Unfulfilled and method-specific contraceptive preferences among reproductive-aged contraceptive users in Arizona, Iowa, New Jersey, and Wisconsin.

OBJECTIVE: To identify characteristics associated with unfulfilled contraceptive preferences, document reasons for these unfulfilled preferences, and examine how these unfulfilled preferences vary across specific method users. DATA SOURCES AND STUDY SETTING: We draw on secondary baseline data from 4660 reproductive-aged contraceptive users in the Arizona, Iowa, New Jersey, and Wisconsin Surveys of Women (SoWs), state-representative surveys fielded between October 2018 and August 2020 across the four states. STUDY DESIGN: This is an observational cross-sectional study, which examined associations between individuals' reproductive health-related experiences and contraceptive preferences, adjusting for sociodemographic characteristics. Our primary outcome of interest is having an unfulfilled contraceptive preference, and a key independent variable is experience of high-quality contraceptive care. We also examine specific contraceptive method preferences according to current method used, as well as reasons for not using a preferred method. DATA COLLECTION/EXTRACTION METHODS: Survey respondents who indicated use of any contraceptive method within the last 3 months prior to the survey were eligible for inclusion in this analysis. PRINCIPAL FINDINGS: Overall, 23% reported preferring to use a method other than their current method, ranging from 17% in Iowa to 26% in New Jersey. Young age (18-24), using methods not requiring provider involvement, and not receiving quality contraceptive care were key attributes associated with unfulfilled contraceptive preferences. Those using emergency contraception and fertility awareness-based methods had some of the highest levels of unfulfilled contraceptive preferences, while pills, condoms, partner vasectomy, and IUDs were identified as the most preferred methods. Reasons for not using preferred contraceptive methods fell largely into one of two buckets: system-level or interpersonal/individual reasons. CONCLUSIONS: Our findings highlight that avenues for decreasing the gap between contraceptive methods used and those preferred to be used may lie with healthcare providers and funding streams that support the delivery of contraceptive care.

Examining sources of post-acute care inequities with layered target matching.

OBJECTIVE: To examine factors associated with racial inequities in discharge location, skilled nursing facility (SNF) utilization, and readmissions. DATA SOURCES: A 20% sample of longitudinal Medicare claims from 2016 to 2018. STUDY DESIGN: We present layered target matching, a method for studying sources of inequities. Layered target matching examines a fixed target population profile representing any race, ethnicity, or vulnerable population, sequentially adjusting for sets of characteristics that may contribute to inequities these groups endure. We use the method to study racial inequities in post-acute care use and readmissions. DATA COLLECTION/EXTRACTION METHODS: We studied Black and non-Hispanic White fee-for-service Medicare beneficiaries aged 66+ admitted to short-term acute-care hospitals for qualifying diagnoses or procedures between January 1, 2016 and November 30, 2018. PRINCIPAL FINDINGS: Admitted Black patients tended to be younger, had significantly higher rates of risk factors such as diabetes, stroke, or renal disease, and were much more frequently admitted to large or academic hospitals. Relative to demographically similar White patients, Black patients were significantly more likely to be discharged to SNFs (21.8% vs. 19.3%, difference = 2.5%, p < 0.0001) and to receive any SNF care within 30 days of discharge (25.3% vs. 22.4%, difference = 2.9%, p < 0.0001). Black patients were also significantly more likely to experience 30-day readmission (18.7% vs. 14.5%, difference = 4.2%, p < 0.0001). Differences in reasons for hospitalization and risk factors explained most of the differences in discharge location, post-acute care use, and readmission rates, while additional adjustment for differences in hospital characteristics and complications made little difference for any of the measures studied. CONCLUSIONS: We found significant Black-White differences in discharge to SNFs, SNF utilization, and readmission rates. Using layered target matching, we found that differences in risk factors and reasons for hospitalization explained most of these differences, while differences in hospitals did not materially impact the differences.

Racial and ethnic disparities in excess mortality among U.S. veterans during the COVID-19 pandemic.

