Insights for Improvement

Transcription

1 2012 Insights for Improvement Reducing Readmissions: Measuring Health Plan Performance An NCQA Insights for Improvement Publication

2 Acknowledgments This publication was developed by the National Committee for Quality Assurance (NCQA) and funded by Boehringer Ingelheim Pharmaceuticals, Inc. Editorial oversight and content decisions are the responsibility of NCQA. NCQA recognizes and thanks Donna Dugan, Erin Giovannetti, Jeremy Gottlich, Judy Lacourciere, Karen Onstad, Gregory Pawlson, Bob Rehm, Robert Saunders, Sarah Thomas, Phyllis Torda and Heather Williams for their contribution to the publication s production. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without the written permission of NCQA. Copyright 2012 by the National Committee for Quality Assurance (NCQA) th Street NW, Suite 1000, Washington, DC All rights reserved. Printed in the U.S.A. NCQA Customer Support: NCQA Policy Clarification Support (PCS):

3 Table of Contents Foreword iii Introduction iv Why Should We Care About Readmissions? Measuring Inpatient Readmissions NCQA s HEDIS Plan All-cause Readmissions Measure Results from the First Year of Measuring Readmissions Using Readmission Results to Improve Care The Future of Readmission Measurement Conclusions Glossary Appendix A Appendix B References HEDIS is a registered trademark of the National Committee for Quality Assurance

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5 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE iii Foreword On behalf of the National Committee for Quality Assurance (NCQA), I am pleased to introduce the third publication in our Insights for Improvement series, Reducing Readmissions: Measuring Health Plan Performance. This publication provides an overview of the development and implementation of the NCQA Plan All-Cause Readmissions measure. Discharge from a hospital is a critical transition point in a patient s care. Research has shown that up to 20 percent of Medicare patients discharged from the hospital will be readmitted within 30 days, and that many of these readmissions could be prevented. Preventable hospital readmissions lead to increased morbidity and to billions of wasted dollars. Readmission may be caused by complications arising directly from the hospital stay, an incomplete handoff at discharge or by poorly managed chronic disease, and thus may indicate poor care (both inpatient and ambulatory) or missed opportunities to coordinate care better. Most readmission measures and interventions target the hospital in preventing readmissions; consequently, data sources for the measures and performance reporting for accountability focus on the hospital. The intent of NCQA s health plan measure is to recognize the important role health plans can play in reducing readmission rates through improvement of post-discharge planning, care coordination and chronic care management. The Plan All-Cause Readmissions HEDIS measure is based on data available to health plans, and is intended to determine whether plans and their networks of providers and members are taking appropriate actions to reduce readmissions. When coupled with other use-of-services and clinical quality data, readmission measure results can be used by plans to evaluate interventions related to care coordination and continuity of care, and to other factors that affect readmission rates. The NCQA measure complements hospital-focused measures. It can be used to foster cooperation across the continuum of care (e.g., inpatient, home care, specialty and primary care, long-term care, ambulatory care). And, it reflects the reality that care received outside the hospital can also contribute to readmissions. We are optimistic that using this measure will increase focus on reducing readmissions and improve outcomes. By fostering accountability across the system, we have an opportunity to achieve both an increase in quality and a decrease in the cost of care. This is a win-win situation for the health care system and for patients. Margaret E. O Kane President, NCQA

6 iv 2012 INSIGHTS FOR IMPROVEMENT Introduction Research shows that up to 20 percent of the 36.1 million Medicare hospitalizations that occurred in resulted in a readmission within 30 days, 2 with substantial variation in rates across and within health care markets. 3 Hospitalizations are costly (in terms of both number of dollars spent and poor patient outcomes) and risk exposing patients to adverse events. Many studies have shown the increasing chance of iatrogenic (unintentionally caused by medical treatment) complications, loss of function and increased morbidity and mortality that are consequences of frequent or prolonged hospitalization, especially in older persons. Readmissions (i.e., hospital admissions that occur within a specified time frame after discharge from the initial admission) may be unavoidable (planned or unrelated to the primary admission) or potentially preventable. Planned readmission is appropriate in certain circumstances related to a patient s condition, such as for cancer (e.g., timed chemotherapy requiring inpatient stays for administration and monitoring). Readmission may also be unrelated to the primary admission, such as for trauma from an accident after an admission for asthma. 4 Potentially preventable readmissions can result (directly or indirectly) from suboptimal care during hospitalization or after discharge, and may be prevented through better care during the hospital stay, chronic disease management, improvement in care coordination during transition out of the hospital or monitoring after hospital discharge. While not all potentially preventable readmissions can be avoided, many can be prevented if optimal care is rendered, both in the hospital and after discharge. 5 Specific problems have been linked to ineffective discharge planning, failure to reconcile medications, insufficient transfer of information at discharge and failures in coordination of care and post-discharge monitoring of patients. For example, if a patient is discharged home from the hospital without a plan for follow-up care and the primary care physician is not made aware of the recent hospitalization, the patient s condition may worsen with no monitoring and result in a readmission. Factors influencing readmission also include patient characteristics and community factors. With regard to patient characteristics, research has shown that likelihood of readmission is associated with age, sex, race, socioeconomic status, illness severity, number Types of Readmissions Hospital readmissions (rehospitalization) are admissions that occur within a specified time frame after discharge from the first, or index, admission. Planned Readmission: Rehospitalization appropriate for a patient s condition, such as cancer (e.g., timed chemotherapy requiring inpatient stays for administration and monitoring). Potentially Preventable Readmission: Suboptimal care provided before hospitalization, during hospitalization or after discharge that leads to rehospitalization.

