References
- Abdel Rahman, S. E., Zhang, M., Bray, B. E., & Kawamoto, K. (2014). A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: Congestive heart failure readmission case study. BMC Medical Informatics and Decision Making, 14(1), 41. https://doi.org/10.1186/1472-6947-14-41
- Abualenain, J., Frohna, W. J., Shesser, R., Ding, R., Smith, M., & Pines, J. M. (2013). Emergency department physician-level and hospital-level variation in admission rates. Annals of Emergency Medicine, 61(6), 638–643. https://doi.org/10.1016/j.annemergmed.2013.01.016
- Alexander, M., Grumbach, K., Remy, L., Rowell, R., & Massie, B. M. (1999). Congestive heart failure hospitalizations and survival in California: Patterns according to race/ethnicity. American Heart Journal, 137(5), 919–927. https://doi.org/10.1016/S0002-8703(99)70417-5
- Almagro, P., Barreiro, B., Ochoa De Echagüen, A., Quintana, S., Rodríguez Carballeira, M., Heredia, J. L., & Garau, J. (2006). Risk factors for hospital readmission in patients with chronic obstructive pulmonary disease. Respiration, 73(3), 311–317. https://doi.org/10.1159/000088092
- Alqahtani, J. S., Njoku, C. M., Bereznicki, B., Wimmer, B. C., Peterson, G. M., Kinsman, L., Aldabayan, Y. S., Alrajeh, A. M., Aldhahir, A. M., & Mandal, S. (2020). Risk factors for all-cause hospital readmission following exacerbation of COPD: A systematic review and meta-analysis. European Respiratory Review, 29(156), 156. https://doi.org/10.1183/16000617.0166-2019
- Amalakuhan, B., Kiljanek, L., Parvathaneni, A., Hester, M., Cheriyath, P., & Fischman, D. (2012). A prediction model for COPD readmissions: Catching up, catching our breath, and improving a national problem. Journal of Community Hospital Internal Medicine Perspectives, 2(1), 9915. https://doi.org/10.3402/jchimp.v2i1.9915
- Amarasingham, R., Moore, B. J., Tabak, Y. P., Drazner, M. H., Clark, C. A., Zhang, S., Reed, W. G., Swanson, T. S., Ma, Y., & Halm, E. A. (2010). An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Medical Care, 48(11), 981–988. https://doi.org/10.1097/MLR.0b013e3181ef60d9
- Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019). Readmission prediction using deep learning on electronic health records. Journal of Biomedical Informatics, 97, 103256. https://doi.org/10.1016/j.jbi.2019.103256
- Bardhan, I., Chen, H., & Karahanna, E. (2020). Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. Management Information Systems Quarterly, 44(1), 185–200. http://doi.org/10.25300/MISQ/2020/14644
- Bardhan, I., Oh, J. H., Zheng, Z., & Kirksey, K. (2014). Predictive analytics for readmission of patients with congestive heart failure. Information Systems Research, 26(1), 19–39. https://doi.org/10.1287/isre.2014.0553
- Barnes, P. J., Shapiro, S. D., & Pauwels, R. (2003). Chronic obstructive pulmonary disease: Molecular and cellularmechanisms. European Respiratory Journal, 22(4), 672–688. https://doi.org/10.1183/09031936.03.00040703
- Basu Roy, S., Teredesai, A., Zolfaghar, K., Liu, R., Hazel, D., Newman, S., and Marinez, A. (2015). Dynamic Hierarchical Classification for Patient Risk-of-Readmission. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, (pp. 1691–1700).
