Previously submitted to: JMIR Medical Informatics (no longer under consideration since Apr 06, 2026)
Date Submitted: Mar 25, 2026
Open Peer Review Period: Apr 6, 2026 - Apr 6, 2026
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A Review and Proposed AI-Driven Framework for MDS Assessment Validation in Skilled Nursing Facilities
ABSTRACT
Background:
Minimum Data Set (MDS) assessments determine both clinical care planning and Medicare reimbursement under the Patient-Driven Payment Model (PDPM) in skilled nursing facilities (SNFs). Documentation errors arising from assessment complexity, workforce instability, and compressed completion timelines are widespread and financially consequential. Traditional retrospective, sample-based audits fail to intercept most errors before submission.
Objective:
This paper reviews the literature on AI-assisted clinical documentation validation and proposes a structured hybrid framework specifically designed for MDS validation in SNF environments.
Methods:
A narrative review of peer-reviewed literature was conducted across PubMed, IEEE Xplore, and Scopus using the terms: artificial intelligence, machine learning, clinical documentation validation, electronic health records, skilled nursing facilities, and MDS assessment. A prototype simulation using 500 synthetic assessments with injected known errors was used to benchmark the proposed framework.
Results:
Existing AI approaches show complementary strengths: rule-based systems offer regulatory traceability while ML classifiers detect subtle non-deterministic patterns. The proposed five-layer framework integrates both paradigms. Prototype simulation results project F1-scores of 0.77 to 0.92 across key MDS sections, with highest performance in Activities of Daily Living scoring (0.92), special treatments (0.91), and ICD-10 diagnosis coding (0.88).
Conclusions:
The proposed framework addresses identified gaps in current MDS validation practice by providing real-time, 100%-coverage validation with automated regulatory versioning. Prospective multi-site evaluation is the essential next step. This work provides a structured blueprint for SNF technology implementers and clinical informatics researchers seeking to reduce documentation burden while improving reimbursement accuracy.
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