Although there are nearly 1 billion outpatient follow-up visits annually in the United  States, few data exist documenting evidence-based follow-up intervals for the most common and costly chronic conditions.

		Evidence-based follow-up intervals must be established based on healthcare outcomes.

		Evidence-based follow-up intervals have the potential to reduce healthcare costs per person and improve access without compromising or restricting care.

Public concern regarding access to care combined with increasing pressure to curtail healthcare costs has prompted physicians to think critically about how best to manage chronic disease. Perhaps surprisingly, Americans face long wait times compared with other industrialized nations. A 2010 Commonwealth Fund study of 11 industrialized countries found waiting times were longer in the United States than in all the other countries except Canada, Norway, and Sweden.

 Moreover, the study showed that only 57% of patients were able to access a same-day or next-day appointment when they were sick or needed care, compared with top-ranking Switzerland, where 93% of patients described being able to secure an appointment under these conditions. Similarly, 19% of   patients in the United States waited 6 or more days for an appointment compared with only 2% of patients in Switzerland.

Several specialties face a shortage of providers, and geographic inequities also exist in almost all areas.

 This problem is confounded by increasing patient demand in an aging population and slow growth in physician supply, which lags behind other countries on a per capita basis, and is further exacerbated by economic disparities.

Where might there be more room for patients in this system? Notably, a substantial portion of outpatient office visits  are follow-up  visits. According to the National Health Statistics Report for 2009, there were nearly 1 billion office visits in 2009, 30% of which were  for routine follow-up of a chronic problem and an additional 26% of which were for preventive care or follow-up of an acute  condition. The remaining 42% were for the evaluation of a new problem or an exacerbation of a chronic condition.

There are 3 obvious questions. (1) Are these follow- up visits and their timing determined scientifically or by convenience and habit?  (2) Is there an evidence base to support physicians’ practice patterns? (3) If there is evidence to support physicians’ practice patterns, are physicians adhering to those guidelines? If not, scheduling habits may be unnecessarily contributing to the problems  f limited access, excessive utilization, and excessive costs, without improvement in healthcare outcomes.

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for performing a systematic review, we used the PubMed database to search for studies discussing evidence-based guidelines for follow-up intervals for the top 5 chronic conditions accounting for the greatest number of outpatient visits in 2010 (mental disorders, back problems, arthritis, chronic obstructive pulmonary disease/asthma, hypertension).

 These 5 conditions accounted for approximately $281  billion in healthcare expenditures in 2010.

This search yielded 330 studies. Abstracts of these studies were reviewed, and guidelines with recommendations for follow-up interval timing were included. Eight studies recommended specific follow-up times (

Some guidelines attempted to recommend specific followup intervals, but the vast majority were not evidence based. Determining the appropriate intervals and modeling their impact are important. For example, patients being medically managed for hypertension are typically seen every 6 months. However, a recent randomized controlled trial determined 6 months to be too short an interval to reflect accurate therapy-induced changes in blood pressure.

 Extending the follow-up interval in this case may not only result in cost savings but also improve patient care, as patients appear to be better managed with more accurate assessment of blood pressure and more appropriate adjustments to therapy. Similarly, recent research into optimizing follow-up intervals for melanoma patients demonstrated that frequency of followup intervals did not impact outcomes.

The next task was to determine the impact on healthcare utilization and expenditures of curtailing inappropriate follow-up visits. With a model that examines the diagnosis of hypertension, the profound effect of small reforms in followup practice becomes substantially clear. Essential hypertension  accounted for $47.4 billion in healthcare expenditures in 2008, with outpatient visits accounting for $13.03 billion.

 There were 79.1 million outpatient visits for hypertension in 2008.18 About 88%, or 69.6 million, of those visits were established patients.16 Using the Medicare national allowable billing amount of $65.30 for a Current Procedural Terminology code 99213 visit, we can estimate the cost savings as a function of the number of visits omitted. If follow-up visits were extended by just 1 month, from 6 to 7 months, there would be a 15% decrease in the number of visits in 1 year. This reduction could correspond to a cost savings of nearly $682 million. If the follow-up interval were extended to 9 months, there would be a 34% reduction in follow-up visits for 1 year and a potential cost savings of $1.5 billion. Decreasing follow-up visits to a yearly interval would result in a 50% reduction in follow-up visit volume and a possible cost savings of about $2.3 billion (

). Additionally, patients may be better managed with more accurate assessment of blood pressure and more appropriate adjustments to therapy.

In this era of healthcare reform, managing follow-up visits and intervals is an evidence-based approach that has the potential to reduce costs per person and improve access without compromising or restricting care. In order to implement this plan, appropriate follow-up intervals must first be established based on healthcare outcomes. Physicians should tackle this first step by focusing research efforts and funding on the development of evidence-based follow-up guidelines for common chronic diagnoses. The same scientific rigor that guides therapeutic decision making should be used to optimize chronic disease management. Rational choice of follow-up intervals is a crucial step in adjusting current utilization patterns to maximize the quality of patient care while minimizing unnecessary costs. It’s a win for everyone.

From University of Massachusetts (EJ, AR), Boston, MA; Harvard Medical School (ABK), Boston, MA.

The authors (ABK, EJ, AR) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (ABK); acquisition of data (AR, EJ); analysis and interpretation of data (ABK, EJ, AR); drafting of the manuscript (ABK, EJ, AR); critical revision of the manuscript for important intellectual content (ABK, EJ, AR); administrative, technical, or logistic support (ABK); and supervision (ABK).

 Alexa Boer Kimball, MD, MPH, Department of Dermatology, Harvard University School of Medicine, 50 Staniford St, #240, Boston, MA 02114. E-mail: akimball@partners.org. 1. Schoen C, Osborn R; for the Commonwealth Fund. 2010 Commonwealth Fund International Health Policy Survey in Eleven Countries. http://www.commonwealthfund.org/Surveys/2010/Nov/2010-International-Survey.aspx. Published November 2010. Accessed October 30,2012.

2. US General Accounting Office. Physician Workforce: Physician Supply Increased in Metropolitan and Nonmetropolitan Areas but Geographic Disparities Persisted. http://www.gao.gov/new.items/d04124.pdf. Published October 2003. Accessed June 11, 2012.