- Bayati, M., Braverman, M., Gillam, M., Mack, K. M., Ruiz, G., Smith, M. S., & Horvitz, E. (2014). Data-driven decisions for reducing readmissions for heart failure: General methodology and case study. PloS One, 9(10), e109264. https://doi.org/10.1371/journal.pone.0109264
- Ben-Assuli, O., & Leshno, M. (2013). Using electronic medical records in admission decisions: A cost effectiveness analysis. Decision Sciences, 44(3), 463–481. https://doi.org/10.1111/deci.12018
- Ben-Assuli, O., & Padman, R. (2018). Analysing repeated hospital readmissions using data mining techniques. Health Systems, 7(2), 1–15. https://doi.org/10.1057/s41306-016-0016-1
- Ben-Assuli, O., Ziv, A., Sagi, D., Leshno, M., & Ironi, A. (2015). Improving diagnostic accuracy using EHR in emergency departments: A simulation-based study. Journal of Biomedical Informatics, 55, 31–40. https://doi.org/10.1016/j.jbi.2015.03.004
- Bolourani, S., Tayebi, M. A., Diao, L., Wang, P., Patel, V., Manetta, F., & Lee, P. C. (2020). Using machine learning to predict early readmission following esophagectomy. The Journal of Thoracic and Cardiovascular Surgery, 161(6), 1926–1939. https://doi.org/10.1016/j.jtcvs.2020.04.172
- Brunner-La Rocca, H.-P., Peden, C. J., Soong, J., Holman, P. A., Bogdanovskaya, M., & Barclay, L. (2020). Reasons for readmission after hospital discharge in patients with chronic diseases—Information from an international dataset. PloS One, 15(6), e0233457. https://doi.org/10.1371/journal.pone.0233457
- Buhr, R. G., Jackson, N. J., Kominski, G. F., Dubinett, S. M., Ong, M. K., & Mangione, C. M. (2019). Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: A comparison of the Charlson and Elixhauser comorbidity indices. BMC Health Services Research, 19(1), 701. https://doi.org/10.1186/s12913-019-4549-4
- Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, United States,1721–1730.
- Centers for Medicare & Medicaid Services. (2012). National medicare readmission findings: Recent data and trends. Office of Information Product and Data Analytics.
- Chamberlain, R. S., Sond, J., Mahendraraj, K., Lau, C. S., & Bl, S. (2018). Determining 30-day readmission risk for heart failure patients: The readmission after heart failure scale. International Journal of General Medicine, 11, 127–141. https://doi.org/https://doi.org/10.2147/IJGM.S150676
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
- Cress, J. C. (2011). Helping reduce hospital readmissions using seven key elements. Geriatric Care Management Journal, 21(2), 25–28. https://www.aginglifecare.org/ALCA_Web_Docs/journal/GCM_journal_Dec2011_FINAL.pdf#page=25
- Cusimano, M. D., Pshonyak, I., Lee, M. Y., & Ilie, G. (2017). A systematic review of 30-day readmission after cranial neurosurgery. Journal of Neurosurgery, 127(2), 342–352. https://doi.org/10.3171/2016.7.JNS152226
- Daly, B. J., Douglas, S. L., Kelley, C. G., O’toole, E., & Montenegro, H. (2005). Trial of a disease management program to reduce hospital readmissions of the chronically critically ill. CHEST Journal, 128(2), 507–517. https://doi.org/10.1378/chest.128.2.507
- Eftekhar, B., Mohammad, K., Ardebili, H. E., Ghodsi, M., & Ketabchi, E. (2005). Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Medical Informatics and Decision Making, 5(1), 1–8. https://doi.org/10.1186/1472-6947-5-3
- Facchinetti, G., D’Angelo, D., Piredda, M., Petitti, T., Matarese, M., Oliveti, A., & De Marinis, M. G. (2020). Continuity of care interventions for preventing hospital readmission of older people with chronic diseases: A meta-analysis. International Journal of Nursing Studies, 101, 103396. https://doi.org/10.1016/j.ijnurstu.2019.103396
- Felker, G. M., Leimberger, J. D., Califf, R. M., Cuffe, M. S., Massie, B. M., Adams, K. F., Gheorghiade, M., & O’Connor, C. M. (2004). Risk stratification after hospitalization for decompensated heart failure. Journal of Cardiac Failure, 10(6), 460–466. https://doi.org/10.1016/j.cardfail.2004.02.011
- Golas, S. B., Shibahara, T., Agboola, S., Otaki, H., Sato, J., Nakae, T., Hisamitsu, T., Kojima, G., Felsted, J., & Kakarmath, S. (2018). A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: A retrospective analysis of electronic medical records data. BMC Medical Informatics and Decision Making, 18(1), 44. https://doi.org/10.1186/s12911-018-0620-z
- Goto, T., Jo, T., Matsui, H., Fushimi, K., Hayashi, H., & Yasunaga, H. (2019). Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease. COPD: Journal of Chronic Obstructive Pulmonary Disease, 16(5–6), 338–343. https://doi.org/10.1080/15412555.2019.1688278
- Goto, T., Yoshida, K., Faridi, M. K., Camargo, C. A., & Hasegawa, K. (2020). Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation. BMC Pulmonary Medicine, 20(1), 1–10. https://doi.org/10.1186/s12890-020-1136-8
- Hagland, M. (2011). Mastering readmissions: Laying the foundation for change. Healthcare Informatics, 28(4), 10–16.
