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Related Experiment Videos

Generative AI in Admission Notes and Diagnostic Completeness: A Pilot Study.

Alfredo Camargo Rodrigues¹, Jason MIsurac², Lindsey A Knake²

¹University of Iowa Health Care, Health Care Information Systems, Department of Anesthesia, IA, United States, Iowa City.

Applied Clinical Informatics

|May 18, 2026

Summary

Related Concept Videos

Documentation of Nursing Diagnosis

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...

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This summary is machine-generated.

Generative AI admission notes can improve inpatient documentation completeness by identifying new diagnoses. Human oversight is crucial for AI-assisted documentation workflows.

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Inpatient documentation, risk adjustment, and reimbursement rely on admission history and physical (H&P) notes.
High-acuity settings face challenges with under-documented comorbidities due to time constraints and fragmented data.
Generative AI offers potential to enhance documentation completeness from electronic health records, but real-world performance needs characterization.

Purpose of the Study:

To evaluate AI-generated admission notes for identifying documentation-relevant diagnoses missed in provider-authored notes.
To assess the concordance of principal diagnoses between AI-generated and provider-authored notes.
To quantify the number and clinical documentation improvement (CDI) support for net-new secondary diagnoses identified by AI.

Main Methods:

Related Experiment Videos

A single-center retrospective pilot study reviewed 22 matched pairs of AI-generated and provider-authored H&P notes.
Principal diagnosis concordance was assessed.
AI-identified secondary diagnoses absent from provider notes were adjudicated by a CDI team.

Main Results:

AI-generated notes showed 91% concordance with provider-authored principal diagnoses.
97% of 104 AI-identified secondary diagnoses were supported for documentation by the CDI team.
A median of 4.5 net-new diagnoses per admission were identified by AI, not present in provider notes.

Conclusions:

AI-generated admission notes demonstrate potential in improving inpatient documentation completeness by surfacing additional diagnoses.
High principal diagnosis concordance and identification of CDI-supported net-new diagnoses were observed.
Human oversight is essential in AI-assisted documentation, and larger multi-center studies are needed to confirm generalizability and safety.