Letters
Table 2. Human Immunodeficiency Virus (HIV) Diagnoses Among Males With Infection Attributed to Male-to-Male Sexual Contact No. of HIV Diagnoses Among Males With Infection Attributed to Male-to-Male Sexual Contact by Year of Diagnosisa 2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
% Change
EAPC (95% CI)
13-24
2976
3207
3748
4107
4690
5449
6208
6528
6912
6919
132.5
10.5 (10.1 to 10.9)
25-34
7957
7474
7657
7506
7379
7867
7868
7854
7738
7929
–0.4
35-44
9782
9296
9284
8642
8563
8125
7264
6537
5824
5417
–44.6
45-54
3936
3933
4090
4145
4217
4564
4471
4185
4040
4145
5.3
≥55
1370
1342
1462
1439
1465
1609
1655
1581
1521
1623
18.5
2.0 (1.0 to 3.0)
26 021
25 251
26 240
25 838
26 313
27 614
27 466
26 685
26 035
26 033
0
0.3 (0.1 to 0.5)
Age group, y
Total
0.3 (0 to 0.6) –6.2 (–6.5 to –5.8) 0.6 (0.1 to 1.1)
Abbreviation: EAPC, estimated annual percentage change. a
The number of HIV diagnoses resulted from statistical adjustment that accounted for missing transmission category.
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Holtgrave reported that Johns Hopkins University received grant support from Johnson & Johnson and the Female Health Company outside of the scope of this article. No other disclosures were reported. Funding/Support: The CDC provides funds to all states and the District of Columbia to conduct the HIV surveillance data used in this study. Role of the Sponsor: The CDC had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclaimer: The findings and conclusions in this study are those of the authors and do not necessarily represent the views of the CDC. 1. Centers for Disease Control and Prevention (CDC); Health Resources and Services Administration; National Institutes of Health; HIV Medicine Association of the Infectious Diseases Society of America. Incorporating HIV prevention into the medical care of persons living with HIV. MMWR Recomm Rep. 2003; 52(RR-12):1-24. 2. Centers for Disease Control and Prevention (CDC). Vital signs: HIV prevention through care and treatment—United States. MMWR Morb Mortal Wkly Rep. 2011;60(47):1618-1623. 3. Centers for Disease Control and Prevention. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data—United States and 6 dependent areas—2011. http://www.cdc.gov/hiv/pdf/2011 _monitoring_hiv_indicators_hssr_final.pdf. Accessibility verified June 24, 2014. 4. Ries LAG, Eisner MP, Kosary CL, et al. SEER Cancer Statistics Review, 1973-1999, 2002. http://www.seer.cancer.gov/archive/csr/1973_1999/overview .pdf. Accessibility verified June 24, 2014. 5. Centers for Disease Control and Prevention (CDC). HIV testing and risk behaviors among gay, bisexual, and other men who have sex with men—United States. MMWR Morb Mortal Wkly Rep. 2013;62(47):958-962. 6. Centers for Disease Control and Prevention (CDC). Vital signs: HIV infection, testing, and risk behaviors among youths—United States. MMWR Morb Mortal Wkly Rep. 2012;61(47):971-976.
