How Do Health Systems Address Patient Flow When Services Are Misaligned With Population Needs? A Qualitative Study

Document Type : Original Article

Authors

1 Department of Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada

2 George & Fay Yee Centre for Healthcare Innovation, Winnipeg Regional Health Authority/University of Manitoba, Winnipeg, MB, Canada

3 Health Systems Evaluation & Evidence, Alberta Health Services, Calgary, AB, Canada

Abstract

Background 
Patient flow through health services is increasingly recognized as a system issue, yet the flow literature has focused overwhelmingly on localized interventions, with limited examination of system-level causes or remedies. Research suggests that intractable flow problems may reflect a basic misalignment between service offerings and population needs, requiring fundamental system redesign. However, little is known about health systems’ approaches to population–capacity misalignment, and guidance for system redesign remains underdeveloped.
 
Methods 
This qualitative study, part of a broader investigation of patient flow in urban Western Canada, explored health-system strategies to address or prevent population–capacity misalignment. We conducted in-depth interviews with a purposive sample of managers in 10 jurisdictions across 4 provinces (N = 300), spanning all healthcare sectors and levels of management. We used the constant comparative method to develop an understanding of relevant strategies and derive principles for system design.
 

Results 
All regions showed evidence of pervasive population–capacity misalignment. The most superficial level of response – mutual accommodation (case-by-case problem solving) – was most prevalent; capacity (re)allocation occurred less frequently; population redefinition most rarely. Participants’ insights yielded a general principle: Define populations on the basis of clusters of co-occurring need. However, defining such clusters demands a difficult balance between narrowness/rigidity and breadth/flexibility. Deeper analysis suggested a further principle: Populations that can be divided into homogeneous subgroups experiencing similar needs (eg, surgical patients) are best served by narrow/ rigid models; heterogeneous populations featuring diverse constellations of need (eg, frail older adults) require broad/ flexible models.
 

Conclusion 
To remedy population–capacity misalignment, health system planners should determine whether clusters of population need are separable vs. fused, select an appropriate service model for each population, allocate sufficient capacity, and only then promote mutual accommodation to address exceptions. Overreliance on case-by-case solutions to systemic problems ensures the persistence of population–capacity misalignment.

