Using the Kitagawa Decomposition to Measure Overall-and Individual Facility Contributions to-Within-facility and Between-facility Differences: Analyzing Racial and Ethnic Wait Time Disparities in the Veterans Health Administration.

BACKGROUND: Identifying whether differences in health care disparities are due to within-facility or between-facility differences is key to disparity reductions. The Kitagawa decomposition divides the difference between 2 means into within-facility differences and between-facility differences that are measured on the same scale as the original disparity. It also enables the identification of facilities that contribute most to within-facility differences (based on facility-level disparities and the proportion of patient population served) and between-facility differences. OBJECTIVES: Illustrate the value of a 2-stage Kitagawa decomposition to partition a disparity into within-facility and between-facility differences and to measure the contribution of individual facilities to each type of difference. SUBJECTS: Veterans receiving a new outpatient consult for cardiology or orthopedic services during fiscal years 2019-2021. MEASURES: Wait time for a new-patient consult. METHODS: In stage 1, we predicted wait time for each Veteran from a multivariable model; in stage 2, we aggregated individual predictions to determine mean adjusted wait times for Hispanic, Black, and White Veterans and then decomposed differences in wait times between White Veterans and each of the other groups. RESULTS: Noticeably longer wait times were experienced by Hispanic Veterans for cardiology (2.32 d, 6.8% longer) and Black Veterans for orthopedics (3.49 d, 10.3% longer) in both cases due entirely to within-facility differences. The results for Hispanic Veterans using orthopedics illustrate how positive within-facility differences (0.57 d) can be offset by negative between-facility differences (-0.34 d), resulting in a smaller overall disparity (0.23 d). Selecting 10 facilities for interventions in orthopedics based on the largest contributions to within-in facility differences instead of the largest disparities resulted in a higher percentage of Veterans impacted (31% and 12% of Black and White Veterans, respectively, versus 9% and 10% of Black and White Veterans, respectively) and explained 21% of the overall within-facility difference versus 11%. CONCLUSIONS: The Kitagawa approach allows the identification of disparities that might otherwise be undetected. It also allows the targeting of interventions at those facilities where improvements will have the largest impact on the overall disparity.

Comparing Postoperative Readmission Rates Between Veterans Receiving Total Knee Arthroplasty in the Veterans Health Administration Versus Community Care.

BACKGROUND: There are growing concerns that Veterans' increased use of Veterans Health Administration (VA)-purchased care in the community may lead to lower quality of care. OBJECTIVE: We compared rates of hospital readmissions following elective total knee arthroplasties (TKAs) that were either performed in VA or purchased by VA through community care (CC) at both the national and facility levels. METHODS: Three-year cohort study using VA and CC administrative data from the VA's Corporate Data Warehouse (October 1, 2016-September 30, 2019). We obtained Medicare data to capture readmissions that were paid by Medicare. We used the Centers for Medicare and Medicaid Services (CMS) methods to identify unplanned, 30-day, all-cause readmissions. A secondary outcome, TKA-related readmissions, identified readmissions resulting from complications of the index surgery. We ran mixed-effects logistic regression models to compare the risk-adjusted odds of all-cause and TKA-related readmissions between TKAs performed in VA versus CC, adjusting for patients' sociodemographic and clinical characteristics. PRINCIPAL FINDINGS: Nationally, the odds of experiencing an all-cause or TKA-related readmission were significantly lower for TKAs performed in VA versus CC (eg, the odds of experiencing an all-cause readmission in VA were 35% of those in CC. At the facility level, most VA facilities performed similarly to their corresponding CC providers, although there were 3 VA facilities that performed worse than their corresponding CC providers. CONCLUSIONS: Given VA's history in providing high-quality surgical care to Veterans, it is important to closely monitor and track whether the shift to CC for surgical care will impact quality in both settings over time.

Is the Availability of Direct-Acting Antivirals Associated with Increased Access to Hepatitis C Treatment for Homeless and Unstably Housed Veterans?

