		There is a link between high levels of social capital and good health.

		Care delivery models have historically ignored opportunities to enhance social capital between providers and patients and among patients themselves.

		 As resources to manage care diminish, medical practices and payers should increasingly consider how medical practices and hospitals can leverage social capital as a means of improving the quality and efficiency of healthcare.

In 2001, the political scientist Robert Putnam described the causes and effects of declining social connectedness in his book

 Among Putnam’s conclusions: Americans are both happier and healthier when they have strong relationships with one another. Not surprisingly, the literature offers several pillars of support for Putnam’s thesis that social connectedness is associated with improved clinical outcomes.

As delivery reform unfolds and payers and delivery systems begin to experiment more broadly with new organizational forms such as accountable care organizations and patientcentered medical homes, “social capital”—the term Putnam and other social scientists use to quantify levels of social connectedness&mdash; will be a powerful concept to incorporate into their design and evaluation. The concept of social capital has been defined variably in the literature. For the purposes of its application to healthcare delivery, “social capital” refers to the collective value of bonds formed between and among individuals within social networks. These networks create a setting of trust and support in which people learn to reciprocate and perform actions that are mutually beneficial to others.

Putnam defines 2 types of social capital: bridging and bonding capital. Bridging capital is the social connectedness that results when members of unlike groups engage with one another. Bridging across occupational lines, racial and ethnic groups, and socioeconomic classes often produces an exchange of information, ideas, norms, and values that cannot be achieved if individuals restrict social contact to others like themselves.

Bonding capital, conversely, is the social connectedness that uniquely follows when individuals from within a particular group relate closely to one another. The experience and background shared by group members draws them close and promotes rewarding interaction. Examples of bonding capital might be the feelings experienced by individuals who share a common racial identity, profession, or interest.

Through the lens of this distinction, the models of care delivery that pervade medical practice are oriented more around maximizing bridging capital between patients and providers—physicians, nurses, and allied professionals&mdash; than toward generating bonding social capital among patients and their families.

Bonding social capital is an overlooked tool to improve the quality and reduce the cost of care for patients with chronic disease. The use of bonding social capital reflects the belief that levels of interpersonal trust and reciprocity norms can enhance cooperation around common medical conditions; patients who participate within a network of other patients with the same illness may be more likely to follow care recommendations and encourage others to follow if they trust the people within their network and receive support gratification from the members for their efforts.

With significant payment and medical education reform simultaneously under way, an important opportunity exists to enhance the importance of both types of social capital in care delivery and physician training models.

Physicians and the organizations in which they work may want to actively consider how they connect willing patients with one another—as supporters, teachers, and advocates for one another. Patient-centered medical homes may include patients and family members as integral members of care teams. The medical practice could become both a center for individualized care and a source for disease group management.

Diabetes management, for example, has been shown to benefit from efforts to promote social capital development among patients. Patients who participate in group visit programs over extended periods have been shown to have better glycemic control and compliance with evidence-based guidelines.

 Patients are more engaged and satisfied with their care and more likely to achieve better outcomes for themselves when involved in helping others achieve their goals. Group visit programs have been successfully implemented in many clinical settings, but have historically struggled to gain traction because third party payers have failed to reimburse them.

Enhancing social capital between patients and family members may also improve care. By focusing on the practical issues of supporting healthier lifestyles, family members and close social contacts have been shown to substantially increase the rates of healthy behaviors.

Physician practices can systematically enhance social capital by more regularly and systematically enrolling family members in the oversight of treatment and in promoting emotional support from family members. Such approaches cost little, and may encourage meaningful behavior change and improved health outcomes.

Online communication tools may be an important facilitator in promoting social capital.

 Online communities focused on specific conditions have been shown to offer rich, supportive environments where patients and their caregivers share personal stories, provide emotional support, and offer advice about up-to-date clinical care. In new care delivery models that integrate online communications into their work flow, clinicians may engage patients with common conditions within their practice to help manage one another’s conditions. Moreover, they may use linkages to other practices and clinicians to seek help for patients with more rare conditions. In these ways, the clinical practice might expand its walls to be available to help patients find support at any time of the day.

The ImproveCareNow network of pediatric gastroenterologists and patients demonstrates how online collaboration can build social capital by encouraging social connectedness to improve health outcomes. Sponsored by the American Society for Pediatric Gastroenterology, Hepatology, and Nutrition and the American Board of Pediatrics, ImproveCareNow has reported improvements in care processes such as appropriate medication dosing, as well as in clinical outcomes

; by sharing data and ideas, the network has improved the remission rate among children and adolescents with ulcerative colitis and Crohn’s disease from 49% to 67%.

Unquestionably, efforts must be made to better understand how best to harness social capital to encourage better selfmanagement of chronic conditions. Clinicians may want to look first to successful models of patient-led groups which are thriving in in-person and online communities alike. They may also aim to explore how they can plug into ongoing efforts within their communities to support them in becoming even more effective.

Ultimately, the power of social capital has largely gone unharnessed as a means of improving health outcomes and lower costs of care that could be easily incorporated into medical home settings, accountable care organizations, and other new models of payment and delivery. As an example, the mean annual cost of living with Crohn’s disease or ulcerative colitis is $8000 and $5000, respectively.

 By leveraging social capital, the ImproveCareNow network reduced remission rates and care utilization rates of patients with both diseases. Although upfront costs to implement programs similar to the ImproveCareNow network may initially exceed savings, if strategies to generate social capital are maintained, they may lead to significant savings in total costs of care borne by payment provider organizations, payers, and patients.

Efforts to promote social capital will not appeal to all patients or clinicians, however. Some patients may be wary of sharing information about health problems with strangers. Some clinicians and patients may be more comfortable at delivering and receiving care in one-on-one patient encounters. For others, participation in online forums may be too time intensive and impersonal.

Attempts to utilize social capital could have potentially adverse effects on patients, where patients may share and adopt negative behaviors in addition to positive ones. Inclusion of family members and close social contacts in the oversight of treatment could also have negative health consequences for the caretakers.

