{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T22:03:32Z","timestamp":1770501812712,"version":"3.49.0"},"reference-count":72,"publisher":"Georg Thieme Verlag KG","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Objective\u2003To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions.<\/jats:p><jats:p>\n          Methods\u2003Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams.<\/jats:p><jats:p>\n          Results\u2003Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p\u2009&lt;\u20090.01) after risk adjustment.<\/jats:p><jats:p>\n          Conclusion\u2003Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.<\/jats:p>","DOI":"10.1055\/s-0041-1726422","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T23:00:32Z","timestamp":1621465232000},"page":"372-382","source":"Crossref","is-referenced-by-count":11,"title":["Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore"],"prefix":"10.1055","volume":"12","author":[{"given":"Christine Xia","family":"Wu","sequence":"additional","affiliation":[{"name":"Quality, Innovation and Improvement, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernest","family":"Suresh","sequence":"additional","affiliation":[{"name":"Department of Medicine, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francis Wei Loong","family":"Phng","sequence":"additional","affiliation":[{"name":"Quality, Innovation and Improvement, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai Pik","family":"Tai","sequence":"additional","affiliation":[{"name":"Quality, Innovation and Improvement, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janthorn","family":"Pakdeethai","sequence":"additional","affiliation":[{"name":"Department of Medicine, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jared Louis Andre","family":"D'Souza","sequence":"additional","affiliation":[{"name":"Department of Medicine, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woan Shin","family":"Tan","sequence":"additional","affiliation":[{"name":"Health Services and Outcomes Research, National Healthcare Group, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phillip","family":"Phan","sequence":"additional","affiliation":[{"name":"Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States"},{"name":"Department of Medicine, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelvin Sin Min","family":"Lew","sequence":"additional","affiliation":[{"name":"Quality, Innovation and Improvement, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gamaliel Yu-Heng","family":"Tan","sequence":"additional","affiliation":[{"name":"Group Medical Informatics Office, National University Health System, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerald Seng Wee","family":"Chua","sequence":"additional","affiliation":[{"name":"Department of Medicine, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi Hong","family":"Hwang","sequence":"additional","affiliation":[{"name":"Quality, Innovation and Improvement, Ng Teng Fong General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"issue":"01","key":"ref1","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1146\/annurev-med-022613-090415","article-title":"Reducing hospital readmission rates: current strategies and future directions","volume":"65","author":"S Kripalani","year":"2014","journal-title":"Annu Rev Med"},{"key":"ref2","volume-title":"HCUP Statistical Brief #172. April 2014","author":"A L Hines"},{"issue":"01","key":"ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00981389.2014.966881","article-title":"Why do patients keep coming back? Results of a readmitted patient survey","volume":"54","author":"H C Felix","year":"2015","journal-title":"Soc Work Health Care"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1186\/1472-6963-12-373","article-title":"Frequent hospital admission of older people with chronic disease: a cross-sectional survey with telephone follow-up and data linkage","volume":"12","author":"J MI Longman","year":"2012","journal-title":"BMC Health Serv Res"},{"issue":"03","key":"ref5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.7812\/TPP\/12-141","article-title":"The readmission reduction program of Kaiser Permanente Southern California-knowledge transfer and performance improvement","volume":"17","author":"P Tuso","year":"2013","journal-title":"Perm J"},{"issue":"07","key":"ref6","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1377\/hlthaff.2014.0041","article-title":"Big data in health care: using analytics to identify and manage high-risk and high-cost patients","volume":"33","author":"D W Bates","year":"2014","journal-title":"Health Aff (Millwood)"},{"issue":"16","key":"ref7","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1001\/jama.2011.1561","article-title":"Hospital readmissions and the Affordable Care Act: paying for coordinated quality care","volume":"306","author":"R P Kocher","year":"2011","journal-title":"JAMA"},{"issue":"07","key":"ref8","doi-asserted-by":"crossref","first-page":"E391","DOI":"10.1503\/cmaj.101860","article-title":"Proportion of hospital readmissions deemed avoidable: a systematic review","volume":"183","author":"C van Walraven","year":"2011","journal-title":"CMAJ"},{"issue":"08","key":"ref9","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1001\/jamainternmed.2013.3023","article-title":"Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model","volume":"173","author":"J Donz\u00e9","year":"2013","journal-title":"JAMA Intern Med"},{"issue":"10","key":"ref10","first-page":"597","article-title":"Classifying general medicine readmissions. Are they