By Geoffrey C. Lamb, Maureen A. Smith, William B. Weeks, and Christopher Queram

Transcription

1 Quality Of Care doi: /hlthaff HEALTH AFFAIRS 32, NO. 3 (2013): Project HOPE The People-to-People Health Foundation, Inc. By Geoffrey C. Lamb, Maureen A. Smith, William B. Weeks, and Christopher Queram Publicly Reported Quality-Of-Care Measures Influenced Wisconsin Physician Groups To Improve Performance Geoffrey C. Lamb (glamb@ mcw.edu) is a professor of internal medicine and director of the Quality Improvement and Patient Safety Pathway at the Medical College of Wisconsin, in Milwaukee. Maureen A. Smith is an associate professor at the School of Medicine and Public Health, University of Wisconsin Madison. William B. Weeks is a professor of psychiatry and of community and family medicine at the Geisel School of Medicine at Dartmouth, in Hanover, New Hampshire. Christopher Queram is president and CEO of the Wisconsin Collaborative for Healthcare Quality, in Middleton. ABSTRACT Public reporting of how physicians and hospitals perform on certain quality of care measures is increasingly common, but little is known about whether such disclosures have an impact on the quality of care delivered to patients. We analyzed fourteen publicly reported quality of ambulatory care measures from 2004 to 2009 for the Wisconsin Collaborative for Healthcare Quality, a voluntary consortium of physician groups. We also fielded a survey of the collaborative s members and analyzed Medicare billing data to independently compare members performance to that of providers in the rest of Wisconsin, neighboring states, and the rest of the United States. We found that physician groups in the collaborative improved their performance during the study period on many measures, such as cholesterol control and breast cancer screening. Physician groups reported on the survey that publicly reported performance data motivated them to act on some, but not all, of the quality measures. Our study suggests that large group practices will engage in quality improvement efforts in response to public reporting, especially when comparative performance is displayed, as it was in this case on the collaborative s website. Public reporting of how health care providers perform on designated measures of care quality is increasingly common in health care. 1 However, the enthusiasm for public reporting is ahead of the science supporting it, especially in the context of physicians and physician groups. 2,3 Constance Fung and coauthors conducted a systematic review of public reporting and found that rigorous evaluation of many public reporting systems was lacking. 3 The authors identified only eleven studies that addressed the impact on quality improvement of publicly reported performance on quality measures. All of these eleven studies were essentially hospital based and focused primarily on mortality or cardiac procedures. 3 6 Fung and coauthors were unable to identify any published studies of the effect of publicly reported performance data on quality improvement activity among physicians or physician groups. 3 The authors stated that more existing reporting systems should be evaluated in studies using rigorous designs with a plausible comparison strategy, so that secular trends and bias from the intervention effect can be distinguished. 3(p121) The Wisconsin Collaborative for Healthcare Quality provides an opportunity to study such a reporting system. The collaborative is a voluntary, statewide consortium of physician groups, hospitals, health plans, and employers working together to improve the quality and cost of health care in Wisconsin. 7,8 Member physician groups care for half of the patients in the state. Since Health Affairs March :3

2 the collaborative has been posting the member physician groups performance on quality of ambulatory care measures on a publicly accessible website. The project that we report on here was designed to study the impact of the first five years of that public reporting on the collaborative as a whole and on the individual participating groups. This is one of the first studies to provide insights into the impact of public reporting on large physician groups. Study Data And Methods This project was designed as a retrospective cohort study focusing on the Wisconsin Collaborative for Healthcare Quality s reporting of ambulatory measures during In the absence of a randomized controlled trial, the analysis was structured using a three-prong approach. The first was to determine whether there was measurable improvement among the collaborative s participants with respect to the outcomes being reported. The second was to survey each clinic site to assess how participants responded to the information reported. And the third was to use the resources of the Dartmouth Atlas Project, through the Dartmouth Institute for Health Policy and Clinical Practice, to obtain an independent, external measurement of the collaborative s performance over time and compare it to the performance of providers not participating in the collaborative. At