Future Directions for the National Healthcare Quality and Disparities Reports
D. Measurement Opportunities for the Framework's Components of Quality Care
Appendix D. Measurement Opportunities for the Framework's Components of Quality Care
In Chapter 3, the committee recommends for the NHQR and NHDR an updated framework that includes components of quality care not previously included (access, efficiency, care coordination, and health systems infrastructure capabilities). The committee examined the current set of health care quality measures presented in the NHQR and NHDR for measures that could satisfy these new care components. The committee determined that AHRQ would need to evaluate new measures in order to adequately report on these quality components. Some measures for the new components are considered either aspirational for AHRQ's work or still too developmental to immediately include in the NHQR or NHDR. In the context of this report, the term aspirational refers to performance areas for which no measures yet exist—at best, there is a proposed way to measure performance, but no evidence that it has been put into practice. Developmental refers to measures that are currently partially developed but not yet well tested or validated, or measures that have been validated but still lack sufficient national data on which to report.
By identifying new framework components for which the capacity for national reporting is still developing or aspirational (i.e., efficiency, care coordination, health systems infrastructure capabilities, end-of-life care), the committee faced a challenge in suggesting measures for immediate or even near-term inclusion in the national healthcare reports. The inclusion of unvalidated or insufficiently tested measures is not an option for the NHQR or NHDR and many of these areas are still in a research phase or undergoing validation.
Nonetheless, the committee presents some available measures that could serve to satisfy reporting on the newly added framework components of access, efficiency, care coordination, and health systems infrastructure capabilities (go to Chapter 3 for the rationale of adding these components). Their implications for quality improvement and disparities reduction are discussed and exploratory ideas for measure development in some of these areas are also presented.
The Current State of Quality Measurement
To date, most health care quality measurement efforts in the United States have focused largely on chronic conditions such as diabetes, asthma, and heart failure, and on measures of recommended preventive or screening services such as immunizations and mammograms. Data on these topics are readily available, and relevant guidelines can be easily translated into measures that express performance as the rate of receipt of recommended services in a defined denominator population. Less is known, however, about other clinical areas of performance measurement that might have a high impact for improving population health and providing value for the investment in care, but for which effectiveness research, measurement development, or sufficient data collection are still needed. Examples include the provision and management of oncology care (not just prevention) and surgical procedures for specific specialties (e.g., orthopedic surgery measures), both of which were brought to the committee's attention as high-cost items that patients, business, and insurers want to ensure are delivered in the most effective and efficient manner. While there may be some validated measures for these topics, the measures often lack either intensive data collection or sufficient ability to support analyses for disparities, and therefore have not been well represented in national reporting efforts.
A review of quality performance measurement in California found a lack of measures in use for mental health, hospital-acquired infections, obesity, and dental care (University of California at Davis, 2008). Given the recent attention to poor oral health (e.g., reported deaths in children due to untreated dental disease) and documented oral health disparities (AHRQ, 2003), additional or more targeted measures could be developed.1 The Children's Health Insurance Program Reauthorization Act (CHIPRA)2 may provide one mechanism for collecting data, for instance, on a children's dental care measure. CHIPRA required the Secretary of HHS to recommend a set of children's health care quality measures for voluntary use by Medicaid, the Children's Health Insurance Program (CHIP), and health insurance issuers and managed care entities that enter into contracts with Medicaid or CHIP programs. The initial set of measures, proposed in January 2010, includes one dental measure: "Total eligibles receiving preventive dental services" (HHS Office of the Secretary, 2009). If AHRQ expands reporting on the priority population of children, the National Survey of Children's Health (NSCH) could provide data on children who received needed mental health treatment, a measurement area on which AHRQ does not currently report, but that AHRQ may determine to be of high impact.3
Efforts to identify developmental performance measures for health are currently being undertaken by Healthy People 2020. A draft of Healthy People 2020's objectives includes several quality indicators identified as developmental, signifying that these measures are still being assessed for their validity and reliability for reporting (HHS, 2009). The National Priorities Partnership (NPP)—a collaborative of 32 major national organizations interested in transformational change in the U.S. health care system convened by the National Quality Forum (NQF)—promotes aspirational measurement for areas where improvement is believed to have potential for high health impact. Although the NPP has identified a number of NQF-endorsed measures compatible with its priorities, for many measures, little or no data are available on a national scale. Work is under way at the NQF to provide a roadmap of measures available in the short- and long-term to support documentation and build consensus among provider, payer, consumer, and community groups for these potential areas and measures.4 These efforts are of interest because they relate to the priorities proposed by the Future Directions committee (go to Box 2-3 in Chapter 2).
