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1 OPERATIONAL ISSUES: MEASURING EFFICIENCY ACROSS EPISODES OF CARE
2 A commissioned paper submitted by Kyle L. Grazier, PhD.
3 This paper supports the NQF Priority Setting Pilot project which is developing a
4 comprehensive measurement framework for chronic care episodes. It addresses many of the
5 operational issues that may arise in attempting to evaluate efficiency, defined as quality and
6 costs, across ―episodes of care‖. It describes the rationale for measuring efficiency across
7 episodes, while examining components of the framework, measures, data integrity, and
8 methods required for implementation. A key issue in adoption and usefulness of the
9 measures is attribution which poses many challenges which are described. The paper
10 concludes with strategies for overcoming barriers which highlights the opportunities for
11 improvement for consumers, providers, and purchasers resulting from measurement of
12 efficiency across episodes.
13
14 The NQF Priority Setting Pilot project is intended to result in movement towards better
15 alignment of measurement development and reporting activities with national priorities and
16 goals, to address critical gaps in the quality measurement agenda, and to begin defining
17 comprehensive, longitudinal performance metrics—focusing on extended episodes of care that
18 include quality and resource use, and are reflective of both care processes and patient
19 outcomes. The paper builds on the Steering Committee’s proposed measurement framework
20 for evaluating efficiency, and it directly relates back to the Steering Committee’s stated
21 purpose of the health care delivery system:
22 To improve health and reduce the burden of illness, and maximize the value of individual and
23 societal resources allocated to health care.
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1 MEASURING EFFICIENCY ACROSS EPISODES
2 Background
3 The discourse on the definition of ―efficiency‖ has been extensive and thorough. 1,2 For
4 purposes of this paper, in accordance with work performed by the National Quality Forum,
5 the Institute of Medicine, the AQA, the HQA and others, efficiency measures encompass both
6 quality and cost. MedPAC defines an episode "as a series of clinically related health care
7 claims over a defined time period."3 Broadening the concept of episodes beyond claims-based
8 measures, episodes describe the patterns of illness, delivery of healthcare services, costs, and
9 resultant patient outcomes. ―Episode efficiency‖ connotes comprehensive measurement
10 across multiple dimensions.
11
12 Decades ago, researchers and clinicians argued for the use of episodes to understand, manage,
13 and evaluate health and health care.4,5, 6,7 There is growing acceptance to conceptualize health,
14 healthcare delivery and associated costs in the context of episodes, rather than as discreet and
15 independent visits, or encounters.8 Most recently, the importance of understanding ―value‖
16 has highlighted the need to include outcomes within the episode model. Increasing in
17 recognition are: the chronicity of many illnesses; the positive relationship between continuing
18 coordinated care and quality, which exposes the importance of integrated information within
19 and across provider groups; the potential consequences of performance measurement based
20 on incomplete data; the importance of payment models that reflect true resource use and short
21 and long term results. Despite these realizations, an episode approach to measuring and
22 rewarding care has not been widely embraced. A review of the framework, criteria for
23 selecting metrics, measures, barriers, and potential consequences may help explain the current
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1 state of the art in measuring efficiency across episodes, and may inform strategies for more
2 rapid and wider adoption.
3
4 Measurement Framework
5 A framework for assessing efficiency through episode measures would: focus on extended
6 periods of illness and care; include quality of care and quality of life; recognize resource use
7 from the start of the condition, not only from the entry into care; and reflect care processes and
8 patient outcomes.
9
10 Consistent with this focus, measures and their underlying data need to be: valid and reliable;
11 precisely defined; of significant clinical and economic importance; applicable to chronic and
12 acute conditions; reflective of processes and outcomes of care; and technologically and
13 economically feasibility. Standardized collection of standardized information allows
14 measurement and comparisons across or within subsets or strata of a delivery system, market,
15 or community. 9
16
17 Basic to implementation of the framework is the capacity to identify and track individuals
18 over time and across delivery systems, regardless of insurance coverage or location of care.
19 Support for creation of episodes that reflect care for chronic conditions depends upon
20 information derived from systems such as electronic health records that can be transmitted to
21 the provider at point of service, individual medical records that ―follow‖ the individual, or
22 dedicated community-level registry-based information systems. Even with such information
23 systems, it can be difficult to create a complete picture of an episode that may include, for
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1 example, one–time acute care services, periodic imaging, a hospital stay, and regular
2 metabolic testing. 10
3
4 Additionally, persons with chronic conditions usually have comorbidities requiring attention.
5 11,12 Often depending on healthcare coverage, patients can access primary care or specialty
6 providers outside of a network or a local medical community, further complicating efforts to
7 capture data on the patient, the use and costs of services, and the outcomes of care.
