A variety of data sources already in use could be leveraged to support measurement. These data sources cover different populations, conditions, and aspects of care and are directed toward a variety of end uses, including direct clinical care, payment decisions, quality assessment, and population tracking, among others. There is also significant variation in data collection processes. This appendix describes data sources available for assessing progress along each of the four study dimensions: population health, quality of care, cost of care, and engagement in health and health care.
POPULATION HEALTH
Data on the health of populations come from a variety of sources:
- individual-level social data (e.g., social and economic status; demographics; access to social and economic services, child and family services, elderly services, and home health services);
- population surveys (e.g., National Health Interview Survey [Census Bureau and Centers for Disease Control and Prevention], National Health and Nutrition Examination Survey [NHANES], U.S. Census);
- reportable diseases (e.g., state notifiable disease reporting systems, National Notifiable Diseases Surveillance System); and
- vital statistics (e.g., local, state, and national vital statistics registries; National Death Index).
These data provide important information about the health of the nation as a whole and may offer insight into the impact of large-scale population health interventions. These data are some of the most comprehensive in the field of health measurement, with coverage of vital statistics and census data approaching 100 percent of the population (Guyer et al., 2000).
One significant challenge for health care measurement is the need to break down the artificial barrier between individual and population health. Doing so could allow for routine comparison of individual health against the health of communities or demographic groups. Furthermore, continuous individual and community health data could allow for more precise, targeted population health interventions tailored to specific environmental and social factors.
QUALITY OF CARE
A variety of data sources can be used to assess the quality of health care, including
- patient-level clinical care data (e.g., electronic health records, registries);
- population-level safety data (e.g., adverse event reporting registries, public health surveillance);
- population-level clinical data (e.g., cancer, chronic condition, and screening registries);
- claims data (e.g., Medicare claims, private payer claims, multipayer and all-payer claims databases);
- patient-reported outcomes (e.g., National Institutes of Health [NIH] Patient Reported Outcomes Measurement Information System [PROMIS], Short Form [36] Health Survey [SF-36]);
- surveys (e.g., National Hospital Care Survey, National Ambulatory Medical Care Survey, Medicare Current Beneficiary Survey, National Home and Hospice Care Survey, Medicare Health Outcomes Survey); and
- operational and financial data for health care organizations.
Care quality measures traditionally have been calculated from administrative data, such as claims, which remain the most common source for quality measurement today (Damberg et al., 2011). Administrative data often have been used because of the absence of other data sources for large-scale analysis, but they also have other advantages—they are broadly available and inexpensive to collect and contain extensive information about medical care. Yet claims data often lack significant clinical details that are important for understanding the appropriateness of medical care
and identifying clinically relevant populations. One study found that claims data were able to identify only 75 percent of patients with diabetes, while automated analyses of electronic health record data were able to identify 97 percent of such patients (Tang et al., 2007). Another study found that claims data recorded several preventive services for patients with diabetes (cholesterol screening, influenza vaccination, nephropathy screenings, and A1C testing) only half of the time (Devoe et al., 2011), and still another study found that claims data failed to capture the provision of many recommended services in pediatric care (Casciato et al., 2012). Claims data also may miss significant subpopulations, including the uninsured, the underinsured, or the discontinuously covered.
An additional barrier to deriving accurate measures from claims data is that individual clinician experience for patients with a given condition (especially rare conditions) is limited by health plan enrollment. As each payer collects and maintains its claims data separately, the statistical accuracy of these performance measures may be low, meaning that two similar clinicians may appear to have very different performance results (Landon and Normand, 2008; Landon et al., 2003; Scholle et al., 2008, 2009). One method for overcoming this challenge is to combine data across multiple payers, an approach that has been piloted successfully in several states (Higgins et al., 2011; Toussaint et al., 2011).
