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. Author manuscript; available in PMC: 2015 Jun 8.
Published in final edited form as: Am J Manag Care. 2013 Oct 1;19(10):e359–e366.

Performance Measurement for People With Multiple Chronic Conditions: Conceptual Model

Erin R Giovannetti 1, Sydney Dy 1, Bruce Leff 1, Christine Weston 1, Karen Adams 1, Tom B Valuck 1, Aisha T Pittman 1, Caroline S Blaum 1, Barbara A McCann 1, Cynthia M Boyd 1
PMCID: PMC4459786  NIHMSID: NIHMS693598  PMID: 24304183

Abstract

Background

Improving quality of care for people with multiple chronic conditions (MCCs) requires performance measures reflecting the heterogeneity and scope of their care. Since most existing measures are disease specific, performance measures must be refined and new measures must be developed to address the complexity of care for those with MCCs.

Objectives

To describe development of the Performance Measurement for People with Multiple Chronic Conditions (PM-MCC) conceptual model.

Study Design

Framework development and a national stakeholder panel.

Methods

We used reviews of existing conceptual frameworks of performance measurement, review of the literature on MCCs, input from experts in the multistakeholder Steering Committee, and public comment.

Results

The resulting model centers on the patient and family goals and preferences for care in the context of multiple care sites and providers, the type of care they are receiving, and the national priority domains for healthcare quality measurement.

Conclusions

This model organizes measures into a comprehensive framework and identifies areas where measures are lacking. In this context, performance measures can be prioritized and implemented at different levels, in the context of patients’ overall healthcare needs.

One-fourth of Americans have 2 or more chronic conditions, yet this population accounts for more than half of overall healthcare expenditures.1 Having multiple chronic conditions (MCCs) can negatively affect quality of life,2 ability to work,3 disability,4 and mortality.5 Despite the high prevalence of MCCs and corresponding negative consequences, care for people with MCCs is often fragmented, incomplete, inefficient, and ineffective,4,6,7 resulting in potentially avoidable inpatient admissions,8,9 adverse consequences of therapeutic interactions,10 and postoperative complications. The Department of Health and Human Services has identified “fostering healthcare and public health system changes to improve the health of individuals with multiple chronic conditions” as one of 4 goals in an overall strategic framework to improve the health of individuals with MCCs.1 Various agencies, including the Centers for Medicare & Medicaid Innovation Center, Centers for Disease Control and Prevention, and Administration on Aging, are funding projects centered around healthcare quality improvement for individuals with MCCs. However, existing healthcare quality measures used in many of these projects are inadequate for measuring quality improvement for this population.1

Existing quality performance measures are often limited by a disease-specific focus,7,11 do not account for patient and family preferences and goals,12,13 and often focus on a single setting such as hospitals. Performance measurement has been largely guided by a single disease heuristic that does not address challenges common to MCCs such as disease interactions and treatment interactions, and little evidence exists to support development or adaptation of performance measures for people with MCCs.14 To improve quality and efficiency of care for people with MCCs, performance measures need to address the heterogeneity and scope of care, individuals’ priorities and care preferences, the high risk of uncoordinated care across settings, and the high risk for adverse health outcomes in the presence of comorbidities. Currently, few measures meet these criteria, and those that exist require further development.7,15

There is a need to develop new performance measures and refine existing measures to address the complexity of care experienced by patients with MCCs and their families over time. Such development efforts would be facilitated by an appropriate conceptual model. Existing conceptual models of quality of care address some of these issues but do not address the broader perspective of people with MCCs.16,17 To help advance development of new performance measures and implementation of existing measures in this area, the Department of Health and Human Services requested that the National Quality Forum convene a multistakeholder steering committee to develop a measurement framework for individuals with MCCs. As part of this effort we developed the Performance Measurement for People with Multiple Chronic Conditions (PM-MCC) conceptual model based on reviews of the literature and existing conceptual models of performance measurement,17–22 input from experts on the National Quality Forum Steering Committee, and input through an open public comment period.