OBJECTIVE: Hepatitis C virus (HCV) treatment has experienced a rapid transformation in the USA. New direct-acting antiviral (DAA) medications make treatment easier, less toxic, and more successful (90% or greater viral cure) than prior, interferon-based HCV medications. We sought to determine whether DAAs may have improved access to HCV treatment for hard-to-reach populations such as the homeless. METHODS: In a retrospective study of VA electronic medical record data, a cohort was created of 63,586 veterans with a positive HCV RNA or genotype test taken at any point from January 1, 2012, through December 31, 2016. Patient data were examined for up to 5 years using a discrete time survival model to assess the relationship between their housing status and receipt of HCV medications in 6-month time periods in both the interferon and DAA eras. RESULTS: In the interferon era, the probability of HCV treatment in a given 6-month window among housed veterans, at 6.2% (95% CI: 5.3-7.1%) was significantly higher than among veterans who were homeless or unstably housed; for example, among currently homeless veterans, the probability of treatment initiation, in a given 6-month window, was 2.6% (95% CI: 1.9-3.3%). With the arrival of DAAs, each housing category had an increased probability of treatment initiation. For housed veterans, the probability was 8.6% (95% CI: 8.3-8.9%) while for currently homeless veterans, it was 6.3% (95% CI: 5.7-6.9%). CONCLUSIONS: We found a clear indication that the likelihood of treatment initiation was greater for all veterans in the DAA era as compared to the interferon era. However, disparities in treatment initiation rates between housed and homeless veterans that were observed in the interferon era persisted in the DAA era.

Did Access to Care Improve Since Passage of the Veterans Choice Act?: Differences Between Rural and Urban Veterans.

BACKGROUND: The 2014 Veterans Choice Program aimed to improve care access for Veterans through expanded availability of community care (CC). Increased access to CC could particularly benefit rural Veterans, who often face obstacles in obtaining medical care at the Veterans Health Administration (VHA). However, whether Veterans Choice Program improved timely access to care for this vulnerable population is understudied. OBJECTIVES: To examine wait times among rural and urban Veterans for 5 outpatient specialty care services representing the top requests for CC services among rural Veterans. RESEARCH DESIGN: Retrospective study using VHA and CC outpatient consult data from VHA's Corporate Data Warehouse in Fiscal Year (FY) 2015 (October 1, 2014 to September 30, 2015) and FY2018 (October 1, 2017 to September 30, 2018). SUBJECTS: All Veterans who received a new patient consult for physical therapy, cardiology, optometry, orthopedics, and/or dental services in VHA and/or CC. MEASURES: Wait time, care setting (VHA/CC), rural/urban status, sociodemographics, and comorbidities. RESULTS: Our sample included 1,112,876 Veterans. Between FY2015 and FY2018, mean wait times decreased for all services for both rural and urban Veterans; declines were greatest in VHA (eg, mean optometry wait times for rural Veterans in VHA vs. CC declined 8.3 vs. 6.4 d, respectively, P<0.0001). By FY2018, for both rural and urban Veterans, CC mean wait times for most services were longer than VHA wait times. CONCLUSIONS: Timely care access for all Veterans improved between FY15 and FY18, particularly in VHA. As expansion of CC continues under the MISSION Act, more research is needed to evaluate quality of care across VHA and CC and what role, if any, wait times play.

Rural Veterans' Experiences With Outpatient Care in the Veterans Health Administration Versus Community Care.

BACKGROUND: The 2014 Veterans Access, Choice and Accountability Act was intended to improve Veterans' access to timely health care by expanding their options to receive community care (CC) paid for by the Veterans Health Administration (VA). Although CC could particularly benefit rural Veterans, we know little about rural Veterans' experiences with CC. OBJECTIVE: The objective of this study was to compare rural Veterans' experiences with CC and VA outpatient health care services to those of urban Veterans and examine changes over time. RESEARCH DESIGN: Retrospective, cross-sectional study using data from the Survey of Healthcare Experiences of Patients (SHEP) and VA Corporate Data Warehouse. Subjects: All Veterans who responded to the SHEP survey in Fiscal Year (FY) 16 or FY19. MEASURES: Outcomes were 4 measures of care experience (Access, Communication, Coordination, and Provider Rating). Independent variables included care setting (CC/VA), rural/urban status, and demographic and clinical characteristics. RESULTS: Compared with urban Veterans, rural Veterans rated CC the same (for specialty care) or better (for primary care). Rural Veterans reported worse experiences in CC versus VA, except for specialty care Access. Rural Veterans' care experiences improved between FY16 and FY19 in both CC and VA, with greater improvements in CC. CONCLUSIONS: Rural Veterans' reported comparable or better experiences in CC compared with urban Veterans, but rural Veterans' CC experiences still lagged behind their experiences in VA for primary care. As growing numbers of Veterans use CC, VA should ensure that rural and urban Veterans' experiences with CC are at least comparable to their experiences with VA care.