Electronic health records offer another opportunity to improve quality measurement, as these data sources contain detailed information on care processes. To achieve that potential, digital record systems must capture the necessary data elements from routine clinical care in a standardized, codified fashion and be able to exchange that information across data systems. Although progress has been made, this capability still is not a reality in many circumstances. Despite a significant investment in electronic health records, for example, a patchwork of such systems exists that capture data elements in inconsistent formats, and it may not be easy to transmit the data to other systems (Chan et al., 2010; Gold et al., 2012; IOM, 2011, 2012; Kern et al., 2013; Parsons et al., 2012). One study found that quality measures calculated automatically from electronic health records could differ significantly from measures derived from manual review of the clinical records—overestimating the provision of some services and underestimating the provision of others (Kern et al., 2013). Other challenges include substantial variation in the use of terminology, such as “shock”; variation in the meaning of different terms used for the same concept; and limited common standards for documentation (Berenson et al., 2013). These challenges highlight the importance of implementation in unlocking the potential of these new data sources.
Further, depending on the site of a clinician’s practice and patient population characteristics, high-quality care that is delivered may result in
very different outcomes because of patients’ exposure to social determinants of health and differential community factors that impact health. Accurate measurement will depend on the use of data sources that capture this information, which can then be used to “equalize” performance and quality based on patient complexity and baseline need for health care services.
One key consideration is that many of the existing technical specifications for measures fail to take advantage of the capabilities of new digital infrastructure, as the measures were designed for other data sources. One study found that measures designed for claims data can be adapted to be calculated from digital records, but the adapted measures do not take full advantage of the new data source, and information may be lost in the transition. For example, the study found that claims-based data showed that fewer than 1 percent of patients had annual body mass index (BMI) documentation, while data from electronic health records showed more than 70 percent (Gold et al., 2012).
Another consideration is that no secondary data source contains all of the relevant information needed (e.g., social determinants of health often are missing from claims and electronic health record data but may be found in survey data). Given the limitations of each data source, some measures are calculated from hybrid data that draw on multiple data sources, such as merging of administrative data with clinical, survey, or operational data (NQF, 2013).
COST OF CARE
The body of data on health care costs is relatively small compared with the volume and variety of data collected on health care quality. Furthermore, cost data are not linked consistently with clinical and demographic data, which limits their usefulness. The data sources currently available for assessing the cost of care include
- single-payer claims data (e.g., Medicare claims data, private payer claims);
- multi-payer claims databases (e.g., state all-payer claims databases, FAIR Health, Health Care Cost Institute);
- surveys (e.g., American Heart Association [AHA] Annual Survey of Hospitals with information technology [IT] supplement, Medical Expenditure Panel Survey, Medicare Current Beneficiary Survey);
- organization operational data;
- organizational chargemasters; and
- the Healthcare Cost and Utilization Project (HCUP).
Claims and billing data account for the majority of health care cost data currently collected. Medicare, for example, maintains a comprehensive database of claims information. A variety of local-, state-, and national-level multi-payer claims databases aggregate cost data across providers for a more complete picture of health care costs and prices. As of May 2013, 10 states—Colorado, Kansas, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, Tennessee, Utah, and Vermont—had implemented an all-payer claims database. These databases can help inform policy initiatives and provide greater knowledge on how costs compare across counties and over time (NCSL, 2013).
Data on health care costs also are collected through routine surveys, including the AHA Annual Survey of Hospitals with IT supplement, the Medical Expenditure Panel Survey, and the Medicare Current Beneficiary Survey. The HCUP, a project of the Agency for Healthcare Research and Quality (AHRQ), collects both nationwide and state-specific longitudinal data from hospitals in the United States, bringing together clinical, administrative, and cost data at the encounter level.
Another challenge is that the prices for health care services generally are confidential or difficult to obtain. Those data that are available show that prices also are highly variable. This variability is due to a variety of factors, including the fragmented billing of different providers for an episode of care; varied negotiated rates for different health plans; and legal factors such as antitrust law, contractual obligations between insurers and providers, and hesitancy to disclose negotiated rates (GAO, 2011). Given the variation in health care prices (Office of Attorney General Martha Coakley, 2011), the lack of data in this area limits the ability of consumers and patients to select the highest-value care.