3. Simoens S, Hurst J. The Supply of Physician Services in OECD Countries. OECD Health Working Papers No. 21. http://www.oecd.org/dataoecd/27/22/35987490.pdf. Published January 2006. Accessed June 11, 2012.

4. Centers for Disease Control and Prevention. National AmbulatoryMedical Care Survey: 2009 Summary Tables. Fact Sheet: Physician Office Visits. http://www.cdc.gov/nchs/data/ahcd/NAMCS_Factsheet_All_2009.pdf. Accessed June 11, 2012.

5. Agency for Healthcare Research and Quality. Table 2: Number of events for selected conditions by type of service: United States, 2010. Medical Expenditure Panel Survey Household Component Data. Rockville, MD: Agency for Healthcare Research and Quality; 2010. Accessed June 11, 2012.

6. Agency for Healthcare Research and Quality. Total expenses and percent distribution for selected conditions by type of service: United States, 2010. Medical Expenditure Panel Survey Household Component Data. Rockville, MD: Agency for Healthcare Research and Quality; 2010. Accessed June 11, 2012.

7. Quinn RR, Hemmelgarn BR, Padwal RS, et al; Canadian Hypertension Education Program. The 2010 Canadian Hypertension Education Program recommendations for the management of hypertension: part I—blood pressure measurement, diagnosis and assessment of risk. Can J Cardiol. 2010;26(5):241-248.

8. Keenan K, Hayen A, Neal BC, Irwig L. Long term monitoring in patients receiving treatment to lower blood pressure: analysis of data from placebo controlled randomised controlled trial. BMJ. 2009;338:b1492.

9. British Thoracic Society Standards of Care Committee. BTS statement on criteria for specialist referral, admission, discharge and follow-up for adults with respiratory disease. Thorax. 2008;63(suppl1):i1-i16.

10. van den Bemt L, Schermer T, Smeele I, et al. Monitoring of patients with COPD: a review of current guidelines’ recommendations. Respir Med. 2008;102(5):633-641.

11. Schulberg HC, Katon W, Simon GE, Rush AJ. Treating major depression in primary care practice: an update of the Agency for Health Care Policy and Research Practice Guidelines. Arch Gen Psychiatry. 1998;55(12):1121-1127.

12. Francken AB, Hoekstra HJ. Follow-up of melanoma patients: the need for evidence-based protocols. Ann Surg Oncol. 2009;16(4): 804-805.

13. Francken AB, Thompson JF, Bastiaannet E, Hoekstra HJ. Detection of the first recurrence in patients with melanoma: three quarters by the patient, one quarter during outpatient follow-up [in Dutch]. Ned Tijdschr Geneeskd. 2008;152(10):557-562.

14. Francken AB, Shaw HM, Accortt NA, Soong SJ, Hoekstra HJ, Thompson JF. Detection of first relapse in cutaneous melanoma patients: implications for the formulation of evidence-based follow-up guidelines. Ann Surg Oncol. 2007;14(6):1924-1933.

15. Einwachter-Thompson J, MacKie RM. An evidence base for reconsidering current follow-up guidelines for patients with cutaneous melanoma less than 0.5mm thick at diagnosis. Br J Dermatol. 2008; 159(2):337-341.

16. Turner RM, Bell KJ, Morton RL, et al. Optimizing the frequency of follow-up visits for patients treated for localized primary cutaneous melanoma. J Clin Oncol. 2011;29(35):4641-4646.

17. Agency for Healthcare Research and Quality. Total expenses and percent distribution for selected conditions by type of service & number of events for selected conditions by type of service: United States, 2008. Medical Expenditure Panel Survey Household Component Data.Rockville, MD: Agency for Healthcare Research and Quality; 2008. AccessedJune 11, 2012.

18. Centers for Disease Control and Prevention. National Ambulatory Medical Care Survey: 2008 Summary Tables: Table 8: Continuity-of- Care Office Visit Characteristics, by Specialty Type: United States, 2008.

Articles

COMMENTARY

Evidence-based guidelines are needed to determine appropriate follow-up intervals for chronic medical conditions to maximize the quality of patient care and minimize unnecessary costs.

Evidence-Based Guidelines to Determine Follow-up Intervals: A Call for Action

 To examine changes in patient experience across key domains of the patient-centered medical home (PCMH) following practice transformation with Lean quality improvement methodology inclusive of payment reform.

Pre-intervention/post-intervention analysis of intervention with a comparison group, a quasi-experimental design. We surveyed patients following office visits at the intervention (n = 2502) and control (n = 1622) practices during the 15-month period before and 14-month period after PCMH Lean transformation (April-October 2009).

We measured and compared pre-intervention and post-intervention levels of patient satisfaction and other indicators of patient-centered care. Propensity weights adjusted for potential case-mix differences in intervention and control groups; propensity-adjusted proportions accounted for physician-level clustering.

More intervention patients were very satisfied with their care after the PCMH Lean intervention (68%) compared with pre-intervention (62%). Among control patients, there was no corresponding increase in satisfaction (63% very satisfied pre-intervention vs 64% very satisfied post-intervention). This comparison resulted in a statistical trend (

 = .10) toward greater overall satisfaction attributable to the intervention. Post-intervention, patients in the intervention practice consistently rated indicators of patient-centered care higher than patients in the control practice, particularly in the personal physician and communication domain. In this domain, intervention patients reported superior provider explanations, time spent, provider concern, and follow-up instructions compared with control participants, whereas control group ratings fell in the post-intervention period (

 In a pilot PCMH transformation including Lean enhancement with payment reform, patient experience was sustained or improved across key PCMH domains.

Using a quasi-experimental design, we evaluated patient experience following patient-centered medical home (PCMH) practice transformation with Lean enhancement coupled with payment reform.

		Compared with controls, intervention participants were more likely to report faster speed of registration (

 = .04) and provide higher ratings across the personal physician and communication domain (

		62% of intervention participants were very satisfied pre-intervention versus 68% post-intervention (

		Future studies are needed to determine if physicians, over time, will thrive under an adjusted salary-based reimbursement system in the PCMH.

The patient-centered medical home (PCMH) has emerged as a patient-focused, quality-driven, cost-effective model to revive primary care. A growing body of literature supports the notion that health systems emphasizing the role of primary care can achieve superior outcomes at a lower cost.

