Performance Measurement for People With Multiple Chronic Conditions: Conceptual Model

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.

Am J Manag Care. 2013;19(10):e359-e366Improving 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 conceptualmodel 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.

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.

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 n 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 opulation 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, re 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 PMMCC 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 ites 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.

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-ofpocket 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 subpopulations 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 PMMCC 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 interactions^37,38). 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 ased 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 ptions, whereas a patient with limited means may prioritize access to care. New systems are needed to capture patientreported 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, recognizingthat 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 andimplementing healthcare performance measures that work in this complex world.Author Affiliations: From Johns Hopkins School of Medicine (ERG, SD, BL, CMB), Baltimore, MD; Johns Hopkins School of Public Health (SD, BL, CW, CMB), Baltimore, MD; National Quality Forum (TBV, AP), Washington, DC; University of Michigan Health Systems (CSB), Ann Arbor, MI; Interim Healthcare (BAM), Sunrise, FL.

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).

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.

Address correspondence to: Erin R. Giovannetti, PhD, Research Scientist, National Committee for Quality Assurance, 1100 13th St NW, Ste 1000, Washington, DC 20005. E-mail: giovannetti@ncqa.org.1. US Department of Health and Human Services. Multiple Chronic Conditions: A Strategic Framework. Optimum Health and Quality of Life for Individuals With Multiple Chronic Conditions. http://www.hhs.gov/ash/initiatives/mcc/mcc_framework.pdf. Published December 2010. Accessed January 2012.

2. Fortin M, Bravo G, Hudon C, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83-91.

3. Naessens JM, Stroebel RJ, Finnie DM, et al. Effect of multiple chronic conditions among working-age adults. Am J Manag Care. 2011;17(2): 118-122.

4. Wolff JL, Boult C, Boyd C, Anderson G. Newly reported chronic conditions and onset of functional dependency. J Am Geriatr Soc. 2005; 53(5):851-855.

5. Marengoni A, von Strauss E, Rizzuto D, Winblad B, Fratiglioni L. The impact of chronic multimorbidity and disability on functional decline and survival in elderly persons: a community-based, longitudinal study. J Intern Med. 2009;265(2):288-295.

6. Parekh AK, Barton MB. The challenge of multiple comorbidity for the US health care system. JAMA. 2010;303(13):1303-1304.

7. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294(6): 716-724.

8. Librero J, Peiró S, Ordiñana R. Chronic comorbidity and outcomes of hospital care: length of stay, mortality, and readmission at 30 and 365 days. J Clin Epidemiol. 1999;52(3):171-179.

9. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162(20):2269-2276.

10. Hohl CM, Dankoff J, Colacone A, Afilalo M. Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department. Ann Emerg Med. 2001;38(6):666-671.

11. Tinetti ME, Bogardus ST Jr, Agostini JV. Potential pitfalls of diseasespecific guidelines for patients with multiple conditions. N Engl J Med. 2004;351(27):2870-2874.

12. Bayliss EA, Edwards AE, Steiner JF, Main DS. Processes of care desired by elderly patients with multimorbidities. Fam Pract. 2008;25(4): 287-293.

13. Fried TR, McGraw S, Agostini JV, Tinetti ME. Views of older personswith multiple morbidities on competing outcomes and clinical decision-making. J Am Geriatr Soc. 2008;56(10):1839-1844.

14. Fortin M, Dionne J, Pinho G, Gignac J, Almirall J, Lapointe L. Randomized controlled trials: do they have external validity for patients with multiple comorbidities? Ann Fam Med. 2006;4(2):104-108.

15. Lind A. Measuring Quality for Complex Medicaid Beneficiaries in New York. New York: Medicaid Institute at United Hospital Fund; 2011.

16. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the Quality Chasm: a New Health System for the 21st Century. Washington, DC: National Academies Press; 2001.

17. National Quality Forum. Measurement Framework: Evaluating Efficiency Across Patient-Focused Episodes of Care. http://www.qualityforum.org/Publications/2010/01/Measurement_Framework__Evaluating_ Efficiency_Across_Patient-Focused_Episodes_of_Care.aspx. Published January 2010. Accessed January 2012.

18. Zapka JG, Taplin SH, Solberg LI, Manos MM. A framework for improving the quality of cancer care: the case of breast and cervical cancer screening. Cancer Epidemiol Biomarkers Prev. 2003;12(1):4-13.

19. Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial [published correction appears in J Am Geriatr Soc. 2004;52(7):1228]. J Am Geriatr Soc. 2004;52(5): 675-684.

20. Cowing M, Davino-Ramaya CM, Ramaya K, Szmerekovsky J. Health care delivery performance: service, outcomes, and resource stewardship. Perm J. 2009;13(4):72-78.

21. McDonald KM, Schultz E, Albin L, et al. Care Coordination Measures Atlas Version 3. Prepared by Stanford University under subcontract to Battelle on Contract No. 290-04-0020. Rockville, MD: Agency for Healthcare Research and Quality; November 2010. AHRQ publication 11-0023-EF.

22. Boyd CM, Fortin M. Future of multimorbidity research: how should understanding of multimorbidity inform health system design? Public Health Rev. 2011;32(2):451-474.

23. Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006;29(3):725-731.

24. Grant RW, Ashburner JM, Hong CS, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician’s perspective: a cohort study. Ann Intern Med. 2011;155(12):797-804.

25. Wolff JL, Roter DL. Older adults’ mental health function and patient-centered care: does the presence of a family companion help or hinder communication? J Gen Intern Med. 2012;27(6):661-668.

26. Boyd C, Leff B, Weiss C, Wolff J, Hamblin A, Martin L. Clarifying multimorbidity patterns to improve targeting and delivery of clinical services for Medicaid populations. Faces of Medicaid Data Brief. http:// www.chcs.org/usr_doc/clarifying_multimorbidity_patterns.pdf. Published December 2010. Accessed February 1, 2013.

27. Woodard LD, Landrum CR, Urech TH, Rofit J, Virani SS, Petersen LA. Treating chronically ill people with diabetes mellitus with limited life expectancy: implications for performance measurement. J Am Geriatr Soc. 2012;60(2):193-201.

28. Guiding principles for the care of older adults with multimorbidity: an approach for clinicians: American Geriatrics Society Expert Panel on the Care of Older Adults with Multimorbidity. J Am Geriatr Soc. 2012;60(10):E1-E25.

29. Werner RM, Asch DA. Clinical concerns about clinical performance measurement. Ann Fam Med. 2007;5(2):159-163.

30. National Committee for Quality Assurance. HEDIS 2010. http://www.ncqa.org/tabid/1044/Default.aspx. Accessed May 4, 2010.

31. Lee SJ, Walter LC. Quality indicators for older adults: preventing unintended harms. JAMA. 2011;306(13):1481-1482.

32. Parekh AK, Goodman RA, Gordon C, Koh HK; HHS Interagency Workgroup on Multiple Chronic Conditions. Managing multiple chronic conditions: strategic framework for improving health outcomes and quality of life. Public Health Rep. 2011;126(4):460-471.

33. Sarver N, Murphy K. Management of asthma: new approaches to establishing control. J Am Acad Nurse Pract. 2009;21(1):54-65.

34. Dalton C, Farrell R, De Souza A, et al. Patient inclusion in goal setting during early inpatient rehabilitation after acquired brain injury. Clin Rehabil. 2012;26(2):165-173.

35. van Lieshout J, Steenkamer B, Knippenberg M, Wensing M. Improvement of primary care for patients with chronic heart failure: a study protocol for a cluster randomised trial comparing two strategies. Implement Sci. 2011;6:28.

36. Carney PA, Eiff MP, Saultz JW, et al. Aspects of the Patient-centered Medical Home currently in place: initial findings from preparing the personal physician for practice. Fam Med. 2009;41(9):632-639.

37. Lindblad CI, Hanlon JT, Gross CR, et al. Clinically important drugdisease interactions and their prevalence in older adults. Clin Ther. 2006;28(8):1133-1143.

38. Schäfer I, von Leitner EC, Schön G, et al. Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions. PLoS One. 2010;5(12):e15941.

39. Boyd CM, Giovannetti ER, Leff B, Weston C, Dy SM. Background Paper for the Development of a Framework for Measurement of Performance for People with Multiple Chronic Conditions Commissioned by the National Quality Forum. Washington, DC: National Quality Forum; 2011.

40. Persell SD, Kho AN, Thompson JA, Baker DW. Improving hypertension quality measurement using electronic health records. Med Care. 2009;47(4):388-394.

41. Persell SD, Wright JM, Thompson JA, Kmetik KS, Baker DW. Assessing the validity of national quality measures for coronary artery disease using an electronic health record. Arch Intern Med. 2006;166(20): 2272-2277.

42. Guyatt GH, Oxman AD, Vist GE, et al; GRADE Working Group. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924-926.

43. Baker DW, Qaseem A; American College of Physicians’ Performance Measurement Committee. Evidence-based performance measures: preventing unintended consequences of quality measurement. Ann Intern Med. 2011;155(9):638-640.

44. Boyd CM, Leff B, Wolff JL, et al. Informing clinical practice guideline development and implementation: prevalence of coexisting conditions among adults with coronary heart disease. J Am Geriatr Soc. 2011; 59(5):797-805.

45. Uhlig K, Leff B, Kent D, et al. A framework for crafting clinical practice guidelines that are relevant to the care and management of peoplewith multimorbidity. J Gen Intern Med. In press.

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