7 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE v and severity of comorbid conditions and availability of support in the community. 6 For instance, older patients and their caregivers may have a harder time following confusing post-discharge instructions involving changes to their medication regimen, which may contribute to rehospitalization. This underscores the importance of risk adjustment for readmission measures. Practice patterns in the community, such as the underlying hospitalization rate, case volume and number of physicians or hospital beds, may be related to readmission rates as well. The readmission rate may be higher in regions where there is a greater propensity to hospitalize a patient. 6 Because there are both planned and unplanned readmissions, it is difficult to determine an optimal readmission rate (although lower rates are likely better than higher rates). Readmission rates reflect a mix of planned and unplanned readmissions, the disease burden, gender and other aspects of the patient population, as well as the care provided in the hospital and in the community. With this in mind, the overall goal should be effective management of acute and chronic conditions, achieved through a reduction in unplanned readmissions resulting from controllable factors in the hospital and the health plan. Because of the high prevalence and cost of readmission (both monetary and in terms of increased morbidity and mortality), and reasonable evidence that some readmissions are preventable, interest in rehospitalization is supported by an increasing number of public policy and payer initiatives. The Medicare Payment Advisory Commission recommended a payment strategy to penalize hospitals with high readmission rates that was included in the Affordable Care Act (ACA). Also included are funded pilots to improve transitions of care. Reducing readmissions is a key aspect of the Health and Human Services Partnership for Patients initiative. Readmissions are a major quality and cost issue that can also be addressed by linking ongoing performance measurement and quality improvement efforts. To date, most readmission measurement efforts have focused on the hospital. This includes the types of measures in widespread use, the data sources used in the measures and the hospital as the unit of accountability. Considering that multiple factors in the hospital and outpatient settings can affect readmission rates, a strategy that incorporates those factors is likely to have more effect than a hospitalonly focused strategy. From a system perspective, a safe transition from a hospital to the community or a nursing home requires care that centers on the patient and transcends organizational boundaries. 2 In 2009, NCQA developed a measure comparing health-plan performance related to readmission that complements existing hospital-based measures. Plan All-Cause Readmissions measures how well the health plan reduces readmissions, with the expectation that the plan can affect readmission rates through pay-for-performance initiatives, data sharing, contracting and other interventions within its network of hospital and outpatient providers and its members. The measure covers all types of discharges, rather than for particular conditions, because factors predicting readmissions and the many interventions to improve them are not likely to be unique to specific conditions.

8 vi 2012 INSIGHTS FOR IMPROVEMENT NCQA s Approach to Improving the Quality and Value of Care NCQA is a private, not-for-profit organization dedicated to improving health care quality. Since its founding in 1990, NCQA has been a central figure in driving improvement throughout the health care system, helping to elevate the issue of health care quality to the top of the national agenda. NCQA builds consensus around important health care quality issues by working with large employers, policymakers, providers, patients and health plans to decide what is important, how to measure it and how to promote improvement. 7 Within the last decade, with the goal of expanding the ability to measure cost, waste and overuse and to explore the relationship between resource use and quality in order to increase the value of care, NCQA has also focused on measures of cost. As the gap in quality of care remains and health care costs continue to rise, the quest continues to find opportunities for improved outcomes. NCQA promotes joint accountability across entities within the health care system to achieve this goal. The HEDIS Plan All-Cause Readmissions measure provides one such opportunity, and demonstrates NCQA s commitment to improving the quality and value of care. Refer to Appendix A for additional information on NCQA.

9 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 1 Why Should We Care About Readmissions? The U.S. exceeds all other countries by nearly 50 percent in per capita health care spending but consistently lags behind other highly developed countries in quality markers related to prevention and chronic illness. Research indicates that not all hospital admissions are necessary and that many hospital readmissions represent failures in discharge planning and follow-up care, and missed opportunities for effective and coordinated outpatient care. Costs of inpatient care have continued to climb despite successful efforts to reduce or stabilize length of hospital stay, leading policymakers to focus on hospital admissions. The idea that some hospitalizations could be avoided by more effective or active care of patients has been strengthened by a number of peer-reviewed studies, including those led by Wennberg and Fisher, et al, at Dartmouth. 8 Their analysis of Medicare utilization and expenditures demonstrates wide variation of hospital use in different regions of the country, even when adjusted for differences in disease prevalence (usually quite small) or socioeconomic factors. For example, in Medicare fee-for-service (FFS) patients, who generally have similar benefits and levels of utilization oversight, the adjusted hospital discharge rates for congestive heart failure (CHF) per 1,000 patients varies from 350 in Mississippi and Alabama, to fewer than 150 in Idaho. At the same time, studies have not shown that higher use of hospitals results in better outcomes or quality of care. Additional data analysis by the Dartmouth group has shown that for some diseases, variation in hospital admissions is most strongly associated (highly correlated) with the number of hospital beds and subspecialists per capita in a given area, rather than with socioeconomic or other factors, including risk adjustment for patient disease burden. This demonstrated variation in hospital care combined with the lack of a significant relationship between utilization and improved quality and outcomes have converged with escalating health care costs to fuel renewed efforts to reduce unnecessary or wasteful hospital use. Not only does potentially avoidable hospital use waste resources, in some cases it results in significant complications, or even death, for patients. Although some readmissions are clearly necessary or are unrelated to the primary admission, numerous studies indicate substantial variation in the rate of readmission per 1,000 admissions. One stunning finding from Jencks 2 was that little more than half of patients readmitted to the hospital within 30 days of discharge had no evidence of a follow-up visit Measuring readmissions provides an opportunity to evaluate and improve the quality of care, while potentially reducing its cost.