 The PCMH joint principles were developed into a set of best practices by the National Committee for Quality Assurance and have been used as the blueprint for organizational change for many recent PCMH demonstration projects. As preliminary results of PCMH demonstration projects emerge, successes have shown that the PCMH has the potential to reduce cost and improve quality,

 although transformation is often challenging

 and short-term declines in satisfaction may be observed following interventions to implement the PCMH model.

 Coupling practice transformation with payment reform may encourage more rapid adoption of the PCMH model, and both private and public payers are starting to invest heavily in such transformation pilots. Lean quality improvement methodology have been incorporated with some PCMH transformations to help facilitate PCMH practice improvements, but did not integrate changes to the current resource-based relative value scale.

 Therefore, if the PCMH model is to be widely utilized, it will be vital to demonstrate that its implementation with concurrent payment reform results in favorable outcomes with respect to quality and cost, as well as patients’ perceptions of care.

Overlooked for many years, patient experience has recently gained increasing prominence. The Affordable Care Act of 2010 calls for novel approaches to increase patient access and enhance patient-centered care in order to improve patient experience and outcomes.

In general, patient-centered care experiences and outcomes-oriented research are becoming increasingly recognized as a core element of quality healthcare delivery in the United States and other countries.

 In their pursuit of a 3-part aim to improve quality, cost, and patient experience, some health plans and health systems have begun to link measures of patient satisfaction to provider payment.

Few data have been available regarding the effect of PCMH transformation, with or without Lean quality improvement methodology, on patient experience in the setting of payment reform. To assess the impact on patient experience of transformation of care to a medical home linked with implementation of a new physician reimbursement scheme, we undertook a 3-year quasi-experimental study. We assessed patients’ experience with office visits at a clinic site where these interventions were implemented compared with a control site, both within a high-performing, large, multispecialty group practice. We evaluated the effect of PCMH transformation, including Lean quality improvement methodology, on patient experience with a widely used survey for measuring patients’ experience with outpatient care.

Following Cook and Campbell’s classification of quasi-experimental studies, we used an Untreated Control Group Design with Pretest and Posttest.

 In both the experimental and control groups, we conducted cross-sectional surveys of patients’ experiences  with care before and after the implementation of the PCMH model in the experimental site. We studied responses to a mail copy of the medical experience survey developed by Press Ganey (www.pressganey.com), a widely used, commercially available tool for measuring patients’ experience with outpatient care. All surveys were mailed 1 week after a patient’s encounter. Survey response rates across all clinic sites in the integrated system were 28.1% in 2008, 28.3% in 2009, and 36.6% in 2010. The study protocol was approved by the Partners Healthcare Institutional Review Board.

The study was conducted within Harvard Vanguard Medical Associates, a large multispecialty group of 14 practices in eastern Massachusetts, frequently recognized for achieving high scores in quality metrics.9 For this study, we included 2 of the 14 practices: 1 practice that underwent PCMH transformation with payment reform in 2009 and 1 practice,12 miles away with a similar demographic composition, that served as the control. Patients who had at least 1 internal medicine office visit from 2008 through 2010 were stratified by primary care provider. For each provider, surveys were mailed to 200 randomly selected patients, evenly distributed over the course of each of the 3 study years; each patient was surveyed only once per year. Between January 2008 and June 2009 (pre-intervention), a total of 2027 patients completed the survey: 1224 at the intervention site and 803 at the control site. Between July 2009 and December 2010 (post-intervention), we collected 2097 completed surveys: 1278 from the intervention site and 819 from the control site. Intervention and control participants were propensity weighted to adjust for possible case-mix differences between these 2 groups (see the Statistical Analysis section for more detail). 

The intervention was a pilot that started in April 2009 in the internal medicine department of 1 clinic site. It consisted of team restructuring in addition to a series of process improvements including use of Lean reengineering,

 centered around optimizing chronic disease management and reassigning tasks traditionally performed by a physician to medical assistants or nurses. For example, nurses were trained to use medication titration protocols for virtual management of many chronic diseases, shifting simple visits off provider schedules, and medical assistants took over many administrative duties traditionally completed by physicians. Components of the intervention are summarized in 

 and are described in greater detail elsewhere.

Changes in physician payment were structured around the proposal by Goroll and colleagues

 that involved replacing productivity-based (fee-for-service) compensation with a salary and bonus scheme. Each physician’s base salary was determined by their highest earnings over the prior 4 quarters. The pilot salary scheme did not have penalties for low productivity; it also incorporated quality-based bonuses for reaching benchmarks in the Healthcare Effectiveness Data and Information Set performance metrics and for responsiveness to patient communication (via telephone or secure message). Initially, 5 physicians were enrolled in the salary and bonus scheme. By the end of the pilot intervention’s implementation in October 2009, a total of 14 physicians (100% of the internal medicine department) had joined the salary scheme. All physicians at the intervention site remained salaried for the duration of the post-intervention period.

The primary outcome was overall visit satisfaction, measured on a 5-point Likert scale. Patients were asked to indicate their “overall rating of care provided” for the survey-tagged visit. Satisfaction was analyzed as a dichotomized variable “very good” versus “good,” “fair,” “poor,” or “very poor.”  Secondary outcomes were drawn from the 5 domains within the Press-Ganey survey that reflect the core principles of the PCMH: enhanced access to care (3 items), visit coordination and care (3 items), physician communication (4 items), and whole-person orientation of care (3 items).

 Responses to items within each domain were measured on the same 5-point Likert scale and dichotomized as described above. Individual survey responses were linked to each patient’s medical record, providing demographic information (age, sex, race, insurance status), healthcare utilization (the number of internal medicine and urgent care visits in the 6 months preceding the surveyed visit), certain medical conditions (hypertension, diabetes, cardiovascular disease), body mass index (dichotomized  as less than 25 kg/m

 and greater), and primary care provider characteristics (sex and number of years since hire).

All variables used in the patient experience analysis are dichotomous, so descriptive statistics of patient characteristics and experiences in the control and intervention groups in the pre-intervention and post-intervention periods were calculated using proportions. Our main analysis was to compare control and intervention groups with respect to patient experiences; however, because of possible baseline differences in patient experiences in the control and intervention groups, we compared the post-intervention minus pre-intervention experiences in the control group with the post-intervention minus pre-intervention experiences in the intervention group, commonly referred to as a difference-in-differences analysis.

