Patient-Centered Medical Home Cost Reductions Limited to Complex Patients
Published Online: November 19, 2012
Thomas J. Flottemesch, PhD; Louise H. Anderson, PhD; Leif I. Solberg, MD; Patricia Fontaine, MD, MS; and Stephen E. Asche, MA
The patient-centered medical home (PCMH) is a topic of interest.1-15 A high-functioning medical home requires coordinated care by a consistent team.16-21 Although few clinics are PCMHs, adults reporting a usual source of primary care are 25% more likely to report positive clinician attributes22 and reduced disparities.23 Observational studies suggest the PCMH approach results in improved satisfaction24 and reduced utilization.25 Pilot studies have found reduced emergency department (ED) use26 and cost reductions.27-31 Clinical practice systems are an important component of a PCMH.32,33 They give access to relevant information, coordinate management of complex conditions, and facilitate delivery of preventive care services. The Physician Practice Connections–Patient-Centered Medical Home (PPC-PCMH), a tool endorsed by the National Committee for Quality Assurance (NCQA), measures practice systems and has been used in PCMH programs.34-38
Prior studies of utilization26,39 have looked at short time frames of 12 to 24 months.16 In theory, the PCMH would reduce medical costs over time by avoiding complications leading to ED visits and inpatient stays, especially among patients with complex illness, as suggested by the chronic care model of Bodenheimer et al.40 This study uses a 2005 measure of the PPC-PCMH and a retrospectively constructed cohort from a large Midwestern health plan to evaluate whether clinical practice systems evaluated at baseline are associated with reduced utilization and costs over a subsequent 5-year period. We present key findings in terms of predicted annual per person amounts to illustrate (1) how predicted costs and utilization change in response to clinical systems and (2) how baseline clinic systems related to different patient groups.
Data Sources, Study Population, and Primary Care Medical Groups
Utilization, billing, provider, medical group, and patient demographic data came from the administrative databases of a large, not-for-profit Midwestern health plan. Practice system measures came from a 2005 survey of medical group directors using the Physician Practice Connections-Research Survey (PPC-RS).41 This instrument is similar to the PPC-PCMH except for fewer questions about the electronic medical record, a focus on 4 chronic conditions (diabetes, cardiovascular disease, asthma, and depression), and graded response categories. A full description of the tool is available on request.
A retrospective cohort over 2005 to 2009 was constructed to compare a baseline measure of clinic systems with subsequent utilization patterns. Subjects needed to meet the following inclusion criteria: (1) have 10 or more months of continuous enrollment in each year; (2) be alive on December 31, 2009; (3) be 19 years or older as of January 1, 2005; and (4) be attributable to the same primary care medical group for 2005 to 2009. Persons were attributed to the medical group with which they had the greatest percentage of primary care visits. Primary care visits were defined as visits with providers in the following specialties: family medicine, internal medicine, general practice, geriatric medicine, and obstetrics and gynecology (Ob-Gyn). Nurse practitioner and physician assistant visits were included. Our decision to include visits with Ob-Gyn providers was made because such visits are a regular source of care for many women of childbearing age. However, such an inclusion does not strictly conform to the personfocused primary care concept of Starfield.42 The implications of this decision are discussed in the Limitations section.
Those with no primary care visits were unattributed and excluded. Those attributed to more than 1 medical group (ie, those who had an equal number of visits to 2 or more groups) were also excluded. We further limited the sample to medical groups with a minimum of 200 attributed members. Per year, of the 318,857 adults attributed in 2005 approximately 5% were excluded due to death or disenrollment, 25% due to a change in attributed medical group, and 5% due to no primary care utilization. This resulted in 58,391 persons across 22 medical groups. Most (n = 48,292) had commercial insurance, 7077 were Medicare enrollees, and 3022 were enrolled in Medicaid. For dually eligible Medicare beneficiaries aged 65 to 75 years, all claims including pharmacy were processed by the health plan in order to track benefits, deductibles, and payer liability. All claims from Medicaid beneficiaries were processed for similar reasons.