COMMENT & RESPONSE
Evaluating a Multipayer Medical Home Intervention To the Editor As physicians involved in the Pennsylvania Chronic Care Initiative (PACCI), a medical home pilot, we are concerned that the evaluation of the first phase had many inadequacies that steered Dr Friedberg and colleagues1 to the wrong conclusions. These conclusions have the potential to impede further payment reform and block continued redesign of primary care practices. This study examined data from the first 3 years of the initiative, a time frame that is too short to detect changes resulting from the intervention. Phase 1 of this program was de434
voted to building the infrastructure and culture to earn National Committee for Quality Assurance (NCQA) Patient-Centered Medical Home (PCMH) recognition and become a true medical home; reduction in costs of care was not a goal. In phase 2, pilot practices added case management and other quality interventions to the PCMH model. Our practice is just starting to observe reductions in emergency department visits, hospitalizations (including reductions in 30-day readmissions), and costs of care attributable to PCMH changes and the PACCI. The use of claims data has significant limitations. For example, one of our laboratories does not report to the payers any claims for blood work done for capitated patients; physicians instead complete gap reports via electronic medical record registries for the payers and submit results to the PACCI. These reports were not included in the analysis. Friedberg et al1 stated that “[p]oint estimates suggested improved performance among pilot practices relative to comparison practices.” However, in the propensity-weighted analyses, many more patients were included in pilot vs comparison practices. It is more difficult to get a larger cohort to goal than a smaller cohort. The smaller number of patients in comparison practices therefore makes it more difficult to find statistically significant differences in outcomes. The authors reported that there was not a strict 1:1 match of pilot vs comparison practices, but they did not adequately highlight potentially important implications of this mismatch. Other factors, such as insurance mix, number of trainees in a practice, and number of new patients enrolling in a practice, may have had a substantial effect on results. Our practice enrolls a large number of new patients monthly, many with poor control of hypertension, diabetes, and cholesterol. Getting new patients to goal is more challenging than keeping a stable cohort at goal. It is unclear if comparison practices faced similar challenges or if the authors accounted for these differences. George Valko, MD Richard Wender, MD Author Affiliations: Department of Family and Community Medicine, Jefferson Medical College, Philadelphia, Pennsylvania (Valko); American Cancer Society, Atlanta, Georgia (Wender). Corresponding Author: George Valko, MD, Jefferson Medical College of Thomas Jefferson University, Department of Family and Community Medicine, 833 Chestnut E, Philadelphia, PA 19107 ([email protected]).
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Letters
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Valko reported that his practice was one of the practices involved with the PACCI and was compensated for the work involved to create and develop the PCMH, attend meetings, and submit monthly reports. Dr Wender reported being the former chair of the Department of Family and Community Medicine at Jefferson Medical College and involved in the PACCI. 1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825.
To the Editor Dr Friedberg and colleagues1 analyzed a multipayer medical home intervention and failed to demonstrate meaningful outcomes. We performed a rigorous mixed-methods evaluation of the same PACCI through on-site observations and interviewing 118 individuals (clinicians, staff, and administrators) at 17 of the 25 participating primary care practices in the PACCI.2-4 We believe our evaluation provides important insights into the challenges of translating the PACCI into meaningful outcomes. The PACCI was modeled around improving outcomes in diabetes care for practices having at least NCQA level 1 PCMH recognition. No practices had NCQA recognition at the start of the initiative. By 18 months (at the start of our evaluation), 9 were level 3, 1 was level 2, and 7 were level 1. The NCQA standards rely heavily on health information technology infrastructure and do little to reflect the organizational culture of practices considered necessary to realizing full PCMH transformation. The PACCI incentives, based on NCQA status, thus potentially widened the gap between PCMH practice behaviors and meaningful outcomes. Our data suggested significant heterogeneity in achieving practice transformation, with many practices lacking the infrastructure and organizational culture necessary to realize the PCMH ideal. Striking differences between high- and low-performing practices were noted in their descriptions of shared vision and