Propensity score weighting was used to adjust for case-mix differences between patients in the control and intervention groups in the pre-intervention and post-intervention periods.

In general, propensity score methods permit control for observed confounding factors that might influence both the outcomes (in our study, patient experiences) and the comparison groups (for control and intervention groups in the pre-intervention and post-intervention periods) using a single composite measure. The propensity score attempts to balance patient characteristics between the comparison groups, as would occur in a randomized experiment. The propensity of being in the 4 groups (control and intervention groups in the pre-intervention and post-intervention periods) was calculated using multinomial logistic regression models with all patient characteristics available to us as predictors: patient age, sex, race, insurance status (Medicare or Medicaid vs private insurance), the number of active medications, healthcare utilization (number of internal medicine and urgent care visits in the 6 months preceding the visit referenced in the patient experience survey), and whether the patient had any or all of the following chronic conditions: diabetes, hypertension, heart disease, or overweight.

To calculate propensity-adjusted proportions using the propensity-weighted approach, each patient was weighted by the inverse propensity score of being in their observed treatment group. 

 values comparing propensity-adjusted proportions across treatment groups were calculated using generalized estimating equations

to account for physician-level clustering.

.05 was considered statistically significant. All analysis was completed using SAS/STAT 9.2 (SAS Institute Inc, Cary, North Carolina).

Between January 2008 and June 2009 (pre-intervention), a total of 2027 patients completed the patient experience survey: 1224 at the intervention site and 803 at the control site. Between July 2009 and December 2010 (post-intervention), we collected 2097 completed surveys: 1278 from the intervention site and 819 from the control site. After propensity weighting on all available demographic and clinical status variables, the intervention and control sites were similar with respect to all baseline variables (

The propensity-weighted proportions, accounting for provider-level clustering (

), show that overall satisfaction was higher at the intervention site following the intervention (68% indicated “very good” post-intervention versus 62% pre-intervention, 

= .003). In contrast, at the control site, 63% of patients felt satisfied pre-intervention versus 64% post-intervention (

 = .58). Using a difference-in-differences approach, we did not detect a statistically significant difference in overall satisfaction when the 2 sites were compared (

 = .10), though a trend was apparent. However, at the intervention site, we noted a clear pattern of consistent or improved patient satisfaction across the personal physician and communication domain, which focused on perceptions of the care provider’s characteristics during the visit referenced in the survey. In this domain, improved ratings of provider explanations, time spent, concern, and follow-up instructions were observed compared with responses from control participants, whose ratings fell during the post-intervention period (

Although intervention participants rated their care equal or better across the access to care, coordination of care, and whole-person orientation of care domains post-intervention, these ratings did not reach statistical significance compared with those of the control group. An exception was observed for speed of registration in the access to care domain: 62% of patients at the intervention site rated this element of care as “very good” pre-intervention, increasing to 66% post-intervention, compared with 63% pre-intervention and 60% post-intervention at the control site (

In this study of patient satisfaction before and after a pilot PCMH implementation with payment reform including Lean quality improvement methodology for chronic conditions within a large multispecialty practice in eastern Massachusetts, we did not observe significant statistical differences in overall patient-reported satisfaction when comparing the intervention and control sites, though there were some trends toward improvement. Notably, contrary to previously reported PCMH transformations without Lean quality improvement methodology, we found no short-term declines in patient experience despite the widespread, practice wide transformation that occurred.

We also found that patients were significantly more satisfied following the pilot implementation at the intervention site when asked to rate physician attributes and communication. These findings may provide reassurance to clinicians and health system administrators who harbor concerns about the potential for medical home transformation initiatives to compromise patient satisfaction with their provider over the short term. In fact, they suggest the possibility that medical home transformation may bolster or at least maintain patient-provider relationships within a team-based setting.

Of interest was the fact that we observed a decline in patients’ perceptions of physician communication in the control practice. Several possible explanations could account for this finding. For example, it may reflect increasing external pressures within primary care following Massachusetts’s 2006 mandate for universal health coverage. The fact that we observed the opposite trend in the intervention site could indicate that PCMH transformation with Lean quality improvement methodology and payment reform may fortify primary practices to weather such external pressures. We did note that 1 of 10 physicians left the internal medicine department in 2010 at the control site, potentially contributing to loss of morale and increased workload among providers during this time period. We were unable to identify additional local-level influences such as support staff turnover, structural renovations, or similar specific attributes to support these findings.

It is possible that the negative pattern in the personal physician and communication domain leading to the observation of relatively superior ratings at the intervention site may also be explained by changes in physician behavior. According to the qualitative analysis of this transformation, the intervention site physicians experienced reduced time pressure as a result of delegation of administrative tasks to the team, freeing up time for longer encounters and more detailed discussions.

 Newly salaried physicians, no longer conforming to the fee-for-service model, may have felt more freedom to prioritize topics on their patients’ agenda or engage in traditionally poorly reimbursed activities such as discussion of social issues or matters of behavioral health. These factors may have contributed to stable ratings at the intervention site, while physicians burdened with maintaining patient volume and executing a myriad of administrative items tasked to a primary care doctor may have felt less inclined to invest in discussion or longer encounters.

A trend toward improvement in overall satisfaction at the intervention site relative to the control site post-intervention was observed in our study. Although participants provided ratings that were either sustained or improved post-intervention across the access to care, coordination of care, and whole-person orientation of care domain, these improvements did not reach statistical significance compared with ratings in the control group. Possible reasons for these findings include the following: (1) Harvard Vanguard already was considered a high-performing medical system, leading to a ceiling effect for detecting substantial change from the pilot intervention, and (2) changes within team structure and work flow in the medical home pilot intervention may have been too subtle for patients to appreciate, or too early to have seen sustained effects. Other surveys of PCMH endeavors with Lean quality improvement methodology have found that sustained outcome changes could take up to 5 years.

Taken together, the findings in this study provide timely information and implications for design and implementation of medical home models. In order to align patient satisfaction with care transformation endeavors, practices interested in transforming toward a medical home model may want to consider physician payment reform in the early phases of implementation in order to potentially enhance patients’ relationships with their provider. This study also underscores potential problems associated with evaluating patient satisfaction following implementation of measures that may be patient centered but less visible to patients and therefore less likely to receive a high rating. Longer follow-up periods may be necessary to tease out sustained effects on patient experience of PCMH transformation with Lean quality improvement methodology and payment reform. It is unclear to what extent Lean practice improvements for patients with chronic conditions may have influenced patient satisfaction ratings; future studies could address this issue.