Comparing Driving Miles for Department of Veterans Affairs-delivered Versus Department of Veterans Affairs-purchased Cataract Surgery.

BACKGROUND: The Veterans Choice Act of 2014 increased the number of Veterans eligible for Department of Veterans Affairs (VA)-purchased care delivered in non-VA community care (CC) facilities. Driving >40 miles from home to a VA facility is a key eligibility criterion for CC. It remains unclear whether this policy change improved geographical access by reducing drive distance for Veterans. OBJECTIVES: Describe the driving distance for Veterans receiving cataract surgery in VA and CC facilities, and if they visited the closest-to-home facility or if they drove to farther facilities. SUBJECTS: Veterans who had cataract surgery in federal fiscal year 2015. MEASURES: We calculated driving miles to the Closest VA and CC facilities that performed cataract surgeries, and to the location where Veterans received care. RESULTS: A total of 61,746 Veterans received 83,875 cataract surgeries. More than 50% of CC surgeries occurred farther than the Closest CC facility providing cataract surgery (median Closest CC facility 8.7 miles vs. Actual CC facility, 19.7 miles). Most (57%) Veterans receiving cataract surgery at a VA facility used the Closest VA facility (median Closest VA facility 28.1 miles vs. Actual VA facility at 31.2 miles). In all, 26.1% of CC procedures occurred in facilities farther away than the Closest VA facility. CONCLUSIONS: Although many Veterans drove farther than needed to get cataract surgery in CC, this was not true for obtaining care in the VA. Our findings suggest that there may be additional reasons, besides driving distance, that affect whether Veterans choose CC and, if they do, where they seek CC.

How hospitals select their patient safety priorities: An exploratory study of four Veterans Health Administration hospitals.

BACKGROUND: Hospitals face ongoing pressure to reduce patient safety events. However, given resource constraints, hospitals must prioritize their safety improvements. There is limited literature on how hospitals select their safety priorities. PURPOSE: The aim of this research was to describe and compare the approaches used by Veterans Health Administration (VA) hospitals to select their safety priorities. METHODOLOGY: Semistructured telephone interviews with key informants (n = 16) were used to collect data on safety priorities in four VA hospitals from May to December 2016. We conducted a directed content analysis of the interview notes using an organizational learning perspective. We coded for descriptive data on the approaches (e.g., set of cues, circumstances, and activities) used to select safety priorities, a priori organizational learning capabilities (learning processes, learning environment, and learning-oriented leadership), and emergent domains. For cross-site comparisons, we examined the coded data for patterns. RESULTS: All hospitals used multiple approaches to select their safety priorities; these approaches used varied across hospitals. Although no single approach was reported as particularly influential, all hospitals used approaches that addressed system level or national requirements (i.e., externally required activities). Additional approaches used by hospitals (e.g., responding to staff concerns of patient safety issues, conducting a multidisciplinary team investigation) were less connected to externally required activities and demonstrated organizational learning capabilities in learning processes (e.g., performance monitoring), learning environment (e.g., staff's psychological safety), and learning-oriented leadership (e.g., establishing a nonpunitive culture). PRACTICE IMPLICATIONS: Leaders should examine the approaches used to select safety priorities and the role of organizational learning in these selection approaches. Exclusively relying on approaches focused on externally required activities may fail to identify safety priorities that are locally relevant but not established as significant at the system or national levels. Organizational learning may promote hospitals' use of varied approaches to guide their selection of safety priorities and thereby benefit hospital safety improvement efforts.

One-year costs of medical admissions with and without a 30-day readmission and enhanced risk adjustment.