Reporting Opportunities for the Four New Components
Just as Chapter 3 did not go into the discussion of the care components that were included in the original framework for the NHQR and NHDR (i.e., effectiveness, safety, timeliness, patient-centeredness), this appendix does not present additional suggestions for reporting measures in those areas. The following sections address measure reporting possibilities for the new components of access, efficiency, care coordination, and health care systems infrastructure. Equity and value are crosscutting dimensions that were also added to the framework but do not have specific measures associated with them, and so are not addressed in this appendix. The committee offers the following information, but counsels that any measure should be assessed according to the process outlined in Chapter 4 to determine if it meets the test for inclusion in the NHQR and NHDR.
Reporting Opportunities for Access Measures
As discussed in Chapter 3, insurance status, affordability, and continuity of care are interrelated aspects of access. Insurance status often determines one's access to a usual or ongoing source of care. In addition, affordability can affect someone's ability to seek necessary, ongoing care, increasing risks of a worsened condition that ultimately may be costlier to treat (Collins et al., 2008).
The 2008 version of the NHDR reported data for 10 access measures that focus mainly on health insurance status, continuity of care, and access to needed services,5 and in Chapter 3, the Future Directions committee suggests the inclusion of data on these measures in future NHQRs. Further, the NHQR contained only partial information on two measures that captured affordability issues related to access, appearing in a summary table at the end of that chapter.6 The more detailed data on those measures and a handful of related measures were reported in an online appendix of data tables for the reports where the measures and data receive little visibility.7 Because affordability is often of concern to patients, data on this topic deserves consideration in the national healthcare reports. Other affordability indicator examples include reporting the percentage of low-income families that spend less than 10 percent of their income on out-of-pocket medical costs and premiums, or the number of adults under the age of 65 living in states where premiums for employer-sponsored health coverage are less than 15 percent of under-age 65 median household income (The Commonwealth Fund Commission on a High Performance Health System, 2008). These measures may give a sense of how close the nation is to providing more affordable health care. Data for these measures are available from the Medical Expenditure Panel Survey (MEPS) database and provide additional insight into the barriers of affordability in the health care system.
Continuity of care, both for primary and specialist care, can be affected as individuals come in and out of the health care system or change insurance companies through new employment. Based on coverage limitations, individuals may need a new physician following a change of insurance, an experience that often causes patients to report less satisfaction and increased access problems (Smith and Bartell, 2004). An emerging model of care, the patient-centered medical home, seeks to address this problem by establishing a usual source of care that is selected by the patient and coordinates care around patient preferences and needs (Kaye and Takach, 2009; NASHP, 2009; NQF, 2009c). Validated measures of medical homes are discussed in the capabilities of health systems infrastructure section of this appendix, as they are more closely related to ensuring structures that promote coordinated care.
Given the importance of health insurance coverage as a predictor of access, it would be useful if AHRQ could stratify insurance information by age. For example, when reporting data on insurance coverage, AHRQ could separate children 0-17 years of age from individuals aged 18-64. Reporting data on insurance status by age is important since a majority of low-income children are covered by state-sponsored programs, such as CHIP and Medicaid, while many adults are not covered by these programs. In 2008, the number of uninsured adults rose to 20.3 percent from 19.6 in 2007, while uninsured children decreased to 9.9 percent, the lowest number since 1987 (U.S. Census Bureau, 2009). As the nature of coverage changes, monitoring the type of coverage available to different age groups will be essential.
Reporting Opportunities for Efficiency Measures
Efficiency refers to producing the best possible outputs from a given set of inputs or producing a specific output with the least costly inputs. Efficiency measures are differentiated from cost measures because they take into account the outputs produced by a process as well as the resources used (inputs), whereas cost measures only take into account the resources used (inputs) relative to costs (i.e., resources used by unit price) (AQA, 2009; Krumholz et al., 2008).
