PharmacoEconomics

, Volume 21, Issue 12, pp 839–851 | Cite as

Integer/linear mathematical programming models

A tool for allocating healthcare resources
Current Opinion

Abstract

In today’s environment, the demand for efficient healthcare resource allocation is increasing. As new technologies become available, allocation decisions become more complex and tools to assist decision makers in determining efficient allocations of healthcare resources are encouraged. Mathematical programs have multiple properties that are desirable for healthcare decision makers such as the simultaneous consideration of multiple constraints and a built-in sensitivity analysis. These models have been well researched and are considered invaluable in other industries. Mathematical programming has also become increasingly visible in facilitating the allocation of healthcare resources in the health services research sector. However, the use of mathematical programming tools has been limited in economic evaluations of new technologies.

Budget allocations, such as formulary, drug development, and pricing decisions may benefit greatly from the use of mathematical programs. As an increasing number of expensive new technologies become available and pressure grows to contain healthcare costs, these tools may help guide a more efficient allocation of resources for technologies under budgetary and other constraints.

Keywords

Decision Variable Healthcare Decision Maker General Algebraic Modelling System Pharmaceutical Therapy Current Allocation 

Notes

Acknowledgements

The authors wish to thank Dr Anke Richter, the journal editor, and the anonymous referees for their helpful comments and thorough reviews of this paper. Their invaluable feedback has resulted in a significantly improved manuscript. The authors have provided no information on sources of funding or on conflicts of interest directly relevant to the content of the review.

