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The American Journal of Managed Care October 2019
Inflammatory Bowel Disease Readmissions Are Associated With Utilization and Comorbidity
Shirley Cohen-Mekelburg, MD, MS; Russell Rosenblatt, MD, MS; Beth Wallace, MD, MS; Nicole Shen, MD, MS; Brett Fortune, MD, MSc; Akbar K. Waljee, MD, MSc; Sameer Saini, MD, MS; Ellen Scherl, MD; Robert Burakoff, MD; and Mark Unruh, PhD
From the Editorial Board: Michael E. Chernew, PhD
Michael E. Chernew, PhD
The Long-term Social Value of Granulocyte Colony-Stimulating Factors
Alison Sexton Ward, PhD; Mina Kabiri, PhD; Aylin Yucel, PhD, MSc, MBA, MHSA, PharmD; Alison R. Silverstein, MPH; Emma van Eijndhoven, MS, MA; Charles Bowers, MD; Mark Bensink, PhD, MSc, MEd; and Dana Goldman, PhD
Physician Clinical Knowledge, Practice Infrastructure, and Quality of Care
Jonathan L. Vandergrift, MS; and Bradley M. Gray, PhD
Variation in US Private Health Plans’ Coverage of Orphan Drugs
James D. Chambers, PhD; Ari D. Panzer, BS; David D. Kim, PhD; Nikoletta M. Margaretos, BA; and Peter J. Neumann, ScD
Ease of Ordering High- and Low-Value Services in Various Electronic Health Records
Eric Schwartz, MD; Allison Ruff, MD; Michael Kinning, DO; and A. Mark Fendrick, MD
Real-World Outcomes Among Patients With Early Rapidly Progressive Rheumatoid Arthritis
Andrew J. Klink, PhD, MPH; Tammy G. Curtice, PharmD, MBA, MS; Kiran Gupta, PhD, MPharm; Kenneth W. Tuell, RPh, BCGP; A. Richard Szymialis, RPh; Damion Nero, PhD; and Bruce A. Feinberg, DO
Can Accountable Care Divert the Sources of Hospitalization?
Jangho Yoon, PhD; Lisa P. Oakley, PhD; Jeff Luck, PhD; and S. Marie Harvey, DrPH
Patients’ Expectations of Their Anesthesiologists
Charlie Lin, MD; Jansie Prozesky, MBChB; Donald E. Martin, MD; and Verghese T. Cherian, MD
Currently Reading
A Deep Learning Model for Pediatric Patient Risk Stratification
En-Ju D. Lin, PhD, MPH; Jennifer L. Hefner, PhD, MPH; Xianlong Zeng, MS; Soheil Moosavinasab, MS; Thomas Huber, PhD, MS; Jennifer Klima, PhD; Chang Liu, PhD; and Simon M. Lin, MD, MBA

A Deep Learning Model for Pediatric Patient Risk Stratification

En-Ju D. Lin, PhD, MPH; Jennifer L. Hefner, PhD, MPH; Xianlong Zeng, MS; Soheil Moosavinasab, MS; Thomas Huber, PhD, MS; Jennifer Klima, PhD; Chang Liu, PhD; and Simon M. Lin, MD, MBA
Artificial intelligence based on medical claims data outperforms traditional models in stratifying patient risk.

Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model—a type of machine learning that does not require human inputs—to analyze complex clinical and financial data for population risk stratification.

Study Design: A comparative predictive analysis of deep learning versus other popular risk prediction modeling strategies using medical claims data from a cohort of 112,641 pediatric accountable care organization members.

Methods: “Skip-Gram,” an unsupervised deep learning approach that uses neural networks for prediction modeling, used data from 2014 and 2015 to predict the risk of hospitalization in 2016. The area under the curve (AUC) of the deep learning model was compared with that of both the Clinical Classifications Software and the commercial DxCG Intelligence predictive risk models, each with and without demographic and utilization features. We then calculated costs for patients in the top 1% and 5% of hospitalization risk identified by each model.

Results: The deep learning model performed the best across 6 predictive models, with an AUC of 75.1%. The top 1% of members selected by the deep learning model had a combined healthcare cost $5 million higher than that of the group identified by the DxCG Intelligence model.

Conclusions: The deep learning model outperforms the traditional risk models in prospective hospitalization prediction. Thus, deep learning may improve the ability of managed care organizations to perform predictive modeling of financial risk, in addition to improving the accuracy of risk stratification for population health management activities.