- Hines, A. L., Barrett, M. L., Jiang, H. J., & Steiner, C. A. (2014). Conditions with the largest number of adult hospital readmissions by payer, 2011: statistical brief# 172. The Agency for Healthcare Research and Quality's (AHRQ).
- Ho, T. K. (1995). Random decision forests. Proceedings of 3rd international conference on document analysis and recognition, Montreal, QC, Canada, 1, 278–282.
- Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601
- Jamei, M., Nisnevich, A., Wetchler, E., Sudat, S., Liu, E., & Upadhyaya, K. (2018). Correction: Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PloS One, 13(5), e0197793. https://doi.org/10.1371/journal.pone.0197793
- Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418–1428. https://doi.org/10.1056/NEJMsa0803563
- Joynt, K. E., Orav, E. J., & Jha, A. K. (2011). Thirty-day readmission rates for medicare beneficiaries by race and site of care. Jama, 305(7), 675–681. https://doi.org/10.1001/jama.2011.123
- Khanna, S., Boyle, J., & Good, N. (2014). Precise prediction for managing chronic disease readmissions. (Ed.),^(Eds.). Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE,Chicago, IL, USA.
- Kociol, R. D., Lopes, R. D., Clare, R., Thomas, L., Mehta, R. H., Kaul, P., Pieper, K. S., Hochman, J. S., Weaver, W. D., & Armstrong, P. W. (2012). International variation in and factors associated with hospital readmission after myocardial infarction. Jama, 307(1), 66–74. https://doi.org/10.1001/jama.2011.1926
- Koekkoek, D., Bayley, K. B., Brown, A., & Rustvold, D. L. (2011). Hospitalists assess the causes of early hospital readmissions. Journal of Hospital Medicine, 6(7), 383–388. https://doi.org/10.1002/jhm.909
- Kohli, R., & Tan, S. S.-L. (2016). Electronic health records: How can IS researchers contribute to transforming healthcare? MIS Quarterly, 40(3), 553–573. https://doi.org/10.25300/MISQ/2016/40.3.02
- Krumholz, H. M., Chen, Y.-T., Wang, Y., Vaccarino, V., Radford, M. J., & Horwitz, R. I. (2000). Predictors of readmission among elderly survivors of admission with heart failure. American Heart Journal, 139(1), 72–77. https://doi.org/10.1016/S0002-8703(00)90311-9
- Lin, Y.-K., Chen, H., Brown, R. A., Li, S.-H., & Yang, H.-J. (2017). Healthcare predictive analytics for risk profiling in chronic care: A bayesian multitask learning approach. MIS Quarterly, 41(2), 473–495. https://doi.org/10.25300/MISQ/2017/41.2.07
- Liu, X., Liu, Y., Lv, Y., Li, C., Cui, Z., & Ma, J. (2015). Prevalence and temporal pattern of hospital readmissions for patients with type I and type II diabetes. BMJ Open, 5(11), e007362. https://doi.org/10.1136/bmjopen-2014-007362
- Mahajan, S. M., Heidenreich, P., Abbott, B., Newton, A., & Ward, D. (2018). Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review. European Journal of Cardiovascular Nursing, 17(8), 675–689. https://doi.org/10.1177/1474515118799059
- Medicare Payment Advisory Commission. (2007). Report to the congress: Promoting greater efficiency in medicare. MedPAC.
