Effect of a Co-Located Bridging Recovery Initiative on Hospital Length of Stay Among Patients With Opioid Use Disorder: The BRIDGE Randomized Clinical Trial.

Importance: Co-located bridge clinics aim to facilitate a timely transition to outpatient care for inpatients with opioid use disorder (OUD); however, their effect on hospital length of stay (LOS) and postdischarge outcomes remains unclear. Objective: To evaluate the effect of a co-located bridge clinic on hospital LOS among inpatients with OUD. Design, Setting, and Participants: This parallel-group randomized clinical trial recruited 335 adult inpatients with OUD seen by an addiction consultation service and without an existing outpatient clinician to provide medication for OUD (MOUD) between November 25, 2019, and September 28, 2021, at a tertiary care hospital affiliated with a large academic medical center and its bridge clinic. Intervention: The bridge clinic included enhanced case management before and after hospital discharge, MOUD prescription, and referral to a co-located bridge clinic. Usual care included MOUD prescription and referrals to community health care professionals who provided MOUD. Main Outcomes and Measures: The primary outcome was the index admission LOS. Secondary outcomes, assessed at 16 weeks, were linkage to health care professionals who provided MOUD, MOUD refills, same-center emergency department (ED) and hospital use, recurrent opioid use, quality of life (measured by the Schwartz Outcome Scale-10), overdose, mortality, and cost. Analysis was performed on an intent-to-treat basis. Results: Of 335 participants recruited (167 randomized to the bridge clinic and 168 to usual care), the median age was 38.0 years (IQR, 31.9-45.7 years), and 194 (57.9%) were male. The median LOS did not differ between arms (adjusted odds ratio [AOR], 0.94 [95% CI, 0.65-1.37]; P = .74). At the 16-week follow-up, participants referred to the bridge clinic had fewer hospital-free days (AOR, 0.54 [95% CI, 0.32-0.92]), more readmissions (AOR, 2.17 [95% CI, 1.25-3.76]), and higher care costs (AOR, 2.25 [95% CI, 1.51-3.35]), with no differences in ED visits (AOR, 1.15 [95% CI, 0.68-1.94]) or deaths (AOR, 0.48 [95% CI, 0.08-2.72]) compared with those receiving usual care. Follow-up calls were completed for 88 participants (26.3%). Participants referred to the bridge clinic were more likely to receive linkage to health care professionals who provided MOUD (AOR, 2.37 [95% CI, 1.32-4.26]) and have more MOUD refills (AOR, 6.17 [95% CI, 3.69-10.30]) and less likely to experience an overdose (AOR, 0.11 [95% CI, 0.03-0.41]). Conclusions and Relevance: This randomized clinical trial found that among inpatients with OUD, bridge clinic referrals did not improve hospital LOS. Referrals may improve outpatient metrics but with higher resource use and expenditure. Bending the cost curve may require broader community and regional partnerships. Trial Registration: ClinicalTrials.gov Identifier: NCT04084392.

Common street drug names for the anesthesiologist and pain physician.

People who use or sell drugs develop their own in-group terms and language, much like any other group of people with a common experience. Slang terms are derived from a wide variety of sources. These might include the physical appearance and/or type of drug, the place where it originates, the effect it has on users, or how it is packaged for sale. To assist and educate the clinical practitioner who may deal with this nomenclature, we have compiled a list of some of the most common street names and some explanations (when known) of their origins.

Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model.

Background: Natural language processing models such as ChatGPT can generate text-based content and are poised to become a major information source in medicine and beyond. The accuracy and completeness of ChatGPT for medical queries is not known. Methods: Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes/no) or descriptive answers. The physicians then graded ChatGPT-generated answers to these questions for accuracy (6-point Likert scale; range 1 - completely incorrect to 6 - completely correct) and completeness (3-point Likert scale; range 1 - incomplete to 3 - complete plus additional context). Scores were summarized with descriptive statistics and compared using Mann-Whitney U or Kruskal-Wallis testing. Results: Across all questions (n=284), median accuracy score was 5.5 (between almost completely and completely correct) with mean score of 4.8 (between mostly and almost completely correct). Median completeness score was 3 (complete and comprehensive) with mean score of 2.5. For questions rated easy, medium, and hard, median accuracy scores were 6, 5.5, and 5 (mean 5.0, 4.7, and 4.6; p=0.05). Accuracy scores for binary and descriptive questions were similar (median 6 vs. 5; mean 4.9 vs. 4.7; p=0.07). Of 36 questions with scores of 1-2, 34 were re-queried/re-graded 8-17 days later with substantial improvement (median 2 vs. 4; p<0.01). Conclusions: ChatGPT generated largely accurate information to diverse medical queries as judged by academic physician specialists although with important limitations. Further research and model development are needed to correct inaccuracies and for validation.