OBJECTIVE: The COVID-19 pandemic disproportionately affected racial and ethnic minorities among the general population in the United States; however, little is known regarding its impact on U.S. military Veterans. In this study, our objectives were to identify the extent to which Veterans experienced increased all-cause mortality during the COVID-19 pandemic, stratified by race and ethnicity. DATA SOURCES: Administrative data from the Veterans Health Administration's Corporate Data Warehouse. STUDY DESIGN: We use pre-pandemic data to estimate mortality risk models using five-fold cross-validation and quasi-Poisson regression. Models were stratified by a combined race-ethnicity variable and included controls for major comorbidities, demographic characteristics, and county fixed effects. DATA COLLECTION: We queried data for all Veterans residing in the 50 states plus Washington D.C. during 2016-2020. Veterans were excluded from analyses if they were missing county of residence or race-ethnicity data. Data were then aggregated to the county-year level and stratified by race-ethnicity. PRINCIPAL FINDINGS: Overall, Veterans' mortality rates were 16% above normal during March-December 2020 which equates to 42,348 excess deaths. However, there was substantial variation by racial and ethnic group. Non-Hispanic White Veterans experienced the smallest relative increase in mortality (17%, 95% CI 11%-24%), while Native American Veterans had the highest increase (40%, 95% CI 17%-73%). Black Veterans (32%, 95% CI 27%-39%) and Hispanic Veterans (26%, 95% CI 17%-36%) had somewhat lower excess mortality, although these changes were significantly higher compared to White Veterans. Disparities were smaller than in the general population. CONCLUSIONS: Minoritized Veterans experienced higher rates excess of mortality during the COVID-19 pandemic compared to White Veterans, though with smaller differences than the general population. This is likely due in part to the long-standing history of structural racism in the United States that has negatively affected the health of minoritized communities via several pathways including health care access, economic, and occupational inequities.

Impact of the Comprehensive Care for Joint Replacement model on patient-reported outcomes.

OBJECTIVE: To determine whether the Comprehensive Care for Joint Replacement (CJR) model, a mandatory episode-based payment program for knee and hip replacement surgery, affected patient-reported measures of quality. DATA SOURCES: Surveys of Medicare fee-for-service beneficiaries who had hip or knee replacement surgery, collected between July 2018 and March 2019, secondary Medicare administrative data, the Provider of Services file, CJR and Bundled Payments for Care Improvement participant lists from the Centers for Medicare & Medicaid Services, and the Area Health Resource Files. STUDY DESIGN: In 2018, participation in the CJR model was mandatory for nearly all hospitals in 34 randomly selected, metropolitan statistical areas (MSAs) that had high historical Medicare payments for lower-extremity joint replacements surgery. The control group included 47 high-payment MSAs randomly assigned as controls. We estimated risk-adjusted differences in self-reported measures of functional status and pain, satisfaction with care, and caregiver help between respondents in CJR hospitals and respondents in hospitals located in the control group. DATA COLLECTION: We selected a census of CJR patients and an equal number of control patients to survey. We fielded two waves of surveys using a mail and phone protocol, yielding 8433 CJR and 9014 control respondents. PRINCIPAL FINDINGS: CJR respondents were slightly more likely to depend on caregivers for certain activities of daily living when they got home (either directly from the hospital or after an institutional post-acute care stay). On a 100-point scale, differences ranged from -2.6 points (p < 0.01) for help needed bathing to -1.7 points (p < 0.05) for help needed using the toilet. However, differences in eight measures of self-reported functional status approximately 90-120 days after hospital discharge were not statistically significant, ranging from -1.1% (p = 0.087) to 0.7% (p = 0.437). CONCLUSIONS: CJR did not harm patient health or affect patient satisfaction on average but did increase reliance on caregivers during recovery.

Development of claims-based measures of unplanned acute care with superior power for assessing the effectiveness of interventions following acute care.

