Path From Predictive Analytics to Improved Patient Outcomes

Harris, Alex H. S.

doi:10.1097/sla.0000000000002023

Cited by 33 publications

(12 citation statements)

References 7 publications

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“…The real test of the value of a predictive model should not only be discrimination or calibration. The effects of specific applications of predictive models (eg, informed consent, shared decision-making, risk stratification) need to be rigorously evaluated in terms of their impact on patient outcomes and satisfaction [34].…”

Section: Discussionmentioning

confidence: 99%

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis

Harris

Kuo

Bowe

et al. 2018

The Journal of Arthroplasty

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Section: Discussionmentioning

confidence: 99%

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis

Harris

Kuo

Bowe

et al. 2018

The Journal of Arthroplasty

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“…Within health care, early successes of PM and ML models have energized the community. 6 , 8 , 9 , 14–19 Despite this high level of enthusiasm about PM and ML, little is known about the barriers that health care organizations face when they attempt to leverage the emerging fields of PM and ML to optimize care. We hypothesize that AMCs, with their access to clinical and informatics pioneers and experts, might be in the best position to provide insights to the rest of the community on how to overcome these barriers.…”

Section: Introductionmentioning

confidence: 99%

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?

et al. 2020

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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.

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“…In addition, by enabling the prediction of ASA-PS when it is not available and potentially improving the measure’s accuracy when ASA-PS is available, we also see our effort as an aid for other researchers and clinicians to build better risk prediction models. Further external validation of this model with non-ACS-NSQIP data will help define the minimal number of elements needed to predict ASA-PS and further refine predictive ability [26, 38, 39].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status

Mudumbai

Pershing

Bowe

et al. 2019

BMC Health Serv Res

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BackgroundThe American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes.MethodsUsing the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator.ResultsOf the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C = 0.77 +/− 0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ConclusionsModel-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.

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Path From Predictive Analytics to Improved Patient Outcomes

Cited by 33 publications

References 7 publications

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis

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?

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status

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