Who Knew? Innovation and Transformation Within Acute Care Case Management
Bibliographic record
Abstract
Mindy Owen, RN, CRRN, CCM, a Charter Board member and Past President of CMSA and Past Chair and Commissioner of CCMC. She is the Principal of Phoenix Healthcare Associates LLC, Coral Springs, Florida, specializing in case management education and management. Her career in health care has included critical care neurosurgery and rehabilitation. She helped design and implement an SCI-TBI rehabilitation department at Wesley Regional Medical Center in Wichita, Kansas. She was the 1st Midwest Regional Director of C.M. for Intracorp and has developed and directed both acute and MCO CM/DM programs nationwide. Laura Ostrowsky, RN, CCM, MUP, is currently the Director of Case Management at Memorial Sloan-Kettering Cancer Center and the 2012 CMSA Case Manager of the Year. She holds a Master's degree in Health Planning and Policy from Hunter College, has been a CMSA member for more than 7 years, and is currently serving on the Board of Directors of the NYC Chapter. Laura has more than 30 years of health care experience, including time as a staff nurse, quality assurance (QA) coordinator, director of utilization review (UR) and QA, followed by directorships in CM at NYP and currently Memorial Sloan-Kettering Cancer Center. She also spent 3 years in information services at the NYP Presbyterian Network overseeing the selection, acquisition, and implementation of an integrated hospital information application for UR, QA, credentialing, and risk management at 5 network hospitals. Address correspondence to Laura Ostrowsky, RN, CCM, MUP, Case Management Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 ([email protected]). The author reports no conflicts of interest.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".