MODEL OVERVIEW

This conceptual model centers on a patient with multiple conditions, represented by overlapping circles (see center circle of the Figure). Included are traditional diseases, but also conditions such as symptoms, disability, substance abuse, and hearing impairment that fall outside the traditional disease model. Any given condition may affect the patient to a greater or lesser magnitude at any one time, and may or may not be a dominant condition (ie, a condition so complex or serious that it eclipses the management of other conditions23). The patient and the family or friends who care for him/her have goals and preferences for care of these diseases and conditions. Performance measurement should center on these goals and preferences.

Figure. The PM-MCC Conceptual Model.

Figure

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PM-MCC indicates Performance Measurement for People with Multiple Chronic Conditions.

The first circle surrounding the individual at the center of the model represents shifting sites and providers that support and care for the individual’s healthcare needs. These sites could include the ambulatory, hospital, postacute, nursing home, community, home (including formal home-based primary and skilled nursing care as well as informal care), and pharmacy settings. This list is not exhaustive; rather, it is intended to be illustrative of the possible sites of care for people with MCCs. At any given site, multiple types of providers may be providing care over time.

Moving outward from the sites and providers circle, there is a circle representing the types of care an individual may receive at any given site of care. The types of care patients receive (eg, screening, prevention, diagnosis, treatment, management, secondary prevention, community services, management of an acute exacerbation, rehabilitation, palliation, end-of-life care) are not necessarily linear or mutually exclusive. For example, a patient with congestive heart failure may be seen in the hospital for an acute exacerbation while receiving ongoing care for diabetes and depression at the same time.

The outermost circle represents the domains of measurement that apply across sites and types of care. These domains are not mutually exclusive, and a given measure could fall into multiple domains; however, measures can be categorized under the 6 priority areas of the National Quality Strategy for improving healthcare: (1) health and well-being, (2) effective prevention and treatment, (3) person- and family-centered care, (4) patient safety, (5) effective communication and care coordination, and (6) affordable care (Figure). These domains intentionally align with the National Quality Strategy to promote harmonization across public- and private-sector programs supporting this population and to provide a way to track progress on filling of critical measure gaps. In each measurement domain there are non–disease-specific measures such as pain screening (which would apply to all patients within a certain population regardless of their condition) and disease-specific measures such as management of antidepressant medications (which would apply to patients with certain conditions).

These circles, described above and shown in the Figure, are intended to be seen as a set of interlocking wheels that rotate over time. At any given point in time, an individual may receive 1 or more types of care in 1 or more settings from multiple providers. At the same time, the domains of performance measurement most important for that patient, site, or type of care vary. Some measures may be specific to a site and type of care, while others (eg, care coordination) apply across sites and types of care.

Each measurement domain can be measured over a period of time, which may be labeled as an episode. However, the PM-MCC model builds on the original National Quality Forum–endorsed patient-focused episodes of care framework by recognizing that distinct beginnings and ends to episodes of care are difficult to define and may need to be defined more arbitrarily in people with MCCs. Additionally, the elements of care highlighted on this model are not meant to be of equal importance. The amplification or minimization of any item in the model will vary with the individual and across time. The PM-MCC model places measures within the larger context of patient’s multiple conditions, sites of care, and types of care. We propose that in this context measurement can be applied and prioritized at the level of the individual, practice, site of care, health plan, or population.

Finally, the PM-MCC model falls within a social and environment context, as well as a public and private health policy context. These contextual factors surrounding the patients, their healthcare needs, and the delivery of healthcare will affect the way performance is measured. By using the PM-MCC conceptual model, measurement developers, researchers, and policy makers can refine and implement performance measurement sets to effectively evaluate and improve care for individuals with MCCs.