Five annualized outcomes were constructed: total cost, total outpatient cost, total inpatient cost, inpatient days, and ED visits. The health plan’s administrative databases contain information concerning insurance product, medical diagnosis, care specialty, costs, and limited demographics (age, address, and sex). These were organized using Evaluation and Management, International Classification of Diseases, Ninth Revision, and Current Procedural Terminology (CPT) codes. Total cost included all reimbursed medical costs, including copays, coinsurance, and deductibles. Outpatient cost included professional services, prescriptions, lab and x-ray tests, and outpatient surgical procedures. Inpatient cost included professional and facility fees for hospital-based services including emergency care. Inpatient days were days with an overnight hospital stay. ED visits included all visits to an ED with reimbursed service. If a subject was not enrolled for the entire year, their cost and utilization was annualized using their monthly average.
To avoid variation in outcomes due to benefit design or provider contract, total, outpatient, and inpatient costs were based on a standardized measure, the relative resource value unit. Relative resource value units are based on Centers for Medicare & Medicaid Services relative value units, inpatient diagnosis-related groups (DRGs), and Ambulatory Payment Classification weights. The logic is to apply a standardized fee schedule across all providers by developing standardized costs for each CPT code, hospital DRG, and National Drug Classification (NDC) code that is dependent upon the type of procedure/service/prescription provided but independent of the place of service, type of insurance coverage, or year. This fee schedule was developed by constructing a weighted average of billed amounts across all contracted providers for each CPT code, hospital DRG, and NDC code. Our measures of costs were developed by adjusting these averages by the ratio of billed to paid amounts across service category and scaled to the base year of 2005.
The PPC-RS41 asks 53 questions related to delivering preventive services, depression, diabetes, cardiovascular disease, and asthma. Of these, 43 are grouped into domains corresponding to the Chronic Care Model: Health Care Organization (n = 3), Delivery System Redesign (n = 8), Clinical Information System (n = 10), Decision Support (n = 9), and Self-Management Support (n = 23). Items are coded as present and work well (1 point), present but need improvement (1/2 point), or absent (0 points). Domain scores represent the proportion of possible items present and utilized. The PPCRS score is a summation of all items with high scores associated with higher-functioning clinical systems.
Plan of Analysis
Multiple regression models were estimated using generalized estimating equations for continuous outcomes and generalized linear models for discrete outcomes. All models were fit with a subject-level autoregressive error structure (AR1 process). Cost outcomes were log transformed43-46 and Duan’s smearing estimator was used.47,48 A majority had neither inpatient nor ED visits within a given year. Thus, 2 outcomes were considered. First, the likelihood of any utilization was modeled using a logistic regression with a subject-level AR1 process. Among those with inpatient costs, a log-transformation was used. For ED utilization and inpatient days, a zero-inflated Poisson model was used.
Our models compared a baseline measure of clinical systems with costs and utilization over a 5-year period. They adjusted for demographics (age and sex), complexity/comorbidity (number of medications and comorbidities), insurance type, and primary care visits. Outpatient prescription medications were our measure of complexity because this information was reliably available from claims data, and it is a validated, easily reproducible measure.49 Certain results categorized subjects by their baseline number of prescriptions, but models allowed that number to vary by year. All of the models were developed in the following manner. First, candidate covariance structures were considered. Second, demographic models were constructed. Covariates significant at the 10% level in univariate models were screened for confounding, multicolinearity, and consistent linear relationships. Appropriate adjustments (transformations, interactions, and polynomial terms) were made. Study year was considered as both a continuous and fixed effect with a fixed-effect specification preferable (likelihood ratio test; P = .0002). Finally, PPC-RS scores were added, and the possibility of interactions with both study year and patient demographics was considered.
To control for variation in outcomes due to differences in patient mix when presenting our results, we used a 2-part strategy. First, as discussed, we incorporated multiple patientlevel factors adjusting for patient demographics (age and sex), ability to pay (insurance type), and medical complexity (prescription drug use and comorbidities) into our final multivariate models. Second, we estimated the impact of changes in PCMH-related systems (PPC-RS scores) holding fixed patient demographics.
Table 1 contains demographic information and average per patient costs. Total costs averaged $10,347 (standard deviation [SD] $22,384) in 2005 and trended upward to $13,637 (SD $32,905) in 2009. The average 2005 age was 52.3 years with 64% being female, 13% using Medicare, and 1.8% using Medicaid. Over the study, an additional 6% enrolled in Medicare; however, Medicaid enrollment was stable.