References

  1. 1.
    Canadian Coordinating Office for Health Technology Assessment. Guidelines for economic evaluation of pharmaceuticals. 2nd ed. Ottawa: Canadian Coordinating Office for Health Technology Assessment (CCOHTA), 1997Google Scholar
  2. 2.
    National Institute for Clinical Excellence. Guidance for manufacturers and sponsors. London: National Institute for Clinical Excellence (NICE), 2001Google Scholar
  3. 3.
    Pharmaceutical Benefits Advisory Committee. Guidelines for the pharmaceutical industry on preparation of submissions to the pharmaceutical benefits advisory committee: including major submissions involving economic analyses. Canberra: Australian Government Publishing Service, 2002Google Scholar
  4. 4.
    Academy of Managed Care Pharmacy. Format for formulary submissions. Alexandria (VA): Academy of Managed Care Pharmacy (AMCP), 2000Google Scholar
  5. 5.
    Petrou S, Wolstenholme J. A review of alternative approaches to healthcare resource allocation. Pharmacoeconomics 2000 Jul; 18 (1): 33–43PubMedCrossRefGoogle Scholar
  6. 6.
    Stinnett AA, Paltiel AD. Mathematical programming for the efficient allocation of health care resources. Health Econ 1996; 15: 641–53CrossRefGoogle Scholar
  7. 7.
    Koh TS, Jezioranski JJ, Haycocks T, et al. A practical approach to inverse planning for high-precision dose escalated conformal prostate radiotherapy. Phys Med Biol 2001; 46 (5): 1473–85PubMedCrossRefGoogle Scholar
  8. 8.
    Altman RB, Tombropoulos R. Probabilistic constraint satisfaction: application to radiosurgery. Proc Annu Symp Comput Appl Med Care. New York: McGraw-Hill, 1994: 780–4Google Scholar
  9. 9.
    D’Souza WD, Meyer RR, Thomadsen BR, et al. An iterative sequential mixed-integer approach to automated prostate brachytherapy treatment plan optimization. Phys Med Biol 2001; 46 (2): 297–322PubMedCrossRefGoogle Scholar
  10. 10.
    Valouxis C, Housos E. Hybrid optimization techniques for the workshift and rest assignment of nursing personnel. Artif Intell Med 2000; 20 (2): 155–75PubMedCrossRefGoogle Scholar
  11. 11.
    Kropp DH, Carlson RC. Recursive modeling of ambulatory health care settings. J Med Syst 1977; 1 (2): 123–35PubMedCrossRefGoogle Scholar
  12. 12.
    Lyons JP, Young JP. A staff allocation model for mental health facilities. Health Serv Res 1976; 11 (1): 53–68PubMedGoogle Scholar
  13. 13.
    Baker JR, Fitzpatrick KE. An integer linear programming model of staff retention and termination based on multiattribute utility theory. Socioecon Plann Sci 1985; 19 (1): 27–34PubMedCrossRefGoogle Scholar
  14. 14.
    Branas CC, MacKenzie EJ, ReVelle CS. A trauma resource allocation model for ambulances and hospitals. Health Serv Res 2000; 35 (2): 489–507PubMedGoogle Scholar
  15. 15.
    Weniger BG, Chen RT, Jacobson SH, et al. Addressing the challenges to immunization practice with an economic algorithm for vaccine selection. Vaccine 1998; 16 (19): 1885–97PubMedCrossRefGoogle Scholar
  16. 16.
    Becker NG, Starczak DN. Optimal vaccination strategies for a community of households. Math Biosci 1997; 139 (2): 117–32PubMedCrossRefGoogle Scholar
  17. 17.
    Sewell EC, Jacobson SH, Weniger BG. ’Reverse engineering’ a formulary selection algorithm to determine the economic value of pentavalent and hexavalent combination vaccines. Pediatr Infect Dis J 2001; 20 (11 Suppl.): S45–56PubMedGoogle Scholar
  18. 18.
    Granata AV, Hillman AL. Competing practice guidelines: using cost-effectiveness analysis to make optimal decision. Ann Intern Med 1998; 128 (1): 56–63PubMedGoogle Scholar
  19. 19.
    Wang LY, Haddix AC, Teutsch SM, et al. The role of resource allocation models in selecting clinical preventive services. Am J Manag Care 1999; 5: 445–54PubMedGoogle Scholar
  20. 20.
    Earnshaw SR, Richter A, Sorenson SW, et al. Optimal allocation of resources across four interventions for type 2 diabetes. Med Decis Making 2002; 22 (5): A80–S91CrossRefGoogle Scholar
  21. 21.
    Zaric GS, Brandeau ML. Optimal investment in a portfolio of HIV prevention programs. Med Decis Making 2001; 21: 391–408PubMedGoogle Scholar
  22. 22.
    Zaric GS, Brandeau ML. Resource allocation for epidemic control over short time horizons. Math Biosci 2001; 171: 33–58PubMedCrossRefGoogle Scholar
  23. 23.
    Kaplan EH, Pollack H. Allocating HIV prevention resources. Socioecon Plann Sci 1998; 32: 257–63CrossRefGoogle Scholar
  24. 24.
    Richter A, Brandeau ML, Owens DK. An analysis of optimal resource allocation for HIV prevention among injection drug users and nonusers. Med Decis Making 1999; 19: 167–79PubMedCrossRefGoogle Scholar
  25. 25.
    von Zon AH, Kommer GJ. Patient flows and optimal health-care resource allocation at the macro-level: a dynamic linear programming approach. Health Care Manag Sci 1999; 2: 87–96PubMedCrossRefGoogle Scholar
  26. 26.
    Winston WL. Operations research: applications and algorithms. 3rd ed. Boston (MA): Duxbury Press, 1997Google Scholar
  27. 27.
    Fylstra D, Lasdon L, Watson J, et al. Design and use of the Microsoft Excel Solver. Interfaces 1998; 28 (5): 29–55CrossRefGoogle Scholar
  28. 28.
    Grossman TA. Spreadsheet add-ins for OR/MS. OR/MS Today 2002 Aug; 29 (4): 46–51Google Scholar
  29. 29.
    Fourer R. 2001 Software Survey: linear programming. OR/MS Today 2001 Aug; 28 (4): 58–68Google Scholar
  30. 30.
    Hillier FS, Lieberman GJ. Introduction to operation research. 7th ed. New York: McGraw Hill, 2000Google Scholar
  31. 31.
    Dexter F, Blake IT, Penning DH, et al. Use of linear programming to estimate impact of changes in a hospital’s operating room time allocation on perioperative variable costs. Anesthesiology 2002; 96 (3): 718–24PubMedCrossRefGoogle Scholar
  32. 32.
    Teng TO, Meyer G, Siegal JE, et al. Oregon’s Medicaid ranking and cost-effectiveness: is there any relationship? Med Decis Making 1996; 16 (2): 99–107CrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2003

Authors and Affiliations

  1. 1.RTI Health SolutionsResearch Triangle InstituteResearch Triangle ParkUSA

Personalised recommendations