BACKGROUND: To overcome the limitations of administrative data in adequately adjusting for differences in patients' risk of readmissions, recent studies have added supplemental data from patient surveys and other sources (e.g., electronic health records). However, judging the adequacy of enhanced risk adjustment for use in assessment of 30-day readmission as a hospital quality indicator is not straightforward. In this paper, we evaluate the adequacy of risk adjustment by comparing the one-year costs of those readmitted within 30 days to those not after excluding the costs of the readmission. METHODS: In this two-step study, we first used comprehensive administrative and survey data on a nationally representative Medicare cohort of hospitalized patients to compare patients with a medical admission who experienced a 30-day readmission to patients without a readmission in terms of their overall Medicare payments during 12 months following the index discharge. We then examined the extent to which a series of enhanced risk adjustment models incorporating code-based comorbidities, self-reported health status and prior healthcare utilization, reduced the payment differences between the admitted and not readmitted groups. RESULTS: Our analytic cohort consisted 4684 index medical hospitalization of which 842 met the 30-day readmission criteria. Those readmitted were more likely to be older, White, sicker and with higher healthcare utilization in the previous year. The unadjusted subsequent one-year Medicare spending among those readmitted ($56,856) was 60% higher than that among the non-readmitted ($35,465). Even with enhanced risk adjustment, and across a variety of sensitivity analyses, one-year Medicare spending remained substantially higher (46.6%, p < 0.01) among readmitted patients. CONCLUSIONS: Enhanced risk adjustment models combining health status indicators from administrative and survey data with previous healthcare utilization are unable to substantially reduce the cost differences between those medical admission patients readmitted within 30 days and those not. The unmeasured patient severity that these cost differences most likely reflect raises the question of the fairness of programs that place large penalties on hospitals with higher than expected readmission rates.

Staff-perceived Conflict between Resident-centered Care and Quality in the Skilled Nursing Facility: Are Both Possible?

OBJECTIVES: The purpose of this study was to understand if and how Veterans Affairs (VA) nursing home (CLC) staff experience difficulty in providing care that is both resident-centered (RCC) and concordant with quality standards. METHODS: Twelve VA CLCs were selected for site visits, stratified based on rankings on a composite quality measure (calculated from various indicators) and resident-centered care (RCC) progress (based on a culture change tool). Staff were interviewed about efforts and barriers to achieving goals in RCC and quality, and the interview transcripts systematically analyzed for themes. RESULTS: We interviewed 141 participants, including senior leaders, middle managers, and front-line staff. An emergent theme was conflict between RCC and quality, although participants varied in their perceptions of its impact. Participants perceived three conflict types: 1) between resident preferences and medically indicated actions; 2) between resident preferences and the needs or safety of others; and 3) limits of staff time or authority. CONCLUSIONS: CLC staff perceive conflicts between RCC and care consistent with quality imperatives. CLINICAL IMPLICATIONS: Variation in perceived RCC-quality conflicts suggests that policy clarifications and additional training may provide guidance in dealing with such dilemmas. It may be prudent to clearly communicate to what boundaries exist to RCC in the evolving CLC environment.

Characteristics of State Policies Impact Health Care Delivery: An Analysis of Mammographic Dense Breast Notification and Insurance Legislation.

BACKGROUND: Increased breast tissue density may mask cancer and thus decrease the diagnostic sensitivity of mammography. A patient group advocacy led to the implementation of laws to increase the awareness of breast tissue density and to improve access to supplemental imaging in many states. Given limited evidence about best practices, variation exists in several characteristics of adopted policies. OBJECTIVE: To identify which characteristics of state-level policies with regard to dense breast tissue were associated with increased use of downstream breast ultrasound. RESEARCH DESIGN: This was a retrospective series of monthly cross-sections of screening mammography procedures before and after implementation of laws. SUBJECTS: A sample of 13,481,554 screening mammography procedures extracted from the MarketScan Research database performed between 2007 and 2014 on privately insured women aged 40-64 years that resided in a state that had implemented relevant legislation during that period. MEASURES: The outcome was an indicator of whether breast ultrasound imaging followed a screening mammography procedure within 30 days. The main independent variables were policy characteristics indicators. RESULTS: Notification of patients about issues surrounding increased breast density was associated with increased follow-up by ultrasound by 1.02 percentage points (P=0.016). Some policy characteristics such as the explicit suggestion of supplemental imaging or mandated coverage of supplemental imaging by health insurance augmented that effect. Other policy characteristics moderated the effect. CONCLUSIONS: The heterogeneous effect of state legislation with regard to dense breast tissue on screening mammography follow-up by ultrasound may be explained by specific and unique characteristics of the approaches taken by a variety of states.