Depression was the most prevalent chronic condition, impacting 24% in 2005 and 27% in 2009. The next most prevalent was asthma (10% in 2005, 9% in 2009), then diabetes (10% in 2005, 14% in 2009) and coronary artery disease (5% in 2005, 5.5% in 2009). Patients averaged 3.5 prescriptions in 2005 and 4.2 prescriptions in 2009. A brief discussion of demographics and specific coefficient estimates is in the eAppendix (available at www.ajmc.com).
Tables 2 to 5 contain predicted amounts from our multivariate models. These predict per person costs (Tables 3 and 5) or utilization (Table 4), adjusting for patient demographics and comorbidities. They illustrate (1) how predicted costs and utilization change according to changes in baseline clinical systems (Table 3) and (2) how a hypothetical 10% improvement in baseline clinical systems potentially affects different groups differently.
Table 2 lists attributed patients and average predicted cost by medical group. Estimated average healthcare costs by medical group ranged from $8717 to $12,155. The medical group closely affiliated with the health plan had the largest attribution (n = 27,331) with an estimated cost of $10,474. Attribution to the remaining groups ranged from 8436 to 227 persons. Table 2 also lists 2005 PPC-RS scores that ranged from 25 to 85.
Relationship Between Costs and PPC-RS Scores
Aside from ED use (P <.001), no samplewide associations with the PPC-RS were found. However, significant relationships with subgroups existed. Among complex patients (>7 medications), higher scores were associated with lower total costs, outpatient costs, inpatient days, and ED use.
Table 3 presents estimated amounts by year and quartile of baseline PPC-RS scores. In 2005, predicted per patient costs in groups whose PPC-RS score was at the sample’s 3rd quartile (PPC-RS = 70.3) were approximately $100 less than those whose medical group was at the sample median (P = .025) and $240 less than those whose PPC-RS was at the lowest quartile (P <.01). This difference decreased over time, becoming negative in 2009 (P = .16). Outpatient utilization differed by $45 (P = .035) and $108 (P <.001) between the 3rd and lower quartiles in 2005, with the difference decreasing over time until it was associated with increases in 2008 and 2009 (P = .35 and P = .023, respectively). Higher PPC-RS quartiles were associated with increased inpatient utilization across all study years but less ED use. In 2005, the 3rd quartile of PPC-RS scores was associated with 8.5 and 18.4 fewer ED visits per 1000 persons compared with the median and 1st quartile (P = .01 and P <.01, respectively), and increased to 10.6 and 23.2, respectively, in 2009 (P <.01).
Tables 4 and 5 estimate the impact of a hypothetical 10% increase in 2005 PPC-RS scores for 3 groups defined by their number of 2005 outpatient prescriptions: 0 to 2 medications (n = 29,657), 3 to 6 medications (n = 19,505), and 7 or more medications (n = 9229). Among those with 0 to 2 medications, the 10% increase was associated with a slight increase in total, outpatient, and inpatient costs not significant at the .05 level. Among those with 3 to 6 medications, the increase was associated with a slight decrease in average total and outpatient costs not significant at the .05 level, but with a slight increase in average inpatient costs not significant at the .05 level. Among those with 7 or more medications, there was a small decrease in average inpatient costs not significant at the .05 level, whereas outpatient costs decreased significantly by 1.12% (P <.01) in 2005 and 0.25% in 2009 (P = .015). The number of ED visits decreased by 3.7% for all 5 years (P <.01), as did the number of impatient days (P <.01).
This work explored how a PCMH could impact utilization over time; however, it has limitations. Our inclusion criteria resulted in a final cohort that tended to be older and sicker than average. They may also have introduced a survivor bias as the top 5% to 10% of high-cost patients frequently have short life expectancies. Thus, our findings should not be interpreted as indicating PCMH’s impact on total healthcare costs. Instead, our findings suggest that higher-functioning PCMH systems are associated with different levels of healthcare costs among medically stable patients who have a consistent source of primary care.
Our attribution scheme assigning patients to medical groups included Ob-Gyn visits. As noted, such a definition does not strictly conform to Starfield’s definition of person-focused care that focuses on first contact: person-focused over time, comprehensiveness, and coordination.50 Similarly, our primary variable of interest, the PPC-RS score, focuses on a medical group’s structural elements and is intended to measure systems supporting patient-centered care. The impact is that our findings may tend toward episodes of care rather than patient care needs over time.