Keywords


  1. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Vol 323. Washington, DC: National Academies Press; 2001.
  2. Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA Jr. A conceptual model of emergency department crowding. Ann Emerg Med. 2003;42(2):173-180. doi:10.1067/mem.2003.302
  3. Crawford K, Morphet J, Jones T, Innes K, Griffiths D, Williams A. Initiatives to reduce overcrowding and access block in Australian emergency departments: a literature review. Collegian. 2014;21(4):359-366. doi:10.1016/j.colegn.2013.09.005
  4. Chan SS, Cheung NK, Graham CA, Rainer TH. Strategies and solutions to alleviate access block and overcrowding in emergency departments. Hong Kong Med J. 2015;21(4):345-352. doi:10.12809/hkmj144399
  5. Winasti W, Elkhuizen S, Berrevoets L, van Merode G, Berden H. Inpatient flow management: a systematic review. Int J Health Care Qual Assur. 2018;31(7):718-734. doi:10.1108/ijhcqa-03-2017-0054
  6. de Grood J, Bota M, Villa-Roel C, et al. Overview of interventions to mitigate emergency department overcrowding. In: Health Quality Council of Alberta. Review of the Quality of Care and Safety of Patients Requiring Access to Emergency Department Care and Cancer Surgery and the Role and Process of Physician Advocacy. Calgary: Health Quality Council of Alberta; 2012:247-322.
  7. De Freitas L, Goodacre S, O'Hara R, Thokala P, Hariharan S. Interventions to improve patient flow in emergency departments: an umbrella review. Emerg Med J. 2018;35(10):626-637. doi:10.1136/emermed-2017-207263
  8. Chang AM, Cohen DJ, Lin A, et al. Hospital strategies for reducing emergency department crowding: a mixed-methods study. Ann Emerg Med. 2018;71(4):497-505.e4. doi:10.1016/j.annemergmed.2017.07.022
  9. Kreindler SA. Six ways not to improve patient flow: a qualitative study. BMJ Qual Saf. 2017;26(5):388-394. doi:10.1136/bmjqs-2016-005438
  10. Morley C, Unwin M, Peterson GM, Stankovich J, Kinsman L. Emergency department crowding: a systematic review of causes, consequences and solutions. PLoS One. 2018;13(8):e0203316. doi:10.1371/journal.pone.0203316
  11. Kolker A. Interdependency of hospital departments and hospital-wide patient flows. In: Hall R, ed. Patient Flow. Boston, MA: Springer; 2013:43-63. doi:10.1007/978-1-4614-9512-3_2
  12. Sullivan CM, Staib A, Flores J, et al. Aiming to be NEAT: safely improving and sustaining access to emergency care in a tertiary referral hospital. Aust Health Rev. 2014;38(5):564-574. doi:10.1071/ah14083
  13. Weber EJ, Mason S, Carter A, Hew RL. Emptying the corridors of shame: organizational lessons from England's 4-hour emergency throughput target. Ann Emerg Med. 2011;57(2):79-88.e1. doi:10.1016/j.annemergmed.2010.08.013
  14. Goldratt EM, Cox J. The Goal: A Theory of Constraints. Barrington, MA: North River Press; 1984.
  15. Schmenner RW, Swink ML. On theory in operations management. J Oper Manag. 1998;17(1):97-113. doi:10.1016/s0272-6963(98)00028-x
  16. Jensen K, Mayer TA, Welch S, Haraden C. Leadership for Smooth Patient Flow: Improved Outcomes, Improved Service, Improved Bottom Line. Chicago, IL: Health Administration Press with the Institute for Healthcare Improvement; 2007.
  17. Kreindler SA. Watching your wait: evidence-informed strategies for reducing health care wait times. Qual Manag Health Care. 2008;17(2):128-135. doi:10.1097/01.QMH.0000316990.48673.9f
  18. Litvak E, Long MC. Cost and quality under managed care: irreconcilable differences? Am J Manag Care. 2000;6(3):305-312.
  19. Radnor ZJ, Holweg M, Waring J. Lean in healthcare: the unfilled promise? Soc Sci Med. 2012;74(3):364-371. doi:10.1016/j.socscimed.2011.02.011
  20. Hollander MJ, Prince MJ. Organizing healthcare delivery systems for persons with ongoing care needs and their families: a best practices framework. Healthc Q. 2008;11(1):44-54, 42. doi:10.12927/hcq.2013.19497
  21. Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001;20(6):64-78. doi:10.1377/hlthaff.20.6.64
  22. Grembowski D, Schaefer J, Johnson KE, et al. A conceptual model of the role of complexity in the care of patients with multiple chronic conditions. Med Care. 2014;52 Suppl 3:S7-S14. doi:10.1097/mlr.0000000000000045    
  23. Kreindler SA. The three paradoxes of patient flow: an explanatory case study. BMC Health Serv Res. 2017;17(1):481. doi:10.1186/s12913-017-2416-8
  24. Anwar MR. A Realist Analysis of Streaming Interventions in Emergency Departments [thesis]. Winnipeg: University of Manitoba; 2019.
  25. Roemeling O, Ahaus K, van Zanten F, Land M, Wennekes P. How improving access times had unforeseen consequences: a case study in a Dutch hospital. BMJ Open. 2019;9(9):e031244. doi:10.1136/bmjopen-2019-031244
  26. Birch S, Mason T, Sutton M, Whittaker W. Not enough doctors or not enough needs? refocusing health workforce planning from providers and services to populations and needs. J Health Serv Res Policy. 2013;18(2):107-113. doi:10.1177/1355819612473592
  27. Pennel CL, McLeroy KR, Burdine JN, Matarrita-Cascante D. Nonprofit hospitals' approach to community health needs assessment. Am J Public Health. 2015;105(3):e103-113. doi:10.2105/ajph.2014.302286
  28. Aoun S, Pennebaker D, Wood C. Assessing population need for mental health care: a review of approaches and predictors. Ment Health Serv Res. 2004;6(1):33-46. doi:10.1023/b:mhsr.0000011255.10887.59