8
9 These and other implementation challenges include episode measurement, data sufficiency
10 and integrity, quality measurement, cost and pricing, attribution and accountability.
11
12 MEASURING EPISODES
13 Empirical work provides several constructs and measures for episodes, which have been
14 comprehensively reviewed in several publications.13,14,15,16,17,18 Hornbook and others offer three
15 distinct types of episodes: a) illness or health problem episodes - health problems requiring
16 medical care as perceived by patients; b) disease episodes - a medical model of a diagnosis and
17 associates physiological processes and related complications; and c) care episodes - the
18 temporally contiguous cluster of services provided for diagnosis and care of an identified
19 disease or health problem. 19
20
21 Most episode of care measures for an acute or chronic condition include: a start and stop date,
22 marked by the initial and final recording of a diagnoses code; and submission of a claim or
23 encounter with the health system within a fixed time period (e.g., one year to eighteen
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1 months). Data typically captured include: diagnosis and procedure codes for primary
2 diagnosis and for co-occurring conditions; dates of services and dispositions at discharges;
3 laboratory, radiology and other ancillary services; pharmacy dispensing; and costs (paid
4 charges). To capture an accurate episode for a chronic condition, these data are needed from
5 multiple providers or facilities, complicating logistics and potentially compromising
6 methodologies.
7
8 To distinguish or mark the duration of an episode, a ―clean‖ period is designated, during
9 which a specified number of days pass with no services, procedures, charges or encounters
10 recorded for the condition. The suggested clean period ranges from 30 to 365 days. Although
11 chronic conditions are persistent by nature, often generating resource use beyond one year,
12 most claims-based episode models use 365 day as the maximum episode duration.
13
14 Some people suggest standardizing the length of an episode for particular conditions, rather
15 than defining the episode by actual use based on recorded services delivered. Under that
16 model, resource use and outcomes could be compared across a specified time frame for
17 consistently defined patients and providers.
18
19 Episode groupers
20 Several ―episode groupers‖ are being used. 20,21 Rosen and colleagues specified four episode
21 grouper types, each of which ―represent[s] a meaningful unit of analysis for assessing the full
22 range of primary and specialty services provided in treatment [of] a particular health
23 problem.‖ The first chapter of the MEDPAC 2006 Report to Congress details their evaluation
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1 of two groupers, Episode Treatment Groups (ETGs) and Medstat Episode Groups (MEGs).22
2 Cave and colleagues created diagnosis episode clusters that included all services in a
3 particular time period, adjusted for severity, comorbidities, and age.23 Episode groupers vary
4 in their methodologies, differing in: the elements and formats of the required data; numbers
5 of conditions and occurrences required; outlier identification and adjustment; identifying the
6 start and end of an episode; assigning claims to an episode; incorporating severity and
7 complexity; measuring comorbidities and their role in creating an episode; and use of quality
8 indicators. Using groupers for performance measurement often requires additional risk
9 adjustment.
10
11 Another approach defines episodes as disease specific or condition specific. 24 deVec offered
12 an example of an episode as a ―…consultation or a series of consultations for low back pain,
13 preceded and followed by at least 3 months without consultation for low back pain.‖
14 Cunningham focused on the creation and use of nursing home episodes, which were defined
15 as continuous periods of institutionalization, that may include multiple stays, if contiguous
16 and separated by short-term hospital stays.25 Wingert constructed five different types of
17 episodes among several diseases. 26
18
19 Recently, researchers and practitioners have proposed that episode and/or clean period
20 constructs include patient outcomes. Given their multidimensional nature, health outcome
21 metrics cross both conceptual and operational boundaries of time, care delivery, health status,
22 quality of life, functionality, and mortality. 27,28 For example, high value, efficient, quality care
23 for diabetes can result in: increased survival; fewer contacts with the emergency department;
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1 an improved sense of well being; an earlier return to work or school; a longer, higher quality
2 life; and lower costs to the patient, purchaser and society. Measures for some of these
3 outcomes can mark the beginning or end of a discreet episode or, alternatively, a ―clean‖
4 period between episodes. The chronicity of some illnesses argues for recognizing interim,
5 periodic, short term or minor outcomes that occur over the course of the disease, the care or
6 the specified episode-related time period. Although such recognition may appear less vital to
7 long term outcomes, they are equally important in measuring progress toward wellness,
8 stages of recovery, and quality of care.
