EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect
C. Bennett, T. Doub, und R. Selove.
(2012)cite arxiv:1204.4927Comment: Keywords: Data Mining; Decision Support Systems, Clinical; Electronic Health Records; Implementation; Evidence-Based Medicine; Data Warehouse; (2012). EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect. Health Policy and Technology. arXiv admin note: substantial text overlap with arXiv:1112.1668.

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.

URL

http://arxiv.org/abs/1204.4927

Suchen auf

Diese Publikation wurde noch nicht bewertet.

Bewertungsverteilung

Durchschnittliche Benutzerbewertung0,0 von 5.0 auf Grundlage von 0 Rezensionen

Bitte melden Sie sich an um selbst Rezensionen oder Kommentare zu erstellen.

@misc{bennett2012connect,
  abstract = {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.},
  added-at = {2012-05-07T16:02:43.000+0200},
  author = {Bennett, Casey and Doub, Tom and Selove, Rebecca},
  biburl = {https://puma.uni-kassel.de/bibtex/25179fd5dce6390bf8e7609544414802e/hotho},
  description = {EHRs Connect Research and Practice: Where Predictive Modeling,   Artificial Intelligence, and Clinical Decision Support Intersect},
  doi = {10.1016/j.hlpt.2012.03.001},
  interhash = {cf79203be0f0993a5d098cdcf14cac83},
  intrahash = {5179fd5dce6390bf8e7609544414802e},
  keywords = {test},
  note = {cite arxiv:1204.4927Comment: Keywords: Data Mining; Decision Support Systems, Clinical; Electronic  Health Records; Implementation; Evidence-Based Medicine; Data Warehouse;  (2012). EHRs Connect Research and Practice: Where Predictive Modeling,  Artificial Intelligence, and Clinical Decision Support Intersect. Health  Policy and Technology. arXiv admin note: substantial text overlap with  arXiv:1112.1668},
  timestamp = {2012-05-07T16:02:44.000+0200},
  title = {EHRs Connect Research and Practice: Where Predictive Modeling,   Artificial Intelligence, and Clinical Decision Support Intersect},
  url = {http://arxiv.org/abs/1204.4927},
  year = 2012
}

%0 Generic
%1 bennett2012connect
%A Bennett, Casey
%A Doub, Tom
%A Selove, Rebecca
%D 2012
%K test
%R 10.1016/j.hlpt.2012.03.001
%T EHRs Connect Research and Practice: Where Predictive Modeling,   Artificial Intelligence, and Clinical Decision Support Intersect
%U http://arxiv.org/abs/1204.4927
%X 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.

PUMA

Tags

Nutzer

Kommentare und Rezensionen

Zitieren Sie diese Publikation