ENGAGEMENT IN HEALTH AND HEALTH CARE
Data on patients’ health care perspectives and experiences are collected primarily through surveys, which usually employ self-reporting or interview instruments. Examples of surveys used today to assess patient perspectives include the Health Center Patient Satisfaction Survey, used by the Health Resources and Services Administration (HRSA), and the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey suite, which considers patient experiences with inpatient care, outpatient care, health plans, and other health care stakeholders and venues. One challenge with survey data is ensuring that they are captured frequently enough to allow clinicians and health care organizations to gauge whether initiatives have improved patients’ experience and satisfaction. One limitation of this type of data is that patients tend to over- or underreport when surveyed as a result of recall and response bias, and there may be systematic differences
in responses among demographic groups. Obtaining an adequate response to surveys also requires significant financial and staff resources, as survey validity depends on a robust patient sample.
A conceptual challenge with assessing the extent to which the health care system aligns with patients’ needs and values is uncertainty in how to measure the patient perspective, as well as how to assess patient involvement in health and health care. Multiple terms are used to describe this goal—including patient satisfaction, patient experience, patient perception, and patient ratings—with each term describing different but overlapping concepts. Part of the conceptual challenge is that patients consider a number of issues in determining whether care is of high quality, including technical expertise, staff interactions, and communications and information availability (Gao et al., 2012; Sofaer and Firminger, 2005); therefore, metrics need to be comprehensive to capture all of the aspects of care that patients consider.
There is further confusion on how well survey measures of patient-centeredness correlate with improved health outcomes. Some studies have found that higher patient satisfaction is correlated with lower readmission rates (Boulding et al., 2011) and lower mortality rates for heart attack patients (Glickman et al., 2010). In contrast, others have found that greater patient satisfaction is associated with higher utilization of health services, higher costs, and increased mortality (Fenton et al., 2012) or that increased patient involvement in decisions is linked to increased hospital lengths of stay and higher costs (Tak et al., 2013). Further research is needed to understand these relationships and to identify the components of patient-centered care that result in improved health.
The concept of health means different things to different people. Broadly, patients tend to define health outside the bounds of the health care system, underscoring their preference for care that considers their individual needs and circumstances rather than just their diseases. The literature on patient views of health—taken largely from surveys and focus groups—reveals some general concepts of how patients often define health, noting that perceptions of health are frequently nuanced and personal:
- Avoiding care: Patients tend to define “health” as the absence of a need for medical care or the absence of physical limitations that adversely affect their daily lives.
- Resolving uncertainty: Patients value care that aids in resolving uncertainty about the current or future state of their health (Detsky, 2011).
- Wellness and happiness: Patients view health in social, environmental, economic, and behavioral, not solely biological, terms.
Significant gaps exist in the delivery of information, tools, and resources that would enable people to make improvements in their own health and the health of their family and community, and their ability to engage with the care system. Improvement will require not only engagement by individuals in their own health management but also—and equally important—engagement of the community with patient needs. In this report, the Committee frames this model of people’s involvement as “engagement in health and health care,” encompassing engagement with resources both within and outside of the health care system, as well as the development and use of critical skills and resources that enable patients to improve their own health and care. This model of engagement represents the subjective experience of the individual, personal priorities, understanding of the actions individuals need to take to improve their health, and the societal factors needed to promote good health. This engagement in health represents a key component of shared accountability for health, with patients being active participants in individual, community, and national health improvement efforts. Additional research and development is needed to ensure that health care—and the measures used to assess it—incorporate the views, needs, and priorities of patients.
While the domains of individual and community engagement includes priority areas such as shared decision making, self-care, and patient satisfaction, the perspective of the individual patient—which includes all members of the public at some point in their lives—was central to the Committee’s selection of core measures across all four of the domains. In this way, the measure set is intended to frame measurement and improvement efforts around what matters most for the health of individuals, communities, and the nation.
People’s Perspectives on Health Care
There are multiple misconceptions about what people want from the health care system, with prior studies indicating that significant differences exist between what clinicians believe patients want and what patients actually value (Hibbard and Sofaer, 2010). Research shows that patients weigh multiple factors in assessing the quality of health care. For example, one study examined patient views through focus groups, surveys, and collaborations with consumer organizations and found that patients valued four broad areas in their care (Bechtel and Ness, 2010):
- Whole-person care: understanding the whole of the patient and the factors that may affect patients’ ability to improve and maintain their health.