To date, few data have been available regarding the effect on patient satisfaction of medical home transformation with payment reform. In this setting, transformation efforts were led by physicians and staff familiar with the Lean reengineering process of improvement, a well-established tool for practice change.

 Another strength of this study was that patients’ recall bias regarding their experience and satisfaction was minimized by surveys being mailed within a week of their encounter.

Several limitations should be considered, primarily the small number of practices and the patient survey response rate. We evaluated only 2 practices in an integrated delivery system in 1 region, and these results may not be representative of other  practices or regions. Although this overall response rate is typical of patient experience surveys in nonresearch settings,

 the possibility of nonresponse bias remains a concern. Our ability to detect differences was limited by a relatively short time frame.  Institutional review board stipulations did not permit identification of nonrespondents to identify systematic differences between the 2 groups. Because  this study was not randomized, it is possible that patients systematically chose 1 clinic site over another;  however, we used propensity scores, an established method for balancing the observed differences between individuals who  self-select to participate in an intervention and those who do not.

 In a practice transformation intervention with many components, we are limited in our ability to associate a single causal factor with an observed outcome.

Many payers have begun to recognize the importance of patient experience and have begun to tie measures of patient satisfaction to provider payment. This recent shift may allow providers and healthcare systems to directly understand patient perceptions of their care delivery and may serve as motivation for providers to dedicate time and resources toward activities and programs without direct reimbursement. Upholding the patient-provider relationship as central to the foundation of primary care, our findings suggest that efforts geared toward medical home transformation coupled with physician payment reform can be met with a favorable patient response, particularly with respect to core patient-physician relationship attributes. Additional research should focus on the relative contribution of different factors in improving physician-patient communication and determine whether these satisfaction levels, and other measures of patient satisfaction, can be sustained in the long term as efforts to implement a medical home continue. Future studies should also examine whether physicians and patients, over time, will thrive under a reimbursement scheme where the traditional fee-for-service is replaced with salary-based compensation for PCMH, with or without Lean quality improvement methodology.

Department of Veterans Affairs Medical Center (LH, SRS), Boston, MA; Brigham and Women’s Hospital (LH, AB, SRL, GDS, DWB, SRS), Boston, MA; Harvard Vanguard Medical Associates, (TS), Boston, MA; Harvard Medical School Center for Primary Care (AB), Boston, MA.

This study was supported by The Commonwealth Fund, a national, private foundation based in New York City that supports independent research on healthcare issues and makes grants to improve healthcare practice and policy. The views presented here are those of the authors and not necessarily those of The Commonwealth Fund, its directors, officers, or staff. Dr Heyworth is supported by Health Resources and Services Administration grant T32 HP10251 and by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development. Dr Bitton is a special advisor to the Center for Medicare and Medicaid Innovation Comprehensive Primary Care initiative. The Centers for Medicare & Medicaid Services (CMS) had no role in this study, and this study in no way reflects any official positions CMS may have.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (LH, AB, TS, SRL, DWB, SRS); acquisition of data (LH, SRS); analysis and interpretation of data (LH, AB, TS, SL, RS); drafting of the manuscript (LH, AB, SRS); critical revision of the manuscript for important intellectual content (LH, AB, TS, SRL, DWB, SRS); statistical analysis (LH, SRL); provision of study materials or patients (TS, SRS); obtaining funding (DWB, AB, SRS); administrative, technical, or logistic support (AB, TS); and supervision (TS, DWB, SRS).

 Leonie Heyworth, MD, MPH, VA Boston Healthcare System, 150 South Huntington Ave, Bldg 9 (152G), Boston, MA 02130.1. Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Q. 2005;83(3):457-502.

CLINICAL

In a pilot patient-centered medical home transformation including Lean quality improvement methodology with payment reform, patient experience was sustained or improved across key domains.

Patient-Centered Medical Home Transformation With Payment Reform: Patient Experience Outcomes

 To examine the association between processes measures of diabetes care and time to progression for 4 diabetes complications: coronary artery disease (CAD), stroke, heart failure (HF), and renal disease (RD).

This retrospective study followed outcomes from 2003 through 2009 in a cohort of 1797 employees with diabetes who worked for a large US manufacturer and were enrolled in the same health insurance plan.

 Quality of care was measured by consensus standards for testing glycated hemoglobin, lipids, and microalbuminuria. Employees with diabetes who received all 3 measures of care in the baseline year (2003) were compared with those who received less complete testing. Cox proportional hazard regression models were used to assess potential associations between diabetes care and time to complications, controlling for potential confounders.

 Observed differences between the 2 groups in time to event were significant for 2 of the 4 complications: HF (hazard ratio [HR] = 0.39, 95% confidence interval [CI], 0.19-0.81; 

 = .0117) and RD (HR = 0.48, 95% CI, 0.24-0.95; 

 = .0339) and any of the 4 complications (HR = 0.66, 95% CI, 0.48-0.91; 

 = .0101). Differences in time to complication for CAD (HR = 0.70, 95% CI, 0.49-1.02;

 = .0635) and stroke (HR = 0.63, 95% CI, 0.38-1.07; 

 = .0891) showed the same trend but were not significant.

Employees with diabetes who received all 3 quality measures experienced fewer complications, risk-adjusting for other factors. These results provide support for the importance of care quality.

The proportion of patients with diabetes receiving standard processes of care based on systematic reviews is disturbingly low and in our fully insured population is just under 15%.

		The patients in our cohort who received standard processes of care developed fewer complications than those who did not, despite being sicker at the outset.

		Our results provide support for process measures and efforts to promote their application in practice.

		If even a modest portion of outcomes are causally associated with better processes of care, there is great potential for benefit.

In 2010, the estimated prevalence of diabetes among adults ranged from 5% to 13% across the United States and its territories,1  accounting for substantial morbidity, mortality, and associated economic costs.

Type 2 diabetes mellitus often develops 9 to 12 years before diagnosis

 and more than 50% of patients have at least 1 complication by that time.

 A significant body of research has established that effective treatments can significantly decrease the development and/or progression of complications.