Tailoring an educational program on the AHRQ Patient Safety Indicators to meet stakeholder needs: lessons learned in the VA.

BACKGROUND: Given that patient safety measures are increasingly used for public reporting and pay-for performance, it is important for stakeholders to understand how to use these measures for improvement. The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) are one particularly visible set of measures that are now used primarily for public reporting and pay-for-performance among both private sector and Veterans Health Administration (VA) hospitals. This trend generates a strong need for stakeholders to understand how to interpret and use the PSIs for quality improvement (QI). The goal of this study was to develop an educational program and tailor it to stakeholders' needs. In this paper, we share what we learned from this program development process. METHODS: Our study population included key VA stakeholders involved in reviewing performance reports and prioritizing and initiating quality/safety initiatives. A pre-program formative evaluation through telephone interviews and web-based surveys assessed stakeholders' educational needs/interests. Findings from the formative evaluation led to development and implementation of a cyberseminar-based program, which we tailored to stakeholders' needs/interests. A post-program survey evaluated program participants' perceptions about the PSI educational program. RESULTS: Interview data confirmed that the concepts we had developed for the interviews could be used for the survey. Survey results informed us on what program delivery mode and content topics were of high interest. Six cyberseminars were developed-three of which focused on two content areas that were noted of greatest interest: learning how to use PSIs for monitoring trends and understanding how to interpret PSIs. We also used snapshots of VA PSI reports so that participants could directly apply learnings. Although initial interest in the program was high, actual attendance was low. However, post-program survey results indicated that perceptions about the program were positive. CONCLUSIONS: Conducting a formative evaluation was a highly important process in program development. The useful information that we collected through the interviews and surveys allowed us to tailor the program to stakeholders' needs and interests. Our experiences, particularly with the formative evaluation process, yielded valuable lessons that can guide others when developing and implementing similar educational programs.

Is Variation in Resident-Centered Care and Quality Performance Related to Health System Factors in Veterans Health Administration Nursing Homes?

The purpose of this research was to explore and compare common health system factors for 5 Community Living Centers (ie Veterans Health Administration nursing homes) with high performance on both resident-centered care and clinical quality and for 5 Community Living Centers (CLC) with low performance on both resident-centered care and quality. In particular, we were interested in "how" and "why" some Community Living Centers were able to deliver high levels of resident-centered care and high quality of care, whereas others did not demonstrate this ability. Sites were identified based on their rankings on a composite quality measure calculated from 28 Minimum Data Set version 2.0 quality indicators and a resident-centered care summary score calculated from 6 domains of the Artifacts of Culture Change Tool. Data were from fiscal years 2009-2012. We selected high- and low-performing sites on quality and resident-centered care and conducted 12 in-person site visits in 2014-2015. We used systematic content analysis to code interview transcripts for a priori and emergent health system factor domains. We then assessed variations in these domains across high and low performers using cross-site summaries and matrixes. Our final sample included 108 staff members at 10 Veterans Health Administration CLCs. Staff members included senior leaders, middle managers, and frontline employees. Of the health system factors identified, high and low performers varied in 5 domains, including leadership support, organizational culture, teamwork and communication, resident-centered care recognition and awards, and resident-centered care training. Organizations must recognize that making improvements in the factors identified in this article will require dedicated resources from leaders and support from staff throughout the organization.

Can Composite Measures Provide a Different Perspective on Provider Performance Than Individual Measures?

BACKGROUND: Composite measures, which aggregate performance on individual measures into a summary score, are increasingly being used to evaluate facility performance. There is little understanding of the unique perspective that composite measures provide. OBJECTIVE: To examine whether high/low (ie, high or low) performers on a composite measures are also high/low performers on most of the individual measures that comprise the composite. METHODS: We used data from 2 previous studies, one involving 5 measures from 632 hospitals and one involving 28 measures from 112 Veterans Health Administration (VA) nursing homes; and new data on hospital readmissions for 3 conditions from 131 VA hospitals. To compare high/low performers on a composite to high/low performers on the component measures, we used 2-dimensional tables to categorize facilities into high/low performance on the composite and on the individual component measures. RESULTS: In the first study, over a third of the 162 hospitals in the top quintile based on the composite were in the top quintile on at most 1 of the 5 individual measures. In the second study, over 40% of the 27 high-performing nursing homes on the composite were high performers on 8 or fewer of the 28 individual measures. In the third study, 20% of the 61 low performers on the composite were low performers on only 1 of the 3 individual measures. CONCLUSIONS: Composite measures can identify as high/low performers facilities that perform "pretty well" (or "pretty poorly") across many individual measures but may not be high/low performers on most of them.