preventable? Veterans Affairs Cooperative Studies in Health Services Group on Primary Care and Hospital Readmissions","volume":"11","author":"E Z Oddone","year":"1996","journal-title":"J Gen Intern Med"},{"key":"ref11","first-page":"35","article-title":"Failed discharges: setting standards for improvement","volume":"18","author":"E G McInnes","year":"1988","journal-title":"Geriatr Med"},{"issue":"05","key":"ref12","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1097\/MD.0b013e3181886f93","article-title":"Early readmissions to the department of medicine as a screening tool for monitoring quality of care problems","volume":"87","author":"U Balla","year":"2008","journal-title":"Medicine (Baltimore)"},{"issue":"02","key":"ref13","first-page":"e104","article-title":"Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm","volume":"5","author":"A Gruneir","year":"2011","journal-title":"Open Med"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/1748-5908-5-88","article-title":"Determinants of preventable readmissions in the United States: a systematic review","volume":"5","author":"J R Vest","year":"2010","journal-title":"Implement Sci"},{"issue":"05","key":"ref15","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1089\/pop.2012.0084","article-title":"The influence of a postdischarge intervention on reducing hospital readmissions in a Medicare population","volume":"16","author":"M E Costantino","year":"2013","journal-title":"Popul Health Manag"},{"issue":"01","key":"ref16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.11622\/smedj.2016110","article-title":"Frequent hospital admissions in Singapore: clinical risk factors and impact of socioeconomic status","volume":"59","author":"L L Low","year":"2018","journal-title":"Singapore Med J"},{"issue":"07","key":"ref17","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1001\/jamainternmed.2014.1608","article-title":"Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials","volume":"174","author":"A L Leppin","year":"2014","journal-title":"JAMA Intern Med"},{"issue":"08","key":"ref18","doi-asserted-by":"crossref","first-page":"520","DOI":"10.7326\/0003-4819-155-8-201110180-00008","article-title":"Interventions to reduce 30-day rehospitalization: a systematic review","volume":"155","author":"L O Hansen","year":"2011","journal-title":"Ann Intern Med"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s40537-019-0217-0","article-title":"Big data in healthcare: management, analysis and future prospects","volume":"6","author":"S Dash","year":"2019","journal-title":"J Big Data"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jbi.2015.05.016","article-title":"A comparison of models for predicting early hospital readmissions","volume":"56","author":"J Futoma","year":"2015","journal-title":"J Biomed Inform"},{"issue":"02","key":"ref22","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1001\/jamacardio.2016.3956","article-title":"Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches","volume":"2","author":"J D Frizzell","year":"2017","journal-title":"JAMA Cardiol"},{"issue":"06","key":"ref23","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1161\/CIRCOUTCOMES.116.003039","article-title":"Analysis of machine learning techniques for heart failure readmissions","volume":"9","author":"B J Mortazavi","year":"2016","journal-title":"Circ Cardiovasc Qual Outcomes"},{"issue":"04","key":"ref24","doi-asserted-by":"crossref","first-page":"e001667","DOI":"10.1136\/bmjopen-2012-001667","article-title":"Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30)","volume":"2","author":"J Billings","year":"2012","journal-title":"BMJ Open"},{"issue":"06","key":"ref25","doi-asserted-by":"crossref","first-page":"e011060","DOI":"10.1136\/bmjopen-2016-011060","article-title":"Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review","volume":"6","author":"H Zhou","year":"2016","journal-title":"BMJ Open"},{"issue":"11","key":"ref26","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1097\/MLR.0b013e3181ef60d9","article-title":"An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data","volume":"48","author":"R Amarasingham","year":"2010","journal-title":"Med Care"},{"key":"ref27","first-page":"103","article-title":"Leveraging derived data elements in data analytic models for understanding and predicting hospital readmissions","volume":"2012","author":"S Cholleti","year":"2012","journal-title":"AMIA Annu Symp Proc"},{"issue":"04","key":"ref29","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1055\/s-0040-1715827","article-title":"Implementation of artificial intelligence-based clinical decision support to reduce hospital readmissions at a regional hospital","volume":"11","author":"S Romero-Brufau","year":"2020","journal-title":"Appl Clin Inform"},{"issue":"15","key":"ref30","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1001\/jama.2011.1515","article-title":"Risk prediction models for hospital readmission: a systematic review","volume":"306","author":"D Kansagara","year":"2011","journal-title":"JAMA"},{"issue":"01","key":"ref32","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1177\/1460458213501095","article-title":"Development of an automated model to predict the risk of elderly emergency medical admissions within a month following an index hospital visit: a Hong Kong experience","volume":"21","author":"E Tsui","year":"2015","journal-title":"Health Informatics J"},{"issue":"06","key":"ref33","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1503\/cmaj.091117","article-title":"Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community","volume":"182","author":"C