ArticleApplied clinical informatics2026
The Effects of Artificial Intelligence in Quality Assessment of Outpatient Mental Health Services on Human Auditors.
Article in Applied clinical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abstract:
backgroundThe use of large language model (LLM)-based artificial intelligence (AI) to assess the quality of outpatient mental health documentation represents an understudied application. Critical questions remain about the effects of exposing human auditors to AI-generated recommendations on auditor efficiency, agreement, and patterns of decision-making. Abstract:
objectivesThe objective of this study is to examine how exposure to LLM-generated quality assessments influences auditor performance during reviews of documents of outpatient mental health intake sessions. Abstract:
methodsWe conducted a pre-post observational evaluation of 10 trained human auditors who reviewed outpatient mental health intake notes and assigned quality ratings using a standardized 13-item chart audit rubric before and after implementation of LLM support. The AI system used Anthropic Claude Sonnet 4 with a fixed chain-prompting strategy. We assessed item-level pass frequency for each auditor across the pre-AI and post-AI periods, AI-human agreement (the alignment between auditor pass/fail determinations and AI-generated recommendations), and auditing time. The pre-AI period included 9,711 notes (126,243 item-level observations) and the post-AI period included 2,677 notes (34,801 item-level observations). Abstract:
resultsFollowing implementation of AI-supported auditing, the mean item-level pass rate increased by approximately 2%. The distribution of auditor-level changes was broad (range: -35 to +35%, standard deviation [SD]: 10%) indicating substantial heterogeneity in individual auditor responses to AI support. High auditor agreement with AI recommendations was associated with larger pass-rate changes for selected rubric items, with some auditors increasing and others decreasing pass rates relative to the pre-AI period. The average time for an auditor to review notes decreased from 8.77 min (SD: 9.64) per review during the pre-AI period to 8.06 min (SD: 10.28) during the post-AI period ( Abstract:
conclusionLLM-based AI tools may improve the efficiency of clinical quality assurance workflows, but exposure to AI recommendations may meaningfully change human auditor behavior.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.