leadership styles.2,3 We identified barriers related to organizational culture and stakeholders’ mental models (ie, an individual’s sense of organizational role and expectations) driving the PCMH transformation process, manifesting as variations in feedback processes and team development and use.4 Our data described significant variation in practices’ reporting of structural capabilities (eg, health information technology) and fiscal stability resulting in technological and financial distractions. Although our evaluation started 18 months into the PACCI and continued into the initiative’s third year, it remains concerning that practices appeared in their infancy of practice transformation. These data call into question how to characterize a PCMH and how best to support regional adoption of the PCMH model. Other PACCI regional rollouts with better data on hospitalizations and readmissions, which are using incentive payments modified beyond NCQA to include required functions (eg, care management) and shared savings, may better demonstrate model performance based more on core functions rather than the limitations of NCQA recognition and what that actually represents. Peter F. Cronholm, MD, MSCE Michelle Miller-Day, PhD Robert Gabbay, MD, PhD
Author Affiliations: Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia (Cronholm); Department of Communication Studies, Chapman University, Orange, California (Miller-Day); Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts (Gabbay). Corresponding Author: Peter F. Cronholm, MD, MSCE, Department of Family Medicine and Community Health, University of Pennsylvania, 142-A Anatomy and Chemistry Bldg, 3620 Hamilton Walk, Philadelphia, PA 19104 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Drs Cronholm, Miller-Day, and Gabbay reported receiving a grant from the Agency for Healthcare Research and Quality. 1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825. 2. Gabbay RA, Friedberg MW, Miller-Day M, Cronholm PF, Adelman A, Schneider EC. A positive deviance approach to understanding key features to improving diabetes care in the medical home. Ann Fam Med. 2013;11(suppl 1): S99-S107. 3. Bleser WK, Miller-Day M, Naughton D, Bricker PL, Cronholm PF, Gabbay RA. Strategies for achieving whole-practice engagement and buy-in to the patient-centered medical home. Ann Fam Med. 2014;12(1):37-45. 4. Cronholm PF, Shea JA, Werner RM, et al. The patient centered medical home: mental models and practice culture driving the transformation process. J Gen Intern Med. 2013;28(9):1195-1201.
To the Editor We participated in the southwestern Pennsylvania version of the PACCI and achieved level 3 NCQA PCMH recognition. Contrary to the findings of Dr Friedberg and colleagues,1 our practice did find a significant decrease in low-density lipoprotein cholesterol and systolic blood pressure levels in 126 patients with diabetes who were aged 18 to 75 years over the 18-month period from 2009 to 2011, when we achieved level 3 PCMH recognition. However, no change was noted in high-density lipoprotein cholesterol levels, diastolic blood pressure, and coronary heart disease risk scores. Hemoglobin A1c levels decreased 1 percentage point on average. Although we do not definitively know the effect of the medical home on patients’ health and well-being, our findings indicate a need to study further the concept of the medical home in an objective manner regarding benefits for patients, the financial outlay, the effort required of physicians, and value to the health care system as a whole. We incurred debt of more than $40 000, despite the $98 000 incentive payment by the PACCI and $36 000 bonus payments with meaningful use certification. In addition, an informal assessment of physician burnout found that it was associated with requirements for extra documentation and registry oversight, which usually occurred in the evenings and on weekends. This corroborates the findings of Lewis et al, 2 who found that a higher PCMH score was associated with lower job satisfaction and increased burnout. Despite some positive changes, we ask if these results are worth it, given the increased cost and great potential for burnout. The apparent lack of value causes us to disagree with Schwenk’s Editorial3 proposing to find a home for the medical home. Joy Boone, MD Edgar A. Boone, MD
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Author Affiliations: University of Pittsburgh McKeesport Family Medicine Residency Program, McKeesport, Pennsylvania (J. Boone); Excela Health Medical Group, Greensburg, Pennsylvania (E. A. Boone). Corresponding Author: Joy Boone, MD, University of Pittsburgh McKeesport Family Medicine Residency Program, 2347 Fifth Ave, McKeesport, PA 15132 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825. 2. Lewis SE, Nocon RS, Tang H, et al. Patient-centered medical home characteristics and staff morale in safety net clinics. Arch Intern Med. 2012;172 (1):23-31. 3. Schwenk TL. The patient-centered medical home: one size does not fit all. JAMA. 2014;311(8):802-803.