Regardless, patients and clinicians who are ready to participate in efforts to promote social capital should be encouraged to do so, whether by means of reimbursement, practice design, or models of medical training. Cost-effectiveness analysis could be used to compare building social capital with alternative approaches to improve patient care or to evaluate different ways of implementation in practice.

As the burden of chronic disease grows and our resources to manage healthcare problems diminish, healthcare providers, health systems, and commercial and government payers would be wise to consider the role of creating greater social capital as a means of improving the quality and efficiency of healthcare. Perhaps a variety of metaphorical “patient bowling leagues” will have their place in the healthcare delivery system of the future.

From Department of Medicine (SHJ), Division of Pharmacoepidemiology (WHS), Brigham and Women’s Hospital, Boston, MA; Harvard University (RG), Harvard College, Boston, MA; Pew Internet Project (SF), Washington, DC.

The authors (SHJ, RG, SF, WHS) 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 (SHJ, SF, WHS); acquisition of data (SHJ); analysis and interpretation of data (SHJ); drafting of the manuscript (SHJ, RG, SF, WHS); critical revision of the manuscript for important intellectual content (SHJ, RG, SF); statistical analysis (SHJ); provision of study materials or patients (SHJ); obtaining funding (SHJ); administrative, technical, or logistic support (SHJ, RG); and supervision (SHJ, WHS).

Sachin H. Jain, MD, MBA, Department of Medicine, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115. E-mail: shjain@gmail.com.1. Putnam, Robert D. Bowling Alone: The Collapse and Revival of American Community. New York, NY: Simon and Schuster; 2000.

2. Fisher CS. To Dwell among Friends: Personal Networks in Town and City. Chicago, IL: University of Chicago Press; 1982.

3. Langford AT, Sawyer DR, Gioimo S, Brownson CA, O’Toole ML. Patient-centered goal setting as a tool to improve diabetes self-management. Diabetes Educ. 2007;33(suppl 6):139S-144S.

4. DiMatteo MR. Social support and patient adherence to medical treatment: a meta-analysis. Health Psychol. 2004;23(2):207-218.

5. Greene JA, Choudhry NK, Kilabuk E, Shrank WH. Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook. J Gen Intern Med. 2011;26(3):287-292.

6. Crandall W, Kappelman MD, Colletti RB, et al. ImproveCareNow: The development of a pediatric inflammatory bowel disease improvement network. Inflamm Bowel Dis. 2011;17(1):450-457.

7. Kappelman M, Rifas-Shiman S, Porter C, et al. Direct health care costs of Crohn’s disease and ulcerative colitis in the US children and adults. Gastroenteroloy. 2008;135(6):1907-1913.

8. Shaw WS, Patterson TL, Semple SJ, et al. Longitudinal analysis of multiple indicators of health decline among spousal caregivers. Ann Behav Med. 1997;19(2):101-109.

9. Schulz R, Beach SR. Caregiving as a risk factor for mortality—the Caregiver Health Effects Study. JAMA. 1999;282(23):2215-2219.

Articles

WEB EXCLUSIVE

As delivery reform unfolds and leads to new models of care delivery, social capital will be a powerful concept to incorporate into their design and evaluation.

Bowling Alone, Healing Together: The Role of Social Capital in Delivery Reform

Accurately measuring the quality of care that ambulatory care physicians provide is an important endeavor. Current measurement instruments, while offering useful information about care systems, remain suboptimal for the measurement of individual physician performance. We offer the quality care opportunities model of ambulatory care physician performance measurement, which may address issues with current instruments while also offering useful information about efficiency and productivity for individual physicians and delivery systems from a patient-centered perspective.

This quality care opportunities model is important in the context of currently flawed systems of measurement to:

		 Improve data on physician performance that might eventually be reported publicly.

		Further involve physicians in the discussion on how to improve models of measurement to more accurately reflect practice realities.

The quality of US healthcare has been called into question.

Reform efforts have prioritized quality improvement aimed at improving care delivery systems and outcomes.

 Accordingly, there is a push to quantify the care quality delivered by systems and individual physicians.

Quality measures, being published at rapid pace, are aimed at assessing physician performance. Most of these measures, while useful in performance measurement of large physician groups, pose serious limitations in quantifying individual physician performance.

 Instead of using isolated diagnosis-specific outcome measures (OMs), process measures (PMs), and composite measures (CMs), we propose that evaluating the sum of opportunities available to each physician for evidence-based patient care and further assessing the fraction addressed over time can increase validity of physician performance measurement. This approach may also offer insights into systems quality.

Studies of physician performance measurement to date have mainly focused on measuring discrete aspects of care applied to groups of diagnosis- related patients.

 Current measures include OMs, PMs, and CMs. Outcome measures evaluate physicians based on targeted evidence-based clinical outcomes within diagnosis-related patient groups (eg, percentage of diabetic patients with glycated hemoglobin [A1C] <7%). Process measures assess physicians on whether evidence-based care processes were used within diagnosis-related patient groups (eg, percentage of time a physician ordered A1C tests for diabetic patients at minimum intervals). Individual OMs have been criticized for failing to account for factors outside of physician control (eg, patient preferences, nonadherence).

 In addition, while PMs do capture actions directly under provider control, critics indicate that PMs are often too disconnected from clinical outcomes to be individually useful.

 Furthermore, most physicians do not have large enough diagnosis-related patient populations to generate a statistically significant result for individual outcome or PMs.

Composite measures are an attempted improvement of the low statistical significance relating to individual OMs and PMs; these CMs combine multiple OMs and PMs into a single measurement (eg, diabetic CM = [A1C <7%] + [low-density lipoprotein cholesterol <100 mg/dL] + [aspirin use] + [nonsmoking status]). Traditionally, CMs, like individual OMs and PMs, are applied across diagnosis- related patient groups cared for by a given physician.

 For example, a diabetes care composite measure such as that proposed by Kaplan et al

 would be applied across all diabetic patients cared for by a single physician in an attempt to illustrate the quality of diabetes care provided by that particular physician. Because more events are measured within this diagnosis-specific CM compared with individual diagnosis-specific OMs or PMs, there is a greater likelihood that a greater number of physicians will have the minimum number of quality events required to generate a statistically significant outcome.