van Walraven","year":"2010","journal-title":"CMAJ"},{"issue":"02","key":"ref35","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1055\/s-0039-1688553","article-title":"Development and prospective validation of a machine learning-based risk of readmission model in a large military hospital","volume":"10","author":"C Eckert","year":"2019","journal-title":"Appl Clin Inform"},{"issue":"06","key":"ref36","doi-asserted-by":"crossref","first-page":"e0235064","DOI":"10.1371\/journal.pone.0235064","article-title":"Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death","volume":"15","author":"Y Zhang","year":"2020","journal-title":"PLoS One"},{"issue":"02","key":"ref37","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.ahj.2006.10.037","article-title":"Socioeconomic disparities in outcomes after acute myocardial infarction","volume":"153","author":"S M Bernheim","year":"2007","journal-title":"Am Heart J"},{"issue":"02","key":"ref38","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s11606-012-2235-x","article-title":"Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review","volume":"28","author":"L Calvillo-King","year":"2013","journal-title":"J Gen Intern Med"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"m958","DOI":"10.1136\/bmj.m958","article-title":"Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review","volume":"369","author":"E Mahmoudi","year":"2020","journal-title":"BMJ"},{"issue":"11","key":"ref40","doi-asserted-by":"crossref","first-page":"833","DOI":"10.7326\/M14-2308","article-title":"Considering the role of socioeconomic status in hospital outcomes measures","volume":"161","author":"H M Krumholz","year":"2014","journal-title":"Ann Intern Med"},{"issue":"05","key":"ref41","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1377\/hlthaff.2013.0816","article-title":"Socioeconomic status and readmissions: evidence from an urban teaching hospital","volume":"33","author":"J Hu","year":"2014","journal-title":"Health Aff (Millwood)"},{"issue":"01","key":"ref42","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s12939-014-0115-1","article-title":"Is socioeconomic status associated with utilization of health care services in a single-payer universal health care system?","volume":"13","author":"D Filc","year":"2014","journal-title":"Int J Equity Health"},{"issue":"05","key":"ref43","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1377\/hlthaff.2013.1148","article-title":"Adding socioeconomic data to hospital readmissions calculations may produce more useful results","volume":"33","author":"E M Nagasako","year":"2014","journal-title":"Health Aff (Millwood)"},{"issue":"04","key":"ref44","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1055\/s-0040-1715650","article-title":"Toward understanding the value of missing social determinants of health data in care transition planning","volume":"11","author":"S S Feldman","year":"2020","journal-title":"Appl Clin Inform"},{"issue":"01","key":"ref45","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1186\/1472-6963-13-366","article-title":"Applicability of a previously validated readmission predictive index in medical patients in Singapore: a retrospective study","volume":"13","author":"S Y Tan","year":"2013","journal-title":"BMC Health Serv Res"},{"key":"ref48","volume-title":"Technical Manual for Indicators in the Public Hospital Performance Report","author":"Ministry of Health","year":"2018"},{"issue":"06","key":"ref50","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/0895-4356(92)90133-8","article-title":"Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases","volume":"45","author":"R A Deyo","year":"1992","journal-title":"J Clin Epidemiol"},{"issue":"11","key":"ref51","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","article-title":"Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data","volume":"43","author":"H Quan","year":"2005","journal-title":"Med Care"},{"key":"ref52","first-page":"109","article-title":"Housing as a social determinant of health in singapore and its association with readmission risk and increased utilization of hospital services","volume":"4","author":"L L Low","year":"2016","journal-title":"Front Public Health"},{"issue":"01","key":"ref57","doi-asserted-by":"crossref","first-page":"12","DOI":"10.11613\/BM.2014.003","article-title":"Understanding logistic regression analysis","volume":"24","author":"S Sperandei","year":"2014","journal-title":"Biochem Med (Zagreb)"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"A Natekin","year":"2013","journal-title":"Front Neurorobot"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/0167-9473(91)90103-9","article-title":"Generalized regression trees","volume":"12","author":"A Ciampi","year":"1991","journal-title":"Comput Stat Data Anal"},{"issue":"09","key":"ref60","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1038\/nbt0908-1011","article-title":"What are decision trees?","volume":"26","author":"C Kingsford","year":"2008","journal-title":"Nat Biotechnol"},{"issue":"02","key":"ref61","first-page":"130","article-title":"Decision tree methods: applications for classification and prediction","volume":"27","author":"Y Y Song","year":"2015","journal-title":"Shanghai Jingshen Yixue"},{"issue":"01","key":"ref62","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Mach Learn"},{"issue":"02","key":"ref63","doi-asserted-by":"crossref","first-page":"e033109","DOI":"10.1136\/bmjopen-2019-033109","article-title":"Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study","volume":"10","author":"A M