In Reply Drs Valko and Wender suggest that a 3-year time horizon may be too short to observe changes in health care costs and utilization, reporting that in their medical home practice these changes occurred later. It is certainly possible that reducing costs and excess health care utilization may require sustained changes and longer observation periods. Continued evaluations are needed. Registry data are attractive sources of performance measurement, but they were not reported by the comparison practices. Despite the inaccuracies that can affect health plan claims and laboratory data, these data had the advantage of being collected using similar methods in both pilot and comparison practices in our study. This methodological consistency enabled us to attribute observed changes in performance to participation in a medical home intervention rather than to modifications to data reporting that occurred only among the pilot practices. In addition, we took steps to mitigate for the possibility of data inaccuracies reported by Valko and Wender by controlling for health plan enrollment in health maintenance organization products. Valko and Wender also raise concerns about unmeasured confounders biasing the results. As always, observational studies are vulnerable to bias due to unmeasured confounders. We controlled for available confounders that were most likely to pose threats to validity, including health plan, insurance product, and patient characteristics. However, other confounders such as those mentioned by Valko and Wender could nonetheless cause bias, thereby increasing or decreasing the apparent effectiveness of the intervention relative to its true effectiveness. Although a large, randomized trial design would overcome this vulnerability, Table 1 of our article demonstrated that pilot and comparison practices were reasonably well matched on observable characteristics at baseline, lessening the likelihood of bias due to unmeasured confounding. Dr Cronholm and colleagues provide valuable insights into the experiences of the 25 nonpediatric practices participating in the pilot we evaluated. Their observations illustrate the formidable efforts needed to achieve medical home transformation and the heterogeneity of these efforts across practices. They also suggest that medical home recognition standards can play important roles in medical 436
home pilots. Balancing these evolving standards with tailored intervention components may help practices transform more successfully. Drs Boone and Boone report improvements in some measures of care from their practice in southwest Pennsylvania, a region we are evaluating currently. However, the financial and personal costs associated with achieving these results are a reminder that medical home interventions are works in progress and that pilot participants are shouldering many of the risks of implementation. We expect that continuing objective evaluation of these efforts will produce the evidence needed to guide improvements in the effectiveness of future medical home interventions. Mark W. Friedberg, MD, MPP Eric C. Schneider, MD, MSc Rachel M. Werner, MD, PhD Author Affiliations: RAND Corporation, Boston, Massachusetts (Friedberg, Schneider); Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, Pennsylvania (Werner). Corresponding Author: Mark W. Friedberg, MD, MPP, 20 Park Plaza, Ste 920, Boston, MA 02116 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Friedberg reported having received compensation from the US Department of Veterans Affairs for consultation related to the medical home model and research support from the Patient-Centered Outcomes Research Institute via subcontract to the National Committee for Quality Assurance. Dr Werner reported having received grants from the Veterans Health Administration, Horizon, and Healthcare Innovation. No other disclosures were reported.
Liver Transplants Among US Veterans To the Editor Dr Goldberg and colleagues1 reported a disparity in the rates of US veterans waitlisted for liver transplantation and, once waitlisted, receiving a liver transplant when the veteran’s local Veteran’s Affairs (VA) medical center was located more than 100 miles from a transplant center. This disparity was observed regardless of whether the veteran received care at a Veterans Affairs Transplant Center (VATC) or a non-VATC. I believe that the authors imply that distance relationships affect veterans more so than similarly situated nonveterans and this requires clarification. Liver transplantation is provided to the US population by 139 specialized centers in 37 states, the District of Columbia, and Puerto Rico, 2 so logically the majority of patients in need of liver transplant services travel long distances, and often more than 100 miles. The Veterans Health Administration (VHA) provides liver transplant services either at 1 of 6 geographically dispersed VATCs or through non-VA purchased care based on clinical circumstances. Goldberg et al1 showed that veterans waitlisted at a VATC had an equivalent rate of receiving a liver transplant compared with those waitlisted at a non-VATC. Furthermore, any benefit of having a local VA hospital within 100 miles of a transplant center applied to both patients waitlisted at a VATC or a non-VATC. In a study in a nonveteran population,3 similar mortality outcome benefits were observed for patients living within 30 miles of a transplant center compared with patients living at greater distances.
