Assessment of Implementation of the Modern Outpatient Information Systems in Indonesia: A Public Health Collaborative Approach.
Bibliographic record
Abstract
Each year, Indonesian hospitals delivers around 5.8 million outpatient appointments of which around one quarter are new and three quarters are return appointments. It is vitally important that people get fast access to advice and support, self-management information, and, where needed, get to see the right health professional as quickly as possible to ensure care is delivered in as responsive and person-centred a manner as possible, and, critically, as close to home as possible. We have also seen significant growth in Outpatient numbers – over 400,000 extra patients now being seen on an annual basis compared to 2017. Looking to the future, a new approach must fit with a wider plan for transformation, in particular transformation of general practice, primary care and community health services. The new approach is about promoting collaboration between clinical teams in primary, secondary and tertiary care to develop solutions that best meet the needs of the local population and at the same time appropriately managing workload across the system, valuing the contribution of all staff. To ensure genuine collaboration we undertake a need assessment to focus our attention on the local interface between primary and secondary care, bringing local decision makers together to promote innovation and the solutions that best meet local needs. The Modern Outpatient Information System will be progressed over a three-year time span starting with strategic planning in December 2019 and will build on the direction set within the Primary Care Transformation Program and National Clinical Strategy in 2020 and 2021 respectively. \n \nKeywords: Outpatient information system, Collaborative healthcare
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".