"Taking death statistics from a sample of Medicare patients and extrapolating it to all hospitalized patients is like turning apples into oranges"
"Using studies that identify medical errors that were followed by death to declare that these medical errors necessarily caused these deaths is not fair. What these studies do not take into account is how long these patients would have lived had they received optimal medical care."
This gets rid of the scams but makes all surviving reviews unreliable since they'd all be pay-to-play. It'd be better to just remove reviews altogether at that point.
Which doesn't seem like such a bad idea now that I've said it out loud.
Thanks, I'm a Windows user and use Chrome myself, and on my machine it works great, but it's a pretty beefy box. I mentioned this downthread to someone else, but I've filed a bug internally and we'll make sure that it's better optimized. Thank you for the report, it's truly helpful.
//The overall wait time to receive care decreased by 71.43% due to this initiative. Additionally, participants received psychological care within three weeks after completing the triage module. In 71.29% of the cases, the artificial intelligence-assisted triage program and the psychiatrist suggested the same treatment intensity and psychotherapy program. Additionally, 63.29% of participants allocated to lower-intensity treatment plans by the AI-assisted triage program did not require psychiatric consultation later.
Potential of ChatGPT in youth mental health emergency triage: Comparative analysis with clinicians
//This study suggests that GPT-4 models could be leveraged as a support tool in mental health telephone triage, particularly for psychiatric emergencies. Although response variability across iterations was minimal, most discrepancies in admission decisions were identified as false positives, reflecting that GPT Models may have a tendency to over-triage relative to clinician judgment. While findings are promising, further research is required to confirm clinical relevance.
A few others that may be of interest. The results from Raita et al.’s study that used a large dataset of adult ED visits revealed that four ML models outperformed the Emergency
Severity Index (ESI) in forecasting outcomes of critical care and hospitalization, with higher discriminatory
abilities and reduced under-triaged patients in levels three to five of ESI triage
The rest of the studies below reinforc the superiority of developed ML models over conventional triage systems - e.g. by demonstrating an AUROC of 0.991 for predicting critical outcomes in pediatric ED visitors, or another study showing LLMs surpassing the performance of the ESI and vital sign triggers
- Yilanli M, McKay I, Jackson DI, Sezgin E Large Language Models for Individualized Psychoeducational Tools for Psychosis: a cross‐sectional study. 2024.07.26.24311075. Preprint at medRxiv. 2024.
-Hwang S, Lee B: Machine learning-based prediction of critical illness in children visiting the emergency
department. PLoS One. 2022, 17:e0264184. 10.1371/journal.pone.0264184
-Joseph JW, Leventhal EL, Grossestreuer AV, et al.: Deep-learning approaches to identify critically Ill
patients at emergency department triage using limited information. J Am Coll Emerg Physicians Open. 2020,
-Liu Y, Gao J, Liu J, et al.: Development and validation of a practical machine-learning triage algorithm for
the detection of patients in need of critical care in the emergency department. Sci Rep. 2021, 11:24044.
-Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA Jr, Hasegawa K: Emergency department triage prediction
of clinical outcomes using machine learning models. Crit Care. 2019,
-Wolff P, Rios SA, Grana M: Setting up standards: a methodological proposal for pediatric triage machine
learning model construction based on clinical outcomes. Expert Syst Appl. 2019, 138:12.
-Ivanov O, Wolf L, Brecher D, et al.: Improving Ed emergency Severity Index acuity assignment using
machine learning and clinical natural language processing. J Emerg Nurs. 2021, 47:265-278.e7.
This is still ER - clinicians using AI tools. The MH triage flow is patient interacts with an AI by typing or speaking. It could work - maybe the systems in the article are just not very well set-up.
> I haven’t been involved with Django since leaving the Journal-World back in September, but now that the framework is open-sourced I look forward to contributing to its further development—both in terms of documentation and actual code.
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