Browsing by Subject "predictive models"

Now showing 1 - 3 of 3

Open Access
Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration.
(MDM policy & practice, 2020-01-10) O'Brien, Cara; Goldstein, Benjamin A; Shen, Yueqi; Phelan, Matthew; Lambert, Curtis; Bedoya, Armando D; Steorts, Rebecca C
Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014-2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79-0.83) versus 0.740 (95% confidence interval = 0.72-0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients' clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.
Open Access
Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?
(JAMIA open, 2020-07) Watson, Joshua; Hutyra, Carolyn A; Clancy, Shayna M; Chandiramani, Anisha; Bedoya, Armando; Ilangovan, Kumar; Nderitu, Nancy; Poon, Eric G
There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (4) technology, and (5) data. Recommendation to the informatics community to overcome these barriers included: (1) development of robust evaluation methodologies, (2) partnership with vendors, and (3) development and dissemination of best practices. For institutions developing clinical PM and ML applications, they are advised to: (1) develop appropriate governance, (2) strengthen data access, integrity, and provenance, and (3) adhere to the 5 rights of clinical decision support. This article highlights key challenges of implementing PM and ML in clinical care at AMCs and suggests best practices for development, implementation, and maintenance at these institutions.
Open Access
USFS Predictive Model Library: Fire and Timber Management
(2020-04-17) Warnell, Katie; Olander, Lydia; Minich, Taylor; Killea, Allison; Fan, Fizzy
The concept of ecosystem services has been formalized into U.S. Forest Service decision-making over the past decade in response to the 2012 Forest Planning Act and Agency regulations and directives, but many practical questions remain about how to do this most effectively. Many USFS decisions use scenarios to assess how different management approaches will meet different objectives and what the trade-offs might be. Often this is done using predictive models developed by the USFS. Some of the models commonly used by the USFS do not yet include many ecosystem services outcomes, but there are other predictive models designed for ecosystem services that might help fill such gaps. This project explores how these non-USFS models could be combined with existing USFS models to provide a fuller analysis of ecosystem services outcomes from different management scenarios. We used an ecosystem service conceptual model as a framework to examine the utility of currently available predictive models for quantifying the effects of fire and timber management on ecosystem services and socioeconomic outcomes.