the time this study was initiated, in mid- 2009, twenty physician practice groups were participating in the collaborative. Each group contained between eight and a hundred affiliated clinics, both primary care and multispecialty. Each member group commits to reporting yearly to the collaborative outcomes for a number of quality measures. The groups are responsible for collecting their own data, using methods that are strictly defined by a committee with representatives from each of the groups. The results are independently audited and validated by the collaborative, with oversight from a multistakeholder audit committee that includes leaders from health care provider organizations, health plans, and purchasing partners. Results for each physician group are posted by name on the collaborative s website, where they are available for public viewing. 8 Performance Of Collaborative Participants Over Time In the initial analysis, we assessed each measure to determine if there was an improvement in the mean performance of the collaborative as a whole. Using the group-level results reported to the collaborative each year, we compared performance on each measure, year to year. Analyses were performed to determine how many years were required to achieve significant improvement on each measure that is, achievement of a difference from baseline with greater than 95 percent confidence. Statistical testing included pairwise t tests and Tukey s range test (a method for analyzing multiple comparisons over time). Next, the trend within each group was analyzed. The documented performance from each annual report was compared with the baseline performance. We controlled for year and correlation among consecutive years within the same group. In addition, performance was compared among consecutive intervention years (year 1 versus year 2, year 2 versus year 3, and year 3 versus year 4). The percent improvement by year and the rate of improvement for each group over the period of participation were then estimated as a linear trend or slope, adjusting for group size and year. Finally, the groups were ordered by their rank during the year they first reported data, and this rank was compared to the subsequent rate of improvement. 9,10 The degree of correlation between the rank and rate of improvement was estimated as a coefficient of determination (R 2 ). Groups with only one year of participation in a measure were excluded from the analysis of that measure. The Survey The University of Wisconsin Survey Center was contracted to conduct a mail survey of the physician groups and their clinics. The survey asked about the clinics characteristics and knowledge of the collaborative s measures, and whether projects had been undertaken specifically in response to the collaborative s reporting and specific types of quality improvement initiatives The survey was conducted in 2009 and addressed efforts through Measures included only those reported prior to 2007 including those for diabetes mellitus, hypertension, and cancer screening so as to ensure incorporating only measures for which the clinics had time to develop an intervention. The strategy included a full mailing to the group leadership, a postcard reminder, a repeat mailing to those who had not returned their surveys, and reminder telephone calls before the first and last mailings to boost response rates. Quality Measures The Dartmouth Institute for Health Policy and Clinical Practice has an existing Medicare administrative data set derived from a 20 percent sample of all fee-forservice beneficiaries in the United States who are fully eligible by age for Medicare Parts A and B. The institute has developed a number March :3 Health Affairs 537

3 Quality Of Care of estimates of quality measures using these administrative data. At the time of this study, data were available for the period The institute worked with a subsidiary of IMS Health, an international company that supplies the pharmaceutical industry with sales data and consulting services, to identify physicians who worked at specific sites. Using the site addresses for the member clinics in the Wisconsin Collaborative for Healthcare Quality, the institute was able to link a list of clinic sites to the physicians working at those sites. Within the Medicare data set, patients were assigned to physicians annually based on a plurality of their outpatient visits, giving priority to primary care physicians. Through this linkage, patients were assigned to clinic sites and identified as being patients of the collaborative or not. Three control populations were created for comparison purposes: Wisconsin residents not assigned to physicians in the collaborative; residents of Iowa and South Dakota combined; and residents of the remainder of the United States. Iowa and South Dakota were selected because they had no active public reporting effort, but the states physician leadership had an existing relationship with the collaborative s leadership. Four annual cohorts were created, one for each year of the study. Thus, assignment