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. 2016 Sep;23(5):872-8.
doi: 10.1093/jamia/ocv197. Epub 2016 Feb 17.

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation

R Scott Evans  1 , Jose Benuzillo  2 , Benjamin D Horne  3 , James F Lloyd  4 , Alejandra Bradshaw  5 , Deborah Budge  6 , Kismet D Rasmusson  6 , Colleen Roberts  2 , Jason Buckway  7 , Norma Geer  7 , Teresa Garrett  8 , Donald L Lappé  9
Affiliations

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation

R Scott Evans et al. J Am Med Inform Assoc. 2016 Sep.

Abstract

Objective: Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients.

Methods: Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients.

Results: The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility.

Conclusions: Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care.

Keywords: clinical decision support; heart failure; risk stratification.

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Conflict of interest statement

None.

Figures

Figure 1:
Figure 1:
Flow chart of patient information needed for the heart failure identification model, predictive tools, and report. EMR, electronic medical record; NLP, natural language processing.
Figure 2:
Figure 2:
Example of an Identification and Risk Stratification Daily Report for heart failure patients.
See this image and copyright information in PMC

References

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