A growing number of CMs have been proposed in the literature, each attempting to measure the quality of care provided by physicians for specific diagnosis-related patient subsets.

 For example, diagnosis-specific CMs used in the Quality Oncology Practice Initiative involve breast cancer care—related measures, lymphoma care–related measures, and so forth.

 Several studies also demonstrate the use of CMs including “preventive care” and “chronic care” CMs.

 These types of CMs are related to types of care rather than specific diagnoses and are typically applied to patients in the same manner as diagnosis-specific CMs. Despite the improved statistical validity of these CMs compared with more granular diagnosis-specific OMs and PMs, several issues limit the clinical utility of these diagnosis-specific (or care type—specific) CMs when used as sole indicators of physician performance.

At the outset, questions about the interpretability of these CMs have limited their use.

 In other words, it is difficult to understand the source of the relative deficit for physicians with lower scores. Further, it is difficult to justify the idea that physicians with otherwise similar composite scores (eg, diabetes care composite scores, preventive care composite scores) could theoretically see major differences in overall patient care quality depending on the source of the deficit in their individual

scores. Overall, scores tend to be less likely to offer meaningful insights for areas of actionable improvement when used as sole indicators of physician performance.

Other issues that limit traditional CM usefulness are problems common to the use of diagnosis-specific OMs, PMs, and CMs as applied only to diagnosis-related patient groups cared for by the physician being measured. Measuring physician performance by applying diagnosis-specific measures generates perverse incentives for physicians to eliminate complex patients or focus on measured care at the expense of unmeasured care.

 Further, there are various methods of applying physician responsibility to individual patients that lead to widely variant performance scores.

 Additionally, these measures fail to address physician productivity and efficiency. Finally, adequately accounting for physician panel complexity has proved difficult when working with diagnosis-specific measures as applied to diagnosis-related patient groups.

REFINING PERFORMANCE MEASURES: QUALITY CARE OPPORTUNITIES

We propose a measurement tool, quality care opportunities (QCOs), that could enhance the utility of diagnosis-specific OMs, PMs, and CMs. It would also potentially address issues associated with those diagnosis-specific measures when applied only to diagnosis-related patient groups for individual physicians.

Over a day’s duration, many opportunities to provide evidence-based care for patients confront physicians. These opportunities include chief complaint management, chronic condition management, and screening/prevention services. Each patient presents with a unique set of needs based on demographics, risk factors, social factors, chronic medical problems, and chief complaint. Each of these needs is reflected by specific outcome or PMs (eg, patient-specific testing, medications, vaccinations, outcomes measures).

To refine performance measurement, the sum of relevant OMs/PMs would first be assigned to each physician’s scope of practice. Each opportunity to provide care (reflected through all relevant OMs/PMs) represents a QCO. Total QCOs for each physician are based on specialty-relevant OMs/PMs applicable to patients seen in the office over a defined time period. Physician performance would be assessed by measuring the fraction of all QCOs met for each patient seen, ie, quality efficiency (QE).

Hence, QCO and QE measures change with each patient and at each point of measurement. That is, each patient presents a unique set of applicable OMs/PMs. For example, a 55-year-old woman with uncontrolled diabetes who has not had healthcare in 5 years will carry a set of QCOs different from those of a 68-year-old male smoker with good care continuity. This QCO-QE system captures those unique intervention opportunities at each point of service.

The QCO concept applied across same-specialty physicians can provide substantive productivity and efficiency insights, related both to absolute numbers of care opportunities confronting physicians and to the proportion of QCOs being met by physicians over equal time intervals. For example, over 1 week, assume that physician A meets 500 of 750 QCOs for his patients, while physician B meets 350 of 400 QCOs for hers. In this example, physician A has an arguably more complex patient load presenting with 750 potential QCOs versus physician B’s 400 QCOs. Further, though physician B has been more efficient (physician B’s QE is 0.875 versus physician A’s QE of 0.667), physician A has been more productive (500 QCOs met over the same period of time that physician B has met 350 QCOs).

Using QCO in combination with QE can help define a benchmark balance between efficiency and productivity for same-specialty physicians. Traditionally, physician productivity has been a measure of patients seen per period of time, where same-specialty physicians are expected to see relatively equal numbers of patients over equal periods of time.

However, for example, assume physicians A and B are both meeting 350 QCOs per week but physician A is confronted with 500 QCOs and physician B is confronted with 1000 QCOs due to patient panel complexity differences. Although both physicians are equally productive in the quality context and by number of patients seen per hour, patients cared for by physician A are arguably receiving better care than patients care  or by physician B because a higher percentage of their needs are being met. Thus, reducing the volume of patients being seen by physician B might be justifiable.

Furthermore, while the QCO-QE model represents an improvement in physician performance measurement, traditional OMs/PMs can be extracted to make relative statements about strengths and weaknesses of individual physicians. This type of information, while subject to aforementioned flaws, remains meaningful for internal quality control and directing continuing medical education efforts. Likewise, extracting OMs/PMs for provider groups can offer the statistically significant information currently measured for health system care quality delivered to diagnosis-related patient populations (eg, diabetic patients).

Finally, QCO and QE measurement utility may be expanded by quantifying the percentage of QCOs met for each patient cared for by the health system and comparing this score with the physician QE. This allows for evaluation of relative nonphysician and physician contributions to system performance. For example, if a targeted fraction of patients cared for by the system have not had minimum QCO percentages met but high physician productivity/efficiency is evident, then greater nonphysician-based concerns may be contributing to poor system performance.

The QCO-QE approach is flexible, reflecting patient changes (eg, QCOs arising from a formerly controlled diabetic patient now presenting with A1C >7%), as well as changes in practice systems (eg, assigning care of diabetic patients to a new, integrated team). Hence, QCO-QE measurement can assess and compare physician action with patient-centered needs, shifts in patient needs, and systems issues related to ongoing opportunities to provide patient-centered quality care.