Referring to descriptions of administrative, operational, and clinical waste put forth by Bentley and colleagues,8 the Future Directions committee identified clinical efficiency measures as those on which AHRQ should focus its immediate developmental attention. Only a small number of health care efficiency measures currently address administrative or operational waste, largely due to the challenges involved in identifying positive administrative or operational costs or activities and teasing them apart from those that constitute waste (Reischauer, 2009). Therefore, these latter measures will need further development before they can be used reliably.
Types of Cost and Efficiency Measures
Most clinical efficiency measures developed thus far consist of cost measures that calculate population-based expenditures, or efficiency measures that address clinical waste by focusing on overuse. Resource use measures, for example, are cost measures that summarize how many services or how much money is spent to provide care (e.g., per capita Medicare costs for certain conditions or cost per admission).
The National Committee for Quality Assurance (NCQA) has developed relative resource use measures that use standardized risk-adjusted data to compare health plans on the average cost of care for people with certain conditions (e.g., diabetes, asthma, cardiovascular conditions, acute low-back pain, uncomplicated hypertensions, chronic obstructive pulmonary disease) for which Healthcare Effectiveness Data and Information Set (HEDIS) effectiveness-of-care measures allow for the evaluation of efficiency (NCQA, 2009b). Health plans apply NCQA-standardized prices to the relevant units of health services. The use of a standardized fee schedule allows the measures to address the intensity of resource utilization within and across these service categories and enables comparisons of different mixes of services using a common scale. The total spending for one health plan can be compared to the spending that would be expected for a similar patient group based on the experience of all similar plans (NCQA, 2009a), providing some insight into system-wide spending for certain services or populations.
Resource use measures can be combined with other health care quality measures, such as episode-of-care measures (more commonly referred to as groupers), to describe the efficiency of care. An episode of care has been defined as "a health problem from its first encounter with a health care provider through the completion of the last encounter related to that problem" (Lamberts and Hofmans-Okkes, 1996). Defining an episode as a fixed unit creates "a clinically meaningful unit of analysis for measuring both the cost and quality of patient care" (Bassin, 1999, p. 319) and has led to the development of evaluation tools that measure both. The basis of these tools are groupers, which are algorithms that bundle together claims for services rendered as part of an episode across a certain period of time. Relying on claims data, software applications determine the cost of an episode from the onset to the end of care from treatments provided that are related to a patient's reason for seeking medical attention (Ingenix, 2008). With these tools, information on multiple episodes of care can be analyzed individually or collectively to assess levels of quality (by comparing the costs incurred by a physician to those of another physician delivering similar levels and types care).
Reporting data on resource use in combination with data on episodes of care may be useful because it would allow comparison of similar kinds of patients, taking into account the different kinds of services used and costs incurred; enable evaluation of care across time, settings, and providers; and potentially encourage care coordination (CMS, 2008). Although the complex process of knitting together measures to fit the parameters of these episodes remains a work in progress,9 it is anticipated that as Health IT systems become more sophisticated, they will be able to provide the types of measures and data desired by multiple stakeholders, and could eventually be reported in the NHQR and NHDR (once they are sufficiently developed and tested for reporting). Growing consensus that episode-based care will provide the basis for many new health care payment models (Pham et al., 2010) will likely also make the development of related measures a priority for national reporting. Until these measures are available, there are examples of more basic strategies that attempt to capture information on resource use relative to quality of care. The example highlighted in Box D-1 is one approach for presenting related cost and quality data.
Related to resource use is the overuse of medical services. Overuse in health care has been defined as "the provision of health services for which the potential risks outweigh the potential benefits" (Chassin et al., 1998) and refers to wasteful services, such as duplicative procedures or avoidable admissions. An example is NCQA's measure of imaging use for low-back pain: percentage of patients with new low-back pain who received an imaging study (plain X-ray, magnetic resonance imaging, CT scan) conducted on the episode start date or in the 28 days following the episode start date (NCQA, 2009b). Other examples of NCQA-developed and NQF-endorsed measures that address overuse include the "appropriate treatment for children with upper respiratory infection (URI)" and "avoidance of antibiotic treatment in adults with acute bronchitis" (NCQA, 2009b).
The NPP, as well as this committee, has identified overuse as priority area for which the goal is to enhance the affordability and safety of care (NPP, 2008). The NPP identified a number of NQF-endorsed measures that could help address this aspect of care (go to Table D-1). Data collection on these measures has yet to be widely established, but the measures present examples of areas in which AHRQ could foster development and strive to report in future versions of the NHQR and NHDR.