- Miller, A. R., & Tucker, C. E. (2011). Can health care information technology save babies? Journal of Political Economy, 119(2), 289–324. https://doi.org/10.1086/660083
- Min, X., Yu, B., & Wang, F. (2019). Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: A case study on COPD. Scientific Reports, 9(1), 1–10. https://doi.org/10.1038/s41598-019-39071-y
- Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
- Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., Das, S. R., de Ferranti, S., Després, J.-P., and Fullerton, H. J. et al. 2015. Heart Disease and Stroke Statistics—2016 Update: A Report from the American Heart Association. Circulation, 133(4), e38-360. https://www.ahajournals.org/doi/pdf/10.1161/cir0000000000000350.
- Muus, K., Knudson, A., Klug, M., Gokun, J., Sarrazin, M., & Kaboli, P. (2010). Effect of post-discharge follow-up care on re-admissions among US veterans with congestive heart failure: A rural-urban comparison. Rural and Remote Health, 10(2), 1447. https://www.rrh.org.au/journal/article/1447
- National Center for Chronic Disease Prevention and Health Promotion. (2020). Chronic Obstructive Pulmonary Disease (COPD), National Center for Chronic Disease Prevention and Health Promotion. (10/12/2020). https://www.cdc.gov/copd/infographics/copd-costs.html
- Oxman, A. D., Cook, D. J., Guyatt, G. H., Bass, E., Brill-Edwards, P., Browman, G., Detsky, A., Farkouh, M., Gerstein, H., & Haines, T. (1994). Users’ guides to the medical literature: VI. How to use an overview. Jama, 272(17), 1367–1371. https://doi.org/10.1001/jama.1994.03520170077040
- Parekh, A. K., & Barton, M. B. (2010). The challenge of multiple comorbidity for the US health care system. Jama, 303(13), 1303–1304. https://doi.org/10.1001/jama.2010.381
- Philbin, E. F., Dec, G. W., Jenkins, P. L., & DiSalvo, T. G. (2001). Socioeconomic status as an independent risk factor for hospital readmission for heart failure. The American Journal of Cardiology, 87(12), 1367–1371. https://doi.org/10.1016/S0002-9149(01)01554-5
- Ranganathan, P., Pramesh, C., & Aggarwal, R. (2017). Common pitfalls in statistical analysis: Logistic regression. Perspectives in Clinical Research, 8(3), 148.
- Rico, F., Liu, Y., Martinez, D. A., Huang, S., Zayas-Castro, J. L., & Fabri, P. J. (2016). Preventable readmission risk factors for patients with chronic conditions. The Journal for Healthcare Quality (JHQ), 38(3), 127–142. https://doi.org/10.1097/01.JHQ.0000462674.09641.72
- Roberts, M. H., Mapel, D. W., Von Worley, A., & Beene, J. (2015). Clinical factors, including all patient refined diagnosis related group severity, as predictors of early rehospitalization after COPD exacerbation. Drugs in Context, 4, 1–15. https://doi.org/10.7573/dic.212278
- Rojas, J. C., Carey, K. A., Edelson, D. P., Venable, L. R., Howell, M. D., & Churpek, M. M. (2018). Predicting intensive care unit readmission with machine learning using electronic health record data. Annals of the American Thoracic Society, 15(7), 846–853. https://doi.org/10.1513/AnnalsATS.201710-787OC
- Rumball-Smith, J., & Hider, P. (2009). The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. The New Zealand Medical Journal (Online), 122(1289), 63-70.
- Russo, A. N., Sathiyamoorthy, G., Lau, C., Saygin, D., Han, X., Wang, X.-F., Rice, R., Aboussouan, L. S., Stoller, J. K., & Hatipoğlu, U. (2017). Impact of a post-discharge integrated disease management program on COPD hospital readmissions. Respiratory Care, 62(11), 1396–1402. https://doi.org/10.4187/respcare.05547
- Salvi, S. S., & Barnes, P. J. (2009). Chronic obstructive pulmonary disease in non-smokers. The Lancet, 374(9691), 733–743. https://doi.org/10.1016/S0140-6736(09)61303-9
- Shams, I., Ajorlou, S., & Yang, K. (2015). A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Management Science, 18(1), 19–34. https://doi.org/10.1007/s10729-014-9278-y
- Sheikh, A., Sood, H. S., & Bates, D. W. (2015). Leveraging health information technology to achieve the “triple aim” of healthcare reform. Journal of the American Medical Informatics Association, 22(4), 849–856. https://doi.org/10.1093/jamia/ocv022
- Shelton, P., Sager, M. A., & Schraeder, C. (2000). The community assessment risk screen (CARS): Identifying elderly persons at risk for hospitalization or emergency department visit. The American Journal of Managed Care, 6(8), 925–933.