- Comprehensive communication and coordination: comprehensive coordination and smooth transitions of care, medical information shared seamlessly, and explanations of care options.
- Patient support and empowerment: partnerships in making care decisions; support for self-management; trust; and respect for patient preferences, privacy, and physical and emotional needs.
- Ready access: ease of obtaining appointments, limited wait times, availability of the care team when needed through different mediums (phone, email, online, in person), and accommodation of the factors that may impede access, such as a lack of physical mobility, cognitive impairment, or language barriers.
The following list consolidates overarching themes from the literature on patient views of health care quality, along with specific descriptive concepts for each theme (Sofaer and Firminger, 2005). While these themes represent an attempt to capture general values and expectations, each patient is different, and many patients value care that is tailored to their particular circumstances and conditions:
- Patient-centered care: having all physical and emotional needs met, receiving care tailored to individual needs and values, being involved with decision making and care, and having family and caregivers involved as needed.
- Access: timeliness of routine and urgent care, affordability, and accommodations for individual preferences and limitations.
- Communication and information: open communication and information flow, listening, understanding what to expect, and prompt communication of test results.
- Courtesy and emotional support: sensitivity, compassion, trust, friendliness, and clinical care that incorporates social and emotional qualities.
- Efficiency of care and effective organization: coordination among clinicians, access to the same care providers over time, accurate billing, efficient referrals, and limited waiting times.
- Technical quality: technical knowledge, competence, experience, credentials, effective treatments, accurate diagnoses, and care that results in good health outcomes and improved quality of life.
- Structure and facilities: easy access to transportation and parking, safety and security, comfort, food quality, and up-to-date technology.
Beyond these themes, focus group research has identified additional areas of importance:
- Relationships: personal relationships with primary care clinicians.
- Science: evidence-based care that accommodates personal choice and preferences (Alston et al., 2012; Carman et al., 2010).
As with people’s views about health, these broad themes describe common perspectives across the population, but individuals’ views may vary based on their background, needs, circumstances, and goals.
People’s Perspectives on Cost, Quality, and Value
While views on health care quality vary significantly from patient to patient, surveys suggest that at the individual level, patients tend to view all health care organizations and clinicians as offering similar-quality care, or they believe that all care meets some minimum standard. This belief is due in part to a lack of transparency, the release of information that is difficult to understand, and the lack of standardization of measures. This belief may discourage patients from seeking out information about care quality or make them uninterested in the quality information they do encounter (Blendon et al., 2011; Carman et al., 2010; Hibbard and Sofaer, 2010; Kaiser Family Foundation, 2008, 2011).
The cost of health care is a relatively new focus for the nation. Historically, there has been little public awareness of the cost associated with health care, with an often deliberate separation of discussion of cost and care by providers and obscured data as a result of the dissociation of care delivery from payment. In general, people may be reluctant to discuss the cost and value of health care (Hibbard and Sofaer, 2010). These perceptions can impede the success of initiatives that encourage people to review cost and value information in making their decisions about clinicians, health care organizations, or care options (O’Kane et al., 2012).
Without useful information about quality, consumers may equate higher cost with higher quality (Hibbard et al., 2012). If this perception leads more people to seek high-cost providers, cost reports lacking information on quality have the potential to increase costs. As a result, cost information needs to be integrated meaningfully with information about the quality of health care services and providers to highlight that higher-quality care can be delivered at lower cost (Carman et al., 2010; Hibbard and Sofaer, 2010; Hibbard et al., 2012; Sinaiko and Rosenthal, 2011). The communication of this information about cost and quality also is critical, as the information must be understandable, relevant, persuasive, and readily accessible if it is to be utilized by individuals.
REFERENCES
Alston, C., L. Paget, G. Halvorson, W. Novelli, J. Guest, P. McCabe, K. Hoffman, C. Koepke, M. Simon, S. Sutton, S. Okun, P. Wicks, T. Undem, V. Rohrbach, and I. Von Kohorn. 2012. Communicating with patients on health care evidence. Washington, DC: Institute of Medicine.
