{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:33:08Z","timestamp":1783524788924,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol\/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented.<\/jats:p>","DOI":"10.3390\/ijerph20064693","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T03:11:39Z","timestamp":1678158699000},"page":"4693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Big Data Analytics to Reduce Preventable Hospitalizations\u2014Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6898-315X","authenticated-orcid":false,"given":"Timo","family":"Schulte","sequence":"first","affiliation":[{"name":"Faculty of Management, Economics and Society, Witten\/Herdecke University, 58455 Witten, Germany"},{"name":"Faculty of Health, Witten\/Herdecke University, 58455 Witten, Germany"},{"name":"Department of Business Analytics, Clinics of Maerkischer Kreis, 58515 Luedenscheid, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tillmann","family":"Wurz","sequence":"additional","affiliation":[{"name":"Department of Project and Change Management, University Clinic Hamburg-Eppendorf, 20251 Hamburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1099-2950","authenticated-orcid":false,"given":"Oliver","family":"Groene","sequence":"additional","affiliation":[{"name":"Faculty of Management, Economics and Society, Witten\/Herdecke University, 58455 Witten, Germany"},{"name":"Department of Research & Innovation, OptiMedis AG, 20095 Hamburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1119-9786","authenticated-orcid":false,"given":"Sabine","family":"Bohnet-Joschko","sequence":"additional","affiliation":[{"name":"Faculty of Management, Economics and Society, Witten\/Herdecke University, 58455 Witten, Germany"},{"name":"Faculty of Health, Witten\/Herdecke University, 58455 Witten, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","unstructured":"(2019, May 01). The Commonwealth Fund 2013 Commonwealth Fund International Health Policy Survey. Available online: https:\/\/www.commonwealthfund.org\/publications\/surveys\/2013\/nov\/2013-commonwealth-fund-international-health-policy-survey."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e058","DOI":"10.5334\/ijic.1514","article-title":"Towards People-Centred Health Services Delivery: A Framework for Action for the World Health Organization (WHO) European Region","volume":"13","author":"Stein","year":"2013","journal-title":"Int. J. Integr. Care"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1001\/jama.2013.393","article-title":"The Inevitable Application of Big Data to Health Care","volume":"309","author":"Murdoch","year":"2013","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.5334\/ijic.5543","article-title":"How Can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review","volume":"22","author":"Schulte","year":"2022","journal-title":"Int. J. Integr. Care"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/2047-2501-2-3","article-title":"Big Data Analytics in Healthcare: Promise and Potential","volume":"2","author":"Raghupathi","year":"2014","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1377\/hlthaff.2014.0147","article-title":"Creating Value In Health Care Through Big Data: Opportunities And Policy Implications","volume":"33","author":"Roski","year":"2014","journal-title":"Health Aff."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e003352","DOI":"10.1136\/bmjopen-2013-003352","article-title":"Choosing a Model to Predict Hospital Admission: An Observational Study of New Variants of Predictive Models for Case Finding","volume":"3","author":"Billings","year":"2013","journal-title":"BMJ Open"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1016\/j.healthpol.2015.08.007","article-title":"Which Hospitalisations Are Ambulatory Care-Sensitive, to What Degree, and How Could the Rates Be Reduced? Results of a Group Consensus Study in Germany","volume":"119","author":"Sundmacher","year":"2015","journal-title":"Health Policy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.12688\/f1000research.73875.2","article-title":"Nursing Home-Sensitive Conditions: Analysis of Routine Health Insurance Data and Modified Delphi Analysis of Potentially Avoidable Hospitalizations","volume":"10","author":"Schulte","year":"2022","journal-title":"F1000Research"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e051403","DOI":"10.1136\/bmjopen-2021-051403","article-title":"Predicting Hospitalisations Related to Ambulatory Care Sensitive Conditions with Machine Learning for Population Health Planning: Derivation and Validation Cohort Study","volume":"12","author":"Yi","year":"2022","journal-title":"BMJ Open"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1093\/eurpub\/ckt019","article-title":"The Central Role of Comorbidity in Predicting Ambulatory Care Sensitive Hospitalizations*","volume":"24","author":"Saver","year":"2014","journal-title":"Eur. J. Public Health"},{"key":"ref_12","first-page":"168","article-title":"Krankenhauskosten ambulant-sensitiver Krankenhausf\u00e4lle in Deutschland","volume":"7","author":"Fischbach","year":"2015","journal-title":"Gesundheitswesen"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1377\/hlthaff.2014.0054","article-title":"Insights From Advanced Analytics At The Veterans Health Administration","volume":"33","author":"Fihn","year":"2014","journal-title":"Health Aff."