- Silverstein, M. D., Qin, H., Mercer, S. Q., Fong, J., & Haydar, Z. (2008). Risk factors for 30-day hospital readmission in patients? 65 years of age. (Ed.),^(Eds.). Dallas, Texas , US: Proceedings. Baylor University Medical Center.
- Sirovich, B., Gallagher, P. M., Wennberg, D. E., & Fisher, E. S. (2008). Discretionary decision making by primary care physicians and the cost of US health care. Health Affairs, 27(3), 813–823. https://doi.org/10.1377/hlthaff.27.3.813
- Tabak, Y. P., Sun, X., Nunez, C. M., & Johannes, R. S. (2013). Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS). Journal of the American Medical Informatics Association, 21(3), 455–463. https://doi.org/10.1136/amiajnl-2013-001790
- Teo, B. K. (2012). EXAFS: Basic principles and data analysis (Vol. 9). Springer Science & Business Media.
- Turgeman, L., & May, J. H. (2016). A mixed-ensemble model for hospital readmission. Artificial Intelligence in Medicine, 72, 72–82. https://doi.org/10.1016/j.artmed.2016.08.005
- Vedomske, M. A., Brown, D. E., & Harrison, J. H. (2013). Random forests on ubiquitous data for heart failure 30-day readmissions prediction. 2013 12th International Conference on Machine Learning and Applications, Miami, Florida, US, 2, 415–421.
- Wang, H., Robinson, R. D., Johnson, C., Zenarosa, N. R., Jayswal, R. D., Keithley, J., & Delaney, K. A. (2014). Using the LACE index to predict hospital readmissions in congestive heart failure patients. BMC Cardiovascular Disorders, 14(1), 1. https://doi.org/10.1186/1471-2261-14-97
- Wiens, J., & Shenoy, E. S. (2018). Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149–153. https://doi.org/10.1093/cid/cix731
- Wijlaars, L. P., Hardelid, P., Woodman, J., Allister, J., Cheung, R., & Gilbert, R. (2016). Who comes back with what: A retrospective database study on reasons for emergency readmission to hospital in children and young people in England. Archives of Disease in Childhood, 101(8), 714–718. https://doi.org/10.1136/archdischild-2015-309290
- Wong, E. L., Cheung, A. W., Leung, M. C., Yam, C. H., Chan, F. W., Wong, F. Y., & Yeoh, E.-K. (2011). Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: A retrospective analysis of Hong Kong hospital data. BMC Health Services Research, 11(1), 149. https://doi.org/10.1186/1472-6963-11-149
- World Health Organization. (2016). Burden of COPD [in:] Chronic respiratory diseases. http://www.who.int/respiratory/copd/burden/en
- Yu, T.-C., Zhou, H., Suh, K., & Arcona, S. (2015). Assessing the importance of predictors in unplanned hospital readmissions for chronic obstructive pulmonary disease. ClinicoEconomics and Outcomes Research: CEOR, 7, 37. https://doi.org/10.2147/CEOR.S74181
- Zhao, Y., and Chen, Y. 2020. Effect of Renal Replacement Therapy Modalities on Renal Recovery and Mortality for Acute Kidney Injury: A Prisma‐Compliant Systematic Review and Meta‐Analysis. Seminars in Dialysis, 33(2), 127–132
- Zhao, Y., Qin, H., Wu, Y., & Xiang, B. (2017). Enhanced recovery after surgery program reduces length of hospital stay and complications in liver resection: A PRISMA-compliant systematic review and meta-analysis of randomized controlled trials. Medicine, 96(31), 1–7. DOI:10.1097/MD.0000000000007628.
- Zhong, X., Lee, S., Zhao, C., Lee, H. K., Bain, P. A., Kundinger, T., Sommers, C., Baker, C., & Li, J. (2019). Reducing COPD readmissions through predictive modeling and incentive-based interventions. Health Care Management Science, 22(1), 121–139. https://doi.org/10.1007/s10729-017-9426-2