National Quality Measures Clearinghouse | Expert Commentaries: Developing and Implementing Quality Measures for Multiple Chronic Conditions

National Quality Measures Clearinghouse | Expert Commentaries: Developing and Implementing Quality Measures for Multiple Chronic Conditions

April 20, 2015

 

Developing and Implementing Quality Measures for Multiple Chronic Conditions

By: Elizabeth A. Bayliss, MD, MPH

The population of persons with multiple chronic conditions (MCC) is large, growing, and consuming a disproportionate share of healthcare resources. While there is broad agreement on general principles of high-quality care for complex patients, there are no measures specific to multimorbidity (1). A priority of the U.S. Department of Health and Human Services (HHS) initiative on MCC is to "identify, develop, endorse, and use key quality metrics, in the form of performance measures, to promote best practices in the general care of individuals with MCC."

Recommendations to improve care for MCC patients range from developing MCC-focused guidelines, routinely incorporating patient priorities into decision-making, and assessing and decreasing treatment burden to empowering patients through community self-management programs, avoiding overuse and underuse, and improving care coordination (2, 3, 4, 5, 6, 7, 8). Implementing these and other recommendations requires systematic approaches to care delivery and an increased awareness of patient complexity. Once developed, MCC quality measures will permit evaluation of interventions that implement such recommendations and help focus attention on MCCs as a distinct entity.

It is easiest to develop quality measures for populations and conditions for which there are evidence-based standards or unambiguous outcomes (9, 10). Development of MCC-related standards has been limited because clinical trials tend to exclude MCC individuals, and observational studies rarely include subanalyses addressing treatment effectiveness in MCC subgroups (1, 2, 11). Where interventions focus on MCCs, population heterogeneity often precludes comparing studies through systematic review (12).

Without foundational standards and guidance for managing MCCs, it has not been possible to develop MCC-specific quality measures. In the absence of these measures, disease-specific quality indicators or preventive service recommendations are often applied to MCC populations—an approach that may result in inefficiencies and unintended consequences, such as medication interactions or overtreatment (13, 14, 15). Detailed measures developed to guide geriatric practice may be relevant for many MCC patients but can be impractical to apply on a population level because of labor-intensive medical record review (14, 15, 16).

Thus, developing quality measures to guide MCC care is a priority. Leaders at the National Quality Forum (NQF), National Committee for Quality Assurance (NCQA), Centers for Medicare and Medicaid Services (CMS), and other stakeholders have compiled the following domains of high-quality MCC care: patient safety, person-centered care with individualized goals, effective prevention and treatment, affordability, and effective communication and care coordination—priorities echoed by multiple MCC researchers and clinicians (1, 8,17, 18, 19). In addition, MCC patients believe that timely access to services, meaningful patient-clinician relationships, and attention to treatment and financial burdens all reflect high quality care (20, 21).

Moving from identifying meaningful quality domains to implementing specific practices is no small task. Traditional approaches to developing quality measures require identifying health outcomes that reflect the selected domains, establishing associations between care processes and outcomes, selecting measures likely to capture such processes, establishing the feasibility of collecting these measures, and validating the measures against the selected outcomes. However, this methodology may need to be adapted for MCC quality measure development, because outcomes are complex and difficult to identify and processes that reflect recommended domains of care are difficult to measure.

Meaningful outcomes related to high-quality MCC care are in the eye of the beholder. For example, utilization and cost outcomes may be of primary interest to policy and health system stakeholders, while communication, relationships, and quality of life may be of more importance to patients and families. In some cases, the most desirable outcomes may be invisible—such as a lack of adverse effects from drug-drug interactions, or seamless transitions to palliative care. In the absence of useful and reliable outcomes, it is necessary to combine consensus-based methods (such as expert opinion) with the available evidence to develop foundational process measures (9, 22).

Given the above domains of interest, process measures will likely prove more valuable than "hard outcomes" measures in quantifying optimal MCC care. Process measures as quality indicators can be applied widely, help to focus on areas for improvement, require less risk adjustment (particularly important for MCC populations), and reveal patients' priorities (23). With a sufficiently rigorous consensus base, process measures themselves can represent best quality care and circumvent the need for "hard outcomes as gold standards." Examples of such process measures might include periodic reassessment of individualized goals, or documenting and using patient-preferred methods of communication.

Although implementing or optimizing specific processes of care may be the best indication of quality for MCC patients, these are difficult to measure efficiently across populations and delivery settings. Electronic health records (EHRs) have improved our ability to quantify disease-specific outcomes, such as target laboratory values, but were not designed to capture processes. Many important MCC care processes require text-based and nuanced documentation that is not easily entered or extracted from EHRs, let alone compiled and evaluated. For example, NQF has recommended shared decision-making and patient-centered care plans as key measurement areas for MCC patients (8). Measuring processes such as shared decision-making have historically required patient surveys or medical record review. Taking MCC quality assessment to scale, however, will require refining EHR technology to document (and extract documentation of) important care processes without such labor intensive practices. Such technological capabilities should also prompt high-quality, patient-centered interactions at the point of care.

Systematic collection of patient-reported outcome measures (PROs) is increasingly considered integral to delivering excellent MCC care (24). However, the best uses of PRO data to improve quality remain unclear. Done correctly, the process of collecting PROs (such as screening for depression and assessing functional status) may itself reflect high-quality patient-centered care that incorporates goals, priorities, and abilities of individual patients. For most MCC patients and populations, the actual values of many PROs (e.g., level of physical function) reveal a multitude of patient factors more than they reflect quality of care. Depression screening and treatment monitoring are a notable exception—suggesting that some measures may better indicate processes of care than others. Optimal use of PROs to improve MCC care quality will require careful measure selection and meaningful application at the level of the individual patient. Assessing PROs on a population level may also help guide health system change.

High-quality patient-centered MCC care incorporates many interpersonal and intangible elements fully appreciated only by patients and their clinicians. Population-based quality measures should be designed to promote and facilitate such care. To foster the delivery of scalable, high-quality, patient-centered care to the growing populace of complex patients, we need to develop, test, and implement a broad range of new measures—most of which will reflect processes rather than easily quantifiable outcomes. Doing so requires ongoing collaboration among patients, clinicians, technology experts, researchers, and operational and policy stakeholders.

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Authors

Elizabeth A. Bayliss, MD, MPH
Director of Scientific Development, Institute for Health Research, Kaiser Permanente Colorado 
Department of Family Medicine, University of Colorado School of Medicine

Disclaimer

The views and opinions expressed are those of the author and do not necessarily state or reflect those of the National Quality Measures Clearinghouse™ (NQMC), the Agency for Healthcare Research and Quality (AHRQ), or its contractor ECRI Institute.

Potential Conflicts of Interest

Dr. Elizabeth A. Bayliss declared no conflicts of interest with respect to this expert commentary.

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