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Table 2. Human Immunodeficiency Virus (HIV) Diagnoses Among Males With Infection Attributed to Male-to-Male Sexual Contact No. of HIV Diagnoses Among Males With Infection Attributed to Male-to-Male Sexual Contact by Year of Diagnosisa 2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
% Change
EAPC (95% CI)
13-24
2976
3207
3748
4107
4690
5449
6208
6528
6912
6919
132.5
10.5 (10.1 to 10.9)
25-34
7957
7474
7657
7506
7379
7867
7868
7854
7738
7929
–0.4
35-44
9782
9296
9284
8642
8563
8125
7264
6537
5824
5417
–44.6
45-54
3936
3933
4090
4145
4217
4564
4471
4185
4040
4145
5.3
≥55
1370
1342
1462
1439
1465
1609
1655
1581
1521
1623
18.5
2.0 (1.0 to 3.0)
26 021
25 251
26 240
25 838
26 313
27 614
27 466
26 685
26 035
26 033
0
0.3 (0.1 to 0.5)
Age group, y
Total
0.3 (0 to 0.6) –6.2 (–6.5 to –5.8) 0.6 (0.1 to 1.1)
Abbreviation: EAPC, estimated annual percentage change. a
The number of HIV diagnoses resulted from statistical adjustment that accounted for missing transmission category.
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Holtgrave reported that Johns Hopkins University received grant support from Johnson & Johnson and the Female Health Company outside of the scope of this article. No other disclosures were reported. Funding/Support: The CDC provides funds to all states and the District of Columbia to conduct the HIV surveillance data used in this study. Role of the Sponsor: The CDC had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclaimer: The findings and conclusions in this study are those of the authors and do not necessarily represent the views of the CDC. 1. Centers for Disease Control and Prevention (CDC); Health Resources and Services Administration; National Institutes of Health; HIV Medicine Association of the Infectious Diseases Society of America. Incorporating HIV prevention into the medical care of persons living with HIV. MMWR Recomm Rep. 2003; 52(RR-12):1-24. 2. Centers for Disease Control and Prevention (CDC). Vital signs: HIV prevention through care and treatment—United States. MMWR Morb Mortal Wkly Rep. 2011;60(47):1618-1623. 3. Centers for Disease Control and Prevention. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data—United States and 6 dependent areas—2011. http://www.cdc.gov/hiv/pdf/2011 _monitoring_hiv_indicators_hssr_final.pdf. Accessibility verified June 24, 2014. 4. Ries LAG, Eisner MP, Kosary CL, et al. SEER Cancer Statistics Review, 1973-1999, 2002. http://www.seer.cancer.gov/archive/csr/1973_1999/overview .pdf. Accessibility verified June 24, 2014. 5. Centers for Disease Control and Prevention (CDC). HIV testing and risk behaviors among gay, bisexual, and other men who have sex with men—United States. MMWR Morb Mortal Wkly Rep. 2013;62(47):958-962. 6. Centers for Disease Control and Prevention (CDC). Vital signs: HIV infection, testing, and risk behaviors among youths—United States. MMWR Morb Mortal Wkly Rep. 2012;61(47):971-976.