10 INSIGHTS FOR IMPROVEMENT of any kind between discharge and readmission. Other important findings from this study noted that patients discharged after surgical procedures were frequently readmitted with medical conditions, most of which were present on the original admission, and length of stay of readmitted patients was nearly a day longer than for primary admissions. Readmission May Indicate Poor Quality of Care The movement of patients from one care setting to another offers many opportunities for quality improvement. Hospital discharges are a critical transition point in care that leaves many patients vulnerable to adverse events. These exchange points contribute to unnecessarily high rates of health service use, [and] spending and expose chronically ill people to lapses in quality and safety. 9 Because of the complexity involved in moving some patients to the hospital or from the hospital to another facility, without appropriate safeguards, mistakes can trigger or contribute to serious adverse clinical events and poor patient satisfaction with care. 10 Problems can arise regarding the effectiveness of discharge planning, transfer of information at discharge or failures in coordination of care, limited access to follow-up care and post-discharge monitoring. For example, if a patient s medication history is not available to the hospital staff on admission, or if medications are not reconciled at discharge and correct information given to the patient s doctors, there could be serious consequences for the patient, including additional morbidity, rehospitalization or mortality. Hospital readmissions not only indicate inadequate coordination of care, but may also indicate poor care in general, including incomplete treatment, poor care of the underlying problem and errors in the hospital (e.g., nosocomial or hospital acquired infections; instrument left inside a patient post-surgery) or in the ambulatory care setting. Although hospitals should work to prevent avoidable readmissions, responsibility may also lie with outpatient care. 11 Poor management of a patient s chronic disease after discharge can also drive rehospitalization. This can stem from limited access to quality primary or specialty care in the community or from the lack of continuity of care between these providers. Research has documented the relationship between readmission and quality of care. A meta-analysis conducted by Ashton, et al. 12 found that the risk of early readmission (within 31 days of discharge) increased by 55 percent when care was rated as low quality. In this study, low quality was defined as the failure to meet accepted standards of routine hospital practice. A study by Van Walravenet, et al. 13 showed greater risk of readmission in patients treated during discharge follow-up by a physician who did not receive a discharge summary. Study results from Hannan et al. 14 showed that 85 percent of readmissions following coronary bypass surgery were associated with complications related to that surgery. Readmissions Burden Patients and Their Families There is substantial literature on the negative impact of hospital stays on patients and their families from the perspective of health and cost. In spite of the relatively widespread presence of health insurance, the leading cause of family bankruptcy throughout most of the last decade was health care-related debt in most cases, debt caused by one or more hospitalizations. Many studies have shown the increasing chance of iatrogenic (induced inadvertently through medical treatment) complications, loss of function and increased morbidity and mortality that are consequences of frequent or prolonged hospitalizations, especially in older persons. The literature also finds that patients and caregivers report a lack of emotional support, inadequate discharge planning and insufficient family participation in care transitions. This is in addition to

11 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 3 feelings of anxiety and confusion and the perception that their preferences are disregarded. 15 Readmissions Increase Cost of Care The sustained, unrelenting rise in health care costs has created a renewed and more urgent push for bending the cost curve. The 2011 release of the National Quality Strategy 16 focused on reducing waste as a quality issue and noted the need to address cost issues overall. Because inpatient services are generally the largest expense, they deserve special emphasis in expenditure considerations. Hospitals account for nearly $1 trillion of the $2.6 trillion (2010 estimate) in health care spending (Figure 1). 17 In 2010 there were more than 37 million acute inpatient admissions to 5,000 hospitals. Although total hospital inpatient days have declined, the number of admissions per 1,000 people and the cost per day or per admission have steadily increased. Admissions are an important focus of the efforts to reduce cost, but readmissions are given special attention because they consume a disproportionate share of expenditures for inpatient hospital care. 4 Preventing avoidable hospital readmissions is considered by many to be the most important opportunity for reducing waste in health care. As referenced earlier, the Jencks study using 2004 Medicare claims data on readmissions found that nearly 20 percent of patients who were discharged after an acute hospital stay were readmitted within 30 days of discharge, and more than one third (34 percent) were readmitted by 90 days. 2 In addition, within a year of admission, nearly 70 percent of patients discharged with medical conditions and 53 percent discharged after surgical procedures had been rehospitalized or had died. Given the more than 11 million hospital admissions in Medicare alone, this translates into 2.5 million Medicare Figure 1: Hospital expenditures as a portion of total 2010 health care spending 17 Total Health Care Spending % 5.52% 3.18% 2.71% Hospital ($814.0 B) Physician/Clinical Services ($515.5 B) Admin, Investment, etc. ($407.6 B) 7.59% 9.99% 31.38% 15.72% 19.88% Prescription Drugs ($259.1 B) Professional/Personal Services ($196.9 B) Nursing Care Facilities ($143.1 B) Dental ($104.8 B) Medical Products ($82.5 B) Home Health Care ($70.2 B)