and location of study participants could change each year. Existing Dartmouth quality measures derived from the Medicare billing data that were similar to the collaborative s measures included lipid profiles and glycohemoglobin (blood sugar) tests for patients with diabetes ages and breast cancer screening for women ages Dartmouth also captured the performance of eye exams for patients with diabetes, which is derived from the same cohort of patients and providers as the other diabetes process measures but is not a measure that the collaborative reports. Performance of all four annual cohorts was analyzed in the same manner, using the four quality measures derived from the administrative data sets. Multivariate analyses were performed to compare the collaborative s member groups in aggregate to the three control populations, adjusting for differences in age and sex (for patients with diabetes only), race, education, and income. Values for education level and income were based on the average for the patient s ZIP code, obtained from Census Bureau data for the year The overall rate of test receipt and rate of change were compared simultaneously. Statistical adjustment was performed to account for multiple comparisons. 16 Limitations Because a randomized controlled study was not possible, there were several potential areas of bias or weakness. The decision to join the Wisconsin Collaborative for Healthcare Quality is voluntary; thus, the members are highly motivated. Patients of members also tend to be somewhat more affluent and less likely to be on Medicaid than the patients in the comparison groups. Patients from a lower socioeconomic status may be less likely to seek care and obtain recommended screening tests than those from a higher socioeconomic status. Despite attempts to account for these differences using regression techniques in the analysis, this may create a bias in favor of better performance among the collaborative s members. The information collected on the group practice and clinic surveys were dependent on respondent recall. Any attribution of improvement efforts to the collaborative s influence was potentially subjective. Finally, the Medicare analysis performed by the Dartmouth Institute was limited by the availability of the data to a four-year time span. This period of time was somewhat short relative to the time frames needed to observe improvement and may not allow for adequate comparisons. Study Results Twenty physician groups representing 582 affiliated clinics were members of the Wisconsin Collaborative for Healthcare Quality and thus eligible to participate in the study. Two groups elected not to participate because of competing responsibilities. Two other groups withdrew from the collaborative during the study period. One group had recently merged with another group; the two chose to report their historic data as two separate entities. Accordingly, our analyses of the performance of individual physician groups include seventeen entities, which represented 409 clinics. For analyses of the collaborative s performance in aggregate, the data include the performance of all twenty physician groups. Performance Measures For the collaborative as a whole, each measure showed an increase in the overall mean, ranging from a low of 1.2 percent for low-density lipoprotein cholesterol control in patients with coronary artery disease to a high of 17.3 percent for monitoring kidney function in patients with diabetes (Exhibit 1). With the exception of cervical cancer screening, a significant improvement was seen in all measures that were implemented before the reporting period and that therefore had at least three years of reporting. At the group level, substantially more groups 538 Health Affairs March :3

4 improved significantly during the years that they reported to the collaborative than stayed the same or worsened with the exception of lowdensity lipoprotein cholesterol control in patients with coronary artery disease (Exhibit 1). There was a strong correlation between the initial numerical rank of a group compared to its peers and that group s subsequent rate of improvement. In general, programs that were initially ranked the lowest compared to their counterparts improved at a greater rate, while the higher-performing groups generally demonstrated less change regardless of the overall compliance rate (Exhibit 2). Response To Public Reporting Of the seventeen groups that responded to our survey, six reported that they managed quality improvement either centrally or regionally. These six groups returned either a single survey for all of their clinics or reported on subgroups of clinics that had the same quality improvement experience. The other eleven groups returned surveys filled out for each clinic separately. Combining the two approaches, we had cliniclevel information on quality improvement activities for 409 of the original 582 clinics, or 70 percent. Sixteen of the groups responded to a question asking whether they formally chose to give priority to any of the