COMMENT & RESPONSE
Evaluating a Multipayer Medical Home Intervention To the Editor As physicians involved in the Pennsylvania Chronic Care Initiative (PACCI), a medical home pilot, we are concerned that the evaluation of the first phase had many inadequacies that steered Dr Friedberg and colleagues1 to the wrong conclusions. These conclusions have the potential to impede further payment reform and block continued redesign of primary care practices. This study examined data from the first 3 years of the initiative, a time frame that is too short to detect changes resulting from the intervention. Phase 1 of this program was de434
voted to building the infrastructure and culture to earn National Committee for Quality Assurance (NCQA) Patient-Centered Medical Home (PCMH) recognition and become a true medical home; reduction in costs of care was not a goal. In phase 2, pilot practices added case management and other quality interventions to the PCMH model. Our practice is just starting to observe reductions in emergency department visits, hospitalizations (including reductions in 30-day readmissions), and costs of care attributable to PCMH changes and the PACCI. The use of claims data has significant limitations. For example, one of our laboratories does not report to the payers any claims for blood work done for capitated patients; physicians instead complete gap reports via electronic medical record registries for the payers and submit results to the PACCI. These reports were not included in the analysis. Friedberg et al1 stated that “[p]oint estimates suggested improved performance among pilot practices relative to comparison practices.” However, in the propensity-weighted analyses, many more patients were included in pilot vs comparison practices. It is more difficult to get a larger cohort to goal than a smaller cohort. The smaller number of patients in comparison practices therefore makes it more difficult to find statistically significant differences in outcomes. The authors reported that there was not a strict 1:1 match of pilot vs comparison practices, but they did not adequately highlight potentially important implications of this mismatch. Other factors, such as insurance mix, number of trainees in a practice, and number of new patients enrolling in a practice, may have had a substantial effect on results. Our practice enrolls a large number of new patients monthly, many with poor control of hypertension, diabetes, and cholesterol. Getting new patients to goal is more challenging than keeping a stable cohort at goal. It is unclear if comparison practices faced similar challenges or if the authors accounted for these differences. George Valko, MD Richard Wender, MD Author Affiliations: Department of Family and Community Medicine, Jefferson Medical College, Philadelphia, Pennsylvania (Valko); American Cancer Society, Atlanta, Georgia (Wender). Corresponding Author: George Valko, MD, Jefferson Medical College of Thomas Jefferson University, Department of Family and Community Medicine, 833 Chestnut E, Philadelphia, PA 19107 ([email protected]).
JAMA July 23/30, 2014 Volume 312, Number 4
Copyright 2014 American Medical Association. All rights reserved.
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Letters
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Valko reported that his practice was one of the practices involved with the PACCI and was compensated for the work involved to create and develop the PCMH, attend meetings, and submit monthly reports. Dr Wender reported being the former chair of the Department of Family and Community Medicine at Jefferson Medical College and involved in the PACCI. 1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825.
To the Editor Dr Friedberg and colleagues1 analyzed a multipayer medical home intervention and failed to demonstrate meaningful outcomes. We performed a rigorous mixed-methods evaluation of the same PACCI through on-site observations and interviewing 118 individuals (clinicians, staff, and administrators) at 17 of the 25 participating primary care practices in the PACCI.2-4 We believe our evaluation provides important insights into the challenges of translating the PACCI into meaningful outcomes. The PACCI was modeled around improving outcomes in diabetes care for practices having at least NCQA level 1 PCMH recognition. No practices had NCQA recognition at the start of the initiative. By 18 months (at the start of our evaluation), 9 were level 3, 1 was level 2, and 7 were level 1. The NCQA standards rely heavily on health information technology infrastructure and do little to reflect the organizational culture of practices considered necessary to realizing full PCMH transformation. The PACCI incentives, based on NCQA status, thus potentially widened the gap between PCMH practice behaviors and meaningful outcomes. Our data suggested significant heterogeneity in achieving practice transformation, with many practices lacking the infrastructure and organizational culture necessary to realize the PCMH ideal. Striking differences between high- and low-performing practices were