ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023
Assessing the Predictive and Analytics Capability of Electronic Clinical Data for High-Cost Patients.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. 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
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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
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Authors and funding
2 authors.
Funding
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Abstract
Hotspotting may prevent high healthcare costs surrounding a minority of patients when void of issues such as availability, completeness, and accessibility of information in electronic health records (EHRs). We performed a descriptive study using Barnes-Jewish Hospital patients to assess the availability and accessibility of information that can predict negative outcomes. Manual electronic chart review produced descriptive statistics for a sample of 100 High Resource and 100 Control patient records. The majority of cases were not predictive. Predictive information and their sources were inconsistent. Certain types of patients were more predictive than others, albeit a small percentage of the total. Among the largest and most predictive groups was the most difficult to classify, "Other." These findings were expected and consistent with previous studies but contrast with approaches for attempting prediction such as hotspotting. Further studies may provide solutions to the problems and limitations identified in this study.
Identifiers
37350924PMC10283137What OpenQuestion holds
Registered trials
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