This change has coincided with a sharp increase in the number of patients who are upset by the time they speak to her. On a typical day, as many as a third of her almost two dozen triage calls are with patients who have struggled to access appropriate care.
Also, amazon injury rates were not paradoxical. If you dont care about people when designing process, it is unsurprising when you end up hurting them.
Take cardiologist Dr. Lee Goldman’s chest pain decision-making algorithm, originally devised at Cook County Hospital to diagnose heart attacks.
Effectively a poster in the ER illustrating a simple decision tree, it outperformed traditional doctor diagnoses; achieving over 95% accuracy and proving 70% better at identifying patients not having a heart attack. This landmark clinical protocol represented a quantum-leap in outcomes, eventually refined to the Revised Cardiac Risk Index (RCRI) used as the gold-standard today.
So not only are algorithmic approaches old-hat in clinical settings, they are in fact a core component of contemporary best practice medicine. But that's somewhat besides the point when much of the issues raised in the article above refer to issues of executive dysfunction - areas in which low-context Agentic AI are basically designed to remedy, e.g.
// Instead of being referred directly by primary care doctors and contacted to schedule appointments, patients were routed through an app, nonclinical call center staff or simply given a phone number to call. “Now the onus is on the patients to be their own care coordinators,” she said... This dynamic can be particularly challenging for some patients. “Lack of motivation and lack of follow-through are the most common symptoms of depression on the planet,”
So the answer here is clearly an increase in appropriate AI resources, rather than throwing the baby out with the bathwater. If we trust a decision-tree poster to do cardiovascular triage better than the instincts of cardiovascular specialists, and OCR models to interpret results better than veteran Radiologists, why not Agentic AI to do the basic work of scheduling appointments?
Is same true for AI triage?
That makes it a lot easier to deal with things like the bad AI transcriptions going around. Get burned by AI that can't hear what you're saying? It's in your file, what it heard and then what it screwed up. Providers don't like everything being on the record like that? Maybe they shouldn't be trusting that tool.
Want to know what the useless AI saw when it sent you to group therapy for acute mania? It's in your file. Providers don't like everything being on the record like that? Maybe they shouldn't be trusting that tool.
It seems really nasty to pair this with reductions in triage staff and the use of algorithmic triage. Their people are already overworked.
This was something I really struggled with getting an ADHD diagnosis as adult. I needed an incredible amount of executive function (dozens of phone calls, navigating the mire of health insurance to figure out who I was allowed to go to, who would take me, being bounced around) to be allowed medication to treat my executive dysfunction. I recall wondering how someone less functional than I was was ever expected to get help.
Evaluation of an Artificial Intelligence and Online Psychotherapy Initiative to Improve Access and Efficiency in an Ambulatory Psychiatric Setting https://pmc.ncbi.nlm.nih.gov/articles/PMC12316677/
//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
https://pubmed.ncbi.nlm.nih.gov/40673126/
//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.
UpsideDownRide•58m ago
People making such decisions should be barred from any kind of social adjacent decision-making.
Bluestein•53m ago