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An analytics approach to designing patient centered medical homes

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Abstract

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.

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References

  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):211–218

    Google Scholar 

  2. Murray M, Davies M, Boushon B (2007) Panel size: answers to physicians’ frequently asked questions. Fam Pract Manag 14(10):29–32

    Google Scholar 

  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):209–214

    Article  Google Scholar 

  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):118–122

    Google Scholar 

  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):53–56

    Google Scholar 

  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):1109–1115

    Article  Google Scholar 

  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):2301–2303

    Article  Google Scholar 

  8. Patient-Centered Primary Care Collaborative (PCPCC). (2009). Available at http://www.pcpcc.org/about/medical-home. Accessed August 14, 2013

  9. Backer LA (2009) Building the case for the patient-centered medical home. Fam Pract Manag 16(1):14–18

    Google Scholar 

  10. Fisher ES (2008) Building a medical neighborhood for the medical home. N Engl J Med 359(12):1202–1205

    Article  Google Scholar 

  11. Beal AC, Fund C (2007) Closing the divide: how medical homes promotes equity in health care. Commonwealth Fund

  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):2038–2040

    Article  Google Scholar 

  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):835–843

    Article  Google Scholar 

  14. Bates DW, Bitton A (2010) The future of health information technology in the patient-centered medical home. Health Aff 29(4):614–621

    Article  Google Scholar 

  15. Bitton A, Martin C, Landon BE (2010) A nationwide survey of patient centered medical home demonstration projects. J Gen Intern Med 25(6):584–592

    Article  Google Scholar 

  16. Klein S (2011) The Veterans Health Administration: implementing patient-centered medical homes in the nation’s largest integrated delivery system. Commonwealth Fund

  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):1319–1337

    Article  Google Scholar 

  18. Moerbeek M (2004) The consequence of ignoring a level of nesting in multilevel analysis. Multivar Behav Res 39(1):129–149

    Article  Google Scholar 

  19. Goldstein H (2011) Multilevel statistical models. Wiley series in probability and statistics, 4 edn. John Wiley & Sons

  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:14030674

  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):357–370

    Article  Google Scholar 

  22. Hosmer Jr DW, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons

  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):331–344

    Article  Google Scholar 

  24. García-Cortés LA, Sorensen D (2001) Alternative implementations of Monte Carlo EM algorithms for likelihood inferences. Genet Sel Evol 33(4):443–452

    Article  Google Scholar 

  25. Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33(2):1–22

    Google Scholar 

  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):583–639

    Article  Google Scholar 

  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):762–791

    Article  Google Scholar 

  28. Gueorguieva R (2001) A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family. Stat Model 1(3):177–193

    Article  Google Scholar 

  29. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Statistical science:457-472

  30. VERISK Heath Inc (2011) Verisk Health DxCG medical classification system – Version 7 structural summary Available at http://www.veriskhealth.com/verisk-advantage/DxCG-Medical-Classification-V7-Structural-Summary.pdf. Accessed March 18, 2014

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Healthcare Systems Engineering Group, Wayne State University, Detroit, MI, 48202, USA

    Saeede Ajorlou, Issac Shams & Kai Yang

Authors
  1. Saeede Ajorlou
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  2. Issac Shams
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  3. Kai Yang
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Correspondence to Issac Shams.

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Ajorlou, S., Shams, I. & Yang, K. An analytics approach to designing patient centered medical homes. Health Care Manag Sci 18, 3–18 (2015). https://doi.org/10.1007/s10729-014-9287-x

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  • DOI: https://doi.org/10.1007/s10729-014-9287-x

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