Beyond shifting the focus toward patient-centered care, the QCO-QE approach may address concerns with current performance measures as related to measuring individual physicians. First, QCO-QE measurement eliminates isolated dependence of performance evaluation on OMs, over which physicians feel they have little control. Given that several processes of care are required to accomplish a given clinical outcome, PMs will be more numerous than OMs in QCO-QE measurements. Consequently, by absolute numbers, actionable PMs will carry heavier weight than uncontrolled OMs. However, because improving clinical outcomes is an important goal in healthcare delivery, OMs will justifiably contribute to the final score.

Second, QE scoring mitigates potential incentives to concentrate only on discrete aspects of measured care. Unlike diagnosis-specific OMs, PMs, and CMs, the QCO-QE system aims to evaluate all aspects of care and, as such, makes it difficult to focus on a single aspect of care (eg, diabetes management) over another (eg, screening and prevention). Again, various OMs and PMs can be extracted from the QCO-QE measurements to identify relative physician problem areas and strengths for internal quality control efforts. Further, while statistically valid diagnosis-specific CMs can also be extracted to inform performance on diagnosis-related care, the QCOQE measurement offers an added dimension of understanding physician performance across the spectrum of care provided.

Third, evaluation of care delivered at the point of service, rather than with groups of diagnosis-related patients who may or may not have been seen, can eliminate the problem of assigning physician responsibility to various patients. We argue that while a health system—whether solo practitioner, multiphysician group, or accountable care organization (ACO)&mdash;should be accountable for all patients assigned to the system, a physician should be individually accountable only for care provided at the point of service. If a physician never sees a patient, despite being the assigned provider, then the QCOs for that patient should not contribute to the individual physician QCO-QE score. However, the patient will be captured by systems scoring; and where physicians are team leaders/members in primary care medical homes or ACOs, these physicians will still be accountable for the quality of care delivered to patients not being actively seen in the office.

Fourth, by providing insights about the absolute number of QCOs met by each physician over a period of time, QCO measurement can provide an added dimension of physician performance measurement not afforded by diagnosisspecific OMs, PMs, and CMs: productivity. Further, this measurement of productivity is more patient centered and quality centered than traditional concepts of productivity that simply use an absolute number of patients seen over a period of time. Given that complex patients will present more QCOs at each visit, QCO is proportional to patient complexity for defined patient volumes and therefore can also obviate the need for a separate system of panel-complexity stratification.

Finally, care efficiency can be elucidated. QE measurement potentially highlights physician efficiency with the fraction of all acute, chronic, and preventive patient care needs met at the point of service. Current measures of care efficiency are largely based on cost-efficiency, which is cost centered instead of patient centered, and thus promotes a number of perverse incentives.

QCO-QE IN THE CONTEXT OF HEALTHCARE REFORM

Healthcare reform has introduced urgency for effective physician assessment. The Centers for Medicare & Medicaid Services have created a public database aimed at publishing the quality scores for physicians serving Medicare beneficiaries next year.

 A quality measurement system capable of parsing out physician performance in a patient-centered and statistically significant manner may assist meaningful quality assessment while mitigating the flaws and perverse incentives inherent to current measurement methods.

Further, the QCO-QE model could be an important evaluation tool for ACO demonstration projects. ACO models are aimed at improving quality via coordinated care systems.

 QCO-QE measurement can assist evaluation of this complex system of coordinated physician groups, hospitals, and intermediate care providers by parsing out individual physician, group, and intermediate provider contributions.

The QCO-QE system, as conceived here, does not measure misuse of care (prescribing dangerous medications to elders or pregnant females) or overuse of care (prescribing unnecessary antibiotics or imaging modalities). However, previous studies have shown that physician performance is generally worse for quality measures characterized by underuse (those at which the QCO is directed) than for misuse or overuse.

 Further, the QCO model could theoretically be adapted to measure misuse and overuse as a greater library of measures are produced and validated for measurement of this type of care.

Furthermore, electronic medical records are a necessary element for implementing the QCO-QE model because claims data do not provide enough clinical detail to measure all available outcomes and PMs.

 Moreover, not all electronic medical records currently on the market are sufficiently sophisticated enough to capture the important level of clinical detail needed to measure the QCO-QE. Thus, to fully implement a QCO/QE model, physicians and groups would have to first implement electronic medical records of sufficient capability, adapt current administrative systems, or participate in record data extraction.

 Also, because the QCO/QE model presupposes evaluating all patients who have had care delivery under an individual physician, not just the patients who fall within a specific plan or set of plans, physician participation and cooperation are necessary features of this model. However, with healthcare reform mandates beginning the process of posting physician quality scores publicly, physician participation will likely be a standard in the future.

The quality care opportunities model can lead to increased utility and validity of current measurement tools and more accurate assessment of physician performance.

Quality Care Opportunities: Refining Physician Performance Measurement in Ambulatory Care

 To determine whether diagnostic coding shifts might undermine apparent improvements resulting from the 2007 Healthcare Effectiveness Data and Information Set (HEDIS) measure on avoidance of antibiotics for the treatment of adults with acute bronchitis (

International Classification of Diseases, Ninth Revision, Clinical Modification

Time series analysis within a primary care network for 3 successive winter seasons from 2006 to 2009. Methods: All initial adult visits with a primary diagnosis code of 466.0 or 490 (bronchitis, not otherwise specified) were analyzed. Multivariable analysis accounted for clustering of observations by physician.

 The percentage of visits treated with antibiotics declined significantly for code 466.0 (76.8% to 74.4% to 27.0% of visits over the 3-year study period; 

 <.0001 for trend) but did not decline for code 490 (86.6% to 87.6% to 82.1% of visits; 

 = .33 for trend). Use of the 490 code rose significantly over the study period, from 1.5% of total bronchitis visits in year 1 to 84.6% of total bronchitis visits in year 3. As a result, the odds of an antibiotic prescription for codes 466 and 490 combined decreased slightly in year 3 compared with year 1 (odds ratio 0.88; 95% confidence interval 0.78-0.99).

