Health Care Management Science

, Volume 18, Issue 1, pp 3–18 | Cite as

An analytics approach to designing patient centered medical homes



Recently the patient centered medical home (PCMH) model has become a popular team based approach focused on delivering more streamlined care to patients. In current practices of medical homes, a clinical based prediction frame is recommended because it can help match the portfolio capacity of PCMH teams with the actual load generated by a set of patients. Without such balances in clinical supply and demand, issues such as excessive under and over utilization of physicians, long waiting time for receiving the appropriate treatment, and non-continuity of care will eliminate many advantages of the medical home strategy. In this paper, by using the hierarchical generalized linear model with multivariate responses, we develop a clinical workload prediction model for care portfolio demands in a Bayesian framework. The model allows for heterogeneous variances and unstructured covariance matrices for nested random effects that arise through complex hierarchical care systems. We show that using a multivariate approach substantially enhances the precision of workload predictions at both primary and non primary care levels. We also demonstrate that care demands depend not only on patient demographics but also on other utilization factors, such as length of stay. Our analyses of a recent data from Veteran Health Administration further indicate that risk adjustment for patient health conditions can considerably improve the prediction power of the model.


Healthcare analytics Patient centered medical home Bayesian Generalized linear model Multivariate Multilevel Healthcare workforce 


  1. 1.
    Green LV, Savin S, Murray M (2007) Providing timely access to care: what is the right patient panel size? Jt comm j on quality and patient saf 33(4):211–218Google Scholar
  2. 2.
    Murray M, Davies M, Boushon B (2007) Panel size: answers to physicians’ frequently asked questions. Fam Pract Manag 14(10):29–32Google Scholar
  3. 3.
    Ostbye T, Yarnall KS, Krause KM, Pollak KI, Gradison M, Michener JL (2005) Is there time for management of patients with chronic diseases in primary care? Ann Fam Med 3(3):209–214CrossRefGoogle Scholar
  4. 4.
    Naessens JM, Stroebel RJ, Finnie DM, Shah ND, Wagie AE, Litchy WJ, Killinger PJ, O’Byrne TJ, Wood DL, Nesse RE (2011) Effect of multiple chronic conditions among working-age adults. The Am j of manag care 17(2):118–122Google Scholar
  5. 5.
    Potts B, Adams R, Spadin M (2011) Sustaining primary care practice: a model to calculate disease burden and adjust panel size. The Permanente j 15(1):53–56Google Scholar
  6. 6.
    Balasubramanian H, Banerjee R, Denton B, Naessens J, Stahl J (2010) Improving clinical access and continuity through physician panel redesign. J Gen Intern Med 25(10):1109–1115CrossRefGoogle Scholar
  7. 7.
    Rittenhouse DR, Shortell SM, Fisher ES (2009) Primary care and accountable care–two essential elements of delivery-system reform. N Engl J Med 361(24):2301–2303CrossRefGoogle Scholar
  8. 8.
    Patient-Centered Primary Care Collaborative (PCPCC). (2009). Available at Accessed August 14, 2013
  9. 9.
    Backer LA (2009) Building the case for the patient-centered medical home. Fam Pract Manag 16(1):14–18Google Scholar
  10. 10.
    Fisher ES (2008) Building a medical neighborhood for the medical home. N Engl J Med 359(12):1202–1205CrossRefGoogle Scholar
  11. 11.
    Beal AC, Fund C (2007) Closing the divide: how medical homes promotes equity in health care. Commonwealth FundGoogle Scholar
  12. 12.
    Rittenhouse DR, Shortell SM (2009) The patient-centered medical home: will it stand the test of health reform? J Am Med Assoc 301(19):2038–2040CrossRefGoogle Scholar
  13. 13.
    Reid RJ, Coleman K, Johnson EA, Fishman PA, Hsu C, Soman MP, Trescott CE, Erikson M, Larson EB (2010) The group health medical home at year two: cost savings, higher patient satisfaction, and less burnout for providers. Health Aff 29(5):835–843CrossRefGoogle Scholar
  14. 14.
    Bates DW, Bitton A (2010) The future of health information technology in the patient-centered medical home. Health Aff 29(4):614–621CrossRefGoogle Scholar
  15. 15.
    Bitton A, Martin C, Landon BE (2010) A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med 25(6):584–592CrossRefGoogle Scholar
  16. 16.
    Klein S (2011) The Veterans Health Administration: implementing patient-centered medical homes in the nation’s largest integrated delivery system. Commonwealth FundGoogle Scholar
  17. 17.
    Liu CF, Sales AE, Sharp ND, Fishman P, Sloan KL, Todd-Stenberg J, Nichol WP, Rosen AK, Loveland S (2003) Case-mix adjusting performance measures in a veteran population: pharmacy- and diagnosis-based approaches. Health Serv Res 38(5):1319–1337CrossRefGoogle Scholar
  18. 18.
    Moerbeek M (2004) The consequence of ignoring a level of nesting in multilevel analysis. Multivar Behav Res 39(1):129–149CrossRefGoogle Scholar
  19. 19.
    Goldstein H (2011) Multilevel statistical models. Wiley series in probability and statistics, 4 edn. John Wiley & SonsGoogle Scholar
  20. 20.
    Shams I, Ajorlou S, Yang K (2014) A multivariate hierarchical Bayesian framework for healthcare predictions with application to medical home study in the Department of Veteran Affairs. arXiv preprint arXiv:14030674Google Scholar
  21. 21.
    Srivastava MS, von Rosen T, von Rosen D (2008) Models with a Kronecker product covariance structure: estimation and testing. Math methods of statistics 17(4):357–370CrossRefGoogle Scholar
  22. 22.
    Hosmer Jr DW, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & SonsGoogle Scholar
  23. 23.
    Damlen P, Wakefield J, Walker S (1999) Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables. J R Stat Soc Ser B (Stat Methodol) 61(2):331–344CrossRefGoogle Scholar
  24. 24.
    García-Cortés LA, Sorensen D (2001) Alternative implementations of Monte Carlo EM algorithms for likelihood inferences. Genet Sel Evol 33(4):443–452CrossRefGoogle Scholar
  25. 25.
    Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33(2):1–22Google Scholar
  26. 26.
    Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol) 64(4):583–639CrossRefGoogle Scholar
  27. 27.
    Petersen LA, Byrne MM, Daw CN, Hasche J, Reis B, Pietz K (2010) Relationship between clinical conditions and use of veterans affairs health care among medicare‐enrolled veterans. Health Serv Res 45(3):762–791CrossRefGoogle Scholar
  28. 28.
    Gueorguieva R (2001) A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family. Stat Model 1(3):177–193CrossRefGoogle Scholar
  29. 29.
    Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Statistical science:457-472Google Scholar
  30. 30.
    VERISK Heath Inc (2011) Verisk Health DxCG medical classification system – Version 7 structural summary Available at Accessed March 18, 2014

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Industrial and Systems Engineering, Healthcare Systems Engineering GroupWayne State UniversityDetroitUSA

Personalised recommendations