OpenRounds Editorial

Healthcare AI Daily Briefing

Monday, October 5, 2026

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Research & Evidence

Coagulation parameters boost SILC difficulty prediction in neural network model.

Journal article

In a retrospective single-center study of 520 patients (364 training, 156 validation), an interpretable neural network using six preoperative predictors—including coagulation profiles—achieved an internal‑validation AUC of 0.867 for difficult single-incision laparoscopic cholecystectomy. Ablation showed removing coagulation parameters lowered AUC by 0.043, indicating complementary predictive value. [1]

Operations & Workflow

Trust in AI‑CDSS higher for stable patients than deteriorating or critically ill.

Journal article

A SEIPS 2.0‑informed mixed‑methods study of 57 ICU and ED clinicians from Emory Healthcare found that trust in AI‑CDSS was reported for 75 % of stable‑patient scenarios, versus 47 % for deteriorating and 44 % for critically ill patients. [2]

Operations & Workflow

AI agent beats human with 15‑minute hold, flipping 90% preference.

Podcast from The Signal Room

In the interview, 90% of respondents said they would rather talk to a human agent than an AI agent with no hold. When the choice was framed as a human agent with a 15‑minute wait versus an AI agent that resolves the issue immediately, 90% chose the AI agent, reversing the baseline preference. [3]

Operations & Workflow

Interview: Administrative documentation drives clinician bernard in behavioural health.

Podcast from AI in Healthcare and Life Sciences Podcast

The interview notes that administrative documentation ranks among the top drivers of clinician bernard in behavioural health, taking time away from direct patient care. [4]

Sources

A Predictive Model Integrating Coagulation Profiles for Preoperative Risk Stratification of Difficult Single-Incision Laparoscopic Cholecystectomy: A Retrospective Cohort Study Based on Explainable Machine Learning. · Surgical laparoscopy, endoscopy & percutaneous techniques Original source
Clinician Trust and Human Factors in AI-Enabled Clinical Decision Support in Acute Care: Mixed Methods Study. · JMIR human factors Original source
The signal room: AI Agents in Healthcare: Why the Best AI Knows When to Stop | Kaled Alhanafi · The Signal Room Original source
AI and Customer Experience in Behavioral Health - with Dwayne Stevens of WestCare · AI in Healthcare and Life Sciences Podcast Original source

Coagulation parameters boost SILC difficulty prediction in neural network model.

Trust in AI‑CDSS higher for stable patients than deteriorating or critically ill.

AI agent beats human with 15‑minute hold, flipping 90% preference.

Interview: Administrative documentation drives clinician bernard in behavioural health.

Sources

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