12 INSIGHTS FOR IMPROVEMENT readmissions within 30 days of discharge, at a cost in excess of $17 billion dollars each year. Estimates of the proportion of readmissions defined as potentially preventable vary substantially. The Medicare Payment Advisory Commission (MedPAC) estimates that 13.3 percent of the 30-day readmissions in Medicare are potentially preventable, 18 which equates to $12 billion per year. 5 Based on observed high variation of readmission rates by region, a Commonwealth Fund study estimated that if readmission rates were lowered to the average level achieved by the top-performing regions, Medicare would save $1.9 billion annually. 5 Readmissions Can Be Prevented or Reduced Readmissions in the immediate post-hospital discharge period are more likely to be related to care during the hospitalization (later readmissions are often the fault of poor outpatient care, including lack of follow-up care). They may also be due to failures in the transition of care between the hospital and outpatient setting or to inappropriate initial responses in the setting to which the patient is discharged (e.g., home, nursing home, rehabilitation facility). Beyond the inferential evidence from studies of variation, a large number of interventional and observational studies, including a number of randomized controlled trials (RCT), address prevention of readmission through interventions in outpatient care. As of May 2011, the Agency for Healthcare Research and Quality (AHRQ) Innovations Exchange ( gov) listed more than 50 innovative programs that reduce hospital readmission. Center for Medicare & Medicaid Innovation (CMMI) grants under the Partnership for Patients are also likely to be a source of activity. 19 Although diabetes, chronic obstructive pulmonary disease (COPD) and some surgical procedures have also been studied, the most compelling evidence has been developed with patients who have congestive heart failure (CHF). Observational and RCT studies have shown significantly positive results for interventions such as nurse case management, 20 physician follow-up visits, 21 home visits, 22 telephone follow-up 23 and care in a patientcentered medical home (PCMH). 24 These studies achieved reductions in readmissions ranging from 5 percent to nearly 30 percent. Other studies that focused on hospital care found that lower readmission rates are related to higher use of nurse transition care managers, 25 overall hospital quality scores, 26 higher patient Preventing avoidable hospital readmissions is considered by many to be the most important opportunity for reducing waste in health care.

13 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 5 satisfaction 27, 28 with care, reengineering of hospital discharges and use of a consulting pharmacist at discharge. 29 Some interventions involving different care transition models are based on results of this research. The Coleman Model 30 and the Naylor Model, 31 for example, aim to reduce hospital readmissions and share some of the same elements, including interdisciplinary communication and collaboration, patient activation and enhanced follow-up. The Coleman Model focuses on four conceptual areas as part of its interdisciplinary intervention approach: 1. Medication self-management. 2. Use of a patient-centered record. 3. Primary care and specialist follow-up. 4. Patient knowledge of and response to red flags (e.g., indications that their condition is worsening). The Naylor model provides comprehensive in-hospital planning and home follow-up, with a focus on patient and caregiver understanding; helping patients manage health issues and prevent decline; and medication reconciliation. At the heart of this approach is a transitional care nurse who transitions with patients from the hospital into the home and provides services designed to improve outcomes. Both models have been used successfully by care delivery systems (e.g., health plans, hospitals) to smooth care transitions, reduce rehospitalizations and improve outcomes. Because multiple factors in hospital and outpatient settings can affect readmission rates, multifaceted interventions involving hospital care, transitional care and ambulatory follow-up care to improve coordination, continuity and patient engagement will likely have a greater effect on readmission than the largely single, intervention-focused studies to date. Evolving payment arrangements including bundled or global payments and payments that create incentives to eliminate waste and ineffective care target hospital readmissions. Accountable care organizations (ACOs), which are rewarded for improving quality and reducing cost, may also contribute to improved outcomes and provide new evidence about the effects of intervention. The PCMH Initiative, where primary care practices focus on organizing care around patients, working in teams and coordinating and tracking care over time, is another example of promising changes in the delivery system that may help reduce unnecessary readmissions.