Bechtel, C., and D. L. Ness. 2010. If you build it, will they come? Designing truly patient-centered health care. Health Affairs 29(5):914-920.
Berenson, R. A., P. J. Pronovost, and H. M. Krumholz. 2013. Achieving the potential of health care performance measures. Washington, DC: Urban Institute.
Blendon, R. J., J. M. Benson, G. K. Steel-Fisher, and K. J. Weldon. 2011. Americans’ views on the quality of health care. Boston: Robert Wood Johnson Foundation/Harvard School of Public Health.
Boulding, W., S. W. Glickman, M. P. Manary, K. A. Schulman, and R. Staelin. 2011. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. The American Journal of Managed Care 17(1):41-48.
Carman, K. L., M. Maurer, J. M. Yegian, P. Dardess, J. McGee, M. Evers, and K. O. Marlo. 2010. Evidence that consumers are skeptical about evidence-based health care. Health Affairs 29(7):1400-1406.
Casciato, A., H. Angier, C. Milano, N. Gideonse, R. Gold, and J. DeVoe. 2012. Are pediatric quality care measures too stringent? Journal of the American Board of Family Medicine 25(5):686-693.
Chan, K. S., J. B. Fowles, and J. P. Weiner. 2010. Review: Electronic health records and the reliability and validity of quality measures: A review of the literature. Medical Care Research and Review 67(5):503-527.
Damberg, C. L., M. E. Sorbero, S. L. Lovejoy, K. Lauderdale, S. Wertheimer, A. Smith, D. Waxman, and C. Schnyer. 2011. An evaluation of the use of performance measures in health care. Santa Monica, CA: RAND Corporation.
Detsky, A. S. 2011. What patients really want from health care. Journal of the American Medical Association 306(12):2500-2501.
Devoe, J. E., R. Gold, P. McIntire, J. Puro, S. Chauvie, and C. A. Gallia. 2011. Electronic health records vs. Medicaid claims: Completeness of diabetes preventive care data in community health centers. Annals of Family Medicine 9(4):351-358.
Fenton, J. J., A. F. Jerant, K. D. Bertakis, and P. Franks. 2012. The cost of satisfaction: A national study of patient satisfaction, health care utilization, expenditures, and mortality. Archives of Internal Medicine 172(5):405-411.
Fishman, P. A., and M. C. Hornbrook. 2009. Assigning resources to health care use for health services research: Options and consequences. Medical Care 47(7, Suppl. 1):S70-S75.
GAO (U.S. Government Accountability Office). 2011. Health care price transparency: Meaningful price information is difficult for consumers to obtain prior to receiving care. Washington, DC: GAO.
Gao, G. G., J. S. McCullough, R. Agarwal, and A. K. Jha. 2012. A changing landscape of physician quality reporting: Analysis of patients’ online ratings of their physicians over a 5-year period. Journal of Medical Internet Research 14(1):e38.
Glickman, S. W., W. Boulding, M. Manary, R. Staelin, M. T. Roe, R. J. Wolosin, E. M. Ohman, E. D. Peterson, and K. A. Schulman. 2010. Patient satisfaction and its relationship with clinical quality and inpatient mortality in acute myocardial infarction. Circulation: Cardiovascular Quality and Outcomes 3(2):188-195.
Gold, R., H. Angier, R. Mangione-Smith, C. Gallia, P. J. McIntire, S. Cowburn, C. Tillotson, and J. E. DeVoe. 2012. Feasibility of evaluating the CHIPRA care quality measures in electronic health record data. Pediatrics 130(1):139-149.
Guyer, B., M. A. Freedman, D. M. Strobino, and E. J. Sondik. 2000. Annual summary of vital statistics: Trends in the health of Americans during the 20th century. Pediatrics 106(6):1307-1317.
Hibbard, J., and S. Sofaer. 2010. Best practices in public reporting no. 1: How to effectively present health care performance data to consumers. Washington, DC: Agency for Healthcare Research and Quality.