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, Y., Talwar, A., Chatterjee, S., and Aparasu, R.R. (2021). Application of Machine Learning in Predicting Hospital Readmissions: A Scoping Review of the Literature. BMC Med. Res. Methodol., 21.","DOI":"10.1186\/s12874-021-01284-z"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ijmedinf.2014.10.002","article-title":"Prediction of Hospitalization Due to Heart Diseases by Supervised Learning Methods","volume":"84","author":"Dai","year":"2015","journal-title":"Int. J. Med. Inf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1097\/MLR.0000000000000171","article-title":"Risk Prediction Models to Predict Emergency Hospital Admission in Community-Dwelling Adults: A Systematic Review","volume":"52","author":"Wallace","year":"2014","journal-title":"Med. Care"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lemke, K.W., Weiner, J.P., and Clark, J.M. (2012). Development and Validation of a Model for Predicting Inpatient Hospitalization, Lippincott Williams & Wilkins.","DOI":"10.1097\/MLR.0b013e3182353ceb"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1097\/MLR.0b013e31827da95a","article-title":"Predicting Risk of Hospitalization or Death Among Patients Receiving Primary Care in the Veterans Health Administration","volume":"51","author":"Wang","year":"2013","journal-title":"Med. Care"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1111\/1475-6773.13586","article-title":"Predicting Preventable Hospital Readmissions with Causal Machine Learning","volume":"55","author":"Marafino","year":"2020","journal-title":"Health Serv. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1097\/MLR.0000000000000041","article-title":"Predicting Potentially Avoidable Hospitalizations","volume":"52","author":"Gao","year":"2014","journal-title":"Med. Care"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e019454","DOI":"10.1136\/bmjopen-2017-019454","article-title":"Predicting Risk of Hospitalisation: A Retrospective Population-Based Analysis in a Paediatric Population in Emilia-Romagna, Italy","volume":"8","author":"Louis","year":"2018","journal-title":"BMJ Open"},{"key":"ref_22","unstructured":"Oliver-Baxter, J., Bywood, P., and Erny-Albrecht, K. (2015). PHCRIS Policy Issue Review. Adelaide: Primary Health Care Research Information Service, Primary Health Care Research and Information Service."},{"key":"ref_23","unstructured":"Wurz, T. (2018). Developing a Model To Predict Ambulatory Care Sensitive Hospitalisations, University of Hamburg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1055\/s-0034-1377033","article-title":"Ambulant-sensitive Krankenhausf\u00e4lle: Eine internationale \u00dcbersicht mit Schlussfolgerungen f\u00fcr einen deutschen Katalog","volume":"77","author":"Faisst","year":"2014","journal-title":"Gesundheitswesen"},{"key":"ref_25","unstructured":"Sundmacher, L., Sch\u00fcttig, W., and Faisst, C. (2015). Krankenhausaufenthalte infolge ambulant-sensitiver Diagnosen in Deutschland, Health Services Management; Ludwig-Maximilians Universit\u00e4t M\u00fcnchen: Ludwig-Maximilians-University."},{"key":"ref_26","unstructured":"Pimperl, A., Schulte, T., and Hildebrand, H. (2016). Analysis of Large and Complex Data: Studies in Classification, Data Analysis, and Knowledge Organization, Springer."},{"key":"ref_27","unstructured":"Ward, J.S., Barker, A., and University of St Andrews, School of Computer Science (2019, May 01). Undefined by Data: A Survey of Big Data Definitions. Available online: https:\/\/arxiv.org\/pdf\/1309.5821v1.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ijmedinf.2018.03.013","article-title":"Concurrence of Big Data Analytics and Healthcare: A Systematic Review","volume":"114","author":"Mehta","year":"2018","journal-title":"Int. J. Med. Inf."},{"key":"ref_29","unstructured":"Stiefel, M., and Nolan, K. (2012). A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and per Capita Cost, Institute for Healthcare Improvement."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1089\/pop.2016.0036","article-title":"Evaluating the Impact of an Accountable Care Organization on Population Health: The Quasi-Experimental Design of the German Gesundes Kinzigtal","volume":"20","author":"Pimperl","year":"2017","journal-title":"Popul. Health Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1055\/s-0034-1396815","article-title":"Gute Praxis Sekund\u00e4rdatenanalyse (GPS): Leitlinien und Empfehlungen","volume":"77","author":"Swart","year":"2015","journal-title":"Gesundheitswesen"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Holzinger, A. (2016). Machine Learning for Health Informatics, Springer International Publishing.","DOI":"10.1007\/978-3-319-50478-0_1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1016\/j.arthro.2017.01.054","article-title":"Research Pearls: The Significance of Statistics and Perils of Pooling. Predictive Modeling","volume":"33","author":"Hohmann","year":"2017","journal-title":"Arthrosc. J. Arthrosc. Relat. Surg."