EHRs connect research and practice: Where predictive modeling, artificial intelligence, and clinical decision support intersect

Abstract

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing healthrelated 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% 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.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.

A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer

“…Although there is an overwhelming amount of clinical and genomic data being captured and collected, the data are not being analyzed in a manner to produce actionable information [1]. As of yet, this represents lost opportunities for making improvements to personalized healthcare.…”

“…The traditional medicine model has relied on best practices emerging from large population studies and dictates a one-size-fits-all approach [1]. Although synthesized evidence is essential to demonstrate the overall safety and efficacy of medical approaches, it falls short in explaining the Manuscript individual variations that exist among patients.…”

Federated Tensor Factorization for Computational Phenotyping

“…It is rewri en with respect to mode-n matricization min 1) . is is our decomposition goal in the rest of this paper.…”

“…By assuming Eq. (3), the horizontal concatenation of the local factor matrices of patient mode A (1) k forms the (global) factor matrix A (1) (Fig. 2):…”

“…Electronic health records (EHRs) become one of most important sources of information about patients, which provide insight into diagnoses [19] and prognoses [11], as well as assist in the development of cost-e ective treatment and management programs [1,12]. X. Jiang and H. Yu are co-corresponding authors.…”