Trends in the Purchase of Surgical Care in the Community by the Veterans Health Administration.

BACKGROUND: The 2014 implementation of the Veterans Choice Program increased opportunities for Veterans to receive care in the community. Although surgical care is a Veterans Health Administration (VHA) priority, little is known about the types of surgeries provided in the VHA versus those referred to community care (CC), and whether Veterans are increasing their use of surgical care through CC with these additional opportunities. OBJECTIVES: To examine national trends across VHA facilities in the frequencies and types of surgeries provided in the VHA and through CC, and explore the association between facilities' purchase of care with rurality and surgical complexity designation. RESEARCH DESIGN: Retrospective study using Veterans Administration (VA) outpatient and CC data from the VA's Corporate Data Warehouse (October 1, 2013-September 30, 2016). MEASURES: Veterans' demographics, outpatient surgeries, facility rurality, and surgical complexity. RESULTS: Our sample included 525,283 outpatient surgeries; 79% occurred in the VHA over the study timeframe. The proportion of CC surgeries increased from 16% in October 2013 to 29% in December 2014, and then subsequently declined, leveling off at 21% in June 2016 (trend, P<0.05). These trends varied by surgery type. Increases in CC surgeries were evident for 4 surgery types: cardiovascular, digestive, eye and ocular, and male genital surgeries (all trends, P<0.05). Rural and low-complexity facilities were more likely to purchase surgical CC than their urban and high-complexity counterparts (P<0.0001). CONCLUSIONS: Although the VHA remains the primary provider of surgical care for Veterans, Veterans Choice Program implementation increased Veterans' use of CC relative to the VHA for certain types of surgeries, potentially bringing challenges to the VHA in delivering and coordinating surgical care across settings.

Does Use of a Hospital-wide Readmission Measure Versus Condition-specific Readmission Measures Make a Difference for Hospital Profiling and Payment Penalties?

BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) use public reporting and payment penalties as incentives for hospitals to reduce readmission rates. In contrast to the current condition-specific readmission measures, CMS recently developed an all-condition, 30-day all-cause hospital-wide readmission measure (HWR) to provide a more comprehensive view of hospital performance. OBJECTIVES: We examined whether assessment of hospital performance and payment penalties depends on the readmission measure used. RESEARCH DESIGN: We used inpatient data to examine readmissions for patients discharged from VA acute-care hospitals from Fiscal Years 2007-2010. We calculated risk-standardized 30-day readmission rates for 3 condition-specific measures (heart failure, acute myocardial infarction, and pneumonia) and the HWR measure, and examined agreement between the HWR measure and each of the condition-specific measures on hospital performance. We also assessed the effect of using different readmission measures on hospitals' payment penalties. RESULTS: We found poor agreement between the condition-specific measures and the HWR measure on those hospitals identified as low or high performers (eg, among those hospitals classified as poor performers by the heart failure readmission measure, only 28.6% were similarly classified by the HWR measure). We also found differences in whether a hospital would experience payment penalties. The HWR measure penalized only 60% of those hospitals that would have received penalties based on at least 1 of the condition-specific measures. CONCLUSIONS: The condition-specific measures and the HWR measure provide a different picture of hospital performance. Future research is needed to determine which measure aligns best with CMS's overall goals to reduce hospital readmissions and improve quality.

Composite Measures of Health Care Provider Performance: A Description of Approaches.