OBJECTIVE: To develop outcome measures that are more sensitive than current measures for evaluating primary or transitional care after hospitalizations, emergency department (ED) visits, or observation stays. DATA SOURCES: Medicare claims data from January 1, 2015, to October 31, 2017, for 1 261 707 Medicare fee-for-service beneficiaries served by (a) primary care practices participating in Track 1 of the Comprehensive Primary Care Plus (CPC+) initiative, and (b) their matched comparison practices. STUDY DESIGN: Given the poor statistical power in many studies to detect effects on readmissions, we developed two novel claims-based measures of unplanned acute care (UAC) following an index acute care event. The first measure assesses the proportion of hospitalizations followed by an unplanned readmission, ED visit, or observation stay within 30 days of discharge; the second assesses the proportion of ED visits and observation stays followed by a hospitalization, ED visit, or observation stay within 30 days. We calculate minimum detectable effects (MDEs) for both measures and for a conventional measure of 30-day unplanned readmissions, using CPC+ data. PRINCIPAL FINDINGS: Repeat UAC events are common among Medicare beneficiaries served by the CPC+ practices. In 2017, 22% of discharges and 21% of ED visits and observation stays had a UAC event within 30 days. Readmissions were the most common UAC event following discharge, whereas ED visits were most common following index ED visits or observation stays. MDEs are 25%-40% lower for the new measures than for the standard 30-day readmissions measure, indicating better statistical power to detect impacts of primary or transitional care interventions. CONCLUSIONS: This study introduces two new claims-based measures to assess quality of care during a patient's vulnerable period following acute care. The new measures complement existing measures, covering a broader range of UAC events than the standard 30-day readmissions measure, and yielding greater statistical power.

Care integration within and outside health system boundaries.

OBJECTIVE: Examine care integration-efforts to unify disparate parts of health care organizations to generate synergy across activities occurring within and between them-to understand whether and at which organizational level health systems impact care quality and staff experience. DATA SOURCES: Surveys administered to one practice manager (56/59) and up to 26 staff (828/1360) in 59 practice sites within 24 physician organizations within 17 health systems in four states (2017-2019). STUDY DESIGN: We developed manager and staff surveys to collect data on organizational, social, and clinical process integration, at four organizational levels: practice site, physician organization, health system, and outside health systems. We analyzed data using descriptive statistics and regression. PRINCIPAL FINDINGS: Managers and staff perceived opportunity for improvement across most types of care integration and organizational levels. Managers/staff perceived little variation in care integration across health systems. They perceived better care integration within practice sites than within physician organizations, health systems, and outside health systems-up to 38 percentage points (pp) lower (P < .001) outside health systems compared to within practice sites. Of nine clinical process integration measures, one standard deviation (SD) (7.2-pp) increase in use of evidence-based care related to 6.4-pp and 8.9-pp increases in perceived quality of care by practice sites and health systems, respectively, and a 4.5-pp increase in staff job satisfaction; one SD (9.7-pp) increase in integration of social services and community resources related to a 7.0-pp increase in perceived quality of care by health systems; one SD (6.9-pp) increase in patient engagement related to a 6.4-pp increase in job satisfaction and a 4.6-pp decrease in burnout; and one SD (10.6-pp) increase in integration of diabetic eye examinations related to a 5.5-pp increase in job satisfaction (all P < .05). CONCLUSIONS: Measures of clinical process integration related to higher staff ratings of quality and experience. Action is needed to improve care integration within and outside health systems.

Systematic review and meta-analysis of patient race/ethnicity, socioeconomics, and quality for adult type 2 diabetes.