 While performance on the specific HEDIS measure improved dramatically during this study period, overall antibiotic prescribing did not decline substantially. Quality measures that assess performance on specific diagnosis codes are imperfect and do not account for shifts in diagnosis coding.

Quality measures that are linked to specific disease diagnoses can create unintended shifts in diagnosis patterns that offset apparent gains in quality.

		Diagnosis shifts are important consequences of quality measures that have narrow diagnostic specificity.

		Programs should examine both intended and unintended effects of quality measures, specifically examining changes in behavior for related conditions not captured by the measure.

Acute respiratory tract infections (ARIs), including acute bronchitis, are the most common reasons for outpatient office visits by adults in the United States.

 Although numerous studies and guidelines have advised against routine use of antibiotics for these predominantly viral illnesses, 75% of all antibiotics prescribed in community settings are for ARIs.

 Extensive data have shown that overprescription of antibiotics is a major contributor to emerging antibiotic resistance,

is costly,5 and exposes patients to potential side effects.6 Efforts utilizing public health campaigns, provider education, and practice guidelines like the Centers for Disease Control and Prevention’s “Principles for Appropriate Antibiotic Use for ARIs in Adults”7 have attempted to reduce antibiotic use for ARIs with varying degrees of success.

 Antibiotic prescriptions for acute bronchitis have been particularly refractory to change.

 Fewer than 10% of cases of acute bronchitis are bacterial in etiology, and antibiotic treatment has been shown to have minimal, if any, benefit.

 Guidelines state that acute bronchitis in healthy adults does not require antibiotic treatment, yet rates of antibiotic use remain very high. National Committee for Quality Assurance data from 2006 estimated that 71% of commercially insured patients diagnosed with acute bronchitis received antibiotics.

In recognition of this issue, avoidance of antibiotics for the treatment of adults with acute bronchitis (defined as 

] code 466.0) was included as a component of the Healthcare Effectiveness Data and Information Set (HEDIS) reported to the National Committee for Quality Assurance in 2007. Data obtained from HEDIS indicators are used by employers and consumers to compare quality measures across health plans, and can have an impact on pay-for-performance reimbursement at both the individual and practice level. Desire to score well on the HEDIS measure is an additional financial incentive for providers and insurers to reduce antibiotic use for bronchitis. However, it remains to be seen if the HEDIS measure will result in true reductions in antibiotic use, or if clinicians will alter diagnostic coding for visits to justify antibiotic prescribing.

Using data from a large, integrated health system, we examined trends in antibiotic use for acute bronchitis from 2006 to 2009, years surrounding the implementation of the HEDIS acute bronchitis measure. In this network, an effort to improve quality of care and performance on the HEDIS measure led to an electronic health record—based intervention that attempted to provide real-time feedback to the physician that antibiotics were not necessary in uncomplicated acute bronchitis. The display text for code 466.0 was changed to “ACUTE BRONCHITIS (ABX NOT INDICATED)” and for code 490 it was changed to “ACUTE BRONCHITIS (COMPLICATED).” We first sought to determine current rates of antibiotic use for acute bronchitis within this health system. We then examined whether shifts in coding patterns occurred during this period, and if the shifts were associated with antibiotic prescription rates. Finally, we sought to determine which patient and provider factors were associated with receipt of an antibiotic in order to identify potential targets for future interventions.

We conducted an analysis of antibiotic prescription patterns for adults with ARIs over a 3-year time period. The initial study population included all adults who sought care for any ARI at any of 33 primary care practice sites within the network.

Data for these analyses were collected from the integrated electronic health record that is utilized across all outpatient and inpatient practice sites within the healthcare system. We extracted data for all visits meeting inclusion criteria for the 3 successive periods of October 1 through March 31 for the years 2006 to 2009. Eligible encounters included outpatient visits to any of the 33 primary care practice sites by adult patients with an ARI (defined below). For each visit, we extracted the date of the visit, primary care site, patient demographic variables (age, sex), primary and secondary diagnoses for the visit, documented vital signs, and the presence of specific comorbidities based on the electronic problem list. In addition, we captured the prescription of an oral antibiotic at each eligible visit based on documentation in the electronic health record.

Visits for ARIs were identified based on the primary diagnosis code, using any of the following 

 codes: 460.0 through 487 (acute respiratory infections,

other diseases of the respiratory tract, pneumonia, and influenza), 490 (bronchitis not specified as acute or chronic, hereafter bronchitis NOS), 034.0 (streptococcal

sore throat), 079.0 (adenovirus), 784.1 (throat pain), and 786.2 (cough). For the analyses in this study, we limited our visits of interest to those with a primary diagnosis code of 466.0 (acute bronchitis) and 490 (bronchitis NOS). In addition, we excluded any visits for acute bronchitis within 30 days of a prior visit for an ARI as defined above. Additional exclusion criteria, based on the HEDIS measure, included age <18 years or >65 years, and the presence of specific comorbidities and/or antibiotic-responsive secondary diagnoses. Specific comorbidities included HIV, cystic fibrosis, malignancy, chronic bronchitis or emphysema, or other chronic respiratory diseases, and were identified based on the problem list maintained in the electronic health record and documented as of the date of visit. Antibiotic-responsive secondary diagnoses were documented at the time of the visit for acute bronchitis. The full list of diagnoses is included in the 

Analyses were limited to visits with a primary diagnosis of either acute bronchitis (466.0) or bronchitis NOS (490). Descriptive statistics were performed to examine the distribution of patient characteristics across all participating sites. We next performed unadjusted comparisons of antibiotic prescription rates across each of the study years using the Mantel-Haenszel chi-square analysis for trend. Analyses were conducted for visits coded as 466.0 and 490 separately and combined. Next, generalized estimating equations (PROC GENMOD) were used to control for clustering of antibiotic prescribing patterns by provider, and to evaluate independent predictors of antibiotic prescribing, including study year and

 diagnosis code. All statistical computations were performed using the SAS statistical software program (version 9.2; SAS Institute Inc, Cary, North Carolina). This study was approved by the Institutional Review Board at the participating clinical site and at the University of Pennsylvania and University of California, San Francisco.