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10462-007-9052-3","article-title":"Machine Learning: A Review of Classification and Combining Techniques","volume":"26","author":"Kotsiantis","year":"2006","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1177\/1479972316642365","article-title":"Use of Predictive Algorithms in Home Monitoring of Chronic Obstructive Pulmonary Disease and Asthma: A Systematic Review","volume":"13","year":"2016","journal-title":"Chron. Respir. Dis."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wright, M.N., and Ziegler, A. (2017). Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. arXiv.","DOI":"10.18637\/jss.v077.i01"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s10729-017-9423-5","article-title":"Classification of Hospital Admissions into Emergency and Elective Care: A Machine Learning Approach","volume":"22","author":"Busse","year":"2019","journal-title":"Health Care Manag. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1016\/j.jclinepi.2004.03.012","article-title":"New ICD-10 Version of the Charlson Comorbidity Index Predicted in-Hospital Mortality","volume":"57","author":"Sundararajan","year":"2004","journal-title":"J. Clin. Epidemiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/0021-9681(87)90171-8","article-title":"A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation","volume":"40","author":"Charlson","year":"1987","journal-title":"J. Chronic Dis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e028744","DOI":"10.1136\/bmjopen-2018-028744","article-title":"Identifying Priorities for Primary Care Investment in Ireland through a Population-Based Analysis of Avoidable Hospital Admissions for Ambulatory Care Sensitive Conditions (ACSC)","volume":"9","author":"McDarby","year":"2019","journal-title":"BMJ Open"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1177\/009885881303900401","article-title":"The Use and Misuse of Biomedical Data: Is Bigger Really Better?","volume":"39","author":"Hoffman","year":"2013","journal-title":"Am. J. Law Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.jbi.2013.12.012","article-title":"PARAMO: A PARAllel Predictive MOdeling Platform for Healthcare Analytic Research Using Electronic Health Records","volume":"48","author":"Ng","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/nrcardio.2016.42","article-title":"Big Data Analytics to Improve Cardiovascular Care: Promise and Challenges","volume":"13","author":"Rumsfeld","year":"2016","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1108\/IJHCQA-07-2014-0080","article-title":"Quality of Big Data in Health Care","volume":"28","author":"Sukumar","year":"2015","journal-title":"Int. J. Health Care Qual. Assur."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.puhe.2018.09.019","article-title":"Hospitalisation of Ambulatory Care Sensitive Conditions and Access to Primary Care in Portugal","volume":"165","author":"Carneiro","year":"2018","journal-title":"Public Health"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Busby, J., Purdy, S., and Hollingworth, W. (2017). How Do Population, General Practice and Hospital Factors Influence Ambulatory Care Sensitive Admissions: A Cross Sectional Study. BMC Fam. Pract., 18.","DOI":"10.1186\/s12875-017-0638-9"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1080\/08839514.2016.1193714","article-title":"A Survey of Big Data Issues in Electronic Health Record Analysis","volume":"30","author":"Cyganek","year":"2016","journal-title":"Appl. Artif. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1377\/hlthaff.2014.0352","article-title":"Implementing Electronic Health Care Predictive Analytics: Considerations and Challenges","volume":"33","author":"Amarasingham","year":"2014","journal-title":"Health Aff."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1136\/bmjqs-2017-006629","article-title":"Preventing Hospital Readmissions: The Importance of Considering \u2018Impactibility,\u2019 Not Just Predicted Risk","volume":"26","author":"Steventon","year":"2017","journal-title":"BMJ Qual. Saf."},{"key":"ref_50","unstructured":"Cottle, M., Hoover, W., Kanwal, S., Kohn, M., Strome, T., Treister, N.W., and Institute for Health Technology Transformation (2019, January 01). Transforming Health Care through Big Data. Available online: http:\/\/c4fd63cb482ce6861463-bc6183f1c18e748a49b87a25911a0555.r93.cf2.rackcdn.com\/iHT2_BigData_2013.pdf."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/6\/4693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:49:40Z","timestamp":1760122180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/6\/4693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,7]]},"references-count":50,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["ijerph20064693"],"URL":"https:\/\/doi.org\/10.3390\/ijerph20064693","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,7]]}}}