The utilization of systematic outcome mapping to improve performance management in health care
Bibliographic record
Abstract
Performance management is an important mechanism for ensuring accountability and improving the quality of health-care services. The last decade has witnessed a proliferation in the development of performance measurement systems for assessing health-care processes and outcomes at the program, hospital, district, system and national level. This has allowed for comparison and benchmarking between similar aspects of care at each of these levels. Unfortunately, most performance systems are devoid of clear mechanisms for translating feedback from measures into strategies for action, thus leaving largely unfulfilled the quality and management aspect necessary to improve health-care services. Therefore, the thinking that goes into designing these systems must change. This article outlines a management framework called systematic outcome mapping that provides for performance management rather than just performance measurement by allowing for quality improvement to be built into performance indicator development. It utilizes evidence-based medicine and expert consensus opinion to establish linkages between processes of care and their outcomes with the clear intent that feedback from information provided by performance indicators can be used to modify health-care activities so as to improve health outcomes. This fulfils the quality improvement aspect of performance measurement and makes it an integral part of a performance management framework that reinforces organizational learning through feedback from outcomes and the assessment of organizational routines.
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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 | 0.258 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.008 |
| 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".