There were 18,542 eligible visits for acute bronchitis (combining 

 codes 466.0 and 490) in the years 2006-2009. 

 displays the total number of visits for bronchitis; when stratified by 

 code, visits for bronchitis were coded almost exclusively as 466.0 for the years 2006-2007 (98.5% of cases) and 2007-2008 (98.4% of cases). The electronic health record diagnosis display text was changed in October 2008. In 2008-2009, the frequency of visits coded as 466.0 declined, and the use of code 490 rose significantly and accounted for a total of 84.6% of visits for bronchitis (

 displays the characteristics of patients

diagnosed with acute bronchitis, stratified by study year and diagnosis code. Overall, vital sign abnormalities were generally uncommon among patients across the 3 study years and comparing both diagnosis codes. Similarly, the documented presence of other comorbidities (not considered exclusion criteria for the HEDIS measure) was uncommon among patients diagnosed with either 466.0 or 490.

summarizes factors associated with the use of code 466.0 versus code 490. In both unadjusted and adjusted models, the odds of the 490 diagnosis went up substantially in year 3 compared with year 1 (odds ratio [OR] 370; 95% confidence interval [CI] 97-1416). Female patients had a lower odds of diagnosis with 490 versus 466.0 (OR 0.7; 95% CI 0.6-0.9); and current smokers had a higher odds of diagnosis with 490 versus 466.0 (OR 1.38; 95% CI 1.16-1.65). No other patient characteristics were associated with the choice of 466.0 versus 490 diagnosis codes.

For 2006-2009, antibiotics were prescribed for 74.9% of all patients with acute bronchitis (466.0 + 490 codes combined), and the overall rate declined from 76.9% in 2006-2007 to 74.6% in 2008-2009 (

). Compared with the first year of the study, the odds of antibiotic prescriptions were slightly lower in the second year (OR 0.9; 95% CI 0.80-1.01) and third year (OR 0.88; 95% CI 0.78-0.99) of the study (

). Across all 3 years, antibiotic prescriptions were significantly more common for visits coded as 490 (82.3% of visits) than for visits coded as 466.0 (71.5% of visits) (OR 2.18 for 490 vs 466.0; 95% CI 1.92-2.47; 

 <.0001). When analyzed by visit year, the percentage of visits where antibiotics were prescribed was consistently high for visits coded as 490 at 86.6%, 87.6%, and 82.1% of visits in 2006-2007, 2007-2008, and 2008-2009, respectively (

 = .33 for trend). In contrast, the percentage of visits where antibiotics were prescribed was high for visits coded as 466.0 in years 2006-2007 (76.8%) and 2007-2008 (74.4%), but dropped substantially to 27.0% in years 2008-2009 (

No patient factors were significantly associated with the odds of an antibiotic prescription, including patient age, sex, ethnicity, current smoking, or presence of comorbidities (Table 3). However, in comparing provider types, patients seen by a nurse practitioner or physician assistant were more likely to have an antibiotic prescription compared with patients seen by a physician (OR 2.77; 95% CI 1.69-4.52).

Acute bronchitis is a very common reason for outpatient office visits.

 Extensive data support avoidance of antibiotics for uncomplicated acute bronchitis,

 and quality improvement measures such as HEDIS have been designed to incentivize evidence-based practice for this condition.

 In this study of outpatient visit data from an integrated health plan, we found that a large decrease (ie, improvement) in antibiotic prescribing for acute bronchitis (466.0) as defined by the HEDIS measure was largely accounted for by diagnostic coding shifts. Antibiotic use for visits coded as bronchitis NOS (490) remained high throughout the period 2006-2009, and antibiotic prescription rates for the combined group of

 codes 466 and 490 showed little change over time.

The high rates of antibiotic use (74.9% overall) for acute bronchitis seen in our study are comparable to those found in earlier studies with HEDIS plans as well as with national ambulatory office visit surveys.

 Previous studies have elucidated many provider, patient, and health system factors that contribute to continued antibiotic use for bronchitis, including physician diagnostic uncertainty (ie, use of antibiotics due to difficulty in ruling out community-acquired pneumonia),

 and patient demographics and knowledge factors, and expectation of antibiotic treatment.

 Efforts at provider and patient education including decision support tools and management algorithms have shown some efficacy in lowering rates of antibiotic use.

The HEDIS bronchitis measure has significant potential to alter practice patterns, as health systems and providers have a financial incentive to prescribe fewer antibiotics. However, it is crucial to ensure that improved performance on the HEDIS measure translates to a true reduction in antibiotic use. This study suggests that diagnostic coding patterns should be analyzed when assessing HEDIS outcomes, and perhaps should be considered in future revisions of the HEDIS bronchitis measure.

The very simple electronic health record coding intervention had a significant effect on the use of antibiotics for acute bronchitis (466.0) after implementation in the third year of the study. However, this small effect was overshadowed by the large impact of coding changes on the overall use of antibiotics for patients with bronchitis. The use of diagnostic coding pattern shifts to improve ratings is a potential concern in the reporting of HEDIS measures. A study from 2000 found that after an educational intervention to reduce antibiotic use for acute bronchitis among family physicians, rates of antibiotic prescription declined, and there was no significant shift in coding from acute bronchitis to other ARIs such as sinusitis or pneumonia; however, coding shifts between acute bronchitis and bronchitis NOS were not assessed.

Because our study was observational, we cannot conclude that the implementation of the HEDIS measure and health system response were directly responsible for the observed code shifting and change in antibiotic prescribing patterns. It is possible that other factors influenced the change from use of code 466.0 to use of code 490, such as differences in patient presentation over the years, or concerns about postinfluenza bacterial illnesses in the wake of the influenza epidemic in 2008-2009. In addition, it is possible that provider turnover during the study period may have contributed to the observed change in coding patterns noted in 2008, if newer providers had different coding habits. Another limitation involves the abstraction of data on bronchitis diagnosis and antibiotic use from medical records, which may overestimate or underestimate actual antibiotic use rates depending on errors in the coding. However, previous studies to determine the accuracy of quality measurements such as HEDIS have shown that review of 

 coding and claims data provides information comparable to that obtained by full chart review and that claims data review appears to underestimate actual antibiotic use by providers.