14 INSIGHTS FOR IMPROVEMENT Measuring Inpatient Readmissions NCQA s Measure Development Process For more than 20 years, NCQA has refined its process for developing, testing, implementing and maintaining health care quality measures. Researchers, clinicians, purchasers, consumers and other stakeholders are essential to developing and improving HEDIS measures. The development process is transparent and incorporates multiple points of review and broad stakeholder input. It is based on NCQA s belief that measures should demonstrate four desirable attributes: importance, scientific acceptability, usability and feasibility. NCQA measures are designed to be useful in public reporting, pay-forperformance programs and quality improvement. Developing a measure is a multistep process. It involves identifying the clinical area to evaluate and conducting an extensive literature review with input from Measurement Advisory Panels (MAP); developing detailed specifications with appropriate HEDIS expert panel input; vetting with various stakeholders; and performing a field-test that looks at feasibility, reliability and validity. All HEDIS measures are established and maintained through a highly refined, systematic process. Figure 2 shows the life cycle of a HEDIS measure from measure selection through measure retirement. Plan All-Cause Readmissions Measure Development Even though the concept of hospital readmission seems straightforward (i.e., admission to an inpatient Figure 2: Life cycle of a HEDIS measure. 32 IV. First Year III. Public Comment II. Development HEDIS Measures Life Cycle V. Public Reporting VI. Evaluation I. Selection VII. Retirement Refer to Appendix B for additional information on the HEDIS measure development process, including the HEDIS measure life cycle.

15 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 7 hospital within a defined interval after a prior admission), trying to define the concept in a way that allows clinically useful, consistent, reliable and valid evaluation of efforts to reduce readmissions was challenging. A number of key issues had to be addressed, including what entity to measure (i.e., hospital, provider network or population), what time interval to use (i.e., 7-day, 30-day or 60-day), what type of readmission (i.e., all causes or a specific cause) and how to apply risk adjustment or a rating of the likelihood that a readmission could have been prevented. To address these issues, NCQA reviewed several readmission measures developed by other organizations. Feasibility of measure implementation by health plans, including complexity and implementation burden, was critical, as were reliability and validity. NCQA decided to model the general measurement approach and risk-adjustment process on the National Quality Forum (NQF)- endorsed, Centers for Medicare & Medicaid Services (CMS) condition-specific discharge measures. As a result, certain definitions between the CMS and NCQA measures were coordinated. Issues for Consideration and NCQA s Chosen Approach Who is accountable for readmissions? Most people think of readmissions as a hospital issue. For example, Medicare Hospital Compare ( includes condition-specific readmission measures. This perspective is natural, because of the retrospective and interventional studies that have shown that care within the hospital directly affects readmission rates; yet, other factors not within the confines of the hospital have also been shown to affect readmission rates. From the perspective of a patient or population, hospital care, transitional care and ongoing care after discharge all influence readmission rates. Thus, a population perspective, as well as a focus on specific entities, is optimal. Health plans have many opportunities to influence readmission by selecting provider networks (including hospitals) who manage and coordinate care, and as payers with a defined, enrolled population of patients (and thus sources of claims data). Where a hospital sees only the care provided within its facilities or through its affiliated providers, as payers, health plans can look across multiple treatment settings to identify and address health needs. Through contracts with hospitals, clinical practices and other community providers, plans can develop programs (including data collection and reporting and developing financial arrangements) that improve care coordination and quality. NCQA s Plan All-Cause Readmissions measure complements hospital-based measures by using a population-based approach and measures how well health plans manage delivery of services to members, both in the hospital and in the broader clinical community beyond hospital walls. Plan All-Cause Readmission aligns with hospital measures so plans can use hospital-based and disease-specific measures to drill down to specific facilities or conditions where readmission is more common, and to target interventions. What is the appropriate time interval for readmissions? Researchers have examined a variety of postdischarge time intervals for measuring readmission rates. Shorter intervals (e.g., 7-day, 15-day, 30-day) place more emphasis on hospital-related factors because hospitals have the most direct effect on readmission during the initial stay and the immediate post-discharge period. In addition, most research suggests that a higher proportion of short term readmissions fall into the potentially preventable category. By contrast, longer intervals (e.g., 60-day, 90-day, 180-day) relate more to the effectiveness of care in outpatient settings.