However, diabetes care remains suboptimal and varied in the United States.

 In an effort to improve outcomes, The National Diabetes Quality Improvement Project (1997) developed a comprehensive set of process and intermediate outcome measures to assess quality of care for diabetes patients that are now considered standards of care.

These measures include annual  screenings for lipids and microalbuminuria, at least 2 measurements of glycated hemoglobin (A1C) at least 30 days apart, and annual flu vaccinations, dilated eye exams, and foot examinations.

 have used these performance measures to assess care provided to patients with diabetes. These   studies provide evidence of the substantial gaps between national performance measures for diabetes care and actual care received by persons with diabetes in the United States.

The present study examines 3 process measures of diabetes care: A1C tests, lipid tests, and urine screening tests for  microalbuminuria; and their association with 4 subsequent complications of diabetes: coronary artery disease (CAD), stroke, heart  failure (HF), and renal disease (RD); in a cohort of employees of a large national manufacturing company. This cohort provides an opportunity to study this issue in a geographically, ethnically, and socioeconomically diverse population with rich and uniform healthcare benefits. This study contributes to the literature by identifying systematic reasons for differences in care among privately insured Americans and examining whether adherence to recommended processes of care is associated with better outcomes.

The study setting was a large (approximately 36,000 employees) US-based manufacturing company with uniform healthcare benefits in 22 states that generated claims data during the years of the study. We examined the relationships between the 3 measures of the quality of diabetes care and the risk of major diabetes complications to determine whether employees with diabetes who received all 3 quality-of-care measures in a baseline year (2003) demonstrated a lower risk for developing any of 4 complications over the 6-year observation period (2004-2009). Baseline characteristics suspected to impact outcome risk and/or likelihood of receiving optimal care included in the multivariate model were age, sex, ethnicity, marital status, income, insulin use, comorbidities, and smoking. Due to data limitations, we were unable to assess the possible confounding roles of obesity and duration of diabetes. Data on annual flu vaccinations, dilated eye exams, and foot examinations were not available; thus, these elements were not included in our model.

This was a retrospective cohort study using administrative data on employees collected between January 1, 2001, and December 31, 2009. The majority of the employees held hourly manufacturing jobs; minorities and women were well represented. Although employees could select from a menu of health benefits, only a single preferred provider organization (PPO) network was available to  the entire population. Employees who did not opt for this PPO were excluded (3%). The panel was restricted to those employed by the manufacturer for some part of 2001 and 2002, and for all of 2003, the year during which quality of care measures were assessed. Health outcomes were assessed from claims between January 1, 2004, and December 31, 2009. 

During the “lead-in” period (2001-2002), diabetes status was ascertained from the claims data based on 

International Classification of Diseases, Ninth Revision

) codes for the condition on a submitted medical claim for at least 1 of the following: a hospitalization, emergency department visit, 2 office visits, or 1 prescription for a diabetes-specific medication. Participants were  included in the final data set if they were between 18 and 64 years old and employed throughout 2003 and for at least 1 month in the observation period (2004-2009). Employees with diabetes who had any 1 of the 4 end point complications coded in a medical claim in 2001 or 2002 and women with a claim containing an

code for gestational diabetes were excluded. Those with claims for 1 of the 4 complications in the baseline year (2003) were included in the panel, but were excluded from the analysis for that complication. A total of 1797 subjects were available for analysis.

Process measures of diabetes care (ie, 2 A1C tests at least 30 days apart and at least 1 test each for lipids and microalbuminuria) were assessed in the baseline year (2003).

 Each employee with diabetes was categorized into 1 of 2 mutually exclusive groups: those who received all 3 measures of care in 2003 and those who did not. Cox proportional hazard regression models were used to assess the associations between diabetes care and time to each of the 4 complications (CAD, stroke, HF, and RD) or any of the 4 complications.

The databases available for analysis have been described in detail in previous publications.

 Briefly, health data were obtained from the health insurance plan, extraction of onsite occupational health medical records available for about 40% of the locations, and other linked employer-managed administrative databases. Data included 

 codes from the Chronic Condition Data Warehouse list

 for disease diagnosis and National Drug Codes

 for prescription information. End point complications were identified based on 

 codes during a face-to-face exam or hospitalization. For end point data, the subset of 

 codes from the Chronic Condition Data Warehouse list for causes of kidney disease and heart disease unrelated to diabetes (eg, infectious or genetic etiology) were excluded. Demographic and health behavior data were obtained from the health medical record and human resources databases.

Vision claims were not available. Therefore, claims for routine eye exams and screening for diabetic retinopathy were not usable as measures of quality of diabetes care.

Sightlines DxCG Risk Solutions software (Verisk Analytics, Jersey City, New Jersey) was used to assign risk scores to the cohort during the baseline assessment and capture potential illness-related influences on treatment decisions. This actuarial program uses medical and pharmacy data with proprietary risk-adjustment algorithms to predict future health spending, and its risk scores are comparable to similar scores used by the Centers for Medicare & Medicaid Services and other insurance bodies for global risk estimates. A score of 1 in 2003 implies an actuarial prediction that an individual would consume the median amount of healthcare  or the population in 2004. Databases were linked by an encrypted unique identifier to ensure human subject privacy. This study was approved by the authors’ institutional review boards.

Hazard ratios for each outcome (CAD, stroke, HF, RD, and any of the 4 conditions) were calculated for quality of diabetes care (exposures) and covariates. Time to event, defined as the number of months from the start of the observation period (January 1, 2004) to the first medical claim for a complication, was used for all primary analyses. Employees with diabetes who no longer  received employer-provided health benefits were censored at the time of their last month of coverage. Records (n = 901) were  censored during the observation period for the following reasons: retirement, layoff or termination (n = 647), change from PPO to health maintenance organization or Consolidated Omnibus Budget Reconciliation Act (COBRA) coverage (n = 77), and death or  long-term disability (n = 30). For 119 subjects, the reason for loss of coverage could not be definitely established from the available  records. Differences between the baseline characteristics of censored and uncensored employees were assessed with x

 tests. The Kaplan-Meier method was used to identify crude time-to-event models for the associations between the exposure and outcome measures.

