Knowledge management in health care: an integrative and result-driven clinical staff management model
Bibliographic record
Abstract
Purpose This paper aims to propose an integrative and result-driven health-care knowledge management (HKM) model and discuss the findings of a research that examines how the KM initiatives of a major private Brazilian hospital system are linked to its health-care performance outcomes. Design/methodology/approach Data were collected from a top-level Brazilian private hospital system (Mater Dei Healthcare System – MDHS), which is composed of three large hospitals internationally accredited by ISO 9001/2000, NIAHO and JCI. Multiple qualitative approaches were used to collect data such as 16 in-depth interviews with health professionals and managers, document analysis, participatory observation and benchmarking interviews with two reference hospital networks in Brazil. Findings The proposed health-oriented KM model is an expansion of the organizational knowing cycle model (Choo, 1996), adding absorptive capacity (ACAP) as a new construct. The paper discusses how ACAP integrates with sense-making, knowledge creation and decision-making processes within the health-care context. Information technology and clinical governance were identified as support factors to the HKM processes. Practical implications The paper presents a pragmatic and result-driven knowledge management (KM) model using health-care-welfare key performance indicators, as well as the emergence of KM as an integrative and strategic approach to hospital management. Originality/value The present study presents a knowledge-based perspective to clinical staff management, demonstrating the tangible results of KM initiatives that contribute to health and management performance outcomes.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".