16 INSIGHTS FOR IMPROVEMENT Research and performance measurement practice suggest that 30 days may be the sweet spot for balancing accountability among the discharging hospital, the transfer and the receiving community s practices or facilities. The 30-day interval may be especially suited to health plans because they are in a position to help coordinate transfers between primary care, home health or other support as the patient progresses through recovery and transitions back to living in the community. What readmissions matter? When analyzing readmissions, two types of hospital stays are of interest: the initial hospitalization, or index stay, and the subsequent readmission. All-cause and specific-cause measurement address types of cases that count in the denominator and the numerator. Most current measures (e.g., CMS HospitalCompare measures) focus on all-cause readmissions for specific-cause index conditions. For example, a patient admitted for a heart attack might be readmitted for something related to the heart attack, for something caused by the initial stay (e.g., staph infection) or for something unrelated, such as appendicitis. A measure that focuses only on specific-cause readmission for the heart attack would miss other readmissions that are actually related to the index hospitalization, and which a hospital or ambulatory care site with more effective care coordination might avoid. Measures that look at all-cause readmission for specific index diseases can still be useful for quality improvement interventions because they focus on a group of related conditions that hospitals or health plans can target with tailored interventions. Many factors that contribute to readmissions, such as problems in discharge planning, failure to reconcile medications and lack of post-discharge care, can be associated with any hospital stay. Thus, NCQA decided to include all-cause readmissions for all-cause index stays (with key exclusions). An important advantage, given the relatively small proportion of hospitalized commercial health plan patients, is that this approach includes a much larger number of admissions in the denominator and readmissions in the numerator. The all-cause approach has other advantages: the narrow diagnostic categories used in specific-cause readmission measures may lead to gaming the measure by avoiding use of the diagnostic codes that count towards readmissions. Specific-cause measurement in the health plan context can result in small-numbers problems for analysis a small change in the number of cases has a large effect on the measure because some conditions may be too rare and some health plans enrollment may be too low to have enough cases. This makes rates more susceptible to random variation and reduces the ability to make fair comparison across health plans. Can planned and unplanned readmissions be distinguished? Although the primary goal of controlling readmissions is to improve aspects of service delivery that lead to rehospitalization, sometimes doctors plan to readmit a patient; for example, to

17 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 9 provide chemotherapy or to perform a follow-up procedure or surgery after the patient gains strength at home. In these instances, readmission represents high-quality care. Ideally, we could distinguish between planned and unplanned readmissions and identify readmissions that are completely outside providers control but coding and available clinical information do not allow this distinction. Consultation with our MAP and other advisory panels did not identify an algorithm in the public domain that could be used freely by all health plans to classify admissions; consequently, NCQA elected not to exclude either type of readmission from the measure. How should risk adjustment be applied? Risk adjustment adjusts for some factors that are not under the control of the health care system, and thus levels the playing field across groups being compared. If one health plan has members whose health status makes readmission more likely (i.e., sicker members), it may not be fair to compare the plan directly with others without first accounting for the higher-risk group. Likewise, it may be harder to avoid readmission for a very complicated case, and comparison should consider that some plans will have more of these difficult-to-treat cases. The risk-adjustment process used by NCQA attempts to account for the ways in which plan populations may differ. Most HEDIS measures are not formally risk adjusted; they define a narrow patient population where some action is almost always appropriate, regardless of disease severity. In addition, performance results are reported separately by product line (commercial, Medicare, Medicaid), so people of similar age or socioeconomic status are compared only with each other. But for readmissions, we must look at other factors that might be associated with severity of illness, to adjust the comparison. Most measures focus on patient demographic factors and index hospital stay (IHS) attributes (e.g., whether surgery was performed; principal diagnosis for the index stay) and try to address coexisting health conditions because these can complicate recovery or postdischarge treatment. NCQA s readmission measure is no different in this regard. The primary issue is how to classify reasons for admission and the coexisting condition. NCQA relied on a standard method developed for and used by CMS for payment to Medicare Advantage plans, the Hierarchical Condition Categories (HCC) system, to account for different risks of readmission for patients with different diagnoses. This approach has the advantages of being transparent; being in the public domain; and being in widespread use by health plans in other measures and, for payment purposes, in Medicare. Use of SES in Risk Adjustment When considering including socioeconomic status (SES), which has been associated with higher readmission rates in some studies, NCQA s HEDIS expert panels cited some limitations and barriers. Health plans do not have a reliable way to identify and report information on SES, and attributing SES to each health plan is complicated and prone to measurement error. Adding SES might adjust away important differences in populations and imply that different levels of performance are acceptable for populations with differing SES. It is not clear how well socioeconomic factors can be overcome with additional focused intervention.

18 INSIGHTS FOR IMPROVEMENT NCQA s HEDIS Plan All-Cause Readmissions Measure After reviewing NCQA s measure specification development, testing and public comment results, NCQA approved the Plan All-Cause Readmissions measure as a first-year measure for HEDIS Organizations collect and are audited on firstyear measure results, although results are not publicly reported in the first year. As of 2012, the measure is in its second year of implementation and has been endorsed by the National Quality Forum. A description of the HEDIS 2012 measure specifications follows. Measure Description The NCQA HEDIS 2012 Plan All-Cause Readmissions measure uses administrative claims to determine the number and percentage of acute inpatient stays during the measurement year, for health plan members 18 years and older, that were followed by an acute readmission for any diagnosis within 30 days, the predicted probability of an acute readmission and the total variance. Data are reported in the following categories: Index Hospital Stay (IHS): An acute inpatient stay with a discharge on or between January 1 and December 1 of the measurement year. Acute inpatient stays include non-maternity general medical and surgical hospital stays where the patient is discharged alive to a community setting. Index Admission Date: The IHS admission date. Index Discharge Date: The IHS discharge date. The index discharge date must occur on or between January 1 and December 1 of the measurement year. Index Readmission Stay: An acute inpatient stay for any diagnosis with an admission date within 30 days of a previous index discharge date. Index Readmission Date: The admission date associated with the index readmission stay. 1. Count of Index Hospital Stays (denominator) 2. Count of 30-Day Readmissions (numerator). 3. Average Adjusted Probability of Readmission. 4. Observed Readmission (Numerator/ Denominator). 5. Total Variance.