noted in their descriptions of shared vision and leadership styles.2,3 We identified barriers related to organizational culture and stakeholders’ mental models (ie, an individual’s sense of organizational role and expectations) driving the PCMH transformation process, manifesting as variations in feedback processes and team development and use.4 Our data described significant variation in practices’ reporting of structural capabilities (eg, health information technology) and fiscal stability resulting in technological and financial distractions. Although our evaluation started 18 months into the PACCI and continued into the initiative’s third year, it remains concerning that practices appeared in their infancy of practice transformation. These data call into question how to characterize a PCMH and how best to support regional adoption of the PCMH model. Other PACCI regional rollouts with better data on hospitalizations and readmissions, which are using incentive payments modified beyond NCQA to include required functions (eg, care management) and shared savings, may better demonstrate model performance based more on core functions rather than the limitations of NCQA recognition and what that actually represents. Peter F. Cronholm, MD, MSCE Michelle Miller-Day, PhD Robert Gabbay, MD, PhD
Author Affiliations: Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia (Cronholm); Department of Communication Studies, Chapman University, Orange, California (Miller-Day); Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts (Gabbay). Corresponding Author: Peter F. Cronholm, MD, MSCE, Department of Family Medicine and Community Health, University of Pennsylvania, 142-A Anatomy and Chemistry Bldg, 3620 Hamilton Walk, Philadelphia, PA 19104 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Drs Cronholm, Miller-Day, and Gabbay reported receiving a grant from the Agency for Healthcare Research and Quality. 1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825. 2. Gabbay RA, Friedberg MW, Miller-Day M, Cronholm PF, Adelman A, Schneider EC. A positive deviance approach to understanding key features to improving diabetes care in the medical home. Ann Fam Med. 2013;11(suppl 1): S99-S107. 3. Bleser WK, Miller-Day M, Naughton D, Bricker PL, Cronholm PF, Gabbay RA. Strategies for achieving whole-practice engagement and buy-in to the patient-centered medical home. Ann Fam Med. 2014;12(1):37-45. 4. Cronholm PF, Shea JA, Werner RM, et al. The patient centered medical home: mental models and practice culture driving the transformation process. J Gen Intern Med. 2013;28(9):1195-1201.
To the Editor We participated in the southwestern Pennsylvania version of the PACCI and achieved level 3 NCQA PCMH recognition. Contrary to the findings of Dr Friedberg and colleagues,1 our practice did find a significant decrease in low-density lipoprotein cholesterol and systolic blood pressure levels in 126 patients with diabetes who were aged 18 to 75 years over the 18-month period from 2009 to 2011, when we achieved level 3 PCMH recognition. However, no change was noted in high-density lipoprotein cholesterol levels, diastolic blood pressure, and coronary heart disease risk scores. Hemoglobin A1c levels decreased 1 percentage point on average. Although we do not definitively know the effect of the medical home on patients’ health and well-being, our findings indicate a need to study further the concept of the medical home in an objective manner regarding benefits for patients, the financial outlay, the effort required of physicians, and value to the health care system as a whole. We incurred debt of more than $40 000, despite the $98 000 incentive payment by the PACCI and $36 000 bonus payments with meaningful use certification. In addition, an informal assessment of physician burnout found that it was associated with requirements for extra documentation and registry oversight, which usually occurred in the evenings and on weekends. This corroborates the findings of Lewis et al, 2 who found that a higher PCMH score was associated with lower job satisfaction and increased burnout. Despite some positive changes, we ask if these results are worth it, given the increased cost and great potential for burnout. The apparent lack of value causes us to disagree with Schwenk’s Editorial3 proposing to find a home for the medical home. Joy Boone, MD Edgar A. Boone, MD
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Letters
Author Affiliations: University of Pittsburgh McKeesport Family Medicine Residency Program, McKeesport, Pennsylvania (J. Boone); Excela Health Medical Group, Greensburg, Pennsylvania (E. A. Boone). Corresponding Author: Joy Boone, MD, University of Pittsburgh McKeesport Family Medicine Residency Program, 2347 Fifth Ave, McKeesport, PA 15132 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815-825. 2. Lewis SE, Nocon RS, Tang H, et al. Patient-centered medical home characteristics and staff morale in safety net clinics. Arch Intern Med. 2012;172 (1):23-31. 3. Schwenk TL. The patient-centered medical home: one size does not fit all. JAMA. 2014;311(8):802-803.
