Gaps in accountable care measure sets can be addressed efficiently using priority measure types and innovative approaches to measurement.
Objectives: A primary objective of accountable care is to support providers in reforming care to improve outcomes and lower costs. Gaps in accountable care measure sets may cause missed opportunities for improvement and missed signals of problems in care. Measures to balance financial incentives may be particularly important for high-cost conditions or specialty treatments. This study explored gaps in measure sets for specific conditions and offers strategies for more comprehensive measurement that do not necessarily require more measures.
Study Design: A descriptive analysis of measure gaps in accountable care programs and proposed solutions for filling the gaps.
Methods: We analyzed gaps in 2 accountable care organization measure sets for 20 high-priority clinical conditions by comparing the measures in those sets with clinical guidelines and assessing the use of outcome measures. Where we identified gaps, we looked for existing measures to address the gaps. Gaps not addressed by existing measures were considered areas for measure development or measurement strategy refinement.
Results: We found measure gaps across all 20 conditions, including those conditions that are commonly addressed in current measure sets. In addition, we found many gaps that could not be filled by existing measures. Results across all 20 conditions informed recommendations for measure set improvement.
Conclusions: Addressing all gaps in accountable care measure sets with more of the same types of measures and approaches to measurement would require an impractical number of measures and would miss the opportunity to use better measures and innovative approaches. Strategies for effectively filling measure gaps include using preferred measure types such as cross-cutting, outcome, and patient-reported measures. Program implementers should also apply new approaches to measurement, including layered and modular models.
Am J Manag Care. 2015;21(10):723-728
Measurement in accountable care programs is essential for promoting quality improvement and balancing financial incentives. This study examines gaps in current accountable care measure sets and proposes solutions to fill those gaps.
Accountable care systems, including accountable care organizations (ACOs), along with other value-based approaches to delivery and payment, have proliferated in the public and private sectors.1 Accountable care is focused on increasing the value of care; that is, improving quality while decreasing costs.2,3 Accountable care systems are implementing innovations in care delivery, including better monitoring systems, decision support tools, care coordination capabilities, and team-based approaches to care that are enabled under flexible new payment models. One of the tools available to promote higher value care is measurement.
Accountable care measure sets are tied to performance-based payment arrangements that reward providers for improving quality and avoiding waste. Waste includes underuse, which could lead to avoidable complications and costlier care overall, as well as overuse and misuse of resources. Measures are also important for balancing financial incentives. Program implementers can use measures to gauge the impact of accountable care reforms, which may be particularly important for high-cost conditions and treatments.
Outcome measures are preferred for assessing accountable care systems because they provide information about the results of care that is meaningful to patients, payers, purchasers, and policy makers.4,5 Cross-cutting measures that address multiple conditions and composite measures that aggregate multiple processes and/or outcomes offer the advantage of assessing many aspects of care simultaneously, thereby improving measurement efficiency. Process measures, which evaluate compliance with care guidelines, provide actionable information to support provider improvement.
Gaps in measure sets represent missed opportunities for monitoring system performance, providing transparency to consumers and purchasers, and encouraging improvement in quality and cost of care. Ideally, meaningful measures would be available for all conditions and dimensions of care, but that would subsequently increase the burden of data collection for providers and could distract them from quality improvement efforts. Understanding the best approach to more efficient and effective measurement—including new measure types and innovative approaches to measurement—would benefit from a comprehensive view of measurement gaps.
This study explored the breadth and depth of gaps in accountable care measure sets and identified methods for improving such measure sets by using preferred measure types and by adopting novel models for applying measures.
Methodology and Key Findings
Our study included an analysis of measure gaps for specific conditions and a 1-day, multi-stakeholder roundtable discussion of national thought leaders to review the analysis and inform the conclusions. To explore gaps in accountable care measure sets, we selected 20 high-priority conditions. We then conducted a literature search for lists of high-impact conditions from authoritative sources, such as the National Quality Forum and the CDC. These lists included conditions that are either highly prevalent, leading causes of death, costly for patients, or financially and administratively burdensome to the healthcare system. Based on the search, we compiled a list of conditions which represents a diverse range of patient demographics (eg, age, gender, acute and chronic conditions, primary and specialty care) and anticipated cost drivers (eg, specialty drugs, surgery, imaging, hospitalization).
The list includes conditions that have been the historical focus of performance measurement (eg, asthma, chronic obstructive pulmonary disease, diabetes, hypertension, ischemic heart disease, influenza); that primarily affect the elderly (eg, osteoarthritis, osteoporosis, glaucoma, stroke); that primarily affect children (eg, attention-deficit/hyperactivity disorder); that affect each gender (eg, breast cancer, prostate cancer); that are related to behavioral and mental health (eg, major depression); and that require specialty pharmaceuticals or advanced imaging (eg, chronic kidney disease, hepatitis C, HIV, multiple sclerosis [MS], rheumatoid arthritis, low back pain).