Currently, five efficiency measures are reported in the NHQR (go to Table D-2). Although they might represent the best available data on certain efficiency measures, the committee felt that the presentation of these measures could be improved. Several points made in the efficiency chapter of the NHQR were confusing to readers, and insufficient data were available to verify or fully understand some of the information presented (go to Appendix H for additional comments on the efficiency chapter of the NHQR). AHRQ additionally reports on several measures for avoidable admissions via the online version of the reports, only one of which is presented in the printed NHDR, framed under a subsection of the access measures, where it does not get much visibility. The committee feels that these measures could be highlighted in the efficiency section of both reports, as better examples of clear cost measures. AHRQ has also commented that data on new efficiency measures will be reported in the 2009 NHQR, including avoidable hospitalizations among patients housed in skilled nursing facilities or patients getting home health care; potentially avoidable emergency room visits; and unnecessary prostate specific antigen testing in men aged 75 years or older.10
In the near term, AHRQ should consider reporting data for several measures discussed in the previous sections, particularly those on resource use and overuse provided by NCQA, for which there are data collected using the HEDIS tool. Similarly, AHRQ should strive to report on the NPP-identified measures of overuse as well (go to Table D-1 above), looking to the Centers for Medicare and Medicaid Services (CMS), other payer databases, and subnational datasets for potential data. (Go to Chapter 5 for discussion on use of subnational datasets for the NHQR and NHDR.)
Reporting Opportunities for Care Coordination Measures
Care coordination and health systems infrastructure are of interest to the extent that they improve effectiveness, safety, timeliness, patient-centeredness, access, or efficiency, which is why they are depicted as foundational elements in the conceptual diagram for the framework (go to Figure 3-1 in Chapter 3). Although evidence for specific measures' impact is limited, the committee examines potentially promising areas for performance reporting for these components.
Current care coordination measures focus primarily on ensuring good communication, smooth transitions, and timeliness of care among and between health care teams and patients. Much of the data for these measures are gathered through surveys that ask patients (or family members) for their perceptions of the care received. Patient-reported measures of care coordination include those presented in the Components of Primary Care Index (Flocke, 1997), which evaluates their experiences with their health care; the Care Evaluation Scale, which measures family experiences with their family members end-of-life care (Morita et al., 2004); and certain questions included on the Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys, such as following up with test results and evaluating how informed a personal doctor is about a patient's care received from other providers (AHRQ, 2009b).
The care transitions measure (CTM) is a validated process of care measure that quantifies hospital performance based on patient or caregiver experience with hospital transitions (Coleman, 2006; Parry et al., 2008). This measure has demonstrated positive health outcomes including reduced readmissions of patients discharged from hospitals and improved self-management and recovery of symptoms (Care Transitions Program, 2009). This measure is currently being collected in all Maine hospitals11,12 (Adams, 2009; NQF, 2009a), and data collected in Maine may serve as a subnational dataset to which AHRQ could look. The NQF has endorsed the CTM measure along with others (e.g., timely initiation of care, medical home system survey) (NQF, 2009d).
AHRQ should consider including data on the measures mentioned for which data are collected, such as the CAHPS measures for test results follow-up, and provider knowledge of patient care at other settings (AHRQ, 2009a). Eventually, AHRQ could begin reporting on the coordination measures currently identified by the NPP (and endorsed by the NQF). Although some of the identified measures are considered developmental due to a lack of national data collection, the committee concludes that they are sufficiently important to strive to include in future reports.
Box D-1. Using Subnational Data to Provide Insight into Potential Efficiency Measures
The Wisconsin Collaborative for Healthcare Quality (WCHQ) is working to develop measures that reflect efficiency in hospitals and health care systems. While each of the WCHQ's six reported efficiency measures is presented with a disclaimer that it is only an attempt to measure the relationship between "quality outcomes and charges/length of hospital stay," understanding these relationships is helpful in moving performance measurement in this area forward. AHRQ could feature efficiency data from the WCHQ as a subnational example of promising practices for measuring efficiency, thereby educating others about ongoing development. At the least, WCHQ's work could serve as an example of alternate ways to present quality and value information.