19 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 11 Measure Details Denominator: All acute inpatient stays with a discharge date on or between January 1 and December 1 of the measurement year for commercial health plan members years of age as of the index discharge date, and for Medicare and Special Needs Plan (SNP) members 18 years and older as of the index discharge date. Members must have been continuously enrolled in the plan for 365 days prior to the discharge date through 30 days after the discharge date. There may not be more than one gap of 45 days or less within the 365 days prior to the discharge date, and no gap during the 30 days following the discharge date. Exclusions are made for maternity related stays; admission to a long-term care facility, including long-term, nonacute hospitals, rehabilitation facilities and nursing homes; if the patient died during the admission or readmission stay or for admissions where the index admission date is the same as the index discharge date. The denominator of the measure is based on discharges rather than on members. Numerator: At least one acute readmission for any diagnosis, with an admission date on or between January 2 and December 31 of the measurement year, within 30 days of the index discharge date. Acute inpatient discharges with a principal diagnosis for codes that identify maternity-related inpatient stays are excluded. Risk Adjustment: To allow fair comparison among plans, the measure is risk adjusted via indirect standardization, using predicted probabilities of readmission estimated through logistic regression. That is, we determine for each health plan what we would expect its readmission rate to be based on and how the average health plan would have treated those cases. Risk adjustment is applied by assigning a weight to each IHS, based on the presence of surgery, discharge condition, comorbidity, age and gender. The Clinical Conditions (CC) and HCC categories identify comorbidities and attach weights to each statistically significant comorbidity by product line (i.e., commercial, Medicare) and age group. Weights were developed separately for each product line using a testing database that includes members from multiple health plans. Reporting: Each health plan reports the following five metrics to NCQA separately, by gender and age group: 1. The number of IHS (denominator). 2. The number of IHS with a subsequent 30-day readmission (numerator). 3. The observed rate of readmission (numerator/ denominator): The observed proportion of IHS that had a subsequent 30-day readmission. 4. The expected rate of readmission (or average adjusted probability of readmission): The rate of admission the plan is expected to have, given its case mix and how health plans across the nation treat similar cases. The expected rate of readmission uses the risk adjustment elements described above to assign weights to each admission. Admissions and their associated weights are aggregated by age, gender, product line and health plan to arrive at an average adjusted probability. 5. The total variance in readmission across cases, in gender and age groups. NCQA uses the total variance numbers to calculate the confidence intervals. NCQA then constructs the observed-to-expected (O/E) ratio for the plan as a whole across all age and gender groups. In 2012, NCQA will publicly report the O/E ratio for each plan for commercial populations years of age and for Medicare populations 65 years and older.* NCQA will also report confidence intervals about the O/E ratio, based on the total variance information submitted by plans. *Medicare plans will submit data for enrollees aged 18 64, but NCQA is using 2012 as an evaluation year to evaluate that the measure performs properly for this age group before reporting publicly (as early as 2013).

20 INSIGHTS FOR IMPROVEMENT Results from the First Year of Measuring Readmissions During 2011, the first year of reporting, health plans collected and reported the measure to NCQA. NCQA then calculated results but publicly reported only national and regional averages. Not reporting plan-level performance during the first year helps NCQA assess whether a measure is feasible and allows health plans time to prepare and develop quality improvement strategies. The results presented below are for commercial health plans, members 18 64, and for Medicare Advantage health plans, members 65 and older. National Results First-year data collection of the readmission measure demonstrated strong stakeholder interest and established that the measure could be reasonably collected, reported and audited on a national level. NCQA was also able to validate the measure s soundness because the first-year results were in accordance with field-test results in terms of variation and the potential for improvement. In addition, NCQA learned that the reference data set used to apply risk adjustment could be made more robust. For future implementation, an additional year s worth of data will be used in order to predict readmissions among the elderly more accurately. Refer to Data limitations. High Rates of Participation In the first year of data collection, more than 95 percent of Medicare Advantage plans (n=424) and 75 percent of commercial plans (n=315) reported the measure (the higher Medicare participation rate Observed Rate of Readmission: The observed proportion of IHS that had a subsequent 30-day readmission. A lower observed rate of readmission is considered better performance. Expected Rate of Readmission: Also called average adjusted probability of readmission. A plan s expected admission rate, given its case mix and how plans across the nation treat similar cases, and what we know about its members age, gender, disease burden and aspects of the index admission (e.g., surgical vs. medical). Members with more complex or greater disease burden tend to have a higher risk of readmission; therefore, the expected rate gives a sense of the severity of cases for the health plan. Observed-to-Expected Ratio: The ratio of the plan s observed rate of readmission to its expected rate of readmission (average adjusted probability). The O/E ratio for a single plan tells us whether the plan had less or more 30-day readmissions than predicted by NCQA s testing model. When the ratio is <1.0, the health plan performed better than expected (as predicted by the model); when the ratio is >1.0, the plan performed worse than expected. resulted from the CMS requirement that Medicare plans report the measure). Both are high rates of participation for a new HEDIS measure, which indicates strong interest and that the measure is feasible to report.

