Skip to main content
SpringerLink
Log in
Book cover

Industrial Conference on Data Mining

ICDM 2017: Advances in Data Mining. Applications and Theoretical Aspects pp 181–193Cite as

Predicting Hospital Re-Admissions from Nursing Care Data of Hospitalized Patients

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10357))

Abstract

Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission. However, most of the EHRs are high dimensional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hilbert, M., López, P.: The world’s technological capacity to store, communicate, and compute information. Science 332(6025), 60–65 (2011)

    Article  Google Scholar 

  2. Chen, Y., Hu, D., Zhang, G.: Data mining and critical success factors in data mining projects. In: Wang, K., Kovacs, George L., Wozny, M., Fang, M. (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 281–287. Springer, Boston (2006). doi:10.1007/0-387-34403-9_39

    Chapter  Google Scholar 

  3. Pan, F., et al.: CARPENTER: finding closed patterns in long biological datasets. In: International Conference on Knowledge Discovery and Data Mining (2003)

    Google Scholar 

  4. Liu, H., et al.: Mining frequent patterns from very high dimensional data: a top-down row enumeration approach. In: 2006 SIAM International Conference on Data Mining (SDM 2006), Bethesda, MD, pp. 280–291 (2006)

    Google Scholar 

  5. Jolliffe, I.: Principal component analysis. Wiley Online Library (2002)

    Google Scholar 

  6. Cox, T.F., Cox, M.A.: Multidimensional scaling. CRC Press (2000)

    Google Scholar 

  7. Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis, vol. 46. John Wiley & Sons (2004)

    Google Scholar 

  8. Harper, E., Sensmeier, J.: Why is Big Data Important to Nurses? Himss 2015. http://www.himss.org/News/NewsDetail.aspx?ItemNumber=43374. (cited September 10, 2015)

  9. Kavilanz, P.B.: Health care’s big money wasters, August 10, 2009. http://www.money.cnn.com/2009/08/10/news/economy/healthcare_money_wasters/. (cited April 29, 2014)

  10. Cuckler, G.: National Health Expenditures Projections 2012-2022. C.f.M.a.M.S. (2014)

    Google Scholar 

  11. Smith, P.C.: Performance measurement for health system improvement: experiences, challenges and prospects. Cambridge University Press (2009)

    Google Scholar 

  12. Billings, J., et al.: Impact of socioeconomic status on hospital use in New York City. Health Affairs 12(1), 162–173 (1993)

    Article  Google Scholar 

  13. Goodman, D.C., et al.: After hospitalization: a Dartmouth atlas report on post-acute care for Medicare beneficiaries. The Dartmouth Institute 28, September 2011

    Google Scholar 

  14. Yam, C., et al.: Measuring and preventing potentially avoidable hospital readmissions: a review of the literature. Hong Kong Medical Journal= Xianggang yi xue za zhi/Hong Kong Academy of Medicine 16(5), 383–389 (2010)

    Google Scholar 

  15. Herzog, R.: 5 Ways Healthcare Providers Can Reduce Costly Hospital Readmissions, March 31, 2013. http://hitconsultant.net/2013/03/31/5-ways-healthcare-providers-can-reduce-costly-hospital-readmissions/. (cited August 29, 2015)

  16. Vest, R.J., et al.: Determinants of preventable readmissions in the United States: A systematic review. Implementation Science 5(88) (2010)

    Google Scholar 

  17. Reducing Hospital Readmission with Enhanced Patient Education. K.P. Education (2010)

    Google Scholar 

  18. Jencks, S.F., Williams, M.V., Coleman, E.A.: Rehospitalizations among patients in the Medicare fee-for-service program. The New England Journal of Medicine 360, 1418–1428 (2009)

    Article  Google Scholar 

  19. Medicare Payment Advisory Commission: Report to the Congress: promoting greater efficiency in Medicare. Medicare Payment Advisory Commission (MedPAC) (2007)

    Google Scholar 

  20. Minott, J.: Reducing Hospital Readmissions (2008). http://www.academyhealth.org/files/publications/ReducingHospitalReadmissions.pdf. (cited August 28, 2015)

  21. Foster, D. Harkness, G.: Healthcare reform: Pending Changes to Reimbursement for 30-day Readmissions, August 2010. http://www.communitysolutions.com/assets/2012_Institute_Presentations/acareimbuesementchanges051812.pdf. (cited August 31, 2015)

  22. A controlled trial to improve care for seriously ill hospitalized patients. The study to understand prognoses and preferences for outcomes and risks of treatments (SUPPORT). The SUPPORT Principal Investigators. Jama 274(20), pp. 1591–1598 (1995)

    Google Scholar 

  23. Yao, Y., et al.: Current state of pain care for hospitalized patients at end of life. Am J Hosp Palliat Care 30(2), 128–136 (2013)

    Article  Google Scholar 

  24. Hasan, O., et al.: Hospital readmission in general medicine patients: a prediction model. Journal of General Internal Medicine 25(3), 211–219 (2010)

    Article  Google Scholar 

  25. Mudge, A.M., et al.: Recurrent readmissions in medical patients: a prospective study. Journal of Hospital Medicine 6(2), 61–67 (2011)

