A case study of systems analysis to improve service in a multidisciplinary outpatient clinic
Bibliographic record
Abstract
Background/objective: Innovative ways of improving service delivery often requires testing new ideas. Systems analysis is a validated tool to investigate adverse incidents. This paper describes an innovative usage of this tool for service improvement and redesign in an out-patient setting involving a multidisciplinary team treating women with diabetes in pregnancy at an outer metropolitan health facility in Australia. Methods: Systems analysis tool was chosen to determine the probable causes for prolongation of clinic time causing dissatisfaction amongst the service users and the staff. It provided the template for an action plan regarding work and environmental, organizational process, team, individual, task and patient factors. Remedial actions were implemented over a six month period following this analysis. Timely completion of clinic was the chosen indicator of successful implementation. Results: Several interlinked layers of contributory factors were identified through systems analysis. The large patient load regardless of disease severity was the major contributor. Space restriction for consultation, lesser continuity in the team structure, dated guideline and limited communication between the team members were other factors. Changes implemented included redistribution of patients, adopting new evidence based guidelines, better patient selection accessing the dedicated one-stop clinic and a small change in capacity involving human resources. The service delivery process was restructured in tandem over six months. As a result of these interventions the clinics finished on time generating much greater level of satisfaction among the women attending the clinic and the staff. Conclusions: Redesigning service is an ongoing quality improvement process linked to user and provider satisfaction. Systems analysis is a tool designed to address adverse incidents and identify contributing factors. This study describes an innovative use of the systems analysis tool to improve outpatient services at a district general hospital.
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.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".