  29. Santibáñez P, Bekiou G, Yip K. Fraser Health uses mathematical programming to plan its inpatient hospital network. INFORMS J Appl Anal. 2009;39(3):196-208. doi:10.1287/inte.1080.0405
  30. Olafson K, Ramsey C, Yogendran M, et al. Surge capacity: analysis of census fluctuations to estimate the number of intensive care unit beds needed. Health Serv Res. 2015;50(1):237-252. doi:10.1111/1475-6773.12209
  31. Cramer GR, Singh SR, Flaherty S, Young GJ. The progress of US hospitals in addressing community health needs. Am J Public Health. 2017;107(2):255-261. doi:10.2105/ajph.2016.303570
  32. Latest update: Structural profile of public health in Canada. National Collaborating Centre for Healthy Public Policy website. https://ncchpp.ca/710/Structural_Profile_of_Public_Health_in_Canada.ccnpps.  Accessed November 5, 2019.
  33. Strauss A, Corbin J. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. 2nd ed. London: SAGE Publications; 1998.
  34. Christensen CM, Grossman JH, Hwang J. The Innovator's Prescription: A Disruptive Solution for Health Care. New York: McGraw-Hill Education; 2009.
  35. Rothkopf MH, Rech P. Perspectives on queues: combining queues is not always beneficial. Oper Res. 1987;35(6):906-909. doi:10.1287/opre.35.6.906
  36. Whitt W. Partitioning customers into service groups. Manage Sci. 1999;45(11):1579-1592. doi:10.1287/mnsc.45.11.1579
  37. Mazzocato P, Thor J, Bäckman U, et al. Complexity complicates lean: lessons from seven emergency services. J Health Organ Manag. 2014;28(2):266-288. doi:10.1108/jhom-03-2013-0060
  38. Damani Z, Conner-Spady B, Nash T, Tom Stelfox H, Noseworthy TW, Marshall DA. What is the influence of single-entry models on access to elective surgical procedures? a systematic review. BMJ Open. 2017;7(2):e012225. doi:10.1136/bmjopen-2016-012225
  39. Breton M, Smithman MA, Sasseville M, et al. How the design and implementation of centralized waiting lists influence their use and effect on access to healthcare - a realist review. Health Policy. 2020;124(8):787-795. doi:10.1016/j.healthpol.2020.05.023
  40. Kodner DL, Spreeuwenberg C. Integrated care: meaning, logic, applications, and implications--a discussion paper. Int J Integr Care. 2002;2:e12. doi:10.5334/ijic.67
  41. MacAdam M. Frameworks of Integrated Care for the Elderly. https://brainxchange.ca/Public/Files/Primary-Care/HQPC/Care-of-the-Eldery-integrate-care.aspx.  Published April, 2008. Accessed November 7, 2019.
  42. Leutz WN. Five laws for integrating medical and social services: lessons from the United States and the United Kingdom. Milbank Q. 1999;77(1):77-110. doi:10.1111/1468-0009.00125
  43. Kreindler SA, Cui Y, Metge CJ, Raynard M. Patient characteristics associated with longer emergency department stay: a rapid review. Emerg Med J. 2016;33(3):194-199. doi:10.1136/emermed-2015-204913
  44. Porter ME, Lee TH. The strategy that will fix health care. Harv Bus Rev. 2013;91(10):1-37.
  45. Doupe M, Fransoo R, Chateau D, et al. Population Aging and the Continuum of Older Adult Care in Manitoba. Winnipeg: Manitoba Centre for Health Policy; 2011.
  46. Liu LK, Guo CY, Lee WJ, et al. Subtypes of physical frailty: latent class analysis and associations with clinical characteristics and outcomes. Sci Rep. 2017;7:46417. doi:10.1038/srep46417
  47. Simo B, Bamvita JM, Caron J, Fleury MJ. Patterns of health care service utilization by individuals with mental health problems: a predictive cluster analysis. Psychiatr Q. 2018;89(3):675-690. doi:10.1007/s11126-018-9568-5
  48. Subbe CP, Goulden N, Mawdsley K, Smith R. Anticipating care needs of patients after discharge from hospital: frail and elderly patients without physiological abnormality on day of admission are more likely to require social services input. Eur J Intern Med. 2017;45:74-77. doi:10.1016/j.ejim.2017.09.029
  49. Yan S, Kwan YH, Tan CS, Thumboo J, Low LL. A systematic review of the clinical application of data-driven population segmentation analysis. BMC Med Res Methodol. 2018;18(1):121. doi:10.1186/s12874-018-0584-9
  50. Foguet-Boreu Q, Violán C, Rodriguez-Blanco T, et al. Multimorbidity patterns in elderly primary health care patients in a South Mediterranean European region: a cluster analysis. PLoS One. 2015;10(11):e0141155. doi:10.1371/journal.pone.0141155
  51. Larsen FB, Pedersen MH, Friis K, Glümer C, Lasgaard M. A latent class analysis of multimorbidity and the relationship to socio-demographic factors and health-related quality of life. A national population-based study of 162,283 Danish adults. PLoS One. 2017;12(1):e0169426. doi:10.1371/journal.pone.0169426
  52. Whitson HE, Johnson KS, Sloane R, et al. Identifying patterns of multimorbidity in older Americans: application of latent class analysis. J Am Geriatr Soc. 2016;64(8):1668-1673. doi:10.1111/jgs.14201
  53. Schneider EC, Sarnak DO, Squires D, Shah A, Doty MM. Mirror, Mirror 2017: International Comparison Reflects Flaws and Opportunities for Better U.S. Health Care. New York: The Commonwealth Fund; 2017.
  54. Martin D, Miller AP, Quesnel-Vallée A, Caron NR, Vissandjée B, Marchildon GP. Canada's universal health-care system: achieving its potential. Lancet. 2018;391(10131):1718-1735. doi:10.1016/s0140-6736(18)30181-8

Articles in Press, Corrected Proof
Available Online from 26 April 2021
  • Receive Date: 29 February 2020
  • Revise Date: 10 November 2020
  • Accept Date: 30 March 2021