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Migration of Patients Between Five Urban Teaching Hospitals in Chicago

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To quantify the extent of patient sharing and inpatient care fragmentation among patients discharged from a cohort of Chicago hospitals. Admission and discharge dates and patient ZIP codes from 5 hospitals over 2 years were matched with an encryption algorithm. Admission to more than one hospital was considered fragmented care. The association between fragmentation and socio-economic variables using ZIP-code data from the 2000 US Census was measured. Using validation from one hospital, patient matching using encrypted identifiers had a sensitivity of 99.3 % and specificity of 100 %. The cohort contained 228,151 unique patients and 334,828 admissions. Roughly 2 % of the patients received fragmented care, accounting for 5.8 % of admissions and 6.4 % of hospital days. In 3 of 5 hospitals, and overall, the length of stay of patients with fragmented care was longer than those without. Fragmentation varied by hospital and was associated with the proportion of non-Caucasian persons, the proportion of residents whose income fell in the lowest quartile, and the proportion of residents with more children being raised by mothers alone in the zip code of the patient. Patients receiving fragmented care accounted for 6.4 % of hospital days. This percentage is a low estimate for our region, since not all regional hospitals participated, but high enough to suggest value in creating Health Information Exchange. Fragmentation varied by hospital, per capita income, race and proportion of single mother homes. This secure methodology and fragmentation analysis may prove useful for future analyses.

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  1. Bourgeois FC, Olson KL, Mandl KD. Patients treated at multiple acute health care facilities: quantifying information fragmentation. Arch Intern Med 2010;170:1989–95.

    Article  Google Scholar 

  2. Elder NC, Hickner J. Missing clinical information: the system is down. JAMA 2005;293:617–9.

    Article  Google Scholar 

  3. Smith PC, Araya-Guerra R, Bublitz C, et al. Missing clinical information during primary care visits. JAMA 2005;293:565–71.

    Article  Google Scholar 

  4. Elder NC, Vonder Meulen M, Cassedy A. The identification of medical errors by family physicians during outpatient visits. Ann Fam Med 2004;2:125–9.

    Article  Google Scholar 

  5. Cwinn MA, Forster AJ, Cwinn AA, et al. Prevalence of information gaps for seniors transferred from nursing homes to the emergency department. CJEM 2009;11:462–71.

    Google Scholar 

  6. Lambrew JM, DeFriese GH, Carey TS, et al. The effects of having a regular doctor on access in primary care. Med Care. 1996;34:138–51.

    Article  Google Scholar 

  7. Santoli JM, Rodewald LE, Maes EF, et al. Vaccines for children program, United States. Pediatrics. 1999;104:e15.

    Article  Google Scholar 

  8. Gross CP, Mead LA, Ford DE, et al.. Physician, heal thyself? Regular source of care and use of preventive health services among physicians. Arch Intern Med. 2000;160:3209–14.

    Article  Google Scholar 

  9. Ettner SL. The timing of preventive services for women and children: the effect of having a usual source of care. Am J Public Health. 1996;86:1748–54.

    Article  Google Scholar 

  10. Kim J, Chuun D, Shah A, et al. Prevalence and impact of information gaps in the emergency department. AMIA Annu Symp Proc 2008:866.

  11. Stiell A, Forster AJ, Stiell IG, et al. Prevalence of information gaps in the emergency department and the effect on patient outcomes. CMAJ 2003;169:1023–8.

    Google Scholar 

  12. Accessed 5/21/2011.

  13. Hripcsak G, Kaushal R, Johnson KB, et al. The United Hospital Fund meeting on evaluating health information exchange. J Biomed Inform. 2007 Dec;40(6 Suppl):S3–10.

    Article  Google Scholar 

  14. Kaelber DC, Bates DW. Health information exchange and patient safety. J Biomed Inform. 2007 Dec;40(6 Suppl):S40–5.

    Article  Google Scholar 

  15. Kho AN, Lemmon L, Commiskey M, et al. Use of a regional health information exchange to detect crossover of patients with MRSA between urban hospitals. J Am Med Inform Assoc 2008 Mar-Apr;15(2):212–6.

    Google Scholar 

  16. Arrow K, Auerbach A, Bertko J, et al. Toward a 21st-Century Health Care System: Recommendations for Health Care Reform. Ann Intern Med. 2009 Mar 2.

  17. Walker J, Pan E, Johnston D, et al. The value of health care information exchange and interoperability. Health Aff (Millwood). 2005 Jan-Jun; Suppl Web Exclusives:W5-10-W5-18.

  18. Tenover, FC, McDonald, LC . Vancomycin-resistant staphylococci and enterococci: epidemiology and control. Current Opinion in Infectious Diseases. 18(4):300–305, August 2005.

    Article  Google Scholar 

  19. Shapiro JS, Mostashari F, Hripcsak G, et al. Using health information exchange to improve public health. Am J Public Health. 2011 Apr;101(4):616–23.7.

    Article  Google Scholar 

  20. Personal experience WG.

  21. Grannis SJ, Biondich PG, Mamlin BW, et al. How disease surveillance systems can serve as practical building blocks for a health information infrastructure: the Indiana experience. AMIA Annu Symp Proc. 2005:286–90.

  22. Adler-Milstein J, Bates DW, Jha AK. A survey of health information exchange organizations in the United States: implications for meaningful use. Ann Intern Med. 2011 May 17;154(10):666–71.

    Google Scholar 


  24. United Kingdom. UK NHS. A one-way encryption function to hide person-identifiable information. 15 July 2002. 11 Nov 2008 <>.

  25. United States Census Bureau: Census 2000 5-Digit ZIP Code Tabulation Areas (ZCTAs) Cartographic Boundary Files. Accessed February 21, 2011.

  26. ESRI’s ArcGIS® 10,

  27. Jenks, George F. 1967. “The Data Model Concept in Statistical Mapping”, International Yearbook of Cartography 7: 186–190.

    Google Scholar 

  28. Accessed May 27, 2011.

  29. Accessed 1/2013.

  30. Accessed 3/2011.

  31. Accessed 5/2011.

  32. Ejlertsson G, Berg S. Continuity of care measures: An analytical and empirical comparison. Medical Care. 1984. 22(3). 231–239.

    Article  Google Scholar 

  33. Breslau N, Haug M. Service delivery structure and continuity of care: a case study of a pediatric practice in process of reorganization. J Health Soc Behav 1976;17:339.

    Article  Google Scholar 

  34. Bice TE, Boxerman SB. A quantitative measure of continuity in ambulatory care. An assessment of alternative approaches. Med Care 1977;15:347.

    Article  Google Scholar 

  35. Ejlertsson G. Assessment of patient/doctor continuity in primary health care. JR Coll Gen Pract 1980;7.

  36. Schrag D, Xu F, Hanger M, et al. Fragmentation of Care for Frequently Hospitalized Urban Residents Med Care 2006;44: 560–567.

    Article  Google Scholar 

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This project was supported by grant number U18HS016973 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. This project was also supported by the Chicago Health Information Technology Regional Extension Center (CHITREC).

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The authors declare that they have no conflicts of interest.

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Correspondence to William L. Galanter.

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Galanter, W.L., Applebaum, A., Boddipalli, V. et al. Migration of Patients Between Five Urban Teaching Hospitals in Chicago. J Med Syst 37, 9930 (2013).

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