Modeling process and information systems: leveraging technology to improve service operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it