In Reply Drs Valko and Wender suggest that a 3-year time horizon may be too short to observe changes in health care costs and utilization, reporting that in their medical home practice these changes occurred later. It is certainly possible that reducing costs and excess health care utilization may require sustained changes and longer observation periods. Continued evaluations are needed. Registry data are attractive sources of performance measurement, but they were not reported by the comparison practices. Despite the inaccuracies that can affect health plan claims and laboratory data, these data had the advantage of being collected using similar methods in both pilot and comparison practices in our study. This methodological consistency enabled us to attribute observed changes in performance to participation in a medical home intervention rather than to modifications to data reporting that occurred only among the pilot practices. In addition, we took steps to mitigate for the possibility of data inaccuracies reported by Valko and Wender by controlling for health plan enrollment in health maintenance organization products. Valko and Wender also raise concerns about unmeasured confounders biasing the results. As always, observational studies are vulnerable to bias due to unmeasured confounders. We controlled for available confounders that were most likely to pose threats to validity, including health plan, insurance product, and patient characteristics. However, other confounders such as those mentioned by Valko and Wender could nonetheless cause bias, thereby increasing or decreasing the apparent effectiveness of the intervention relative to its true effectiveness. Although a large, randomized trial design would overcome this vulnerability, Table 1 of our article demonstrated that pilot and comparison practices were reasonably well matched on observable characteristics at baseline, lessening the likelihood of bias due to unmeasured confounding. Dr Cronholm and colleagues provide valuable insights into the experiences of the 25 nonpediatric practices participating in the pilot we evaluated. Their observations illustrate the formidable efforts needed to achieve medical home transformation and the heterogeneity of these efforts across practices. They also suggest that medical home recognition standards can play important roles in medical 436
home pilots. Balancing these evolving standards with tailored intervention components may help practices transform more successfully. Drs Boone and Boone report improvements in some measures of care from their practice in southwest Pennsylvania, a region we are evaluating currently. However, the financial and personal costs associated with achieving these results are a reminder that medical home interventions are works in progress and that pilot participants are shouldering many of the risks of implementation. We expect that continuing objective evaluation of these efforts will produce the evidence needed to guide improvements in the effectiveness of future medical home interventions. Mark W. Friedberg, MD, MPP Eric C. Schneider, MD, MSc Rachel M. Werner, MD, PhD Author Affiliations: RAND Corporation, Boston, Massachusetts (Friedberg, Schneider); Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, Pennsylvania (Werner). Corresponding Author: Mark W. Friedberg, MD, MPP, 20 Park Plaza, Ste 920, Boston, MA 02116 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Friedberg reported having received compensation from the US Department of Veterans Affairs for consultation related to the medical home model and research support from the Patient-Centered Outcomes Research Institute via subcontract to the National Committee for Quality Assurance. Dr Werner reported having received grants from the Veterans Health Administration, Horizon, and Healthcare Innovation. No other disclosures were reported.
Liver Transplants Among US Veterans To the Editor Dr Goldberg and colleagues1 reported a disparity in the rates of US veterans waitlisted for liver transplantation and, once waitlisted, receiving a liver transplant when the veteran’s local Veteran’s Affairs (VA) medical center was located more than 100 miles from a transplant center. This disparity was observed regardless of whether the veteran received care at a Veterans Affairs Transplant Center (VATC) or a non-VATC. I believe that the authors imply that distance relationships affect veterans more so than similarly situated nonveterans and this requires clarification. Liver transplantation is provided to the US population by 139 specialized centers in 37 states, the District of Columbia, and Puerto Rico, 2 so logically the majority of patients in need of liver transplant services travel long distances, and often more than 100 miles. The Veterans Health Administration (VHA) provides liver transplant services either at 1 of 6 geographically dispersed VATCs or through non-VA purchased care based on clinical circumstances. Goldberg et al1 showed that veterans waitlisted at a VATC had an equivalent rate of receiving a liver transplant compared with those waitlisted at a non-VATC. Furthermore, any benefit of having a local VA hospital within 100 miles of a transplant center applied to both patients waitlisted at a VATC or a non-VATC. In a study in a nonveteran population,3 similar mortality outcome benefits were observed for patients living within 30 miles of a transplant center compared with patients living at greater distances.
JAMA July 23/30, 2014 Volume 312, Number 4
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