Аннотация
Objectives: Electronic health records (EHRs) are only a first step in
capturing and utilizing health-related data - the challenge is turning that
data into useful information. Furthermore, EHRs are increasingly likely to
include data relating to patient outcomes, functionality such as clinical
decision support, and genetic information as well, and, as such, can be seen as
repositories of increasingly valuable information about patients' health
conditions and responses to treatment over time. Methods: We describe a case
study of 423 patients treated by Centerstone within Tennessee and Indiana in
which we utilized electronic health record data to generate predictive
algorithms of individual patient treatment response. Multiple models were
constructed using predictor variables derived from clinical, financial and
geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved
and in 99 there was no change in clinical condition. Based on modeling of
various clinical indicators at baseline, the highest accuracy in predicting
individual patient response ranged from 70-72% within the models tested. In
terms of individual predictors, the Centerstone Assessment of Recovery Level -
Adult (CARLA) baseline score was most significant in predicting outcome over
time (odds ratio 4.1 + 2.27). Other variables with consistently significant
impact on outcome included payer, diagnostic category, location and provision
of case management services. Conclusions: This approach represents a promising
avenue toward reducing the current gap between research and practice across
healthcare, developing data-driven clinical decision support based on
real-world populations, and serving as a component of embedded clinical
artificial intelligences that "learn" over time.
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