Accuracy and Reliability of Chatbot Responses to Physician Questions.

Importance: Natural language processing tools, such as ChatGPT (generative pretrained transformer, hereafter referred to as chatbot), have the potential to radically enhance the accessibility of medical information for health professionals and patients. Assessing the safety and efficacy of these tools in answering physician-generated questions is critical to determining their suitability in clinical settings, facilitating complex decision-making, and optimizing health care efficiency. Objective: To assess the accuracy and comprehensiveness of chatbot-generated responses to physician-developed medical queries, highlighting the reliability and limitations of artificial intelligence-generated medical information. Design, Setting, and Participants: Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes or no) or descriptive answers. The physicians then graded the chatbot-generated answers to these questions for accuracy (6-point Likert scale with 1 being completely incorrect and 6 being completely correct) and completeness (3-point Likert scale, with 1 being incomplete and 3 being complete plus additional context). Scores were summarized with descriptive statistics and compared using the Mann-Whitney U test or the Kruskal-Wallis test. The study (including data analysis) was conducted from January to May 2023. Main Outcomes and Measures: Accuracy, completeness, and consistency over time and between 2 different versions (GPT-3.5 and GPT-4) of chatbot-generated medical responses. Results: Across all questions (n = 284) generated by 33 physicians (31 faculty members and 2 recent graduates from residency or fellowship programs) across 17 specialties, the median accuracy score was 5.5 (IQR, 4.0-6.0) (between almost completely and complete correct) with a mean (SD) score of 4.8 (1.6) (between mostly and almost completely correct). The median completeness score was 3.0 (IQR, 2.0-3.0) (complete and comprehensive) with a mean (SD) score of 2.5 (0.7). For questions rated easy, medium, and hard, the median accuracy scores were 6.0 (IQR, 5.0-6.0), 5.5 (IQR, 5.0-6.0), and 5.0 (IQR, 4.0-6.0), respectively (mean [SD] scores were 5.0 [1.5], 4.7 [1.7], and 4.6 [1.6], respectively; P = .05). Accuracy scores for binary and descriptive questions were similar (median score, 6.0 [IQR, 4.0-6.0] vs 5.0 [IQR, 3.4-6.0]; mean [SD] score, 4.9 [1.6] vs 4.7 [1.6]; P = .07). Of 36 questions with scores of 1.0 to 2.0, 34 were requeried or regraded 8 to 17 days later with substantial improvement (median score 2.0 [IQR, 1.0-3.0] vs 4.0 [IQR, 2.0-5.3]; P < .01). A subset of questions, regardless of initial scores (version 3.5), were regenerated and rescored using version 4 with improvement (mean accuracy [SD] score, 5.2 [1.5] vs 5.7 [0.8]; median score, 6.0 [IQR, 5.0-6.0] for original and 6.0 [IQR, 6.0-6.0] for rescored; P = .002). Conclusions and Relevance: In this cross-sectional study, chatbot generated largely accurate information to diverse medical queries as judged by academic physician specialists with improvement over time, although it had important limitations. Further research and model development are needed to correct inaccuracies and for validation.

Inhibition of multiple voltage-gated calcium channels may contribute to spinally mediated analgesia by epipregnanolone in a rat model of surgical paw incision.

Voltage-activated calcium channels play an important role in excitability of sensory nociceptive neurons in acute and chronic pain models. We have previously shown that low-voltage-activated calcium channels, or T-type channels (T-channels), increase excitability of sensory neurons after surgical incision in rats. We have also found that endogenous 5ß-reduced neuroactive steroid epipregnanolone [(3ß,5ß)-3-hydroxypregnan-20-one] blocked isolated T-currents in dorsal root ganglion (DRG) cells in vitro, and reduced nociceptive behavior in vivo, after local intraplantar application into the foot pads of heathy rats and mice. Here, we investigated if epipregnanolone exerts an antinociceptive effect after intrathecal (i.t.) application in healthy rats, as well as an antihyperalgesic effect in a postsurgical pain model. We also studied if this endogenous neurosteroid blocks currents originating from high voltage-activated (HVA) calcium channels in rat sensory neurons. In in vivo studies, we found that epipregnanolone alleviated thermal and mechanical nociception in healthy rats after i.t. administration without affecting their sensory-motor abilities. Furthermore, epipregnanolone effectively reduced mechanical hyperalgesia after i.t application in rats after surgery. In subsequent in vitro studies, we found that epipregnanolone blocked isolated HVA currents in nociceptive sensory neurons with an IC50 of 3.3 µM in a G-protein-dependent fashion. We conclude that neurosteroids that have combined inhibitory effects on T-type and HVA calcium currents may be suitable for development of novel pain therapies during the perioperative period.