Cox proportional hazard models were used to evaluate bivariate and multivariate association, which enabled time-to-outcome assessment with risk adjustment based on socioeconomic and lifestyle risk factors, comorbidities, and severity of diabetes. The base model, calculating time to event as a function of quality of care, was specified with demographic covariates. Covariates including modifiable risks (smoking), clinical characteristics (insulin use and health severity risk score), and socioeconomic status indicators (individual income, job status [hourly or salary], and marital status) were then added to the base model. Finally, the fully adjusted model was used to assess the combined explanatory effects for all measured factors. Hazard ratios (HRs) for primary outcomes were calculated based on Cox proportional hazard analyses stratified by hourly job status, salary, sex, and insulin use. As a sensitivity test, kappa statistics, correlation analyses, and Cox proportional hazard analyses were used to assess outcome  differences based on 2 years (2003 and 2004) of continuous high-quality care (leaving 2005-2009 available for outcome observation). Other sensitivity tests included looking at each stratum of sex, job status, and insulin use in the full model. All analyses were conducted using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina). 

A total of 1797 patients met criteria for inclusion. The cohort consisted primarily of white (69.2%), male (83.1%), married (74.2%), hourly employees (75.7%), a significant number of whom earned less than $35,423 per year (27.2%). More than half (55.1%) were between the ages of 18 and 51 years. The mean age for the total population was 49.0 years (standard deviation = 8.4). Smoking data were reported as never (14.4%), past/current (17.3%), and unknown (68.3%). The unknown group represents primarily locations for which health medical record data were unavailable. As a measure of diabetes severity, 20.1% of the population had insulin prescriptions during the baseline year. Health severity risk scores were also used to assess comorbidities, with 75.1% of the cohort receiving a score of 2.1 or lower (1 = average health risk spending probability, 2 = risk likelihood of double the average health expenditures). Regarding the quality of diabetes care process measures, a majority of patients received at least 1 A1C test (67.6%) and lipid testing (61.8%) in the baseline year. Other measures of care were less common: 43% of patients had at least 2 tests for A1C and only 24.0% were tested for microalbuminuria in the baseline year.

Characteristics of the baseline sample are shown in 

; the same subjects stratified by treatment status are shown in 

. At baseline, the group receiving all 3 process measures was smaller (n = 267) than the comparison group  (n = 1530). Differences between the 2 groups were assessed with 2-tailed

 tests for independent samples. Despite the sample size differences, there were no statistically significant differences between the 2 groups during the baseline period (2003) for demographic, socioeconomic status, or modifiable risk variables. However, there was evidence that the group receiving all 3 process measures was sicker at baseline. They were significantly more likely to be receiving insulin than the comparison group (27.7% vs 18.8%; 

 = .0007) and were also more likely to fall into the higher quartiles of health severity risk score (

 shows the characteristics of those who were censored compared with those who remained in employment throughout the   follow-up period. Although workers who left were older and less well paid on average at baseline, differences between censored employees and non-censored employees were not significant for race, marital status, occupational group (salary or hourly), insulin use, health severity risk score, and importantly, likelihood of receiving the treatment.

In total, 366 persons with diabetes (20%, n = 1797) had medical claims for at least 1 of the 4 complications with a mean time to complication of 29.1 months. The most frequent complication in the cohort was CAD (16.9%) with a mean time to complication of 26.6 months, followed by stroke (8.7%, 33.1 months), HF (5.8%, 29.7 months), and RD (4.9%, 38.1 months). Those getting all 3 process measures of care fared better for all end points. Hazard ratios for 2 of the 4 complications were significant: HF (HR = 0.39, 95% confidence interval [CI], 0.19-0.81; 

 = .012) and RD (HR = 0.48, 95% CI, 0.24-0.95; 

 = .034). The HRs for CAD (HR = 0.70, 95% CI, 0.49-1.02; 

 = .064) and stroke (HR = 0.63, 95% CI, 0.38-1.07; 

 = .089) showed the same trend but were not significant. Hazard ratios with CIs for each and any of the  4 end points are summarized in 

The HR for submitting a medical claim for any of the 4 complications was significantly lower for those receiving all 3 process measures (HR = 0.66, 95% CI, 0.48-0.91;

 = .01). Covariates associated with increased risk included increasing age 46-51 years (HR = 1.88, 95% CI, 1.36-2.61;

= .0001), 52-56 years (HR = 2.06, 95% CI, 1.47-2.89; 

 <.0001), and 57-64 years (HR = 3.09, 95% CI, 2.15-4.46;

 <.0001); health severity  risk scores of 2.1 or higher (HR = 1.91, 95% CI, 1.36-2.68; 

 = .0002); and smoking (HR = 1.44, 95% CI, 1.01-2.07; 

 = .047).  Differences in all other covariates were not statistically significant. The Kaplan-Meier estimates of cumulative hazards for all end points are depicted in 

We conducted 2 sensitivity analyses. The first used 2 years (2003 and 2004) of continuous process of care measures to reduce the likelihood of random misclassification. Results showed the same trends and similar point estimates (data not shown), although with shorter follow-up (2005-2009) these results were no longer significant. Likewise we tested for robustness within strata by sex, hourly versus salaried job status, and initial insulin use. Results revealed comparable point estimates for all strata with the exception of the women, in whom HR estimates for each complication hovered around 1. In general, the point estimates within strata were similar to those found for the full cohort, but with wider CIs.

This study compared 2 groups of employees with diabetes to assess how differences in process measures of care might have affected the onset of complications associated with diabetes. Significant differences in time to complication were observed between the 2 groups for 2 of the 4 complications  (HF and RD) and for any of the 4 complications. Remarkably, those employees getting optimal process scores were, on average, sicker at the outset based on greater insulin use and increased risk severity scores; hence, they would have been expected a priori to have worse outcomes.

Many studies have assessed whether interventions at the provider level improve processes of care and intermediate outcomes, but the effect of such process improvements on complications remains less clear because the outcomes themselves are rarely assessed.

 This study estimates the impact of process measures of quality care by analyzing data in a national sample across multiple physician groups administering diabetes care within the same health insurance plan structure. This study is the first to our knowledge confirming, in practice, the expectations for benefit from compliance with recommended process measures of care in which follow-up was long enough to observe the major clinical end points of interest.