9
10 Integrity of data
11 Challenges exist in capturing information across settings and over the time necessary to
12 evaluate the quality and cost of care for a chronic condition. 29 Access to the necessary data in
13 the necessary settings requires: the ability to capture data from multiple organizations with
14 multiple data systems; compatible data formats and platforms among users; access to
15 disaggregated data on care processes and patient outcomes.
16
17 Many current frameworks and episode measures rely solely on the use of administrative data;
18 however, these data can be collected inconsistently within some provider organizations and
19 across different sites for care, particularly across physician groups, hospitals, long term or
20 post-acute care facilities .30 Although standardized claims and uniform billing formats have
21 improved timely construction of datasets for analysis, administrative data can require
22 considerable manipulation for use in episode measurement. The longitudinal nature of
23 chronic illness episodes, including the multi-year nature of patterns and phases of illness and
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1 patient outcomes, requires compilation of claims over several years. This task becomes
2 particularly difficult with changes in plan enrollment and insurance coverage, the one-year
3 length of standard plan contracts , and multiple payment models for different sources and
4 types of care.
5
6 For many patient outcome measures, other sources may be required. Such sources may
7 include patient medical records, disability data, vital statistics, and employer-based data on
8 absenteeism and employee health services31,32 Other sources offer their own access,
9 coordination, and validity challenges that need to be weighed against the importance of the
10 information. 33,34,35,36
11
12 For medical records that are not electronic, it is costly to gather and extract relevant pieces of
13 information. Electronic record systems are improving in prevalence and connectivity, but
14 they remain, for the most part, isolated from other institutions or providers possessing needed
15 episode data. Despite progress, cost data are often omitted from the EMR, and linkages with
16 finance and cost management systems may be weak. Without an identifier that is unique and
17 matches the medical data, information on short-term or long-term disability, whether or not
18 work related, are difficult to link with other medical data. Collection of information from the
19 Social Security Administration or from the Supplemental Security Income program is too
20 cumbersome to be feasible. Employer-based data, whether from worksite clinics, employee
21 assistance programs or attendance records, could be useful in portraying the full use of
22 resources and the work-based outcomes related to an episode; however, these data are costly
23 to collect and often lack a linking mechanism that would allow inclusion in episode
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1 construction. Despite these drawbacks, many critically important outcomes demand some of
2 this information to understand value.
3
4 Measuring Quality
5 Work by the National Quality Forum, the Institute of Medicine and others has substantiated
6 the importance and feasibility of measuring quality of health care. (NQF; IOM) According to
7 Jencks, there is "substantial consensus that quality can be measured in some important areas of
8 health care." 7 Which dimensions of quality are measured, how the measures are
9 implemented, the extent of evaluation, and the cost and transparency of the infrastructure or
10 superstructure are critical. MEDPAC's analysis illustrated the difficulty of measuring quality,
11 independent of resource use, using claims.37 Singularly and in combination, the factors listed
12 determine ease of acceptance, feasibility of dissemination, and sustainability of efficiency
13 measurement across episodes.
14
15 Substantial research has been conducted on measuring selected processes of care, much of
16 which is directly applicable to an episode-based efficiency metric. These process measures are
17 used as benchmarks for organizations or providers, as well as for process improvement.
18 Evidence and consensus-based guidelines exist for a number of clinical processes believed to
19 suggest high quality care. 38 Use of processes in the metric requires extensive clinical input
20 into critical inclusion conditions, such as age of onset, prior conditions, or an unambiguous
21 diagnosis - and into exclusions, such as evidence of certain comorbidities, symptoms, or prior
22 medical care. The role of co-occurring conditions in the measurement of quality and patient
23 outcomes continues to be studied.