APPLYING THE CONCEPTUAL MODEL

To demonstrate how the PM-MCC model can be applied, as well as its utility, we present the case of a 60-year-old woman with known congestive heart failure (CHF), diabetes, hypertension, and depression as just 1 example of a complex patient.24 Over the course of 6 months, this patient received care from an internist in primary care, a cardiologist in specialty care, a spousal caregiver at home, and a pharmacist in the pharmacy. The types of care provided included screening, prevention, treatment, and management. Over the episode of care, the patient had some weight loss and fatigue. There was no acute exacerbation of her conditions or change in the sites or types of care provided, although it is not clear that her depression was adequately treated initially given her fatigue and weight loss. Examples of the complex medical decision making her providers faced include how low to push her blood pressure and the choice and dose of antidepressants in light of a previous fall and her ongoing risk of falls. It is not rare for patients to be more complex than the patient described here—cognitive impairment due to traumatic brain injury or dementia, or a history of substance abuse, are just 2 examples. Cognitive impairment would increase the involvement of her spouse in her healthcare decision making and management, and would make care coordination more challenging.25,26

The ideal measurement framework would assess the goals and priorities of this patient, and tailor the performance measures to the priorities of the patient. For example, this patient, her spousal caregiver, and her physician may be more concerned about depression control and antidepressant medication management than blood pressure or diabetes control. This information could theoretically drive which denominators of performance measures should be applied to her (eg, the denominator of an antidepressant medication adherence measure but not the denominator of a glycated hemoglobin control measure) or the relative weight given to performance measures for this patient (with higher weight given to achieving antidepressant medication adherence than to achieving glycated hemoglobin control). This method of selecting or weighting performance measures based on individual patient goals and priorities is not currently feasible in most settings. However, personal health records and health information technology may make this type of patient-centered measurement more of a reality.

To demonstrate how this model can be used with current performance measurement methods, we will examine this case at the health plan level. We can start by looking at the subpopulation of patients similar to the case example: adults with MCCs receiving care from more than 1 outpatient provider with no acute medical events over the course of a stable episode. Healthcare performance related to treatment, management, and secondary prevention in both the primary and specialty care settings can be measured across all 6 measure domains that map to the national priorities for improving healthcare (see Table). Many of these measures would apply to all patients in the population (ie, regardless of conditions), such as patient and family experience of care, medication review, and assessment of treatment interactions. Some measures are specific to the diseases of the patient (ie, diabetes, hypertension, CHF, and depression). It is important to note that although guidelines exist for the treatment of each of these conditions individually, it is not clear that specific disease guidelines are appropriate when a patient has more than 1 condition; thus, disease-specific performance measures may not all be appropriate.7,11,27 For example, applying the Optimal Diabetes Care composite measure (a measure of processes and outcome associated with ideal diabetes care) to people with MCCs (eg, the patient described above) may not be clinically appropriate and may misdirect the focus of care from other more pressing conditions for the patient, such as depression or heart failure.28,29 Although disease-specific performance measures are still important for patients in this adult MCC population, they should be implemented with an acute awareness of how the treatment of one disease can affect the treatment of any or all other diseases or conditions.

Table.

Case Example of the PM-MCC Model Applicationa

Case: A 60-year-old woman with previous diagnoses of heart failure, diabetes, hypertension, and depression. Over the course of 6 months, this patient received care from an internist in primary care, a cardiologist in specialty care, a spousal caregiver at home, and a pharmacist in the pharmacy. The types of care being provided included screening, prevention, treatment, and management. Over the episode of care, the patient experienced some weight loss and fatigue. But there was no acute exacerbation or change in the sites or types of care being provided, although it is not clear that her depression was adequately treated initially given her fatigue and weight loss.
Site of Care/Type of Care National Priorities for Improving Healthcare Non–Disease-Specific Measures (NQF #) Disease-Specific Measures (NQF #)
Primary and specialty care/treatment and management Person- and family-centered care

  • CAHPS survey of patient access, experience, and satisfaction with care (0005)

  • Availability of self-management education resources
Health and well-being: enable healthy living; optimize function