We applied a 6-step analysis to each condition to identify key gaps between clinical practice guidelines and accountable care measure sets. For this comparison, we selected the Medicare Shared Savings Program (MSSP)6 and National Committee for Quality Assurance (NCQA) ACO Accreditation7 measure sets. In step 1, we identified diagnostic and treatment goals for each condition using evidence-based clinical practice guidelines from nationally-recognized sources, such as medical specialty societies and patient advocacy groups. In step 2, we compared the care goals for each condition with the measures in the ACO sets and identified measures that either directly or indirectly addressed the care goals. In step 3, we identified care goals that were not covered by the ACO sets as measure gaps. In step 4, we scanned various databases for measures that would cover the gaps identified in the ACO sets. In step 5, we identified opportunities for measure development to fill gaps that were not addressed by available measures. In step 6, we reviewed the gap assessment results across all 20 conditions to identify cross-cutting gaps and inform new measurement solutions.
For each condition, we identified a number of care goals defined by clinical guidelines that were not assessed by measures currently available in either the MSSP or NCQA ACO measure sets.8 Although treatment for some chronic diseases (eg, diabetes, hypertension) were assessed in both sets by several process and fewer outcome measures, other conditions (eg, MS, HIV) had no measures relevant to treatment or health outcomes. Eighteen of the 33 measures in the 2015 MSSP set are outcomes—including patient-reported Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey measures and admission/readmission measures—and 13 of the MSSP measures directly apply to 10 of the conditions on our list. Thirteen of 40 measures in the NCQA ACO set are outcomes—including resource use measures—and 23 of the NCQA ACO measures directly apply to 12 of the conditions on our list.
We found cross-cutting measures in the ACO sets that indirectly covered important aspects of care for a range of conditions simultaneously. These include the CAHPS survey measures (eg, functional status, communication, access to care) and non—survey-based wellness and prevention measures (eg, body mass index [BMI], tobacco screening and follow-up, immunizations).
We identified measures available outside the ACO sets that could be used to fill gaps; however, the majority are process, not outcome, measures (). We also found aspects of care that are not assessed by any available measures, which represent opportunities for measure development. Gaps requiring development included measures of health risk assessment, monitoring for disease progression, and referrals to nonphysician services such as physical or behavioral therapy. For MS, we found no measures.
Our findings illustrate the scope of quality measurement gaps in accountable care for many high-priority conditions. Whereas our analysis does not evaluate the impact of these gaps, the importance of measurement in accountable care arrangements implies missed opportunities and possible risks that could be addressed through better quality measurement systems. Extrapolating our findings to the universe of conditions—or even just high-priority conditions—illustrates the difficulty of attempting to address every important measure gap with the same types of measures and current approaches; it would require hundreds of measures and would be impractical, costly, and burdensome. The results demonstrate the need to use priority measure types and novel strategies to efficiently address these important gaps in measurement.
Preferred Measure Types
Leveraging use of current measures and developing better measures would help to fill gaps related to the most important opportunities for improvement. As Figure 1 depicts, relatively few outcome measures are available; however there are outcome measures that have been tested and endorsed but are not being used, including measures of functional status or rates of avoidable adverse events. The recent inclusion of the depression remission measure to the MSSP ACO set is a positive step by CMS toward the increased use of outcome measures. Additional patient-reported outcome measures that are available, such as health status and symptom control measures, should be included in measure sets to assess what matters most to patients. Where development of new measures is required to fill gaps, resources should be directed toward development of outcome measures, including patient-reported outcomes.
Cross-cutting measures enable efficient assessment of quality of care across multiple conditions as well as to evaluate important aspects of care for conditions that are not directly addressed. Cross-cutting measures include patient engagement (eg, shared decision making, education); population health (eg, BMI, smoking cessation); and care coordination/safety (eg, readmissions, medication management). However, large gaps in available cross-cutting measures remain, including patient self-management capability, activity level, and assessment of environmental factors that affect health.
Measurement Strategies to Fill Critical Gaps
Layered and modular approaches offer alternate strategies for targeting measures to address gaps, while minimizing the number of measures that must be reported for external accountability. The layered approach reflects that accountable care systems use measures at multiple levels: for external accountability reporting at the population level, for internal management of performance at the system level, and for internal improvement at the provider level. The key to the layered approach is to ensure that the measures the system chooses for internal management and improvement map to the external accountability measure set. In this way, the layered approach allows for coordinated, focused, and flexible, yet comprehensive, measurement.
