Adapting inpatient addiction medicine consult services during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: We describe addiction consult services (ACS) adaptations implemented during the Novel Coronavirus Disease 2019 (COVID-19) pandemic across four different North American sites: St. Paul's Hospital in Vancouver, British Columbia; Oregon Health & Sciences University in Portland, Oregon; Boston Medical Center in Boston, Massachusetts; and Yale New Haven Hospital in New Haven, Connecticut. EXPERIENCES: ACS made system, treatment, harm reduction, and discharge planning adaptations. System changes included patient visits shifting to primarily telephone-based consultations and ACS leading regional COVID-19 emergency response efforts such as substance use treatment care coordination for people experiencing homelessness in COVID-19 isolation units and regional substance use treatment initiatives. Treatment adaptations included providing longer buprenorphine bridge prescriptions at discharge with telemedicine follow-up appointments and completing benzodiazepine tapers or benzodiazepine alternatives for people with alcohol use disorder who could safely detoxify in outpatient settings. We believe that regulatory changes to buprenorphine, and in Vancouver other medications for opioid use disorder, helped increase engagement for hospitalized patients, as many of the barriers preventing them from accessing care on an ongoing basis were reduced. COVID-19 specific harm reductions recommendations were adopted and disseminated to inpatients. Discharge planning changes included peer mentors and social workers increasing hospital in-reach and discharge outreach for high-risk patients, in some cases providing prepaid cell phones for patients without phones. RECOMMENDATIONS FOR THE FUTURE: We believe that ACS were essential to hospitals' readiness to support patients that have been systematically marginilized during the pandemic. We suggest that hospitals invest in telehealth infrastructure within the hospital, and consider cellphone donations for people without cellphones, to help maintain access to care for vulnerable patients. In addition, we recommend hospital systems evaluate the impact of such interventions. As the economic strain on the healthcare system from COVID-19 threatens the very existence of ACS, overdose deaths continue rising across North America, highlighting the essential nature of these services. We believe it is imperative that health care systems continue investing in hospital-based ACS during public health crises.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".