Modeling process and information systems: leveraging technology to improve service operations
Bibliographic record
Abstract
This thesis considers the relationship between service quality, operational flow and technological integration through process modeling methodologies. Mixed methods research is presented in a series of process improvement case studies which incorporate Lean and Total Quality Management (TQM) principles. The studies are in context of clinical and administrative departments within a single organization; each department has undergone change to adopt a new information system. Data was collected using semi-structured interviews, focus groups and observations. We apply user-centric process modeling methodologies, Patient Journey Modeling Architecture (PaJMA) or Customer-Centric Process Improvement Methodology (CCPIM), and incorporate Electronic Health Record (EHR) access data to develop and validate process models which reflect the patient care journey or business service operations. Our aim was to identify opportunities for quality improvement of services and technological integration. The second aim was to provide a common language for process improvement across the organization. We conclude with a combination of case study results to provide overall process improvement and change management recommendations to senior management of the organization.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".