collaborative s quality improvement measures, and whether the decision was in response to reporting. It was common for member organizations to focus on the collaborative s measures during the study period, and fifteen groups reported formally giving priority to at least one quality improvement measure in response to the collaborative s reporting. Nine groups indicated that they always or nearly always set their priorities in response to the collaborative s reporting; six groups reported that they sometimes did so. The decisions of groups to focus on collaborative measures evolved over time, with an increasing proportion of the groups taking on collaborative measures each year (Exhibit 3). Groups reported a substantial amount of activity in implementing systems and procedures to improve care quality and outcomes. 17 As reported previously by our group, over time the mean number of quality improvement interventions for each condition increased. This increase was particularly true for diabetes, with the mean number of interventions rising from 5.0 (standard deviation: 3.9) to 8.7 (standard deviation: 4.5) between 2003 and For hypertension, there was a noticeable increase beginning in 2006, with the mean number of interventions adopted across clinics rising from 1.7 (standard deviation: 2.4) in 2006 to 3.9 Exhibit 1 Improvement In Aggregate Performance On Ambulatory Care Measures By Members Of The Wisconsin Collaborative For Healthcare Quality, Measure (mean number of projects initiated) Diabetes (8.7) Initial year reported Significant improvement (standard deviation: 2.7) in The most common initiatives implemented by the collaborative s members at care sites were adopting guidelines (87 percent), and patient reminders (82 percent). Comparing Collaborative Participants With Other Providers Based on the Dartmouth Institute s analysis of Medicare billing data, providers in the collaborative outperformed those in the comparison groups the rest of Wisconsin, the nearby states of Iowa and South Dakota together, and the rest of the United States in measures of glycohemoglobin testing and low-density lipoprotein cholesterol testing in patients with diabetes and in breast cancer screening, all three of which are publicly reported through the collaborative (Exhibit 4). In each of these measures, there was a trend toward a higher rate of improvement Number of years before improvement HbA1c control 2004 Yes HbA1c testing 2006 No a 2.0 Kidney function monitored 2004 Yes LDL control 2004 Yes LDL testing 2004 Yes Blood pressure control 2007 No a 2.0 Coronary artery disease b LDL control 2008 No a 1.2 LDL testing 2008 No a 1.9 Uncomplicated hypertension (3.9) Blood pressure control 2005 Yes Screening or preventive measures (6.9) Pneumococcal vaccinations b 2008 No a 4.3 Breast cancer screening c (2.6) 2006 Yes Cervical cancer screening c (2.5) 2006 No a 4.3 Colorectal cancer screening c (1.8) 2006 Yes Improvement since first year (percentage points) SOURCE Authors analysis of data reported to the Wisconsin Collaborative for Healthcare Quality. NOTES Significant improvement is a difference from baseline with greater than 95 percent confidence. Hemoglobin A1c (HbA1c) control is < 7:0%. Low-density lipoprotein cholesterol (LDL) control is < 100 mg=dl. Blood pressure control is < 130=80 mmhg in people with diabetes and < 140=90 mmhg for people with uncomplicated hypertension. a Not applicable. b Mean number of projects initiated not assessed. c Patients with a history of cancer no longer excluded as of March :3 Health Affairs 539

5 Quality Of Care Exhibit 2 Rate Of Improvement Of Physician Groups In The Wisconsin Collaborative For Healthcare Quality On Ambulatory Care Measures, Measure Diabetes Years reported Groups improved a Groups worsened a Rate of improvement Rank versus slope (R 2 ) b HbA1c control ** 0.17 HbA1c testing Kidney function monitored ** 0.51 LDL control ** 0.47 LDL testing ** 0.30 Blood pressure control Coronary artery disease LDL control LDL testing ** 0.36 Screening or preventive measures Pneumococcal vaccinations ** 0.14 Breast cancer screening ** 0.36 Cervical cancer screening ** 0.35 Colorectal cancer screening ** 0.27 SOURCE Authors analysis of data reported to the Wisconsin Collaborative for Healthcare Quality. NOTES Hemoglobin A1c (HbA1c) control is < 7:0%. Low-density lipoprotein cholesterol (LDL) control is < 100 mg=dl. Blood pressure control is < 130=80 mmhg. a Groups with only one year of participation in a measure were excluded from the analysis of that measure. Thus, there were only fifteen groups for some measures. b R 2 equals coefficient of determination for the linear equation estimating the relationship between rank and rate of improvement (slope). **p < 0:05 Exhibit 3 during the study years for participants in the collaborative, compared to the comparison groups, but this did not reach significance. In contrast, patients in Iowa and South Dakota Reported Reasons For Initiating Quality Improvement Measures, Physician Groups In The Wisconsin Collaborative For Healthcare Quality (WCHQ) SOURCE Authors analysis of University of Wisconsin Survey Center surveys of physician groups in the Wisconsin Collaborative for Healthcare Quality. were more likely to have received a diabetesrelated eye examination than were patients of providers in the collaborative. This measure is not publicly reported by the collaborative (Exhibit 4). The patients in the comparison groups were comparable to patients in the collaborative, in terms of age and sex. The collaborative s patients differed more from those in the comparison groups in terms of racial makeup, income, and percentage of Medicaid patients (Exhibit 4). Discussion The three components of this study provide useful insights into the impact of voluntary public reporting of ambulatory care measures on large independent physician provider groups. Although much of this evidence is circumstantial, this study takes advantage of realist evaluation methods, reflecting how concepts and improvement efforts are taken up in actual practice. 18 During the period of this study, which reflects the first five years of the public reporting effort of the Wisconsin Collaborative for Healthcare Quality, the overall performance of the collaborative s members in the aggregate improved significantly. All of the physician groups saw some improvement on a majority of the measures. In particular, the groups whose baseline performance ranked the lowest among their peers tended to improve at the greatest rate. This improvement occurred independently of the actual compliance rate or of the spread between the top and bottom performers, which suggests that it was more than just a regression to the mean or a ceiling effect among the top performers. The survey component reinforced the concept that the annual public reports influenced improvement. Most participants reported on the survey that they focused at least some improvement efforts in response to their performance on reported measures. More than half of the clinics chose their improvement efforts solely in response to reported measures. Nevertheless, it was clear that none of the physician groups was able to address all of the collaborative s measures at the same time. This suggests that groups chose to focus their improvement efforts on certain measures. The Dartmouth Institute was able to provide an independent measurement of provider performance based solely on Medicare billing data, thus establishing a common platform to use in comparing physicians performance that was separate from the collaborative s internal data. It was reassuring to note that participants in the collaborative tended to perform at a higher level 540 Health Affairs March :3

6 than comparison groups in places where such public reporting is not available. Although performance on these measures improved elsewhere in the country as well, the members of the collaborative consistently performed at a higher level and tended to improve at a faster rate than the comparison groups. Because the collaborative s members performed well to begin with, it is difficult to attribute the rate of improvement solely to the public reporting effort. However, the role of public reporting of the measures is reinforced by the observation that on eye exams for patients with diabetes, the collaborative s members performed no better than the comparison groups. This measure involves the same diabetes patient population as the glycohemoglobin and lipid testing. All three tests are recommended as best practices nationally, and only the eye exam was not one of the collaborative s publicly reported measures. It is hard to identify a plausible explanation other than the influence of public reporting for why the collaborative s members should perform so much better on glycohemoglobin and lipid testing than they did on eye examination. The findings of this study are consistent with those of Lawrence Casalino and coauthors, 11 who demonstrated that large group practices are more likely to incorporate care management processes in response to incentives, such as external recognition, than if such incentives are absent. It is not clear whether these findings can be extrapolated to small or medium-size groups. In essence, public reporting creates a milieu in which practices compete for external recognition and strive to avoid the negative aspect of publicly being identified at the bottom of the list. Those measures on which a provider group ranked the lowest compared to its peers were the measures on which the group was most likely to demonstrate the most rapid improvement. Thus, comparative public reporting of quality measures was associated with overall improvement in performance on those measures among members of the participating groups. In our cohort, provider groups focused at least some of their improvement efforts on measures that were reported publicly. However, most groups limited the number of measures on which they chose to work, suggesting that the nature and number of quality measures had to be chosen carefully. John Colmers has pointed out that the most successful approaches to public reporting and transparency have resulted from partnerships involving the public and private sectors, as well as purchasers and providers. 