Improving community care for patients discharged from hospital through zone-wide implementation of a seamless care transition policy
Bibliographic record
Abstract
BACKGROUND: Several studies within the psychiatry literature have illustrated the importance of discharge planning and execution, as well as accessibility of outpatient follow-up post-discharge. We report the results of implementing a new seamless care transition policy to expedite post-discharge follow-up in the community Addiction and Mental Health (AMH) program in the Edmonton Zone, Alberta, Canada. The policy involved a distribution mechanism for assessment by a mental health therapist (MHT) within 7 days of discharge as well as a dedicated roster of community psychiatrists to accept newly discharged patients. OBJECTIVE: Our aim was to assess the feasibility of this novel policy and to assess its effect on our outcome measures of wait time to first outpatient MHT assessment and re-admission rate to hospital. METHODS: Our study involved a retrospective clinical audit with total sampling design and a comparison of data 1 year before (2015/2016 fiscal year) and 1 year after (2017/2018 fiscal year) the implementation of the seamless care policy within the Edmonton Zone. Extracted data were analyzed with simple descriptive statistics and presented as percentages, mean and median. RESULTS: Overall, with the enactment of this policy, follow-up volumes ultimately increased, while wait times for initial assessment decreased on average for patients discharged from the hospital. In the 2015/2016 fiscal year, MHT completed 128 assessments of post-discharge patients who were new to the community AMH program compared to 298 completed new assessments for the 2017/2018 fiscal year. The corresponding wait times for the new MHT assessments were 12.7 days (median of 12 days) and 7.8 days (median of 6 days), respectively. Similarly, psychiatrists completed only 59 assessments of post-discharge patients who were new to AMH compared to 133 new psychiatric assessments for the 2017/2018 fiscal year. The corresponding wait times for the new psychiatric assessments were 15.3 days (median of 14 days) and 8.8 days (median of 7 days), respectively. We correspondingly found a slight decline in readmission rates after the implementation of our model in the subsequent fiscal year. CONCLUSION: We envision that this policy will set a precedent with regard to streamlining post-discharge follow-up care for admitted inpatients, ultimately improving mental health outcomes for patients.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".