The PPC-RS scores were measured at the start of our study time frame and represent a snapshot of each medical group’s initial clinical systems. They do not capture system change, innovation, and improvement. Those groups with the poorest functioning systems in 2005 had both the greatest incentive and the greatest opportunity to improve. The potential for unobserved convergence is likely and could explain why differences were greatest in 2005. This highlights the need for systematic, prospective analysis.
The results of this analysis build on previous work and demonstrate how the PCMH and its associated clinical systems differ across patient groups over time. That prior work demonstrated that systems implemented at the clinic level lead to decreased utilization over relatively short periods of time. Here, over a longer, 5-year time frame, we show that among complex yet medically stable patients, who likely require the greatest amount of management and care coordination, PCMH systems measured at baseline were associated with lower costs and utilization. That is particularly true of ED care. However, as we noted, our measure of clinic performance and 5-year enrollment requirement may have introduced certain biases. Thus, findings are illustrative and not definitive.
This work is enlightening because it highlights several points regarding clinical systems and their potential impact. Initially, our approach attempted to parallel a similar analysis of quality.51 We intended to measure the impact of implementing a PCMH using a proxy approach comparing an integrated medical group whose clinics had all achieved level III PCMH recognition with primary care medical groups that had unrecognized clinics. That analysis hypothesized that an NCQA-recognized PCMH will have significantly different trends compared with other groups. That initial comparison suggested the PCMH was unassociated with healthcare utilization and costs. However, closer inspection indicated our proxy measure (ie, recognized vs nonrecognized PCMHs) was suspect. Our medical group’s PPC-RS scores, and hence the functioning of its PCMH-related systems, did not significantly differ from those of other groups. When a direct measure of PCMH-related clinical systems was used (2005 PPC-RS scores), significant relationships were found. Specifically, among relatively healthy patients as indicated by a low level of prescription drug use, higher-functioning clinical systems appeared to be unassociated with medical costs and ED use. However, among the sickest and most costly patients, improved PMCH-related systems appear to have been associated with reduced costs and utilization across multiple categories.
The number of outpatient prescription medications was used as a measure of medical complexity. Other measures (the Charlson Comorbidity52 and the Elixhauser53 indices) were considered in sensitivity analysis with no significant change in our predicted results. We retained the number of prescription medications because it is easy to interpret, does not rely on complex algorithms, and has been shown to have higher predictive validity in similar contexts.49
These findings have important implications. The first is that PCMH recognition does not necessarily imply superior functioning. Many medical groups have implemented systems associated with the PCMH, even though they have not gained formal recognition. These systems appear to be having a positive effect in terms of reducing utilization by the most complex patients. However, this association could also imply that different types of patients need different types of care models. The PCMH may be useful and cost-effective for the medically complex; however, an alternative model may be preferable for others. Clearly, additional research is needed that carefully compares how the PCMH is implemented in different settings and how and why formal recognition is sought.
Another implication is that improved PCMH functioning should not necessarily imply a universal decrease in utilization and costs. Instead, it should be viewed as a changed approach to patient management and activation. For some patients, this change may lead to slightly increased utilization as they are encouraged to seek preventive care and screenings. That, in turn, could lead to reduced reliance on expensive emergency care. For other patients, reductions in utilization may indeed be realized as a result of improved management of complex, chronic disease. A better understanding of how the PCMH changes the provider/patient relationship and its long-term impact on patient outcomes is required to fully understand this dynamic.
Author Affiliations: From HealthPartners Research Foundation (TJF, LHA, LIS, PF, SEA), Minneapolis MN.
Funding Source: The Agency for Healthcare Research and Quality (HHSA 290-2007-10010).
Author Disclosures: The authors (TJF, LHA, LIS, PF, SEA) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (TJF); acquisition of data (TJF, LHA, LIS, PF); analysis and interpretation of data (TJF, LHA, LIS, SEA); drafting of the manuscript (TJF, PF, SEA); critical revision of the manuscript for important intellectual content (TJF, LHA, LIS, PF, SEA); statistical analysis (TJF, LHA); and obtaining funding (TJF, LIS).
Address correspondence to: Thomas J. Flottemesch, PhD, HealthPartners Research Foundation, PO Box 1524, MS 21111R; Minneapolis, MN 55440- 1524. E-mail: Thomas.J.Flottemesch@healthpartners.com.
1. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington DC: National Academies Press; 2001.