Antoniadi","year":"2020","journal-title":"BMJ Open"},{"issue":"04","key":"ref64","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s13312-011-0055-4","article-title":"Receiver operating characteristic (ROC) curve for medical researchers","volume":"48","author":"R Kumar","year":"2011","journal-title":"Indian Pediatr"},{"key":"ref65","doi-asserted-by":"crossref","first-page":"2976","DOI":"10.1016\/j.csda.2010.03.004","article-title":"Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods","volume":"54","author":"S Borra","year":"2010","journal-title":"Comput Stat Data Anal"},{"issue":"12","key":"ref66","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1002\/jhm.2106","article-title":"The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30-day readmission","volume":"8","author":"C A Baillie","year":"2013","journal-title":"J Hosp Med"},{"issue":"08","key":"ref67","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1001\/jama.297.8.831","article-title":"Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care","volume":"297","author":"S Kripalani","year":"2007","journal-title":"JAMA"},{"issue":"05","key":"ref68","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s11606-010-1256-6","article-title":"Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: an analysis of medication reconciliation errors and risk factors at hospital admission","volume":"25","author":"K M Gleason","year":"2010","journal-title":"J Gen Intern Med"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"26S","DOI":"10.1016\/S0002-9343(01)00966-4","article-title":"The impact of follow-up telephone calls to patients after hospitalization","volume":"111","author":"V Dudas","year":"2001","journal-title":"Am J Med"},{"issue":"17","key":"ref70","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1001\/jama.2010.533","article-title":"Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure","volume":"303","author":"A F Hernandez","year":"2010","journal-title":"JAMA"},{"issue":"08","key":"ref72","first-page":"663","article-title":"Advance care planning: let's start sooner","volume":"61","author":"M Howard","year":"2015","journal-title":"Can Fam Physician"},{"issue":"12","key":"ref73","doi-asserted-by":"crossref","first-page":"837","DOI":"10.7326\/0003-4819-157-12-201212180-00003","article-title":"Associations between reduced hospital length of stay and 30-day readmission rate and mortality: 14-year experience in 129 Veterans Affairs hospitals","volume":"157","author":"P J Kaboli","year":"2012","journal-title":"Ann Intern Med"},{"issue":"01","key":"ref74","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1186\/s12884-018-1971-2","article-title":"Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study","volume":"18","author":"S Kuhle","year":"2018","journal-title":"BMC Pregnancy Childbirth"},{"issue":"03","key":"ref75","doi-asserted-by":"crossref","first-page":"e34312","DOI":"10.1371\/journal.pone.0034312","article-title":"A mathematical model for interpretable clinical decision support with applications in gynecology","volume":"7","author":"V M Van Belle","year":"2012","journal-title":"PLoS One"},{"issue":"03","key":"ref77","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1002\/bimj.201700067","article-title":"Variable selection: a review and recommendations for the practicing statistician","volume":"60","author":"G Heinze","year":"2018","journal-title":"Biom J"},{"issue":"05","key":"ref78","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.jbi.2013.06.011","article-title":"Development and validation of a continuous measure of patient condition using the Electronic Medical Record","volume":"46","author":"M J Rothman","year":"2013","journal-title":"J Biomed Inform"},{"issue":"03","key":"ref79","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1093\/jamia\/ocv110","article-title":"Real-time prediction of mortality, readmission, and length of stay using electronic health record data","volume":"23","author":"X Cai","year":"2016","journal-title":"J Am Med Inform Assoc"},{"issue":"03","key":"ref80","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11606-009-1196-1","article-title":"Hospital readmission in general medicine patients: a prediction model","volume":"25","author":"O Hasan","year":"2010","journal-title":"J Gen Intern Med"},{"issue":"02","key":"ref81","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/1355819614565498","article-title":"Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada","volume":"20","author":"Y Cui","year":"2015","journal-title":"J Health Serv Res Policy"},{"issue":"09","key":"ref82","first-page":"e348","article-title":"Predictive model for emergency hospital admission and 6-month readmission","volume":"17","author":"S L\u00f3pez-Aguil\u00e0","year":"2011","journal-title":"Am J Manag Care"},{"issue":"11","key":"ref83","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1097\/MLR.0000000000000435","article-title":"Nonelective rehospitalizations and postdischarge mortality: predictive models suitable for use in real time","volume":"53","author":"G J Escobar","year":"2015","journal-title":"Med Care"},{"key":"ref84","volume-title":"Event prediction in healthcare analytics: beyond prediction accuracy","author":"L Fu","year":"2016"},{"issue":"10","key":"ref85","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1038\/gim.2013.72","article-title":"The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future","volume":"15","author":"O Gottesman","year":"2013","journal-title":"Genet Med"}],"container-title":["Applied Clinical 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