Our study highlights the importance of ensuring that quality assessment measures obtain a complete picture of their designated outcomes. While the HEDIS bronchitis measure is an important step to encourage adoption of evidence-based management of acute bronchitis and reduce antibiotic overuse, the potential to work around its data collection measurements, either intentionally or accidentally, should be acknowledged. In future versions of the HEDIS bronchitis measure, the inclusion of bronchitis NOS (490) may be useful to obtain a complete assessment of antibiotic practice patterns.

 From the Department of Medicine (SR, JPM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Medicine (RG, JHM), University of California, San Francisco, San Francisco, CA; and the Geisinger Health System (TH-A, FJB, TG, MSS), Danville, PA.

Supported by grant R01-CI000611 from the Centers for Disease Control and Prevention and grant K24-AI073957 from the National Institutes of Health.

A quality measure reduced antibiotic use for patients with acute bronchitis but led to use of an alternative diagnosis, offsetting most of the observed improvement.

Unintended Consequences of a Quality Measure for Acute Bronchitis

To evaluate the clinical and economic impact of positive airway pressure (PAP) among patients with obstructive sleep apnea (OSA).

Retrospective claims-based analysis of OSA patients diagnosed with polysomnography (PSG) between January 1, 2005, and April 30, 2008.

Patients were required to have 2 or more claims for OSA diagnosis within 1 year after their first PSG test, and a minimum of 12 months’ baseline and 24 months’ follow-up continuous health plan enrollment. Patients with pulmonary disease or PAP use before the first PSG test were excluded. Outcomes included all-cause and sleep apnea—related hospitalization and healthcare costs. Multivariable analyses were performed to adjust for baseline characteristics.

Of the 15,424 patients identified, 90.7% used PAP and 9.3% did not. The PAP group had lower all-cause (19.0% vs 24.2%, 

 <.001) and sleep apnea—related (8.0% vs 11.3%, 

<.001) hospitalization rates than the non-PAP group during the follow-up period. After adjusting for baseline characteristics, patients in the PAP group were less likely to have an all-cause (odds ratio [OR] 0.70; 95% confidence interval [CI] 0.61-0.80]) or sleep apnea—related (OR 0.69; 95% CI 0.58-0.83) hospitalization than non-PAP patients. PAP users on average incurred 10% lower all-cause costs than non-PAP patients ($705 per member per month vs $786 per member per month, 

Among OSA patients in real-world practice, PAP users had significantly lower hospitalization risks and all-cause healthcare costs.

This study demonstrated that patients with obstructive sleep apnea (OSA) who initiated positive airway pressure (PAP) had clinically significant lower hospitalization risk and lower all-cause healthcare costs than patients who did not use PAP.

		 To our knowledge, this is the first study that used a large administrative claims database to compare economic outcomes of OSA patients who used PAP with those of OSA patients who did not.

		 With data from real-world practice, findings can be used by health plans in educating patients and physicians about screening and diagnostic testing for OSA, and for selecting appropriate treatment of OSA.

Obstructive sleep apnea (OSA) is a common breathing disorder characterized by the recurrence of partial or complete collapse of the soft palate and associated soft tissues of the upper airway during sleep.

 This results in oxygen deprivation from poor gas exchange, which leads to arousals from sleep and overall sleep fragmentation.

 Available population-based studies indicate that the prevalence of OSA ranges from 3% to 7% in adult men and 2% to 5% among adult women.

Among middle-aged adults, the prevalence of OSA is 26% for men and 9% for women.

 The prevalence of OSA has been reported to increase with age at a steady rate, especially among men.

The most common symptoms of OSA include daytime sleepiness, loud snoring, interrupted breathing, and choking-related awakenings.

 Recent epidemiologic studies have demonstrated that OSA is associated with cardiovascular disease,9 heart failure, coronary artery disease, cerebrovascular disease, diabetes mellitus,

 Additionally, OSA has also been identified as a primary independent risk factor in the development of hypertension

 Despite known health risks, current literature suggests that more than 80% of patients with moderate to severe OSA have not been clinically diagnosed.

Two commonly accepted tests for OSA are in-laboratory polysomnography (PSG) and in-home testing facilitated by portable monitors.

For more than 2 decades now, PSG has gained acceptance as the gold standard for the diagnosis of sleep-associated disordered breathing.

 The use of PSG is typically associated with supervised overnight laboratory testing to diagnose all cases of suspected OSA. In addition to the PSG diagnostic test, a variety of monitoring devices have been used to measure breathing disturbances during sleep in studies,

 and for the titration of continuous positive air pressure (CPAP) in the treatment of OSA.

 Commercial health plans and the Centers for Medicare & Medicaid Services include tests associated with breathing-affected sleep in their benefits package.

Among the available treatments for OSA, positive air pressure(PAP) devices have become the acknowledged gold standard for treating patients with moderate to severe OSA.

The most common form of PAP, CPAP, works through the application of positive pressure to the upper airway at a constant level to keep the pharyngeal airway open during sleep. The bilevel PAP, which provides 2 levels of pressure to maintain set airway pressures, is also commonly used to treat OSA, especially for patients who are intolerant of or unresponsive to initial fixed-pressure CPAP. The Agency for Healthcare Research and Quality in the United States has not developed guidelines for the treatment of OSA, but does promote those introduced by the United Kingdom’s National Institute of Health and Clinical Excellence, which recommended CPAP and bilevel PAP as technology-based treatment options for adults diagnosed with moderate or severe OSA.

 The Adult OSA Task Force of the American Academy of Sleep Medicine has also recommended the use of CPAP for the treatment of moderate to severe OSA patients and bilevel PAP in the management of OSA in CPAP-intolerant patients.

Costs attributable to the diagnosis and treatment of moderate to severe OSA are estimated at $2 billion to $10 billion per year. This reflects only a small proportion of the overall estimated economic burden of the disease, which ranges from $65 billion to $165 billion after accounting for absenteeism, loss of productivity, and workplace and traffic accidents.

