The Michigan Spine Surgery Improvement Collaborative: a statewide Collaborative Quality Initiative
Bibliographic record
Abstract
OBJECT Given the scrutiny of spine surgery by policy makers, spine surgeons are motivated to demonstrate and improve outcomes, by determining which patients will and will not benefit from surgery, and to reduce costs, often by reducing complications. Insurers are similarly motivated. In 2013, Blue Cross Blue Shield of Michigan (BCBSM) and Blue Care Network (BCN) established the Michigan Spine Surgery Improvement Collaborative (MSSIC) as a Collaborative Quality Initiative (CQI). MSSIC is one of the newest of 21 other CQIs that have significantly improved-and continue to improve-the quality of patient care throughout the state of Michigan. METHODS MSSIC focuses on lumbar and cervical spine surgery, specifically indications such as stenosis, disk herniation, and degenerative disease. Surgery for tumors, traumatic fractures, deformity, scoliosis, and acute spinal cord injury are currently not within the scope of MSSIC. Starting in 2014, MSSIC consisted of 7 hospitals and in 2015 included another 15 hospitals, for a total of 22 hospitals statewide. A standardized data set is obtained by data abstractors, who are funded by BCBSM/BCN. Variables of interest include indications for surgery, baseline patient-reported outcome measures, and medical history. These are obtained within 30 days of surgery. Outcome instruments used include the EQ-5D general health state score (0 being worst and 100 being the best health one can imagine) and EQ-5D-3 L. For patients undergoing lumbar surgery, a 0 to 10 numeric rating scale for leg and back pain and the Oswestry Disability Index for back pain are collected. For patients undergoing cervical surgery, a 0 to 10 numeric rating scale for arm and neck pain, Neck Disability Index, and the modified Japanese Orthopaedic Association score are collected. Surgical details, postoperative hospital course, and patient-reported outcome measures are collected at 90-day, 1-year, and 2-year intervals. RESULTS As of July 1, 2015, a total of 6397 cases have been entered into the registry. This number reflects 4824 eligible cases with confirmed surgery dates. Of these 4824 eligible cases, 3338 cases went beyond the 120-day window and were considered eligible for the extraction of surgical details, 90-day outcomes, and adverse events. Among these 3338 patients, there are a total of 2469 lumbar cases, 862 cervical cases, and 7 combined procedures that were entered into the registry. CONCLUSIONS In addition to functioning as a registry, MSSIC is also meant to be a platform for quality improvement with the potential for future initiatives and best practices to be implemented statewide in order to improve quality and lower costs. With its current rate of recruitment and expansion, MSSIC will provide a robust platform as a regional prospective registry. Its unique funding model, which is supported by BCBSM/BCN, will help ensure its longevity and viability, as has been observed in other CQIs that have been active for several years.
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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 | 0.076 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".