WCHQ's six efficiency measures compare quality of heart attack care, heart failure care, and pneumonia care to either hospital charges or length of stay, often used as indicators/proxies for how efficiently hospitals deliver care. The figure below depicts the Heart Failure Care Hospital Charges and Quality Comparison measure. This quadrant analysis "attempts to quantify the value each hospital provides." Hospitals that performed well on the congestive heart failure composite measure (y-axis) and with low severity adjusted charges (x-axis) are in the upper left quadrant and may be assumed to be providing higher value care. Conversely, hospitals that are not performing as well on the heart failure measure while having high severity-adjusted charges are depicted in the lower right quadrant. These hospitals may be said to be providing lower value care. These data can lead to further investigations of what might contribute to the variation and the resolution of such variation.
Heart Failure Care Hospital Charges and Quality Comparison Measure
Source: Wisconsin Collaborative for Healthcare Quality, 2009.
Opportunities for Reporting on Health Systems Infrastructure Capabilities
A handful of validated measures track the adoption of health information systems and care models for effective organization capacity; as of yet, these measures lack national data for reporting but data may become available over the next 5 years.
Currently, health information technology (HIT) measures are not widely reported in health care settings across the country. However, tens of thousands of hospitals, community health centers, physicians, and other Medicare and Medicaid providers are anticipated to begin implementing Health IT systems over the next few years in order to be eligible for Health IT incentive payments under HITECH and MIPPA.13,14 Examples of potential measures for Health IT include the NQF's recently endorsed measures, which are listed in Table D-3. They address various aspects of Health IT including electronic prescribing, computerized order entry, and electronic laboratory ordering and reporting. As data collection on these measures increases over time, AHRQ should review these for their applicability to future versions of the reports. Adoption may have the potential to substantially improve the quality of care delivered to patients nationwide, and tracking that progress over time might foster adoption.
With greater adoption, there will also eventually be opportunities to better assess the impact on quality of HIT adoption. The NHQR could play a role in tracking progress in the availability, quality, integration, and "meaningful use" of Health IT across inpatient and ambulatory care facilities. Likewise, the NHDR could track equity in these measures by monitoring the relative availability of these capabilities to providers (including safety net providers) serving priority populations.
To further support an effective infrastructure, organizational capacity is needed. Organizational capacity refers to leadership and staff commitment to quality improvement in an organization (whether health plan, hospital, or private practice); policies, procedures, processes, and organizational practices are needed to effectively implement quality improvement for safe, high-quality care (McLaughlin and Kaluzny, 2006); and the implementation of specific care processes designed to optimize quality and the integration of ongoing systems for assessing quality improvement throughout an organization. To date, there are few validated quality measures for organizational capacity, yet measuring the degree to which health care organizations or systems are successful in creating cultures that embrace and encourage continual quality improvement could be an important step for realizing more goals for high-value care (Bodenheimer et al., 2004; Bradley et al., 2005; Corrigan and McNeil, 2009; Singer et al., 2009).
More immediately, examples of measures for care models designed to optimize well-integrated, quality care through system structure include the chronic care model and the patient-centered medical home. The Assessment of Chronic Illness Care measure seeks to evaluate how organizations deliver care for populations with chronic conditions by the degree of system redesign outlined by the Chronic Care Model that they have in place (Bonomi et al., 2002). For gauging the elements of a patient-centered medical home that a practice has adopted, there are two separate validated measures. One is the Medical Home Index, which measures effectiveness of medical homes in pediatric primary care (Cooley et al., 2003). The other is the Medical Home System Survey, which is the survey version of NCQA's Physician Practice Connections-Patient-Centered Medical Home program, which is NQF-endorsed and the most widely used tool for qualifying practices in patient-centered medical home demonstrations (Patient-Centered Primary Care Collaborative, 2009).15 Beal and colleagues have suggested ways to capture elements that characterize a medical home using MEPS data (2009), as has the California Health Interview Survey, both of which may be accessible to AHRQ (go to Chapter 5, Box 5-2). As described in Chapter 3, these care models promote well-coordinated care and help enhance patient experiences by employing interventions targeted at fundamental system practices. Encouraging data reporting from health care settings that employ these care models, or aspects of them, may help establish a stronger evidence base of their effectiveness. Such data would also inform the extent of their adoption in clinical practice, which has been growing nationwide (Coleman et al., 2009; Patient-Centered Primary Care Collaborative, 2008).