 Given the increased morbidity associated with OSA, it is plausible that undiagnosed or untreated OSA may have an economic burden on the healthcare system. Kapur et al reported that patients diagnosed with OSA have $1336 higher annual medical costs in the year prior to their diagnosis compared with matched controls, which could well represent an estimate of the medical costs of untreated OSA.

 A Canadian study compared OSA patients with matched controls over a 10-year prediagnosis period and found that patients with OSA used about twice as many healthcare services during the prediagnosis period as patients in the control group, adding to the evidence that patients with OSA are heavy users of healthcare services.

Available evidence shows that CPAP and bilevel PAP therapy are safe.

 The economic impact of treating OSA patients with PAP, however, has not been well evaluated and documented. One small Swedish study, which compared hospitalizations before and after CPAP initiation among 88 OSA patients within a 2-year period, found that CPAP therapy reduced hospital admissions among OSA patients and resulted in reduced consumption of healthcare resources.

To date, no study has used a large population sample to assess the clinical and economic effects of PAP in realworld practice. This study was designed to evaluate the impact of PAP on hospitalization risks and healthcare costs among patients with PSG-diagnosed OSA using a large health insurance administrative claims database.

This was a retrospective cohort study using patient-level administrative claims data from the HealthCore Integrated Research Database, which consists of 12 regional commercial health insurance plans spread across the northeastern, southeastern, mid-Atlantic, Midwestern, and western regions of the United States. At the time this study was conducted, the HealthCore Integrated Research Database included medical claims, pharmacy claims, eligibility files, and laboratory test results data for approximately 43 million health plan enrollees. All data used in this observational study were deidentified and accessed using protocols compliant with the regulations of the Health Insurance Portability and Accountability Act of 1996. Patient confidentiality was preserved and the anonymity of all patient data was safeguarded throughout the study.

Patients between 18 and 64 years old with a medical claim for a PSG diagnostic test (Current Procedural Terminology [CPT] codes of 95806-95811) between January 1, 2005, and April 30, 2008, were identified. Patients were required to have 2 or more claims with a diagnosis code for OSA (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] code of 327.23) within 1 year after their first PSG test date. Patients older than 65 years were excluded from this study to avoid confounding due to potential dual coverage, with Medicare as an additional insurer. To make sure that patients were newly treated with PAP, subjects who had claims indicating the use of PAP (Healthcare Common Procedure Coding System [HCPCS] codes of E0601, E0470, or E0471), PAP-related supplies (HCPCS codes of A7027- A7039, A7045, or A7046), or PAP management (CPT code of 94660) prior to the first PSG test date were excluded. To avoid confounding by other primary respiratory conditions, patients with asthma (

492.xx), extrinsic allergic alveolitis (ICD-9 495.xx), cystic fibrosis (ICD-9 277.0x), other alveolar and parietoalveolar pneumonopathy (ICD-9 516.xx), other sleep apnea (ICD-9 327.2x except 327.23), and pneumoconioses and other lung disease due to external agents (

 500.xx-506.xx) were also excluded due to the potential contradiction and compromise for the evaluation of the effectiveness of PAP use among OSA patients (

The index date for patients using PAP (PAP group) was defined as the date of the first medical claim for PAP use. For patients not using PAP (non-PAP group), an index date was randomly assigned through Monte Carlo simulation based on the length of time between the PSG test and the initiation of PAP for corresponding PAP users. After simulation, a minimum of 36 months (12 months before the index date and 24 months after the index date) of continuous eligibility was required for both groups to ensure an adequate follow-up period for economic and clinical evaluation.

The main outcomes of interest were all-cause and sleep apnea— related hospitalization and direct costs over a 24-month postindex period. The all-cause hospitalization referred to inpatient admission with any cause of diagnosis, whereas the sleep apnea–related hospitalization referred to any inpatient admission with a sleep apnea diagnosis (

 327.23, 780.53, 780.57, or 780.51). Hospitalization risk was defined as the probability of having 1 or more hospitalizations during the follow-up period. Annualized hospital admission rates were used to assess the number of admissions per 1000 persons per year. The total all-cause costs were defined as the sum of medical and pharmacy costs associated with any condition from the claims data. Sleep apnea—related costs referred to costs associated with the treatment of sleep apnea incurred from inpatient, emergency department, and outpatient services, which were a subset of the all-cause costs. Costs were calculated on a per member per month (PMPM) basis, using the allowable amount reimbursed by health plans.

Baseline or the preindex period was defined as the 12 months prior to the index date (not including the index date) whereas the follow-up or postindex period was defined as 24 months after the index date (including the index date). Patients’ characteristics including age, sex, health plan type, and geographic region were compared between the 2 cohorts. Patients’ comorbid conditions were measured using the Deyo-Charlson Comorbidity Index (DCI) and specific sleep apnea—related comorbid conditions during the 12-month baseline period. The DCI consists of 19 diagnoses identified by 

codes, with a weight from 1 to 6 identified for each diagnosis.

The final score consists of a sum of weighted values for the present comorbidities, and higher scores indicate greater comorbidity burden. In addition, baseline allcause and sleep apnea—related hospitalization and direct costs were captured.

The hospitalization risks and costs of positive airway pressure were evaluated among patients with obstructive sleep apnea in a real-world setting.

June 2012

Bowling Alone, Healing Together: The Role of Social Capital in Delivery Reform

Quality Care Opportunities: Refining Physician Performance Measurement in Ambulatory Care

Unintended Consequences of a Quality Measure for Acute Bronchitis

Impact of Positive Airway Pressure Among Obstructive Sleep Apnea Patients

Barriers to Transition of Obese Patients From Hospital to Community

Thromboembolism Prophylaxis in Medical Inpatients: Effect on Outcomes and Costs

Racial Disparities in Lipid Control in Patients With Diabetes

Automating Care Quality Measurement With Health Information Technology

Role of Insurance, Income, and Affordability in Human Papillomavirus Vaccination