Expert system for hospitals' multi standard accreditation Jordanian study
Bibliographic record
Abstract
is a process of evaluating business activities based upon a set of pre-determined standards [1]. Hospitals and health care centers seek international and local accreditations to win the confidence of patients and increase a competitive edge in the health services' marketplace [11, 12]. Improvements on accreditation standards are encouraged by three parties; governments, voluntaries and independent agencies [1, 2]. Join Commission International (JCI)[ 9], and Canadian Council on Health Services (CCHSA) 20 are two globally recognized international accreditation standards providers. Health Care Council (HCAC) is Jordanian accreditation standards provider [18]. Hospitals and health care centers do great efforts to achieve international accreditation certificate despite the many difficulties and pitfalls awaiting them along the way. Trial and error lead to long time to success meaning escalating costs and late to gain large number of benefits of that certificate. In this paper we propose an Expert System for Hospitals' Multi Standard Accreditation that aims to walk medical users towards achieving and maintaining accreditation in most productive, efficient, and user friendly manner. To facilitate these goals the proposed system will produce large number of evaluation reports, statistics, and comparison graphics in addition to prompt and timely notifications that will be sent automatically to responsible parties about fault point for follow up procedures. This pioneer expert system aims to provide medical professionals and organizations' administrative staff necessary expertise in dealing with complicated information subtleties, tackled with one day to day basis, as to comply with standards and achieve this esteemed accreditation in systematic and coherent manner. What distinguishes our methodology from others is the flexibility of expert system in selecting specific standard (local or international), following up fault points, and analyzing results. The flexibility is provided to make settings for evaluation process adaptable to the selected standard, and also standard itself can be easily changed upon need. Henceforth it is suitable for both direct clients (hospitals) and indirect evaluator organization. The proposed system will be built in multiple phases. In first phase we will take HCAC as a sample for proposed system. We use power designer to design the proposed system database entities relationships, Oracle database, Developer 6i, Report Builder and Graphics to implement the proposed expert system. All these tools are utilized under Microsoft Windows OS.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".