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A Survey of Health Care Models that Encompass Multiple Departments

A Survey of Health Care Models that Encompass Multiple Departments. Peter Vanberkel, PhD Candidate University of Twente. Outline. Part 1: Literature Overview Part 2: ORchestra – online bibliography. Introduction. Background:

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A Survey of Health Care Models that Encompass Multiple Departments

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  1. A Survey of Health Care Models that EncompassMultiple Departments Peter Vanberkel, PhD Candidate University of Twente

  2. Outline • Part 1: Literature Overview • Part 2: ORchestra – online bibliography

  3. Introduction • Background: • Although there is an abundance of models for health care processes, few consider multiple units or departments. – Jun et al. 1999 • Our Intention: • What relationships were being accounted for? (i.e. What areas of the hospital?) • How were they being modelled? (i.e. What techniques?) • Identify examples of multi-department models. • What factors are holding modellers back?

  4. Search Method • Jun, J., Jacobson, S., and Swisher, J. (1999). Application of Discrete-Event Simulation in Health Care Clinics: A Survey. Journal of the Operational Research Society, 50(2):109–123. • Cited by over 70 papers • 21.4% Tutorial or Instructional • 10% Surveys • 40% Applications / Case Studies of a Single Department • 28.6% Applications / Case Studies of a Multiple Department • Other papers found via citations • In Total: Identified 78 papers which we considered to be of “Multiple Department”

  5. What relationships were being accounted for? (i.e. What areas of the hospital?) • How were they being modelled? (i.e. What techniques?) • Identify examples of multi-department models. • What factors are holding modellers back?

  6. What relationships were being accounted for? (i.e. What areas of the hospital?)

  7. Our Idealized Hospital Diagnostics (Lab / Pharmacy / X-Ray) Outpatient Clinics Operating Room Inpatient Wards Emergency Department Diagnostics (Lab / Pharmacy / X-Ray)

  8. Emergency Department (12) OR(1) ICU(1) Ward(10) Emergency Department Referrals(1) Ambulances(1) Lab/DI(5)

  9. Chart depicting the successive loss of French Army soldiers during Napoleon’s Russian Campaign (1812-13) Charles Joseph Minard

  10. Surgical Care (36) DI(2) PACU(10) Ward(22) ICU(9) Operating Room Waiting List(11) Emergency(0)

  11. Inpatient Wards(20) Lab/DI(0) ICU(9) Inpatient Wards Community Care(1) OR(8) Emergency Dept (9)

  12. Outpatient Clinics (4) DI/Lab(2) Pharmacy(1) Outpatient Clinics Operating Room(0) Emergency Dept (0) Primary Care(0) (3)

  13. Diagnostics (3) DI / Lab Pharmacy OR(1) Ward(2)

  14. OR in Health Care Campaign Diagnostics (Lab / Pharmacy / X-Ray) Outpatient Clinics Operating Room Inpatient Wards Emergency Department Diagnostics (Lab / Pharmacy / X-Ray)

  15. How were they being modelled? (i.e. What techniques?) Systems Dynamics(6) Simulation(36) Queueing Theory(7) Mathematical Programming(16) Other(17)

  16. Department by Department

  17. Departments is Scope Two Dept. (38) Three Dept. (34) Four Dept. (5) Five Dept. (1) Six Dept. (1)

  18. Identify examples of multi-department models.

  19. Notable References • Brailsford, S. et al (2004). Emergency and on-demand health care: modelling a large complex system. Journal of the Operational Research Society, 55(1):34– 42. • Scope: Referral, Ambulances, ED, Lab/DI, ICU, Ward • Technique: Systems Dynamics • Dexter, F. (2009). Bibliography of Operating Room Management Articles. Retrieved October 10,2008 from http://www.franklindexter.net. • Scope: Surgical Services

  20. Belien, J., et al. (2006). Visualizing the Demand for Various Resources as a Function of the Master Surgery Schedule: A Case Study. Journal of Medical Systems, 30(5):343–350. • Scope: OR, Lab/DI • Technique: Software • Cochran, J. and Bharti, A. (2006a). A multi-stage stochastic methodology for whole hospital bed planning under peak loading.International Journal of Industrial and Systems Engineering, 1(1):8–36. • Scope: OR, ICU, Ward • Technique: Queueing Theory & Simulation

  21. Fletcher, A. and Worthington, D. (2007). What is a ‘generic’ hospital model? Retrieved October 13, 2008: http://eprints.lancs.ac.uk/7051/1/004583.pdf

  22. What factors are holding modellers back?

  23. Factors • Ambiguous Care Paths • Complexity & Variability • Hospital Culture

  24. Problem 1: Ambiguous Care Paths • “patient care plans for the individual patient are rarely formally recorded, as such, they tend to evolve with the patient stay, and exist in a piece-meal fashion in the minds of physicians, nurses, and discharge planners” (Kopach-Konrad et al., 2007).

  25. Overcoming:Ambiguous Care Paths • Discussions with managers and care providers • Information system protocol HL7 • Medical record audits • Billing code audits • Radio frequency identifiers • Bar codes • Patient tracking systems • Clinical Pathways*

  26. Problem 2: Complexity & Variability • The complexity and variability that is inherent in health care either greatly limits the scope of models or forces modellers to take a more macro view. • Either way, researchers loose a certain amount of perspective and perhaps draw conclusions on a model that does not incorporate the entire set of circumstances

  27. Coping with: Complexity & Variability • distinguish between those complicating factors that have the greatest influence and those factors which are simply attributes. • To limit the amount of variability time should initially be spent eliminating the variability caused by the system itself. • good protocols or work practices • a clear understanding of the patient care trajectories.

  28. Problem 3: Hospital Culture • “management does not consider the total care chain from admission to discharge, but mainly focuses on the performance of individual units. Not surprisingly, this has often resulted in diminished patient access without any significant reduction in costs” (de Bruin et al., 2005). • People working in the health care system are very knowledgeable about their own area but have relatively little understanding of what goes on in the next department. (Carter 2002)

  29. Coping with: Hospital Culture From an Operational Research Perspective: • Better Models • Larger Scopes with a more sophisticated understanding of the requirements of the environment • Practically Relevant • Results which illustrate the benefits of coordination between departments

  30. Part 2: ORchestra Bibliography

  31. ORchestra Bibliography • A comprehensive overview of scientific literature in the field of “Operations Research in Health Care” • Can be accessed at: http://www.choir.utwente.nl/ORchestra. • Is maintained by the Center for Health Care Operations Improvement and Research (CHOIR)

  32. ORchestra Bibliography • Categorized According to: • Medical Category (MeSH terms) • Model Category (Mathematics Subject Classification) • Publication Type (MeSH terms) • Multiple Departments? • Interacting Patient Flows?

  33. ORchestra Bibliography • What’s Available Online? • Detailed descriptions of the categorization method • Sorted pdf’s of all articles in each category • Free Text Searching of all Articles (Coming soon)

  34. Discussion & Questions? http://beta.ieis.tue.nl/home (Literature Review) www.choir.utwente.nl/ORchestra/ (ORchestra)

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