21 REDUCING READMISSIONS: MEASURING HEALTH PLAN PERFORMANCE 13 Table A: First-year results for commercial health plans (ages 18 64) and Medicare health plans (ages 65 and older). Percentiles Product Line Nplans Mean 90th 75th 50th 25th 10th Commercial Observed rate % 7.6% 7.9% 8.3% 8.7% 9.3% Expected rate % 7.6% 8.7% 9.1% 9.4% 10.0% O/E ratio Medicare Observed rate % 11.9% 13.0% 13.9% 15.3% 16.2% Expected rate % 13.8% 14.6% 15.6% 16.4% 17.4% O/E ratio NOTE: Commercial and Medicare results should not be compared; the models are based on different datasets. Observed and Expected Rates Showed Variation Among Commercial Plans As shown in Table A, about 8 percent of index hospitalizations in commercial health plans had at least one subsequent readmission within 30 days. The difference between the 90th and 10th percentiles reveals variation among commercial health plans, with 80 percent of plans reporting readmission rates between 7.6 percent and 9.3 percent. This compares with expected rates (based on NCQA s predictive model) of 9 percent and a range of 7.6 percent 10.0 percent. The expected rate indicates what the plan s rate would be, given what we know about its members age, gender, disease burden and aspects of the index admission (e.g., surgical vs. medical). Members with more complex or greater disease burden tend to have a higher risk of readmission; therefore, the expected rate gives a sense of the severity of the health plan s cases. testing model. When the ratio is less than 1.0, the health plan performed better than expected, because the observed rate of readmission, which indicates the actual readmission rate of a health plan, is lower than expected. Similarly, a value greater than 1.0 indicates worse-than-expected performance. The O/E ratio shows that commercial health plans performed slightly less than expected (i.e., better), based on NCQA s predictive model. The average O/E ratio was 0.935, or about 7 percent less than in NCQA s reference datasets. However, the range of performance Commercial Plans Mean Performance Slightly Less (i.e., Better) Than Expected; O/E Range of Performance Larger Than Expected The observed-to-expected (O/E) ratio for a single health plan tells us whether the plan had less or more 30-day readmissions than predicted by NCQA s

22 INSIGHTS FOR IMPROVEMENT was much greater, with some plans doing nearly 18 percent less (i.e., better) than expected (O/E of 0.822) and some as much as 13 percent more (i.e., worse) than expected (O/E of 1.13). Medicare Plans Mean Performance Less (i.e., Better) Than Expected; O/E Range Slightly Larger Than Expected Medicare health plans reported an average 30-day readmission rate of 14 percent, which is comparable to rates reported in Medicare Advantage elsewhere 33 and superior to the nearly 20 percent rate observed in fee-for-service (FFS) Medicare percent of plans reported 30-day readmission rates of between 12 percent and 16 percent; in fact, 90 percent of health plans reported a readmission rate that was less (i.e., better) than the average in the FFS Medicare program (19.6 percent). 2 Better performance among MA plans may reflect better discharge planning or more favorable case mix relative to FFS; we cannot currently determine how much of the difference is attributable to each cause. The expected rate of performance for MA plans, based on NCQA s predictive model, was about 15 percent, with a range of 13.8 percent 17.4 percent. The average O/E ratio for MA plans that reported in the first year was about 9 percent less (i.e., better) than expected (O/E of 0.909) based on NCQA s predictive model, but with a range of 21 percent (O/E of 0.783) less than to 4 percent (O/E of 1.043) more than expected. Regional Results NCQA calculated results for each of the 10 U.S. Department of Health and Human Services (HHS) regions in the country. Each region generally consists of several states. For example, region 1 (Boston) consists of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont. For additional information on HHS regions, see Commercial Participation Varies by Region As displayed in Table B, the Chicago region (n=57) had the greatest number of health plans, and Denver (n=15) and Seattle (n=18) had the fewest to report in the first year. Slight Variation in Observed Rate Performance Seattle (7.8 percent) and Denver (8.0 percent) had the lowest observed 30-day readmission rates, while Kansas City (8.7 percent) and Philadelphia (8.7 percent) had the highest average rates. In terms of assessing variability in performance, the Denver region had the least variation among health plan (0.9 percent difference) and the Chicago region had the greatest variation in performance between plans (2.5 percent difference). A higher expected rate gives us a sense of the severity of cases by region. The Atlanta region had the lowest expected 30-day readmission rate (8.5 percent), and Boston had the highest rate (9.4 percent). Examining the difference between the 90th and 10th percentiles of performance shows the San Francisco region with the least variation among health plans (0.8 percent difference), and Boston with the most variation (3.3 percent difference). Commercial Plans Perform Less (i.e., Better) Than Expected Seattle-region plans had the best average performance (O/E ratio = 0.844), or about 15 percent less (i.e., better) than expected. Kansas City plans had the worst (i.e., highest) O/E ratio, with about half a percent more readmissions than expected. Assessing variability in performance shows the Philadelphia region with the least variation among health plans (0.125 difference) and Boston with the most variation (0.451 difference).