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1 While process measures are important components of many current performance, quality and
2 profiling systems, they are usually condition-specific or diagnosis-specific, requiring extensive
3 labor to develop and test. If chronic care were delivered across different settings, the recorded
4 diagnosis could differ, depending on the specialty of the provider, the billing systems, or
5 practices for coding primary diagnosis versus secondary conditions as the reason for the
6 encounter. In addition to the inclusion and exclusion criteria associated with using the
7 condition-specific process measures, the metric must also be evaluated for each provider or
8 provider type, for example, for individual physicians, groups of practitioners, hospital based
9 care, or ambulatory care services. 39
10
11 Measuring cost
12 Measurement of ―cost‖ in an episode based efficiency framework offers several challenges due
13 to the history of cost accounting in health care, traditional claims-based billing systems, the
14 different sources of cost data, the long-term nature of chronic illnesses, and the ability to
15 detect the costs associated with improvement, cure, recovery, deterioration and disability over
16 prolonged periods of time. 40
17
18 Traditional insurance plan claims usually include service-related billed charges, eligible
19 charges, paid amounts, or some variation of these. The amounts actually paid for services, as
20 recorded on claims records are often dependent on a patient’s source of payment (e.g., self,
21 public, private, health plan, traditional fee for service contractor), the services eligible for
22 payment, and the contract between the provider and purchaser. Many studies use
23 standardized pricing models in view of the historical lack of a direct relationship among actual
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1 costs of care delivered, adjusted for wages and cost of living, and the charged, billed or paid
2 amounts. This approach results in a standardized monetary value for delivered services or
3 bundles of services, as defined by standardized codes ( e.g., NDC, CPT, DRG) and by ―type‖
4 of provider, independent of individual accounting and billing policies. The source of the
5 standardized costs, the methods of aggregation, and outlier adjustment are paramount to the
6 success of this standardization. Evidence exists that health plan payment records and
7 insurance claims are feasible sources of cost and use data, if combined, multi-year, and edited
8 for outliers.41,42
9
10 The data generated from these sources exclude nontraditional services, services with no
11 connection to the formal medical system, or services for which no billing record or claim is
12 filed. Standardized price measures require periodic adjustments and recalibrations,
13 dependent upon: indexes to market price changes; technological changes that might impact
14 the standardized price; and corrections or additions to the cost units. Given the complexity
15 inherent in creating these standard prices, it is important that the models be transparent to
16 users.
17
18 ATTRIBUTION
19 Attribution of services, costs and quality to individual or groups of providers remains
20 conceptually and technically challenging. To what extent can patient outcomes be attributed
21 to the efficiency and quality of care delivered by or assigned to multiple providers across
22 multiple systems over time? Attribution assigns accountability to providers; however,
23 positive and negatives health outcomes of care are the result of factors over which the
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1 provider may have little or no control. Such factors may include the severity of the condition,
2 the stage at which the patient sought care, and any prior care received for it or other
3 conditions.43
4
5 Measuring efficiency across episodes may require ―risk‖ adjustments for different levels of
6 illness severity, comorbidities, and potential complications - factors often encapsulated in the
7 concept of "risk.‖ The research literature and existing models for physician profiling,
8 physician performance, and case-based payment offer several alternatives for measuring
9 disease-specific severity and resource use intensity. 44 Using episodes as the unit of
10 measurement, rather than individual visits or admissions, additionally requires aggregation of
11 adjusters across multiple conditions and providers or groups, and over time. One approach
12 uses models in which severity scores are generated for each condition and then aggregated to
13 estimate the overall severity of an episode. While most models use administrative data to
14 generate risk scores, Rosen promotes a patient’s perspective on severity of illness, using
15 empirically derived dimensions of suffering, disabilities, risk of suffering, risk of disabilities
16 and risk of death.
17
18 Although risk adjustment is incorporated into most models of efficiency measurement,
19 including models of episode based alternatives, attribution and accountability remain complex
20 components of episode based efficiency measurement. It is to the benefit of society, patients,
21 payers, and the medical community that there be accountability for the quality and quantity of
22 health care delivered. Yet the current delivery, payment and data systems are not based on
23 efficiency, quality or value. As a result, a measurement framework requires a method for
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1 recognizing the contributions from those involved in the continuum of care required for the
2 chronically ill. Since patients experience care in episodes, these contributions need to be
3 captured and quantified within episodes, regardless of their duration.
4
5 These challenging attribution issues have been met with proposals that borrow from earlier
6 research and recent conceptual innovations. They include: the use of formulae in which
7 quality and cost measures are weighted by their contribution to value or to outcomes;
8 identification of a medical ―home‖; and shared accountability.
9
10 Within an episode, cost and quality can be attributed to individual, group, primary care and
11 specialty providers according to several alternative formulae. Methods vary the proportional
12 assignment of charges, paid claims, encounters, admissions, or discharges to one or more
13 providers within the period of the episode. As noted above, much of the current knowledge
14 on attribution has been generated from research on individual physician performance
15 measurement. For instance, researchers have assigned actual and standardized costs, subject
16 to different outlier adjustments, to the most frequently visited provider, to the provider with
17 the highest proportion of claims costs, or tothe provider with claims noting the dominant
18 diagnosis.4546 Most of this work has been performed using available episode groupers. Some
19 models are more robust than others, depending on physician specialty, numbers of patients
20 per provider, or number and type of physicians in the group.
21
22 Similar issues have arisen in episode based efficiency measurement within the chronic care
23 model, due to variability in the patterns of care, specifically combinations of primary care, care
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1 management, and specialist services provided in different settings. For instance, in a nine
2 month episode a patient might see a cardiac specialist once as part of post-AMI care, for which
3 most of the cost of the episode was incurred, while most services within the episode may have
4 been delivered by the primary care provider group.
5
6 Individual physician or provider groups
7 Researchers and policymakers have sought greater understanding of the implications of
8 holding individual providers, groups of providers, or health systems accountable for quality,
9 cost, and outcomes. Arguments rest, for example, on the adequacy of sample sizes for robust
10 statistical analysis. Predictive models of performance have shown the variability of resource
11 use within a practice or a physician's panel of patients due to the small numbers of episodes of
12 being assessed. Particularly in episodes involving chronic illness, numerous providers may be
13 involved in the care. On what basis would any one provider be held accountable, over what
14 could be a long period of time, for the resources consumed, the quality of care, and the health
15 of the patient?
16
17 Although most physicians practice in small, single specialty groups, patients with chronic
18 conditions often seek care outside of these arrangements - in multi-specialty group practices,
19 at academic medical centers, in nursing homes, and at acute and chronic care hospitals. Given
20 the benefits of long-term, integrated care, a measurement framework that promotes
21 communication and coordination among providers would benefit patients, plans and
22 providers. Attributing performance to group practices, particularly to primary care group
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1 practices, with appropriate risk adjustment, could encourage a systems approach, recognizing
2 the chronicity of conditions and multiple needs of these populations.
3
4 The issues span new proposals to create attribution formulae that weight quality and cost
5 measures within an episode by their contribution to value. These proposals require defining
6 and standardizing measurement units of quality, cost and value. Substantial research has
7 been conducted on many dimensions of these components and on the components themselves,
8 including measurement of technical quality of care, longitudinal resource use and costs,
9 functional and quality of life outcomes, and efficiency. 47,48 Although use of such formulae
10 adds a layer of measurement complexity, such models promote the basic goals of the
11 measurement framework.
12
13 Medical Home
14 The concept of medical home presents an alternative to assigning accountability to individual
15 or group practice providers. A medical home, "defined as a health care setting that provides
16 patients with timely, well-organized care, and enhanced access to providers," has been
17 proposed for many reasons, including as a partial solution to the attribution challenge. 49
18 Implementation of the concept, accompanied by insurance, has been found to improve access
19 to preventive and acute medical services, as well as management of chronic conditions.50
20 Given the importance of smooth and timely transitions to appropriate providers, care
21 coordinated thorough a medical home has been shown to be of higher quality than care
22 received in less integrated systems. From a technical perspective, the necessary relationships
23 between patients with chronic conditions and their providers could help generate information
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1 that would streamline tracking and collecting necessary cost, quality, and outcome data for
2 chronic episode-based efficiency measurement. The medical home would be held accountable
3 for the care received and the resultant outcomes. Although this approach would not eliminate
4 sample size issues, or the complexities of attributing efficiency within the medical home, it
5 would offer a model in which to test formulae or other allocation methods within episodes
6 and across providers.
7
8 Shared accountability
9 Given the challenges seen in recent efforts to establish accountability and attribute efficiency
10 in performance measurement, quality-based payment, and physician profiling, Fisher and
11 colleagues propose a model of shared accountability.51,52 All physicians who work with and
12 around an acute hospital create an "extended hospital medical staff," from whom data can be
13 gathered, performance assessed, and quality evaluated and promoted. The concept and
14 model are supported by empirical work using Medicare data. The authors note the
15 advantages of the model for accountability and attributing performance within episodes as 1)
16 related to performance measurement, "larger sample sizes, a broader scope of potential
17 measures, and the feasibility of including all physicians who contribute to the care of a
18 population within the frame of measurement"; 2) establishing "accountability for local
19 decisions about capacity" among hospitals and their affiliated medical staffs; 3) enhancing "the
20 capacity to invest in improving quality and lowering costs.‖ Although a strong case is made
21 for the model, challenges are acknowledged, including: an increase in competition among
22 provider organizations, making integration more difficult; the culture of independence among
23 physicians; legal issues with physician, hospital collaboration; variability on the degree of
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1 alignment between hospitals and physicians; and practical challenges to involving data,
2 politics, and the current payment structure. Nonetheless, this model could be tested for its
3 appropriateness for measuring efficiency across chronic condition-based episodes.
4
5 STRATEGIES FOR OVERCOMING BARRIERS
6 A number of challenges complicate implementation of a measurement framework that
7 captures, over a defined episode, the quality, cost, efficiency and outcomes of care for a person
8 with chronic disease, and then attributes that care to the appropriate provider(s) and/or
9 patient.
10
11 Although the overall intent of the framework is to recognize patients at the center of the
12 model, and episodes are the manner in which patients experience the health system, potential
13 consequences of adoption differ among consumers, providers, payers, plans, and
14 communities. Consumers consequences from an efficiency measurement framework across
15 episodes include: increased value of care through improved access to appropriate, high
16 quality providers; optimized health and healthcare outcomes; and better alignment of health
17 care cost with value. 53,54,55 For providers, the framework can: further the integration of health
18 care across systems, through information sharing and improved management; aid medical
19 decision making by promoting guideline-concordant and evidence-based practices; provide
20 necessary standardization for developing benchmarks; promote consistency between payment
21 and resource expenditure; and increase provider satisfaction.56,57 Framework implementation
22 could help purchasers be better agents for their consumers and organizations. Consistent,
23 valid, standardized measurement across episodes of care aligns the information on health,
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1 health care, resource consumption, and value.58 This result supports transparency in decision
2 making, pricing and payment.
3
4 Evaluating efficiency across episodes should result in better use of limited resources by
5 standardizing measurement, improving the accuracy of clinical and economic data,
6 supporting data platform compatibility, and improving outcomes of healthcare and the health
7 of communities.59
8
9 Selecting strategies with greatest leverage depends to a large extent on the overall objectives of
10 stakeholders. If widespread and immediate adoption satisfies these objectives, then such near-
11 term strategies as engaging stakeholders, widely disseminating information on the processes,
12 phasing implementation, and rewarding early adopters may accelerate implementation of the
13 framework. Throughout its development, the measurement framework could be used to
14 educate participants, organize data efforts, or expedite integration of services.
15
16 If more deliberative pilot testing is necessary for buy-in or sustainability, then pursuit of
17 research sites and methodological innovation could satisfy goals and leverage resources for
18 future implementation.
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1 REFERENCES
1 McGlynn EA. A Typology of Efficiency in Health Care: Implications for Measurement, RAND, presentation
May 2006
2 AQA Principles of “Efficiency” Measures AQA Parameters for Selecting Measures for Physician Performance.
Version 2, April 2006.
3
Medicare Payment Advisory Commission. 2006 Report to the Congress: Increasing the Value of Medicare.
Using Episode Groupers to Assess Physician Resource Use. Ch 1. Washington, DC: MedPAC. 2006
4 Solon JA, Feeney JJ, Jones SJ et al. Delineating episodes of medical care. Am J Public Health Nations Health.
1967;57(3)401-408.
5 Hornbrook MC, Hurtado AV, Johnson RE. Health care episodes: definition, measurement and use. Med Care
Rev 1985;42(2):163-218
6 Hornbrook MC, Monheit AC. The contribution of case-mix severity to the hospital cost-output relation. Inquiry.
1985;22(3):259-271.
7 Hornbrook MC, Berki SE. Practice mode and payment method. Effects on use, costs, quality, and access. Med
Care. 1985;23(5):484-511.
8 Rosen AK, and Mayer-Oakes A. "Episodes of Care: Theoretical Frameworks Versus Current Operational
Realities." Jt Comm J Qual Improv. 1999;25(3):111–128.
9 Tisnado DM, AdamsJL, Damberg CL, et al. Does Concordance Between Data Sources Vary by Medical
Organization Type? AJMC, 2007;13, No. 6 - Part 1.
10 Lydon-Rochelle MT, Holt VL, Cardenas V, et al. "The Reporting of Pre-Existing Maternal Medical Conditions
and Complications of Pregnancy on Birth Certificates and in Hospital Discharge Data." Am J Obstet Gynecol.
2005;193(1):125–134.
11 Higashi T, Wenger NS, Adams JL, etl al. Relationship between Number of Medical Conditions and Quality of
Care. N Engl J Med. 2007;356:2496-2504.
12 Finlayson EVA,Birkmeyer JD, Stukel TA,et al. Adjusting surgical mortality rates for patient comorbidities:
More harm than good? Surgery. 2002;132:787-794.
13 Institute of Medicine. Redesigning health insurance performance measures, payment, and performance
improvement programs: Subcommittee on Performance Measures. 2005.
14
Lamberts H, Hofmans-Okkes I. Episode of care: a core concept in family practice. J Fam Pract.
1996;42(2):161-169.
15
Krane DW, McNair RM Jr, Greenwood BJ, et al. Episodes of care: an overview of legal and regulatory issues.
Manag Care Q. 2001;9(3):52-65.
16
Medicare Payment Advisory Commission. 2006 Report to the Congress: Increasing the Value of Medicare.
Using Episode Groupers to Assess Physician Resource Use. Ch 1. Washington, DC: MedPAC. 2006
17
The Leapfrog Group, Bridges To Excellence. Measuring Provider Efficiency:A collaborative multi-stakeholder
effort. 2004
DRAFT DO NOT CITE, QUOTE, REPRODUCE, OR CIRCULATE 19
18
McGlynn EA. A Typology of Efficiency in Health Care: Implications for Measurement, RAND, presentation
May 2006
19
Hornbrook MC, Whitlock EP,Berg CJ et al. Development of an Algorithm to Identify Pregnancy Episodes in an
Integrated Health Care Delivery System. Health Serv Res. 2007;42(2):908-927.
20
Symmetry Health Data Systems, Inc. "Episode Treatment Groups:™ An Illness Classification and Episode
Building System". Available at www.symmetry-health.com/ETGTut_Desc1.htm. Last accessed October 25, 2005.
21
Dang DK, Pont JM, Portmoy MA. Episode treatment groups: an illness classification and episode building
system--Part II. Med Interface. 1996;9(4):122-128.
22
Medicare Payment Advisory Commission. 2006 Report to the Congress: Increasing the Value of Medicare.
Using Episode Groupers to Assess Physician Resource Use. Ch 1. Washington, DC: MedPAC. 2006
23
Cave DG. Profiling physician practice patterns using diagnostic episode clusters. Med Care. 1995;33(5):463-
486.
24
Lucas J, Gunter MJ, Byrnes J, et al. Integrating outcomes measurement into clinical practice improvement
across the continuum of care: a disease-specific episode of care model.
Manag Care Q. 1995;3(2):14-25
25
Cunningham PJ, Short PF, Feinleib SE. Estimating nursing home episodes from a sample of discharges. The
importance of adjusting for prior and subsequent stays. Med Care. 1995;33(4):432-440.
26
Wingert TD, Kralewski JE, Lindquist TJ, et al. Constructing episodes of care from encounter and claims data:
some methodological issues. Inquiry. 1995-1996;32(4):430-443.
27
Wickizer TM, Franklin G, Fulton-Kehoe D, et al. Patient Satisfaction, Treatment Experience, and Disability
Outcomes in a Population-Based Cohort of Injured Workers in Washington State: Implications for Quality
Improvement. Health Serv Res. 2004;39(4, Part I):727-748.
28 Coleman EA, Parry C, Chalmers S. et al. The Care Transitions Intervention: Results of a Randomized
Controlled Trial. Arch Intern Med. 2006;166:1822-1828.
29 Hersh WR. Adding Value to the Electronic Health Record Through Secondary Use of Data for Quality
Assurance, Research, and Surveillance. AJMC. 2007;13, No. 6 - Part 1.
30 Schulman KA, Yabroff KR, Kong J, et al. A claims data approach to defining an episode of care. Health Serv
Res. 1999; 34(2):603-621.
31
Martin JA, Hamilton BE, Sutton PD et al. "Births: Final Data for 2002." Natl Vital Stat Rep. 2003;52 (10): 1–
113
32
Iezzoni LI, Daley J, Heeren T, et al. Using administrative data to screen hospitals for high complication rates.
Inquiry. 1994;31(1):40-55.
33 Nuttall M, Van der Meulen J, Emberton M. Charlson scores based on ICD-10 administrative data were valid in
assessing comorbidity in patients undergoing urological cancer surgery. J Clin Epidemiol. 2006; 59(3):265-273.
34 Birkmeyer JD, Siewers AE,Finlayson E, et al. Hospital volume and surgical mortality in the United States. N
Engl J Med. 2002;346:1515, 1128-1137.
DRAFT DO NOT CITE, QUOTE, REPRODUCE, OR CIRCULATE 20
35 Forster AJ, Murff HJ, Peterson JF, et al. The incidence and severity of adverse events affecting patients after
discharge from the hospital. Ann Intern Med. 2003;138:161-167.
36 Birkmeyer, JD, Dimick JB, Birkmeyer NJO. Measuring the quality of surgical care: Structure, process, or
outcomes? J Am Coll Surg. 2004;198:44, 626-632.
37
Medicare Payment Advisory Commission. 2006 Report to the Congress: Increasing the Value of Medicare.
Using Episode Groupers to Assess Physician Resource Use. Ch 1. Washington, DC: MedPAC. 2006
38
Birkmeyer JD, Dimick JB, Birkmeyer NJO. Measuring the quality of surgical care: Structure, process, or
outcomes? J Am Coll Sur. 2004;198:44, 626-632.
39
Katherine L, Kahn DM, Tisnado JL, et al. Does Ambulatory Process of Care Predict Health-Related Quality of
Life Outcomes for Patients with Chronic Disease? Health Serv Res. 2007;42:1p1 63-70.
40 Foster EM, Xuan F. An Episode-Based Framework for Analyzing Health Care Expenditures: An Application of
Reward Renewal Models. Health Serv Res. 2005;40(6, Part I):1953-1971.
41 Thomas JW, Ward K. Economic profiling of physician specialists: use of outlier treatment and episode
attribution rules. Inquiry. 2006;43(3):271-282.
42 Thomas WT, Grazier KL, Ward K. Economic Profiling of Primary Care Physicians: Consistency among Risk
Adjusted Measures. Health Serv Res. 2004;39(4), 985-1004.
43 Bynumm JPW,Bernal-Delgado E, Gottlieb D, et al. Assigning Ambulatory Patients and Their Physicians to
Hospitals: A Method for Obtaining Population-Based Provider Performance Measurements. Health Serv Res.
2007; 42:1p1, 45-51.
44 Alemi F, Walters SR.A mathematical theory for identifying and measuring severity of episodes of care. Qual
Manag Health Care. 2006;15(2):72-82.
45
Thomas WT, Grazier KL, Ward K. Economic Profiling of Primary Care Physicians: Consistency among Risk
Adjusted Measures. Health Serv Res. 2004;39(4), 985-1004.
46
Thomas JW, Ward K. Economic profiling of physician specialists: use of outlier treatment and episode
attribution rules. Inquiry. 2006;43(3):271-282.
47 Grazier KL Efficiency/Value-Based Measures for Services, Defined Populations, Acute Episodes, and Chronic
Conditions Performance Measurement: Accelerating Improvement (Pathways to Quality Health Care Series) 2006.
48
Fisher ES,Staiger DO,Bynam JPW,Gotlieb DJ.Creating Accountable Care Organizations: The Extended
Hospital Medical Staff: A new approach to organizing care and ensuring accountability. Health Affairs 26, no. 1
(2007): w44–w57 (published online 5 December 2006; 10.1377/hlthaff.26.1.w44).
49
Beal AC, Doty MM, Hernandez SE, Shea KK,, Davis K, Closing the Divide: How Medical Homes Promote
Equity in Health Care: Results From The Commonwealth Fund 2006 Health Care Quality Survey, The
Commonwealth Fund, June 2007
50
Starfield B, Shi L The Medical Home, Access to Care, and Insurance: A Review of Evidence. Pediatrics
113, no. 5 Supp. (2004): 1493–1498;
DRAFT DO NOT CITE, QUOTE, REPRODUCE, OR CIRCULATE 21
51
Institute of Medicine, Performance Measurement: Accelerating Improvement (Washington: National Academies
Press, 2006.
52
Fisher ES,Staiger DO,Bynam JPW,Gotlieb DJ.Creating Accountable Care Organizations: The Extended
Hospital Medical Staff: A new approach to organizing care and ensuring accountability. Health Affairs 26, no. 1
(2007): w44–w57 (published online 5 December 2006; 10.1377/hlthaff.26.1.w44).
53
Nyman JA, Nathan A, Barleen BS et al. Quality-of-Life Weights for the US Population: Self-Reported Health
Status and Priority Health Conditions, by Demographic Characteristics. Med Care. July 2007;45; 7:618-628.
54 Landon BE, Hicks LS, O’Malley AJ ,et al. Improving the Management of Chronic Disease at Community
Health Centers. N Engl J Med. 2007;356:921-934.
55
Anderson RT, Weisman CS,Camacho F, et al. Women's Satisfaction with Their On-Going Primary Health Care
Services: A Consideration of Visit-Specific and Period Assessments. Health Serv Res. April 2007;42(2):663-681.
56
Ross JS, Siu AL. The Importance of Population-Based Performance Measures. Health Serv Res. February
2007;42:1p1.
57
Weinberg DB, Gittell JH, Lusenhop RW, et al. Beyond Our Walls: Impact of Patient and Provider Coordination
across the Continuum on Outcomes for Surgical Patients. Health Serv Res. February 2007;42:1p1, 7-14.
58
Bynum JPW, Bernal-Delgado E, Gottlieb D, et al. Assigning Ambulatory Patients and Their Physicians to
Hospitals: A Method for Obtaining Population-Based Provider Performance Measurements. Health Serv Res.
February 2007;42:1p1: 45-52.
59
Jencks S. Clinical Performance Measurement—A Hard Sell. JAMA. 2000;283:2015-2016.
DRAFT DO NOT CITE, QUOTE, REPRODUCE, OR CIRCULATE 22
