Knowledge management in health care: an integrative and result-driven clinical staff management model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it