CONTEXT: Since the Institute of Medicine's 2001 report Crossing the Quality Chasm, there has been a rapid proliferation of quality measures used in quality-monitoring, provider-profiling, and pay-for-performance (P4P) programs. Al-though individual performance measures are useful for identifying specific processes and outcomes for improvement and tracking progress, they do not easily provide an accessible overview of performance. Composite measures aggregate individual performance measures into a summary score. By reducing the amount of data that must be processed, they facilitate (1) benchmarking of an organization's performance, encouraging quality improvement initiatives to match performance against high-performing organizations, and (2) profiling and P4P programs based on an organization's overall performance. METHODS: We describe different approaches to creating composite measures,discuss their advantages and disadvantages, and provide examples of their use. FINDINGS: The major issues in creating composite measures are (1) whether to aggregate measures at the patient level through all-or-none approaches or the facility level, using one of the several possible weighting schemes; (2) when combining measures on different scales, how to rescale measures (using z scores,range percentages, ranks, or 5-star categorizations); and (3) whether to use shrinkage estimators, which increase precision by smoothing rates from smaller facilities but also decrease transparency. CONCLUSIONS: Because provider rankings and rewards under P4P programs may be sensitive to both context and the data, careful analysis is warranted before deciding to implement a particular method. A better understanding of both when and where to use composite measures and the incentives created by composite measures are likely to be important areas of research as the use of composite measures grows.

A probability metric for identifying high-performing facilities: an application for pay-for-performance programs.

BACKGROUND: Two approaches are commonly used for identifying high-performing facilities on a performance measure: one, that the facility is in a top quantile (eg, quintile or quartile); and two, that a confidence interval is below (or above) the average of the measure for all facilities. This type of yes/no designation often does not do well in distinguishing high-performing from average-performing facilities. OBJECTIVE: To illustrate an alternative continuous-valued metric for profiling facilities--the probability a facility is in a top quantile--and show the implications of using this metric for profiling and pay-for-performance. METHODS: We created a composite measure of quality from fiscal year 2007 data based on 28 quality indicators from 112 Veterans Health Administration nursing homes. A Bayesian hierarchical multivariate normal-binomial model was used to estimate shrunken rates of the 28 quality indicators, which were combined into a composite measure using opportunity-based weights. Rates were estimated using Markov Chain Monte Carlo methods as implemented in WinBUGS. The probability metric was calculated from the simulation replications. RESULTS: Our probability metric allowed better discrimination of high performers than the point or interval estimate of the composite score. In a pay-for-performance program, a smaller top quantile (eg, a quintile) resulted in more resources being allocated to the highest performers, whereas a larger top quantile (eg, being above the median) distinguished less among high performers and allocated more resources to average performers. CONCLUSION: The probability metric has potential but needs to be evaluated by stakeholders in different types of delivery systems.

Medical and surgical readmissions in the Veterans Health Administration: what proportion are related to the index hospitalization?

BACKGROUND: Readmissions are an attractive quality measure because they offer a broad view of quality beyond the index hospitalization. However, the extent to which medical or surgical readmissions reflect quality of care is largely unknown, because of the complexity of factors related to readmission. Identifying those readmissions that are clinically related to the index hospitalization is an important first step in closing this knowledge gap. OBJECTIVES: The aims of this study were to examine unplanned readmissions in the Veterans Health Administration, identify clinically related versus unrelated unplanned readmissions, and compare the leading reasons for unplanned readmission between medical and surgical discharges. METHODS: We classified 2,069,804 Veterans Health Administration hospital discharges (Fiscal Years 2003-2007) into medical/surgical index discharges with/without readmissions per their diagnosis-related groups. Our outcome variable was "all-cause" 30-day unplanned readmission. We compared medical and surgical unplanned readmissions (n=217,767) on demographics, clinical characteristics, and readmission reasons using descriptive statistics. RESULTS: Among all unplanned readmissions, 41.5% were identified as clinically related. Not surprisingly, heart failure (10.2%) and chronic obstructive pulmonary disease (6.5%) were the top 2 reasons for clinically related readmissions among medical discharges; postoperative complications (ie, complications of surgical procedures and medical care or complications of devices) accounted for 70.5% of clinically related readmissions among surgical discharges. CONCLUSIONS: Although almost 42% of unplanned readmissions were identified as clinically related, the majority of unplanned readmissions were unrelated to the index hospitalization. Quality improvement interventions targeted at processes of care associated with the index hospitalization are likely to be most effective in reducing clinically related readmissions. It is less clear how to reduce nonclinically related readmissions; these may involve broader factors than inpatient care.

Racial and Ethnic and Rural Variations in Access to Primary Care for Veterans Following the MISSION Act.

