global norms and requirements. We describe the implementation of Manav Sampada at scale and elaborate on key learnings and adoption strategies. Manav Sampada was based on key principles of integration and data-sharing with other digital systems, included functional components, a minimum dataset, used a lifecycle-based approach, and a workflow-based system, all of which acted to improve human resource data quality. The eHRMS emerged as a valuable tool for key stakeholders in reviewing worker performance, identifying skill-building needs, and allocating resources for training, leading to improved availability and equity in the distribution of a few critical cadres. The eHRMS in UP is well positioned to become an integral part of the Ayushman Bharat Digital Mission, the backbone of India's integrated digital health infrastructure. Linking eHRMS to a planned beneficiary-centric unitized health service delivery system (capturing information at the individual level rather than the aggregate level) will enable the measurement of service delivery and quality, leading to improved workforce management.
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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 | 0.019 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".