  • Change in daily activity function (0430)

  • General quality of life

  • Improving or maintaining mental and physical health function

  • Treatment burden

  • Diabetes-related quality of life

  • Depression-related quality of life

  • Heart failure–related quality of life

  • A1C control (0575)

  • BP control (0018)

  • Lipid control (0064)
Primary and secondary care/treatment and management Effective prevention and treatment

  • Patient goal attainment

  • Availability of self-management resources

  • Optimal diabetes care (0792)

  • Eye examination (0055)

  • Lipid profile (0063)

  • Antidepressant medication management (0105)

  • Depression care follow-up (0418)
Effective communication and care coordination

  • Use of EHR (0489)

  • Medication review (0553)

  • Advance care plan (0326)

  • Shared decision making (0007)

  • Patient-centered communication

  • Continuity of care
Patient safety

  • Adoption of medication e-prescribing (0486)

  • Diabetes short-term complication hospital admission rate (0272)

  • CHF hospital admission (0277)

  • Adverse drug-disease interaction
Affordable care

  • Total healthcare costs (1604)

  • Total resource use (1598)

  • Site-specific healthcare costs

  • Diabetes-specific resource use (1557)

  • Cardiovascular condition resource use (1558)
Primary and specialty care/screening and prevention Health and well-being: enable healthy living; optimize function

  • Adult BMI assessment (0421)

  • Falls risk assessment (0035)

  • Counseling on physical activity (0029)

  • Functional status assessment (0430)

  • Pain screening (1737)

  • Flushot (0039)

  • Breast cancer screening (0031)

  • Colorectal cancer screening (0034)

  • Pneumonia vaccination (0043)

  • Osteoporosis screening (0046)

  • Cognitive assessment
Home/treatment and management Person- and family-centered care

  • Availability of transportation resources
Cost and resource use

  • Total out-of-pocket costs to patient and family

  • Caregiver and patient work-productivity loss
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A1C indicates glycated hemoglobin; BMI, body mass index; BP, blood pressure; CAHPS, Consumer Assessment of Healthcare Providers and Systems; CHF, congestive heart failure; EHR, electronic health record; NQF, National Quality Forum; PM-MCC, Performance Measurement for People with Multiple Chronic Conditions.

a

This list of measures is not intended to be exhaustive, but illustrative of the breadth of the potential measure. Where a measure is fully developed and endorsed by the National Quality Forum, a measure number is listed. Where we were not able to identify a fully specified measure, the measure concept is listed in italics.

At the same time this population is receiving ongoing treatment and management, screening and prevention of primary and secondary conditions are also important. In the adult MCC population, some screening measures may be more important for some subgroups than for others. For example, urinary incontinence screening is recommended for adults 65 years and older, but not for younger adults. Alternatively, cervical cancer screening is recommended for women aged 21 to 64 years.30 In addition to performance measures for providing appropriate screening, measures should be developed to reward plans or physicians for avoiding inappropriate screenings that could potentially cause harm to the patient.31

Finally, it is important to consider the other sites and providers where patients in this population may be receiving care. As new delivery systems and Accountable Care Organizations (ACOs) develop, measures outside the current scope of health plans should be considered, such as patient out-of-pocket costs or availability of transportation services in the community.

Now let us examine the case described above when the patient has an exacerbation of her CHF, is hospitalized, and is diagnosed concurrently with community-acquired pneumonia. Hospital care is added to the patient’s measurement framework, and the patient begins receiving care for the acute exacerbation of CHF and pneumonia. From the level of the health plan measuring performance, this patient may now fall into a different subpopulation for measurement: adults with MCCs hospitalized for an acute episode with subsequent follow-up care. Similar to the application of the framework to a stable episode, performance measures can be non–disease specific, with a focus on the prevention of delirium and infection during the hospitalization and subsequent readmissions; or they can be disease specific, with a focus on the use of antibiotics and continued assessment of oxygen saturation.

The measures outlined above and in the Table are not intended to be exhaustive. Rather, they illustrate the potential performance measures that fall into the measure domains that map to the 6 National Quality Strategy priority areas. On the other hand, implementing this entire list of measures could create a significant burden of measurement on patients, providers, and health plans. What the PM-MCC model offers is a framework for organizing existing measures and for prioritizing those measures based on the characteristics of the population being examined, and eventually, individual patient preference. This model can also be used to highlight measure gap areas where we currently lack rigorous tools for the purposes of quality measurement (see italicized text in the Table). As can be seen from the Table, particular areas where we lack rigorous quality measurement are non–disease-specific measures of effective treatment and prevention and measures of health and well-being. These measure gap areas may cut across condition, site, and type of care. For example, treatment burden and goal attainment may provide an important addition to the measurement of quality of healthcare and may be higher patient priorities than disease-specific measures.

DISCUSSION

Healthcare quality improvement is moving toward care delivery models that aim to improve care for individuals with MCCs. The Patient Protection and Affordable Care Act provides multiple opportunities for the development of these new models (eg, ACOs, Center for Medicare & Medicaid Innovation, Independence at Home Demonstration, Patient-Centered Outcomes Research Institute32); however, we lack the appropriate tools and approaches to measure the performance of these new healthcare delivery models for individuals with MCCs, who are often the most costly at-risk population. We present a measurement model that will move performance measurement on the path toward measurement that cross-cuts across conditions, sites of care, and types of care, and focuses on the goals and preferences of the patient and family. Policy makers, researchers, and health plans can use this model to select and prioritize existing performance measures for sub-populations of people with MCCs in the context of the complex healthcare experience, as well as to identify areas where measures need to be developed or improved.

Building on the National Quality Forum patient-focused episodes of care measurement framework, which examines an episode of care for a single condition,17 the PM-MCC model shows the heterogeneity and scope of care required by people with MCCs. By illustrating this complexity, the risk of uncoordinated care across settings and the resulting adverse health outcomes becomes more apparent. Although the PM-MCC model does not provide a solution for shared accountability across silos of care, it is a model for the development of future measures that can be applied across multiple conditions (eg, goal attainment),33–35 measures that can be applied across settings (eg, use of a coordinated electronic health record [EHR]36), and measures that can be applied across types of care (eg, assessment for drug-disease interactions37,38). To date, many performance measures have been designed to be setting specific so as to assign accountability for performance on a measure to a single setting or provider. These siloed measures are partially a reflection of a siloed healthcare system where each care setting or provider is only responsible for 1 piece of a patient’s continuum of care. However, new models of care delivery such as ACOs and integrated delivery systems provide an opportunity to measure performance and assign accountability across the continuum of care. The PM-MCC model is well suited to guide ACO performance measure prioritization and development because it focuses on patients in the context of the multiple settings and providers where they are receiving multiple types of care, often simultaneously.

There are several important issues to consider when applying this model. Although the center of this model is a patient with individual goals and preferences for care, current data sources are not designed to track this type of information. Therefore, tailoring quality measures to the individual’s goals and preferences for care is not currently feasible on a widespread basis, and many measures that can apply to a population of people with MCCs will not reflect the care priorities of the individual patient. In the example described in “Applying The Conceptual Model,” although the patient and her provider decided depression was a higher priority of care than diabetes at the moment, there is not currently a methodology for excluding patients from measurement of diabetes outcomes based on preferences for care. Similarly, the measures within a domain may represent different priorities to different patient populations. For example, informed decision making may be of the highest priority to a patient with multiple treatment options, whereas a patient with limited means may prioritize access to care. New systems are needed to capture patient-reported data that are integrated with EHRs to facilitate the use of measures based on patient preferences and priorities for care. In the meantime, consumers, policy makers, researchers, health plans, and practices need to prioritize which measures are most important for subgroups of individuals, recognizing that priorities may differ by age, dominant condition, or other characteristics.1,39

There are significant methodologic and logistical challenges to developing and implementing new performance measures for people with MCCs. Current data sources are not optimal to support performance measurement for people with MCCs. Physicians and health plans frequently cite measurement burden as a significant drain on resources. While the spread of EHRs may hold particular promise for performance measurement by providing the structure for documenting key elements and allowing for easier extraction of data, misclassification of performance data can happen.40,41 Furthermore, EHRs are not designed to collect outcomes often of the greatest importance to patients and families, such as symptom burden, daily function, and quality of life.

