Scoping review of balanced scorecards for use in healthcare settings: development and implementation
Bibliographic record
Abstract
OBJECTIVE: Balanced scorecards (BSCs) were developed in the early 1990s in corporate settings as a strategic performance management tool that emphasised measurement from multiple perspectives. Since their introduction, BSCs have been adapted for a variety of industries, including to healthcare settings. The aim of this scoping review was to describe the application of BSCs in healthcare. METHODS: Medline, Embase and CINHAL databases were searched using keywords and medical subject headings for 'balanced scorecard' and related terms from 1992 to 17/04/2020. Title and abstract screening and full text review were conducted in duplicate by two reviewers. Studies describing the development and/or implementation of a BSC in a healthcare setting were included. Data were abstracted using pilot-tested forms and reviewed for key themes and findings. RESULTS: 8129 records were identified and 841 underwent a full text review. 87 articles were included. Over 26 countries were represented and the majority of BSCs were applied at a local level (54%) in hospital settings (41%). While almost all discussed Kaplan and Norton's original BSC (97%), only 69% described alignment with a strategic plan. Patients/family members were rarely involved in development teams (3%) which typically were comprised of senior healthcare leaders/administrators. Only 21% of BSCs included perspectives using identical formatting to the original BSC description. Lessons learnt during development addressed three main themes: scorecard design, stakeholder engagement and feasibility. CONCLUSIONS: BSC frameworks have been used in various healthcare settings but frequently undergo adaptation from the original description in order to suit a specific healthcare context. Future BSCs should aim to include patients/families to promote patient-centred healthcare systems. Considering the heterogeneity evident in development approaches, methodological guidance in this area is warranted.
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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 | 0.070 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.027 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".