Importance: The 2018 Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks (VA MISSION) Act was implemented to increase timely access to care by expanding veterans' opportunities to receive Veterans Affairs (VA)-purchased care in the community (community care [CC]). Because health equity is a major VA priority, it is important to know whether Black and Hispanic veterans compared with White veterans experienced equitable access to primary care within the VA MISSION Act. Objective: To examine whether utilization of and wait times for primary care differed between Black and Hispanic veterans compared with White veterans in rural and urban areas after the implementation of the VA MISSION Act. Design, Setting, and Participants: This cross-sectional study used VA and CC outpatient and consult data from the VA's Corporate Data Warehouse for fiscal years 2021 to 2022 (October 1, 2020, to September 30, 2022). Separate fixed-effects multivariable models were run to predict CC utilization and wait times. Each model was run twice, once comparing Black and White veterans and then comparing Hispanic and White veterans. Adjusted risk ratios (ARRs) were calculated for Black and Hispanic veterans compared with White veterans within rurality status for both outcomes. Main Outcomes and Measures: VA and CC primary care utilization as measured by primary care visits (utilization cohort); VA and CC primary care access as measured by mean wait times (access cohort). Results: A total of 5 046 087 veterans (994 517 [19.7%] Black, 390 870 [7.7%] Hispanic, and 3 660 700 [72.6%] White individuals) used primary care from fiscal years 2021 to 2022. Utilization increased for all 3 racial and ethnicity groups, more so in CC than VA primary care. ARRs were significantly less than 1 regardless of rurality status, indicating Black and Hispanic veterans compared with White veterans were less likely to utilize CC for primary care. There were 468 246 primary care consultations during the study period. The overall mean (SD) wait time was 33.3 (32.4) days. Despite decreases in wait times over time, primary care wait times remained longer in CC than in VA. Black veterans compared with White veterans had significantly longer wait times in CC (ARRs >1) but significantly shorter wait times in VA (ARRS <1) regardless of rurality status in VA and CC. CC wait times for Hispanic veterans compared with White veterans were longer in rural areas only and in VA rural and urban areas (ARRs >1). Conclusion and Relevance: The results of this cross-sectional study suggest that additional research should explore the determinants and implications of utilization differences among Black and Hispanic veterans compared with White veterans. Efforts to promote equitable primary care access for all veterans are needed so that policy changes can be more effective in ensuring timely access to care for all veterans.

Quality improvement lessons learned from National Implementation of the "Patient Safety Events in Community Care: Reporting, Investigation, and Improvement Guidebook".

OBJECTIVE: To evaluate nationwide implementation of a Guidebook designed to standardize safety practices across VA-delivered and VA-purchased care (i.e., Community Care) and identify lessons learned and strategies to improve them. DATA SOURCES AND STUDY SETTING: Qualitative data collected from key informants at 18 geographically diverse VA facilities across 17 Veterans Integrated Services Networks (VISNs). STUDY DESIGN: We conducted semi-structured interviews from 2019 to 2022 with VISN Patient Safety Officers (PSOs) and VA facility patient safety and quality managers (PSMs and QMs) and VA Facility Community Care (CC) staff to assess lessons learned by examining organizational contextual factors affecting Guidebook implementation based on the Consolidated Framework for Implementation Research (CFIR). DATA COLLECTION/EXTRACTION METHODS: Interviews were conducted virtually with 45 facility staff and 10 VISN PSOs. Using directed content analysis, we identified CFIR factors affecting implementation. These factors were mapped to the Expert Recommendations for Implementing Change (ERIC) strategy compilation to identify lessons learned that could be useful to our operational partners in improving implementation processes. We met frequently with our partners to discuss findings and plan next steps. PRINCIPAL FINDINGS: Six CFIR constructs were identified as both facilitators and barriers to Guidebook implementation: (1) planning for implementation; (2) engaging key knowledge holders; (3) available resources; (4) networks and communications; (5) culture; and (6) external policies. The two CFIR constructs that were only barriers included: (1) cosmopolitanism and (2) executing implementation. CONCLUSIONS: Our findings suggest several important lessons: (1) engage all collaborators involved in implementation; (2) ensure end-users have opportunities to provide feedback; (3) describe collaborators' purpose and roles/responsibilities clearly at the start; (4) communicate information widely and repeatedly; and (5) identify how multiple high priorities can be synergistic. This evaluation will help our partners and key VA leadership to determine next steps and future strategies for improving Guidebook implementation through collaboration with VA staff.