Am J Manag Care. 2019;25(10):e310-e315
Takeaway Points

The present study benchmarked a new deep learning methodology for patient risk stratification using clinical and financial data for a small pediatric accountable care organization (ACO) data set. The predictive validity of the deep learning model was higher than that of other popular population risk prediction modeling strategies that, unlike deep learning, require practitioner expertise and domain knowledge. The deep learning model, although preliminary, may:
  • Enable population risk stratification without the investment of human resources that other modeling techniques require
  • Improve the ability of pediatric ACOs and others conducting population health management to perform predictive modeling of healthcare utilization
The current US healthcare climate is focused on delivery transformation and reform through value-based models that increasingly hold healthcare organizations accountable for population-based outcomes. The accountable care organization (ACO) is a popular type of value-based model that relies on patient risk stratification to identify high-risk patient populations for targeted care coordination and population health management activities.1 Risk stratification of patients using predictive regression models has long been an analytic challenge because of sparse, high-dimensional, and noisy data from insurance claims.2

Currently, the state-of-the-art risk stratification models rely on groupers of diagnosis codes developed in the early 2000s, such as Diagnostic Cost Group (DCG) and its Medicare equivalent DxCG,3 Clinical Classifications Software (CCS),2 and the Johns Hopkins Adjusted Clinical Groups (ACG) model.4 These grouper models are important tools for conducting risk stratification in the field of managed care, including in quality measurement and reporting, risk adjustment, and reimbursement. A 2013 study by Haas and colleagues explored the predictive validity of 6 popular risk stratification modeling techniques involving predictive multivariate modeling of administrative data into categories or weighted scores to predict future healthcare utilization.5 Although the study found that each of the 6 models had at least fair predictive validity, the evaluation was centered on an adult population, as were most published studies of risk adjustment.

As researchers and actuarial experts pointed out, many traditional risk adjustment methodologies were developed using a standard population and hence optimized with greater emphasis on adults, potentially limiting their predictive power in pediatric populations.6,7 In an effort to develop pediatric-focused risk adjustment, researchers have noted that due to the persistence of healthcare costs among certain groups of high-cost children, pediatric risk models benefit from including patient-reported measures of health status.7 Specifically, among Medicaid patients, the predictive validity of pediatric cost prediction models can be improved by adding survey-based measures to models of administrative data7,8; however, this methodology requires the collection of a large volume of survey data from the target patient population.

Recently, deep learning has made the implementation of predictive analytics easier due to an unsupervised learning approach with automatic feature engineering. The deep learning method is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers and connections. This approach allows the computer to learn complex concepts by building them out of simpler ones, identifying patterns and dependencies in the data. Outside the healthcare domain, deep learning and artificial intelligence have transformed our daily lives, with applications in face recognition, credit rating, and instant approval of life insurance.9 Within healthcare, deep learning is in use in a variety of health information technology contexts, including genomic analysis and biomedical image analysis.10 The application of deep learning to patient-level risk prediction is a new area of exploration. Early uses of deep learning in electronic health record data have shown promising results, including the use of recurrent neural networks to predict future diagnoses and medication orders11 and autoencoders to predict patients’ health status.12

Deep learning can handle and leverage the complex relationships in large, sparse, high-dimensional, and noisy data, with no data supervision or labeling needed. Embedding is a popular technique in which all the input concepts (ie, diagnosis and procedure codes) from the data set are mapped to vectors of real numbers in a low-dimensional continuous space where the distance between medical concepts conveys similarity between concepts. A 2016 study by Miotto et al explored the use of deep learning to predict a patient’s future medical conditions.12 The authors’ work suggested that deep learning can replace hands-on algorithm creation.

In the present study, we further develop this deep learning approach through the hypothesis that if the future diagnosis of a patient can be predicted, then deep learning can also predict clinical and financial risks. More specifically, the primary objective of our study is to develop a framework for pediatric patient risk prediction that can be used to improve patient risk stratification. If successful, the model derived from this study could enable healthcare organizations to identify high-risk patients for care management interventions and improve financial risk forecasting. Our specific aims are (1) to use a deep learning model to develop a predictive algorithm for future healthcare utilization from ACO data (including diagnosis, demographic, financial, and provider data) and (2) to assess the predictive validity of this model. To our knowledge, this is the first deep learning study to utilize complex clinical and financial data for population risk stratification. To demonstrate the feasibility of this approach, we benchmarked a deep learning method on a small pediatric data set. We present the results of this preliminary work and discuss the implications of this model for patient health, healthcare practice, and public health policy and management.

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