    Article  Google Scholar 

  26. Billings, J., et al.: Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 333(7563), 327 (2006)

    Article  Google Scholar 

  27. Cui, Y., et al.: Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada. Journal of Health Services Research and Policy 20(2), 83–91 (2015)

    Article  Google Scholar 

  28. Howell, S., et al.: Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Services Research 9, 96 (2009)

    Article  Google Scholar 

  29. Holloway, J., Medendorp, S., Bromberg, J.: Risk factors for early readmission among veterans. Health Services Research 25(1 Pt 2), 213 (1990)

    Google Scholar 

  30. Meldon, S.W., et al.: A Brief Risk-stratification Tool to Predict Repeat Emergency Department Visits and Hospitalizationsin Older Patients Discharged from the Emergency Department. Academic Emergency Medicine 10(3), 224–232 (2003)

    Article  Google Scholar 

  31. Rowland, K., et al.: The discharge of elderly patients from an accident and emergency department: functional changes and risk of readmission. Age and Ageing 19(6), 415–418 (1990)

    Article  Google Scholar 

  32. van Walraven, C., et al.: Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Canadian Medical Association Journal 182(6), 551–557 (2010)

    Article  Google Scholar 

  33. Phillips, R.S., et al.: Predicting emergency readmissions for patients discharged from the medical service of a teaching hospital. Journal of General Internal Medicine 2(6), 400–405 (1987)

    Article  Google Scholar 

  34. Keenan, G., et al.: Maintaining a consistent big picture: Meaningful use of a Web-based POC EHR system. International Journal of Nursing Knowledge 23(3), 119–133 (2012)

    Article  MathSciNet  Google Scholar 

  35. Nebraska Academy of Nutrition and Dietetics (NAND) Association: Nursing Diagnoses. North American Nursing Diagnosis Association (2007)

    Google Scholar 

  36. Moorhead, S., Johnson, M., Maas, M.: Iowa Outcomes Project, Nursing outcomes classification (NOC). Mosby, St. Louis, MO (2004)

    Google Scholar 

  37. Bulechek, G.M., Butcher, H.K., Dochterman, J.M.: Nursing interventions classification (NIC). Mosby (2008)

    Google Scholar 

  38. Gronbach, K.W.: The Age Curve: How to Profit from the Coming Demographic Storm (2008)

    Google Scholar 

  39. Hospital utilization (in non-federal short-stay hospitals). Centers for Disease Control and Prevention (2014)

    Google Scholar 

  40. Benner, P.: From novice to expert. American Journal of Nursing 82(3), 402–407 (1982)

    Google Scholar 

  41. Quinlan, J.: C4.5: Programs for machine learning. M. Kaufmann, San Francisco (2003)

    Google Scholar 

  42. Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)

    Google Scholar 

  43. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273 (1995)

    MATH  Google Scholar 

  44. Pearl, J.: Bayesian networks. In: The handbook of brain theory and neural networks. MIT Press (1998)

    Google Scholar 

  45. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier (2005). Gray, J.

    Google Scholar 

  46. Lewis, D.: Naive (Bayes) at forty: the independence assumption in information retrieval. In: Proceedings of 10th European Conference on Machine Learning, pp. 4–15 (1998)

    Google Scholar 

  47. Whitmarsh, C.: Hospitals Facing Economic Challenges. http://www.businesslife.com/articles.php?id=1104, (cited September 6, 2015)

  48. Gugliotta, G.: Rural hospitals, beset by financial problems, struggle to survive. In: The Washington Post (2015)

    Google Scholar 

  49. Campbell, D.: NHS cuts: One in three hospitals face financial crisis as result of cash squeeze. In: The Guardian (2013)

    Google Scholar 

  50. Desikan, P., et al.: Predictive Modeling in Healthcare: Challenges and Opportunities. http://lifesciences.ieee.org/publications/newsletter/november-2013/439-predictive-modeling-in-healthcare-challenges-and-opportunities. (cited September 27, 2014)

  51. Norrish, A., et al.: Validity of self-reported hospital admission in a prospective study. American Journal of Epidemiology 140(10), 938–942 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Illinois at Chicago, Chicago, IL, USA

    Muhammad K. Lodhi & Rashid Ansari

  2. University of Florida, Gainesville, FL, USA

    Yingwei Yao, Gail M. Keenan & Diana Wilkie

  3. Iowa State University, Ames, IA, USA

    Ashfaq A. Khokhar

Authors
  1. Muhammad K. Lodhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rashid Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingwei Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gail M. Keenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diana Wilkie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ashfaq A. Khokhar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad K. Lodhi .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Sachsen, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Lodhi, M.K., Ansari, R., Yao, Y., Keenan, G.M., Wilkie, D., Khokhar, A.A. (2017). Predicting Hospital Re-Admissions from Nursing Care Data of Hospitalized Patients. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2017. Lecture Notes in Computer Science(), vol 10357. Springer, Cham. https://doi.org/10.1007/978-3-319-62701-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62701-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62700-7

  • Online ISBN: 978-3-319-62701-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation