--- title: "Predict all-cause risk of 30-day hospital readmission. | Sutter Health" description: "We demonstrate that neural networks are good candidates to capture complexity and interdependency of various EHR data fields to predict 30-day readmission risk." url: "https://www.sutterhealth.org/research/publications/predict-all-cause-risk-of-30-day-hospital-readmission-1055312686" --- 1. [Home](https://www.sutterhealth.org/) / 2. [Research](https://www.sutterhealth.org/research) / 3. [Research publications](https://www.sutterhealth.org/research/publications) / 4. Predict all-cause risk of 30-day hospital readmission. # Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. Jamei M, Nisnevich A, Wetchler E, Sudat S, & Liu E. _PLoS One. 12(7):e0181173. doi: 10.1371/journal.pone.0181173. eCollection 2017._ 2017-07-14 ## Abstract Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of electronic health records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of social determinants of health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions. [PubMed abstract](https://www.ncbi.nlm.nih.gov/pubmed/28708848) ![Sylvia Sudat, Ph.D.](https://dyn.mktgcdn.com/p/2AHRaDJwevdbwzC2ecrwY3nYIxbiqxd9NbpxBlI8Kpk/fit=crop,gravity=auto,dpr=2,width=188,height=250,quality=100) Sylvia Sudat, Ph.D. Principal investigator [View profile](https://www.sutterhealth.org/research/investigators/sylvia-sudat-phd-1049681027) ## Research topics - [Biostatistics](https://www.sutterhealth.org/research-search-results?query=%22%5C%22Biostatistics%5C%22%22) - [Health Services](https://www.sutterhealth.org/research-search-results?query=%22%5C%22Health+Services%5C%22%22) - [Medical Informatics](https://www.sutterhealth.org/research-search-results?query=%22%5C%22Medical+Informatics%5C%22%22) ## Related publications [View all publications](https://www.sutterhealth.org/research-search-results?verticalKey=research_publication) - Publication ### [Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.](https://www.sutterhealth.org/research/publications/predict-need-for-palliative-care-in-primary-care-with-ehrs-1055312675) Jung K, Sudat SEK, Kwon N, Stewart WF, Shan NH. J Biomed Inform. 92:103115. 2019 Apr 01 - Publication ### [Patient-centric scheduling with the implementation of health information technology to improve the patient experience and access to care: retrospective case-control analysis.](https://www.sutterhealth.org/research/publications/pt-centric-scheduling-with-hit-to-improve-pt-experience-1055312790) Chung S, Martinez MC, Frosch DL, Jones VG, Chan AS. J Med Internet Res. 22(6):e16451. doi: 10.2196/16451. 2020 Jun 10 - Publication ### [Pragmatic study of clinician use of a personalized patient decision aid integrated into the electronic health record: an 8-year experience.](https://www.sutterhealth.org/research/publications/clinician-use-of-patient-decision-aid-integrated-into-ehr-1055312641) Coylewright M, Keevil JG, Xu K, Dodge SE, Frosch D, Field ME. Telemed J E Health. doi: 10.1089/tmj.2019.0112. \[Epub ahead of print\] 2019 Aug 05 - Publication ### [Leveraging electronic health records to develop measurements for processes of care.](https://www.sutterhealth.org/research/publications/leverage-ehrs-to-measure-care-processes-1055312664) Tai-Seale M, Wilson CJ, Panattoni L, Kohli N, Stone A, Hung DY, Chung S. Health Serv Res. 49(2):628-44. doi: 10.1111/1475-6773.12126. Epub 2013 Nov 18. 2014 Apr 01 - Publication ### [NIH-Academia-Industry Workshop on Healthcare Intelligence Innovation.](https://www.sutterhealth.org/research/publications/nih-workshop-healthcare-intelligence-innovation-1055312779) Tai-Seale M. Carnegie Mellon University Silicon Valley campus website. 2010 Oct 04 [View all publications](https://www.sutterhealth.org/research-search-results?verticalKey=research_publication) ```json {"@context":"https://schema.org","@type":"MedicalOrganization","name":"Sutter Health","@id":"https://www.sutterhealth.org/#medicalorganization","url":"https://www.sutterhealth.org","logo":"https://www.sutterhealth.org/static/images/sutter-health-logo.svg","address":{"@type":"PostalAddress","streetAddress":"2200 River Plaza Drive","addressLocality":"Sacramento","addressRegion":"CA","postalCode":"95833","addressCountry":"US"},"areaServed":{"@type":"AdministrativeArea","name":"California"},"sameAs":["https://www.facebook.com/SutterHealth","https://www.instagram.com/sutterhealth/","https://x.com/SutterHealth","https://www.youtube.com/sutterhealth","https://www.linkedin.com/company/sutter-health","https://en.wikipedia.org/wiki/Sutter_Health"]} ``` ```json {"@context":"https://schema.org","@type":"BreadcrumbList","@id":"https://www.sutterhealth.org/research/publications/predict-all-cause-risk-of-30-day-hospital-readmission-1055312686#breadcrumblist","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://www.sutterhealth.org/"},{"@type":"ListItem","position":2,"name":"Research","item":"https://www.sutterhealth.org/research"},{"@type":"ListItem","position":3,"name":"Research publications","item":"https://www.sutterhealth.org/research/publications"},{"@type":"ListItem","position":4,"name":"Predict all-cause risk of 30-day hospital readmission."}]} ``` ```json {"@context":"https://schema.org","@type":"MedicalScholarlyArticle","@id":"https://www.sutterhealth.org/research/publications/predict-all-cause-risk-of-30-day-hospital-readmission-1055312686#medicalscholarlyarticle","name":"Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.","headline":"Predict all-cause risk of 30-day hospital readmission.","abstract":"Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. 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In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. 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