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.
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.
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% to 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.
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.
► EHRs are increasingly likely to contain data and functionality that can support computational approaches to healthcare. ► Predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline. ► Clinical decision support can be conceptualized as a form of artificial intelligence embedded within clinical systems. ► Despite challenges, data-driven clinical decision support based on real-world populations offers numerous advantages. ► Such approaches may also contribute to better implementation of research into real-world clinical practice.
C.C. Bennett, T.W. Doub, R. Selove, "EHRs connect research and practice: Where predictive modeling, artificial intelligence, and clinical decision support intersect", Health Policy and Technology, Volume 1, Issue 2, 2012, Pages 105-114, ISSN 2211-8837, https://doi.org/10.1016/j.hlpt.2012.03.001.