The layered approach assumes that certain measure types are preferred at the various levels. Outcome measures—including patient-reported measures—and cross-cutting measures are best suited for external accountability because they allow for efficient reporting of the information that is most meaningful to program implementers and patients. Composite measures, along with indicator data such as utilization rates, are ideal for internal management dashboards to identify the largest improvement opportunities and monitor system-level improvement activities. Process measures, which are the most actionable, are valuable for supporting quality improvement initiatives on the front lines of care.
Cross-cutting measures, which assess care across multiple conditions, can also cross multiple layers of accountability, contributing to cohesive measurement through use of the same or similar measures in different layers. Where the same cross-cutting measure is applied in multiple layers, the results can be aggregated from the individual level to the population level.
Whereas relatively few outcome and cross-cutting measures would be the focus for external accountability, under the layered approach, provider organizations gain the flexibility to choose the types and numbers of measures they need to support management and improvement efforts. Providers could focus on the measures they deem essential for improving performance on outcome-oriented external accountability measures through their own quality improvement activities.
Figure 2A illustrates the layered measurement approach with the following layers: 1) externally reported population-level outcome measures, such as functional status improvement or complication rates (eg, preventable admissions due to diabetes complications); 2) internally reported system-level measures to monitor outcomes, such as composite measures (eg, optimal diabetes composite of outcome [glycated hemoglobin control] and process measures [eye exam]; mortality composite for selected conditions [acute myocardial infarction, congestive heart failure, stroke, gastrointestinal hemorrhage, hip fracture, pneumonia]); and 3) internal process improvement measures, such as ordering appropriate tests and treatments that the providers deem important for improving outcomes.
illustrates the modular measurement approach, which is another strategy that allows for more targeted and flexible use of measures to address gaps in accountable care measure sets. In the modular approach, a more granular measure set could be used to focus attention on improving quality and cost for a specific subpopulation within an accountable care program. Modules could be implemented when monitoring indicators show problems with care for a subpopulation. This model could also be used for Centers of Excellence, implementation of bundled payment arrangements, or for developing and testing new measures for widespread use.
As an example of the modular measurement approach, a specific measure set for oncology patients could be implemented under the broader accountable care measure set. Although the broader measure set, including the cross-cutting measures, would apply to the entire population, the additional measures of the oncology module would apply only to cancer patients. Measures in the module would include oncology-specific outcomes such as survival and complications, and processes such as appropriate treatment and use of palliative care, along with the cross-cutting measures from the broader accountable care measures set.
Using preferred measure types and the layered and modular measurement approaches would be particularly advantageous for assessing care for individuals with multiple chronic conditions (MCCs), whether in Medicare, Medicaid, or commercial accountable care programs. Measures of disease-specific processes—even those that address common comorbidities—often are not applicable to individuals with MCCs; moreover, it would be impractical to measure every aspect of care for each individual with MCCs.
Accountable care depends on having meaningful measures and efficient approaches to measurement. Although data systems are improving, the cost of developing and using measures effectively remains significant. To achieve success with new models of accountable care delivery and financing, the measurement strategies we have presented can help prioritize efforts to increase the benefit of measurement. To address widespread gaps in accountable care measure sets while limiting the burden of measurement, program implementers should use outcome, patient-reported, and cross-cutting measures and should promote development of those types of measures to fill gaps. In addition, program implementers should apply layered and modular approaches to broaden the reach of measurement in a targeted manner.
The authors acknowledge the valuable contributions of David Blaisdell, David Sloan, Avis Hixon, and Guy D’Andrea of Discern Health, and Adam Lustig, Andrea Hofelich, and Kathryn Gleason of the National Pharmaceutical Council in the preparation of this manuscript. The authors also acknowledge the insightful contributions of the participants in the multi-stakeholder roundtable, co-chaired by Drs Mark McClellan and Jerry Penso.
Author Affiliations: Discern Health (TV, DD), Baltimore, MD; National Pharmaceutical Council (RWD, KW), Washington, DC; American Medical Group Association (JP), Alexandria, VA; The Brookings Institution (MM), Washington, DC.
Source of Funding: Funding for this project was provided by the National Pharmaceutical Council.
Author Disclosures: Ms Westrich and Dr Dubois are employees of the National Pharmaceutical Council, an industry-funded health policy research group that is not involved in lobbying or advocacy. Dr McClellan is a member of the board for Johnson & Johnson. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (TV, KW, MM, JP, RWD, DD); acquisition of data (TV, DD); analysis and interpretation of data (TV, KW, MM, RWD, DD); drafting of the manuscript (TV, KW, MM, JP, RWD, DD); critical revision of the manuscript for important intellectual content (TV, KW, MM, JP, RWD, DD); obtaining funding (TV, RWD); administrative, technical, or logistic support (DD); and supervision (TV, MM, RWD, DD).
Address correspondence to: Tom Valuck, MD, JD, MHSA, 1120 North Charles St, Baltimore, MD, 21201. E-mail: firstname.lastname@example.org.
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