1 Exhibit 4 Comparisons Between Providers In The Wisconsin Collaborative For Healthcare Quality (WCHQ) And Control Populations WCHQ members Patients demographic characteristics Control populations WI, not WCHQ Certainly this characterizes the nature of the Wisconsin Collaborative for Healthcare Quality. Conclusion This study supports the concept that voluntary reporting of rigorously defined quality measures, as done by the collaborative, helps to drive improvement for all participants. However, this study has broader importance. It is one of the first studies of the impact of public reporting on providers and provider groups. As such, it addresses one of the key gaps in our knowledge of public reporting, as emphasized by Fung and coauthors, 3 and it provides useful insights into how independent provider groups might respond to public reporting of ambulatory measures. Unfortunately, this study was not structured IA and SD US, not IA, SD, or WI Number 42,620 56,680 78,568 4,582,626 Mean age (years) Female (%) Black (%) Medicaid during period (%) median household income ($) 46,292 45,058 38,622 43,945 Ambulatory care measures HbA1c testing 2004 (%) 88.3 a (%) 90.7 a * Odds ratio b 1.06 c 1.06 c 1.05 c Eye testing 2004 (%) a (%) a 68.6 Odds ratio b LDL testing 2004 (%) 79.4 a (%) 85.2 a Odds ratio b c 1.07 a Breast cancer screening 2004 (%) 74.9 a (%) 76.8 a Odds ratio b SOURCE Dartmouth Institute for Health Policy and Clinical Practice analysis of Medicare administrative data. NOTES All four years from 2004 to 2007 were included in analysis of change. Because of multiple comparisons, a p value of < 0:017 was required for significance. Odds ratios are annual change in the Wisconsin Collaborative for Healthcare Quality compared to annual change in the population in the relevant column. WI is Wisconsin. IA is Iowa. SD is South Dakota. HbA1c is hemoglobin A1c. LDL is low-density lipoprotein cholesterol. a p < 0:017 b Not applicable. c p < 0:05 March :3 Health Affairs 541

7 Quality Of Care to determine whether specific improvement efforts correlated with observed outcomes. This remains an opportunity for further research. From a public policy perspective, our results suggest that large group practices will engage in quality improvement efforts in response to public reporting, especially when comparative performance is displayed. The selection of measures being reported and the group s relative performance on those measures will contribute to how it prioritizes its efforts. No group was able to respond to all reported measures, which suggests that it is important to carefully select the measures that are chosen for public reporting efforts. Nevertheless, participation in a public reporting effort is associated with overall improvement for all the participants. These results were presented at the annual meeting of the Society of General Internal Medicine, Phoenix, Arizona, May 2011, and at an invited presentation to the AQA alliance, Washington, D.C., October This study was supported through a grant from the Commonwealth Fund (No ). The article would not have been possible without the contributions of the study coordinator, Lucy Stewart; thoughtful editing by Alexandra Wright; statistical analyses contributed by Daniel Gottlieb, Daniel Eastwood, and Matt Gigot (in particular, Exhibit 1); and the unselfish efforts of the many site coordinators and members of the Wisconsin Collaborative for Healthcare Quality. NOTES 1 Colmers JM. Public reporting and transparency [Internet]. New York (NY): Commonwealth Fund; 2007 Jan [cited 2013 Jan 31]. (Publication No. 988). Available from: doc/colmers_pubreporting transparency_988.pdf 2 Marshall MN, Romano PS, Davies HT. How do we maximize the impact of the public reporting of quality of care? Int J Qual Health Care. 2004; 16(Suppl 1):i Fung CH, Lim YW, Mattke S, Damberg C, Shekelle PG. Systematic review: the evidence that publishing patient care performance data improves quality of care. Ann Intern Med. 2008;148(2): Hibbard JH, Stockard J, Tusler M. Does publicizing hospital performance stimulate quality improvement efforts? Health Aff (Millwood). 2003;22(2): Hibbard JH, Stockard J, Tusler M. Hospital performance reports: impact on quality, market share, and reputation. Health Aff (Millwood). 2005;24(4): Romano PS. Improving the quality of hospital care in America. N Engl J Med. 2005;353(3): Greer AL. Embracing accountability: physician leadership, public reporting, and teamwork in the Wisconsin Collaborative for Healthcare Quality [Internet]. New York (NY): Commonwealth Fund; 2008 Jun [cited 2013 Jan 31]. Available from: documents/embracing_ Accountability.pdf 8 Wisconsin Collaborative for Healthcare Quality [home page on the Internet]. Middleton (WI): WCHQ; [cited 2013 Jan 21]. Available from: 9 Liang K, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1): Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J Am Stat Assoc. 