Hibbard, J., J. Greene, S. Sofaer, K. Firminger, and J. Hirsh. 2012. An experiment shows that a well-designed report on costs and quality can help consumers choose high-value health care. Health Affairs 31(3):560-568.
Higgins, A., T. Zeddies, and S. D. Pearson. 2011. Measuring the performance of individual physicians by collecting data from multiple health plans: The results of a two-state test. Health Affairs (Millwood) 30(4):673-681.
IOM (Institute of Medicine). 2011. Digital infrastructure for the learning health system: The foundation for continuous improvement in health and health care: Workshop summary. Washington, DC: The National Academies Press.
IOM. 2012. Digital data improvement priorities for continuous learning in health and health care: Workshop summary. Washington, DC: The National Academies Press.
Kern, L. M., S. Malhotra, Y. Barron, J. Quaresimo, R. Dhopeshwarkar, M. Pichardo, A. M. Edwards, and R. Kaushal. 2013. Accuracy of electronically reported “meaningful use” clinical quality measures: A cross-sectional study. Annals of Internal Medicine 158(2):77-83.
KFF (Kaiser Family Foundation). 2008. 2008 update on consumers’ views of patient safety and quality information. Menlo Park, CA: KFF.
KFF. 2011. Trends in the use of hospital and provider quality ratings. Menlo Park, CA: KFF.
Landon, B. E., and S. L. Normand. 2008. Performance measurement in the small office practice: Challenges and potential solutions. Annals of Internal Medicine 148(5):353-357.
Landon, B. E., S. L. Normand, D. Blumenthal, and J. Daley. 2003. Physician clinical performance assessment: Prospects and barriers. Journal of the American Medical Association 290(9):1183-1189.
NCSL (National Conference of State Legislatures). 2013. All-payer claims databases—health cost containment. http://www.ncsl.org/issues-research/health/collecting-health-data-allpayer-claims-database.aspx (accessed June 25, 2013).
NQF (National Quality Forum). 2013. Map pre-rulemaking report: 2013 recommendations on measures under consideration by HHS. Washington, DC: NQF.
Office of Attorney General Martha Coakley. 2011. Examination of health care cost trends and cost drivers. Boston, MA: Massachusetts Attorney General.
O’Kane, M., K. Buto, T. Alteras, K. Baicker, J. Fifield, R. Griffin, J. Hansen, and R. Saunders. 2012. Demanding value from our health care: Motivating patient action to reduce waste in health care. Washington, DC: Institute of Medicine.
Parsons, A., C. McCullough, J. Wang, and S. Shih. 2012. Validity of electronic health record-derived quality measurement for performance monitoring. Journal of the American Medical Informatics Association 19(4):604-609.
Scholle, S. H., J. Roski, J. L. Adams, D. L. Dunn, E. A. Kerr, D. P. Dugan, and R. E. Jensen. 2008. Benchmarking physician performance: Reliability of individual and composite measures. American Journal of Managed Care 14(12):833-838.
Scholle, S. H., J. Roski, D. L. Dunn, J. L. Adams, D. P. Dugan, L. G. Pawlson, and E. A. Kerr. 2009. Availability of data for measuring physician quality performance. American Journal of Managed Care 15(1):67-72.
Sinaiko, A. D., and M. B. Rosenthal. 2011. Increased price transparency in health care: Challenges and potential effects. New England Journal of Medicine 364(10):891-894.
Sofaer, S., and K. Firminger. 2005. Patient perceptions of the quality of health services. Annual Review of Public Health 26:513-559.
Tak, H. J., G. W. Ruhnke, and D. O. Meltzer. 2013. Association of patient preferences for participation in decision making with length of stay and costs among hospitalized patients. JAMA Internal Medicine 1-8.
Tang, P. C., M. Ralston, M. F. Arrigotti, L. Qureshi, and J. Graham. 2007. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: Implications for performance measures. Journal of the American Medical Informatics Association 14(1):10-15.
Toussaint, J. S., C. Queram, and J. W. Musser. 2011. Connecting statewide health information technology strategy to payment reform. American Journal of Managed Care 17(3):e80-e88.
