Examining the effects of an obstetrics interprofessional programme on reductions to reportable events and their related costs
Bibliographic record
Abstract
There are many ways to account for the return on investment (ROI) in healthcare: improved communication, teamwork, culture, patient satisfaction, staff satisfaction, and clinical outcomes are but a few. Some of these are easier to quantify and associate to an intervention than others. What if the outcomes listed were not just independent results, but beget one another? In 2001, the Society of Obstetricians and Gynaecologists of Canada created the Managing Obstetrical Risk Efficiently (MOREOB) programme, to improve healthcare culture and patient outcomes in obstetrics by leveraging front-line ownership. Our study provides evidence that MOREOB lowers the frequency and cost of reportable events in maternity units. We sought to review the impact of this intervention on the frequency and cost of reportable events at the insurer level of a clinically focused, three-year interprofessional culture change intervention applied to the maternity unit. We compared the impact of reportable events both in the obstetrical and in the non-obstetrical areas of the same hospitals during the same time periods. We analysed these data using an interrupted time series (ITS) design, among 34 Ontario Canada hospitals. The ITS design assessed changes in the frequency and cost of reportable events before and after the implementation of the intervention. The method was ideally suited as the various maternity units had differing programme commencement and completion dates. The frequency of reportable events showed little change during the three-year intervention. As culture change grew, the changes in behaviour and processes that impact patient outcomes took longer to accrue. A large reduction in the frequency of reportable events occurred in the following three-year (14% reduction) and six-year (25% reduction) tranches. Our results show statistically significant reductions in the frequency and costs associated with reportable events at the level of an insurer. The results also give insight as to the investment of time required to achieve a level of sustainability.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".