1995;90(429): Casalino L, Gillies RR, Shortell SM, Schmittdiel JA, Bodenheimer T, Robinson JC, et al. External incentives, information technology, and organized processes to improve health care quality for patients with chronic diseases. JAMA. 2003; 289(4): Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001;20(6): Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998;1(1): Bonomi AE, Wagner EH, Glasgow RE, VonKorff M. Assessment of Chronic Illness Care (ACIC): a practical tool to measure quality improvement. Health Serv Res 2002; 37(3): Alemi F, Safaie FK, Neuhauser D. A survey of 92 quality improvement projects. Jt Comm J Qual Improv. 2001;27(11): We used a Bonferroni adjustment for multiple comparisons. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. 1995;310(6973): Smith MA, Wright A, Queram C, Lamb GC. Public reporting helped drive quality improvement in outpatient diabetes care among Wisconsin physician groups. Health Aff (Millwood). 2012;31(3): Pawson R, Tilley N. Realistic evaluation. London: Sage; Health Affairs March :3

8 ABOUT THE AUTHORS: GEOFFREY C. LAMB, MAUREEN A. SMITH, WILLIAM B. WEEKS & CHRISTOPHER QUERAM Geoffrey C. Lamb is a professor of internal medicine at the Medical College of Wisconsin. In this month s Health Affairs, Geoffrey Lamb and coauthors report on their study of whether and how public reporting of measures of ambulatory care quality influenced members of the Wisconsin Collaborative for Healthcare Quality, a voluntary consortium of physician groups. Analyzing a survey of the collaborative s membersand Medicare billing data, the authors found that physician groups in the collaborative improved their performance on many measures, such as cholesterol control and breast cancer screening and the groups themselves reported on the survey that the publicly reported performance data, displayed on the collaborative s website,did motivate them to act on some of the quality measures. The authors view these facts as proof that public reporting will prompt large group practices to engage in quality improvement efforts, especially when comparative performance is displayed. Lamb is a professor of internal medicine and directs the Quality Improvement and Patient Safety Pathway, Medical College of Wisconsin. He is section chief of hospital medicine and associate director of the Joint Quality Office at Froedtert Memorial Lutheran Hospital and the Medical College of Wisconsin. Lamb s research interests include screening tests, resident education, and venous thromboembolism. He earned a medical degree from Dartmouth. Maureen A. Smith is an associate professor at the University of Wisconsin School of Medicine and Public Health. Maureen Smith is an associate professor at the School of Medicine and Public Health, University of Wisconsin Madison, where she leadseffortstocreate,consolidate, and translate new and existing knowledge into practice to improve health care delivery and health outcomes. Her leadership roles include faculty director of the Health Innovation Program, director of the communityacademic partnerships core of the Institute for Clinical and Translational Research, and associate director of Population Sciences at the University of Wisconsin Paul P. Carbone Comprehensive Cancer Center. Smith s research focuses on improving the quality of the health care system for aging people. She earned a master s degreeinpublic health and a medical degree from Yale University, as well as a doctorate in health services research, with a concentration in policy and administration, from the University of Minnesota. William B. Weeks is aprofessoratthe Geisel School of Medicine at Dartmouth. WilliamWeeksisaprofessorof psychiatry and of community and family medicine at the Geisel School of Medicine at Dartmouth. His research interests focus on business and economic aspects of health services delivery, particularly as they relate to improving the value added by health care systems. Weeks has also studied dual use of the Department of Veterans Affairs and Medicare among veterans living in rural areas. He was named researcher of the year by the National Rural Health Association in Weeks holds an MBA from Columbia University and a medical degree from the University of Texas Medical Branch at Galveston. Christopher Queram is president and CEO of the Wisconsin Collaborative for Healthcare Quality. Christopher Queram is president and CEO of the Wisconsin Collaborative for Healthcare Quality, a voluntary consortium of organizations working to improve the quality and affordability of health care, and the health of the population in Wisconsin. He is a member of numerous advisory bodies, including the Agency for Healthcare Research and Quality s National Advisory Council, the AQA Steering Committee, the Quality Alliance Steering Committee, and the editorial advisory group of the Commonwealth Fund publication Quality Matters. Queramearneda master s degree in health services administration from the University of Wisconsin Madison. March :3 Health Affairs 543