Performance measurement must be based on rigorous evidence that a process of care is the best possible treatment because it leads to improved outcomes.42,43 However, clinical trials often exclude medically complex patients, older people, people with MCCs, or people undergoing multiple medical interventions.44 When treating patients with MCCs, providers must use clinical judgment in deciding whether to intervene without evidence indicating what treatment is most likely to cause benefit rather than harm. They should use informed decision making that involves both patients and their families. Development of new performance measures must be guided by clinical research into new treatments and guidelines that address people with MCCs.45

Despite these challenges, the PM-MCC model is a useful tool for organizing and prioritizing existing performance measures on multiple levels (population, health plan, or practice) and identifying areas for future development of measures that examine quality of care across conditions, providers, and sites of care over time. Given the increasing prevalence of individuals with MCCs in the United States, the rising cost of providing care for this population, and the current investment in quality improvement initiatives, it is imperative that we know how to measure healthcare performance beyond the traditional process-based, disease-specific framework. We present the PM-MCC model as a step toward developing and implementing healthcare performance measures that work in this complex world.

Take-Away Points.

Improving the quality of care for people with multiple chronic conditions (MCCs) requires performance measures reflecting the heterogeneity of, and scope of care required by, these people.

  • The Performance Measurement for People with Multiple Chronic Conditions conceptual model is designed to facilitate development and refinement of performance measures to address the complexity and dynamic nature of care for those with MCCs.

  • The model centers on patient and family goals and preferences for care in the context of multiple care sites and providers, the type of care they are receiving, and the national priority domains for healthcare quality measurement.

Acknowledgments

The authors would like to thank the Multiple Chronic Conditions Steering Committee and the National Quality Forum for its guidance in the development of this model.

Funding Source: This work was funded by a subcontract from the National Quality Forum through a contract from Department of Health and Human Services (Contract HHSM-500-2009-0010C). Dr Giovannetti’s work was supported during her postdoctoral fellowship under a National Institute on Aging (NIA) T32 Training Grant (T32 AG00021 052507). Dr Boyd is a Robert Wood Johnson Foundation Physician Faculty Scholar and is supported by the Paul Beeson Career Development Award Program (NIA K23 AG032910, American Federation for Aging Research, The John A. Hartford Foundation, The Atlantic Philanthropies, The Starr Foundation, and an anonymous donor). Dr Leff is supported by The John A. Hartford Foundation and The Atlantic Philanthropies, and is an American Political Science Association Health and Aging Policy Fellow. This article is based on material that was prepared under, and paid for by, the Centers for Medicare & Medicaid Services under Contract HHSM-500-2009-00010C.

Footnotes

Author Disclosures: Dr Giovannetti reports employment with the National Committee for Quality Assurance and has received honoraria for guest lectures at Johns Hopkins. Drs Leff and Boyd report that they have received grants from the National Quality Forum. The other authors (SD, KA, TBV, ATP, CSB) 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 (ERG, BL, KA, TBV, ATP, CSB, CMB); acquisition of data (ERG, BL, CMB); Concept and design (ERG, SD, BL, CW, CMB, KA, TBV, ATP, CSB, BAM); drafting of the manuscript (ERG, SD, BL, CW, CMB); critical revision of the manuscript for important intellectual content (SD, BL, CW, CMB, KA, TBV, ATP, CSB, BAM); obtaining funding (CBM, TBV, KA); and supervision (CMB).

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