That said, there remain significant limitations to our methods and our ability to draw causal inference from the results. We relied on claims data for recognition of our population with diabetes, for ascertainment of complications, and for assessment of the critical covariate of interest—execution of all of the quality of care diagnostic measures in the baseline year. Each step entails opportunity for  misclassification. Although the diagnosis of diabetes is readily inferred from claims with high sensitivity and specificity,

comparable data comparing claims with verifiable sources such as medical records or examinations do not exist for any of the major complications with the exception of RD,

which has been found to be identified with high specificity but less sensitivity than diabetes. That would not, however, bias our findings for patients who had been identified as having RD. Thus, it is possible that the end points misclassified cohort members in both directions and that some with complications already evident were not appropriately excluded. The impact of such misclassifications, assuming they were random, would likely drive our result toward the null, but there is no assurance that such errors were random.

Another concern is the impact on our conclusions of unobserved variables (eg, body mass index, duration of diabetes) as noted above. Although there is no strong prior reason to suspect that such variables were distributed in such a way as to confound our  result, more obese subjects might have received consistently poorer care and, independently, had a higher risk of bad outcomes. The impact of censorship may also have resulted in possible bias, as those faring most poorly might be expected to leave prematurely more often. It is reassuring that censorship was not associated with baseline characteristics of disease severity nor  with treatment group, but the size of loss—more than half of the cohort&mdash;introduces concern for unmeasured bias.

The employees in this study may not represent a generalizable population, as they work for a stable employer with rich and uniform benefits in the heavy manufacturing sector, both less common features than a generation ago. Our study panel requirements demanded a group stably employed during a period of years, further rendering it less representative of the larger workforce.

Perhaps most difficult to fully address is the possibility of endogenous differences among persons with diabetes leading some to both seek better care for their disease—picking better qualified doctors and/or advising them what tests to order&mdash;and take better are of themselves in other ways, collectively resulting in better outcomes. Nonetheless, it is noteworthy that in the primary test of  the hypothesis, the baseline characteristics created an uneven playing field, with more ostensibly sick patients having a higher rate of better care; thus, the deck was stacked against those with better care having better outcomes, an effect that began to be visually apparent in the third observation year (see Figure 5). It is also reassuring that our result survived the various sensitivity tests. We are exploring instrumental variables and other approaches to examine the causal pathway further, including assessment of the relationships between the process measures and intermediate outcomes such as medication change, adherence, frequency of exams, and the like.

Limitations notwithstanding, these results provide further support for process measures and efforts to promote their application in  practice, starting with the obvious; the proportion of employees with diabetes getting the standard of care based on systematic  reviews

 was disturbingly low (just under 15%) in our fully insured population. If even a modest portion of the association with outcome is causal, there is a considerable opportunity for benefit from driving better processes of care.

From Alcoa Aluminum (FJB), Pittsburgh, PA; Yale Occupational & Environmental Medicine (DG, MS, OT), Yale School of Medicine, New Haven, CT; Stanford University School of Medicine (IMC, MRC), General Medical Disciplines, Stanford, CA.

 Dr Bayer reports employment with Alcoa Aluminum, who provided data for this study. The other authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (FJB, DG, MS, OT, MRC); acquisition of data (FJB, MRC); analysis and interpretation of data (FJB, DG, MS, IMC, OT, MRC); drafting of the manuscript (FJB, IMC, MRC); critical revision of the manuscript for important intellectual content (FJB, DG, IMC, OT, MRC); statistical analysis (FJB, DG, MS, MRC); provision of study materials or patients (FJB); obtaining funding (FJB, MRC); administrative, technical, or logistic support (FJB, IMC); and supervision (MRC).

This study was supported by National Institutes for Health grant 5RO1AG026291-04 and continuing support from Alcoa Aluminum. The funders had no role in the design of this study; collection, management, analysis, and interpretation of the data; conduct of this study; or preparation or approval of the manuscript. Alcoa Aluminum reviewed the manuscript prior to publication.

Adherence to process of care measures was associated with reduced risk of 2 diabetes complications or any of 4 complications in a national industrial cohort.

Process of Care Compliance Is Associated With Fewer Diabetes Complications

 To examine uptake patterns of the electronic health risk assessment (eHRA) and compare characteristics of early adopters among adults in 1 healthcare system with those of the potentially eligible population.

 Retrospective cohort study (September 2006-March 2009).

 We designed and implemented an integrated eHRA to improve individual health plan members’ self-management of their health risks and chronic illness. We included individuals aged 21 to 85 years who had been enrolled at Group Health Cooperative for 12 or more months before September 2006, when the eHRA was first introduced. Study participants had to be registered users of the health plan’s secure Web portal by March 2009 in order to complete the eHRA.

A total of 332,381 adults were potentially eligible; of these 39.3% were eligible to complete the eHRA. One or more eHRAs were completed by 22.4% of registered Web portal users over the study period with slow but consistent uptake. Completers were more likely to be women, to be middle-aged (41-65 years), and to have had a recent well-care visit and fewer comorbid conditions.

Significant additional outreach, engagement strategies, and incentives are likely required by health systems to increase adoption and ongoing use of an eHRA among target populations. Future research on eHRA uptake in primary care should focus on whether the use of these tools leads to patient action and provider engagement that improve health outcomes in moderate-risk and  high-risk individuals, as well as on modalities to reach broader audiences for higher completion rates.

The demographic and health risk profile of early electronic health risk assessment (eHRA) adopters was primarily characterized by age (41-65 years), sex (women), recent well-care visit, and fewer comorbid conditions.

		Significant additional outreach and engagement strategies and incentives are likely required to increase eHRA adoption and use among target populations and may decrease demographic differences between early adopters and later adopters.

		Future research on uptake of risk assessment tools in primary care should also address whether their use leads to increased uptake of activities that improve health outcomes in moderate-risk and high-risk individuals.

Unhealthy lifestyles have a substantial impact on the incidence of chronic conditions and impaired health outcomes for US residents.

Rising healthcare expenditures are associated with increasing chronic disease incidence and prevalence, as exemplified by the association of obesity and increased healthcare  costs.

 Some of these conditions or complications can be prevented or averted by reducing behaviors that increase health risk such as the use of tobacco, poor diet, physical inactivity, and risky alcohol use.

Significant additional outreach and engagement strategies and incentives are likely required to increase adoption and ongoing use of health risk assessment tools among target populations.

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