2. Kohn LT, Corrigan JM, Donaldson MS, eds; Committee on Quality of Health Care in America, Institute of Medicine. To Err is Human: Building A Safer Health System. Washington, DC: National Academies Press; 1999.
3. McGlynn EA, Asch SM, Adams J, et al. The quality of health care delivered to adults in the United States. N Engl J Med. 2003;348(26):2635-2645.
4. McGlynn EA. An evidence-based national quality measurement an reporting system. Med Care. 2003;41(1)(suppl):l8-l15.
5. Mangione-Smith R, DeCristofaro AH, Setodji CM, et al. The quality of ambulatory care delivered to children in the United States. N Engl J Med. 2007;357(15):1515-1523.
6. Bodenheimer T. High and rising health care costs: part 1: seeking an explanation. Ann Intern Med. 2005;142(10):847-854.
7. Bodenheimer T. High and rising health care costs: part 3: the role of health care providers. Ann Intern Med. 2005;142(12, pt 1):996-1002.
8. Barr M, Ginsburg J. The Advanced Medical Home: A Patient-Centered, Physician-Guided Model of Health Care. A Policy Monograph of the American College of Physicians. http://www.acponline.org/ advocacy/events/state_of_healthcare/statehc06_5.pdf. Published 2006. Accessed September 26, 2012.
9. Sia C, Tonniges TF, Osterhus E, Taba S. History of the medical home concept. Pediatrics. 2004;113(5)(suppl):1473-1478.
10. Barr MS. The need to test the patient-centered medical home. JAMA. 2008;300(7):834-835.
11. Medical Home Initiatives for Children With Special Needs Project Advisory Committee. American Academy of Pediatrics. The medical home. Pediatrics. 2002;110(1, pt 1):184-186.
12. Rogers JC. The patient-centered medical home movement—promise and peril for family medicine. J Am Board Fam Med. 2008;21(5):370-374.
13. Rosenthal TC. The medical home: growing evidence to support a new approach to primary care. J Am Board Fam Med. 2008;21(5):427-440.
14. Starfield B. Access, primary care, and the medical home: rights of passage. Med Care. 2008;46(10):1015-1016.
15. Starfield B, Shi L. The medical home, access to care, and insurance: a review of evidence. Pediatrics. 2004;113(5)(suppl):1493-1498.
16. Patient Centered Primary Care Collaborative. Patient-Centered Medical Home Building Evidence and Momentum. A compilation of PCMH pilot and demonstration projects. http://www.pcpcc.net/content/pcpcc_pilot_report.pdf. Published 2008. Accessed September 26, 2012.
17. Toomey SL, Chan E, Ratner JA, Schuster MA. The patient-centered medical home, practice patterns, and functional outcomes for children with attention deficit/hyperactivity disorder. Acad Pediatr. 2011;11(6):500-507.
18. Richmeier S. Will the real PCMH step forward? MGMA Connex. 2011;11(4):19-20.
19. Crabtree BF, Chase SM, Wise CG, et al. Evaluation of patient centered medical home practice transformation initiatives. Med Care. 2011;49(1):10-16.
20. Crosby JB. PCMH: a revolutionary model of care. J Am Osteopath Assoc. 2010;110(4)(suppl 5):eSii.
21. Kellerman R. The patient centred medical home—a new model of practice in the USA. Aust Fam Physician. 2009;38(5):279.
22. DeVoe JE, Wallace LS, Pandhi N, Solotaroff R, Fryer GE Jr. Comprehending care in a medical home: a usual source of care and patient perceptions about healthcare communication. J Am Board Fam Med. 2008;21(5):441-450.
23. Beal AC, Doty MM, Hernandez SE, Shea KK, Davis K. Closing the Divide: How Medical Homes Promote Equity in Health Care: Results From The Commonwealth Fund 2006 Health Care Quality Survey. New York: The Commonwealth Fund; http://www.commonwealthfund.org/Publications/Fund-Reports/2007/Jun/Closing-the-Divide--How-Medical-Homes-Promote-Equity-in-Health-Care--Results-From-The-Commonwealth-F.aspx. Published June 27, 2007. Accessed September 26, 2012.
24. Daaleman TP. The medical home: locus of physician formation. J Am Board Fam Med. 2008;21(5):451-457.
25. Gilfillan RJ, Tomcavage J, Rosenthal MB, et al. Value and the medical home: effects of transformed primary care. Am J Manag Care. 2010;16(8):607-614.