The updated framework implies looking to areas of measurement that are now underdeveloped and collaborating with other entities to develop measures and data sources. Consideration of priority areas (Chapter 2) and identification of measurement areas with the highest impact on population health, quality, value, and equity may result in the NHQR and NHDR containing a different compendium of measures and data over the next five years. The measures presented in this appendix are meant to be illustrative examples of measurement areas and measures that could be examined in the course of measure selection and development.
1 In the NHQR and NHDR, AHRQ currently reports on three dental care measures: percent of children age 2-17 with a dental visit in the past year (note: this measure is reported in the NHQR in alternate years); people who were unable to get or delayed in getting needed dental care in the past 12 months; people who were unable to get or delayed in getting needed dental care due to financial or insurance reasons (note: these two latter measures are usually reported as part of composite measures in the NHDR). Another measure, "People who had a dental visit in the calendar year," is reported in an online appendix to the reports.
2 Children's Health Insurance Program Reauthorization Act, Public Law 111-3, 111th Cong., 1st sess. (January 6, 2009).
3 AHRQ and CMS are implementing the quality provisions of CHIPRA and have identified mental health and substance abuse services for children as a priority area for pediatric quality measurement. A proposed core set of children's health care quality measures for use by Medicaid and CHIP programs includes a measure on follow-up after hospitalization for mental illness (HHS Office of the Secretary, 2009).
4 Personal communication, Karen Adams, National Priorities Partnership, National Quality Forum, November 17, 2009.
5 Examples of access measures presented in the printed 2008 NHDR include people under age 65 with health insurance; people under age 65 who were uninsured all year; people with a specific source of ongoing care; people with a usual primary care provider; people without a usual source of care who indicated a financial or insurance reason for not having a source of care; people who were unable to get or delayed in getting needed care; people unable to get or delayed in getting needed care due to financial or insurance reasons; people who had a dental visit in the calendar year; perforated appendixes per 1,000 admissions with appendicitis; adults who received mental health treatment or counseling in the past 12 months; and people age 12 and older who received any treatment for illicit drug or alcohol abuse in the past 12 months.
6 Those two measures are "People without a usual source of care who indicated a financial or insurance reason for not having a source of care" and "People unable to get or delayed in getting needed care due to financial or insurance reasons" (AHRQ, 2009c, pp.164-165).
7 Related measures include "People unable to get or delayed in getting needed medical care, dental care, or prescription medicines in the last 12 months due to financial or insurance reasons" and "People unable to get or delayed in getting needed medical care, dental care, or prescription medicines due to financial or insurance reasons."
8 "Administrative waste is the excess administrative overhead that stems primarily from the complexity of the U.S. insurance and provider payment systems [e.g., billing/claims processing, sales/marketing practices, compliance procedures, benefits design], operational waste refers to other aspects of inefficient production process [e.g., unnecessary or duplicative procedures, use of defective devices that cause errors, or wasted time transporting people or materials], and clinical waste is created by the production of low-value outputs [e.g., overuse of certain procedures]" (Bentley et al., 2008, p. 632).
9 The committee notes that there has been criticism of episode-based groupers because of the difficulty in defining which services should be grouped together (e.g., is the cardiac care for someone with diabetes a separate episode, or part of the diabetes episode of care?). NQF is currently attempting to capture the performance of episode-based care through the development of a conceptual model of a longitudinal episode that incorporates ambulatory, acute, and post-acute care for conditions such as back pain and acute myocardial infarction.
10 Personal communication, Ernest Moy, Agency for Healthcare Research and Quality, October 13, 2009.
11 Personal communication, Eric Coleman, University of Colorado at Denver, October 30, 2009.
12 Personal communication, Susan E. Schow, Maine Health Data Organization, November 2, 2009.
13 Medicare Improvements for Patients and Providers Act of 2008, Public Law 110-275, 110th Cong., 2nd sess. (July 10, 2008).
14 American Recovery and Reinvestment Act of 2009, Public Law 111-5 §4001(b)(2)(B)(vii), 111th Cong., 1st sess. (February 17, 2009).
15 This is the same Medical Home System Survey endorsed as an Health IT measure by NQF (go to Table D-3). The various aspects of care delivery that the medical home addresses (e.g., care coordination, Health IT use) allow it to potentially serve as a measure for more than one framework component.
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