PSCPF: planning, scheduling and control of patient flow
Bibliographic record
Abstract
Paper aims Lack of operational planning in healthcare organizations may result in service disconnection and reduced value per effort to the patient. Production Planning, Scheduling and Control assists in determining “what,” “how much,” “when” and “where” to deliver, which may be of great relevance to healthcare services. This study proposes a structural framework and analyzes the implementation of PPSC concepts in OR management. Originality The application of concepts from manufacturing applied to healthcare as the PPSC, present in this text, demonstrates the crossing and alignment of global management techniques and tools applicable to both areas. Research method The discussion is based on data gathered through action-research in a Brazilian public hospital. Main findings In the hospital under study, the PSCPF implementation resulted in increased production control of surgeries, increased efficiency in management of waiting lists, increased operational efficiency, and motivational gains from staff integration. Implications for theory and practice The understanding and proper implementation of the concept of PPSC contributed to mitigate problems related to capacity management, reduced the unawareness of different levels of demand and waiting lists, cut down scheduling errors, and prevented the provision of false information from the disconnection between the various areas of the 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".