26. Reid RJ, Fishman PA, Yu O, et al. Patient-centered medical home demonstration: a prospective, quasi-experimental, before and after evaluation. Am J Manag Care. 2009;15(9):e71-e87.
27. McMullin ST, Lonergan TP, Rynearson CS. Twelve-month drug cost savings related to use of an electronic prescribing system with integrated decision support in primary care. J Manag Care Pharm. 2005;11(4):322-332.
28. McMullin ST, Lonergan TP, Rynearson CS, Doerr TD, Veregge PA, Scanlan ES. Impact of an evidence-based computerized decision support system on primary care prescription costs. Ann Fam Med. 2004;2(5):494-498.
29. Fischer MA, Vogeli C, Stedman M, Ferris T, Brookhart MA, Weissman JS. Effect of electronic prescribing with formulary decision support on medication use and cost. Arch Intern Med. 2008;168(22):2433-2439.
30. Fischer MA, Avorn J. Potential savings from increased use of generic drugs in the elderly: what the experience of Medicaid and other insurance programs means for a Medicare drug benefit. Pharmacoepidemiol Drug Saf. 2004;13(4):207-214.
31. Paulus RA, Davis K, Steele GD. Continuous innovation in health care: implications of the Geisinger experience. Health Aff (Millwood). 2008;27(5):1235-1245.
32. Scholle SH, Saunders RC, Tirodkar MA, Torda P, Pawlson LG. Patient-centered medical homes in the United States. J Ambul Care Manage. 2011;34(1):20-32.
33. Scholle SH, Roski J, Dunn DL, et al. Availability of data for measuring physician quality performance. Am J Manag Care. 2009;15(1):67-72.
34. Bergeson SC, Dean JD. A systems approach to patient-centered care. JAMA. 2006;296(23):2848-2851.
35. Glasgow RE, Orleans CT, Wagner EH. Does the chronic care model serve also as a template for improving prevention? Milbank Q. 2001; 79(4):579-612, iv-v.
36. O’Connor PJ, Sperl-Hillen JM, Pronk NP, Murray T. Primary care clinic-based chronic disease care: features of successful programs. Dis Manage Health Outcomes. 2001;9(12):691-698.
37. Feifer C, Ornstein SM, Nietert PJ, Jenkins RG. System supports for chronic illness care and their relationship to clinical outcomes. Top Health Inf Manage. 2001;22(2):65-72.
38. Rothman AA, Wagner EH. Chronic illness management: what is the role of primary care? Ann Intern Med. 2003;138(3):256-261.
39. Flottemesch TJ, Fontaine P, Asche SE, Solberg LI. Relationship of clinic medical home scores to health care costs. J Ambul Care Manage. 2011;34(1):78-89.
40. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the chronic care model, part 2. JAMA. 2002;288(15):1909-1914.
41. Solberg LI, Asche SE, Pawlson LG, Scholle SH, Shih SC. Practice systems are associated with high-quality care for diabetes. Am J Manag Care. 2008;14(2):85-92.
42. Starfield B. Is patient-centered care the same as person-focused care? Perm J. 2011;15(2):63-69.
43. Manning WG. The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ. 1998;17(3):283-295.
44. Manning WG Jr. Panel on cost-effectiveness in health and medicine recommendations: identifying costs. J Clin Psychiatry. 1999;60(suppl 3):54-56.
45. Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. J Health Econ. 2005; 24(3):465-488.
46. Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461-494.
47. Duan N. Smearing estimate: a non-parametric retransformation method. J Am Stat Assoc. 1983;78(383):605-610.
48. Duan N, Manning WG Jr, Morris CN, Newhouse JP. A comparison of alternative models for the demand for medical care. J Bus Econ Stat. 1983;1(2):115-126.
49. Perkins AJ, Kroenke K, Unützer J, et al. Common comorbidity scales were similar in their ability to predict health care costs and mortality. J Clin Epidemiol. 2004;57(10):1040-1048.
50. Starfield B. Family medicine should shape reform, not vice versa. Fam Pract Manag. 2009;16(4):6-7.
51. Solberg L Asche SE, Fontaine P, Flottemesch TJ, Anderson LH. Trends in quality during medical home transformation. Ann Fam Med. 2011;9(6):515-521.
52. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.
53. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27.