OBJECTIVE: To review the evidence of the association between performance in eight indicators of diabetes care and a patient's race/ethnicity and socioeconomic characteristics. DATA SOURCE: Studies of adult patients with type 2 diabetes in MEDLINE published between January 1, 2000, and December 31, 2018. STUDY DESIGN: Systematic review and meta-analysis of regression-based studies including race/ethnicity and income or education as explanatory variables. Meta-analysis was used to quantify differences in performance associated with patient race/ethnicity or socioeconomic characteristics. The systematic review was used to identify potential mechanisms of disparities. DATA COLLECTION: Two coauthors separately conducted abstract screening, study exclusions, data extraction, and scoring of retained studies. Estimates in retained studies were extracted and, where applicable, were standardized and converted to odds ratios and standard errors. PRINCIPAL FINDINGS: Performance in intermediate outcomes and process measures frequently exhibited differences by race/ethnicity even after adjustment for socioeconomic, lifestyle, and health factors. Meta-analyses showed black patients had lower odds of HbA1c and blood pressure (BP) control (OR range: 0.67-0.68, P < .05) but higher odds of receiving eye or foot examination (OR range: 1.22-1.47, P < .05) relative to white patients. A high school degree or more was associated with higher odds of HbA1c control and receipt of eye examinations compared to patients without a degree. Meta-analyses of income included a handful of studies and were inconsistently associated with diabetes care performance. Differences in diabetes performance appear to be related to access-related factors such as uninsurance or lacking a usual source of care; food insecurity and trade-offs at very low incomes; and lower adherence among younger and healthier diabetes patients. CONCLUSIONS: Patient race/ethnicity and education were associated with differences in diabetes quality measures. Depending on the approach used to rate providers, not adjusting for these patient characteristics may penalize or reward providers based on the populations they serve.

Hospital quality indicators are not unidimensional: A reanalysis of Lieberthal and Comer.

OBJECTIVE: To evaluate the dimensionality of hospital quality indicators treated as unidimensional in a prior publication. DATA SOURCE/STUDY DESIGN: Pooled cross-sectional 2010-2011 Hospital Compare data (10/1/10 and 10/1/11 archives) and the 2012 American Hospital Association Annual Survey. DATA EXTRACTION: We used 71 indicators of structure, process, and outcomes of hospital care in a principal component analysis of Ridit scores to evaluate the dimensionality of the indicators. We conducted an exploratory factor analysis using only the indicators in the Centers for Medicare & Medicaid Services' Hospital Value-Based Purchasing. PRINCIPAL FINDINGS: There were four underlying dimensions of hospital quality: patient experience, mortality, and two clinical process dimensions. CONCLUSIONS: Hospital quality should be measured using a variety of indicators reflecting different dimensions of quality. Treating hospital quality as unidimensional leads to erroneous conclusions about the performance of different hospitals.

Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.

OBJECTIVE: To improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services' (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity. DATA SOURCES/STUDY SETTING: Data from 284 627 respondents to the 2014 Medicare CAHPS survey. STUDY DESIGN: We compared performance (cross-validated Pearson correlation of estimates and self-reported race/ethnicity) for several alternative models predicting self-reported race/ethnicity in cross-sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework. DATA COLLECTION/EXTRACTION METHODS: We linked survey-reported race/ethnicity to CMS administrative and US census data. PRINCIPAL FINDINGS: MBISG 2.0 removed 25-39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self-reported race/ethnicity for these groups. CONCLUSIONS: MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS' public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.

Geographic context of black-white disparities in Medicare CAHPS patient experience measures.

OBJECTIVE: To examine whether black-white patient experience disparities vary by geography and within-county contextual factors. DATA SOURCES: 321 300 Medicare beneficiaries responding to the 2015-2016 Medicare Consumer Assessment of Health care Providers and Systems (MCAHPS) Surveys; 2010 Census data for several within-county contextual factors. STUDY DESIGN: Mixed-effects regression models predicted three MCAHPS patient experience measures for black and white beneficiaries from geographic random effects, contextual fixed effects, and beneficiary-level case-mix adjustors. PRINCIPAL FINDINGS: Black-white disparities in patient experiences were smaller in counties with higher average patient experiences. Black-white disparities in patient experiences were not associated with county-level poverty or racial segregation. However, county racial segregation and some measures of poverty were significantly associated with all beneficiaries' level of health care access. Getting Needed Care scores were higher with greater racial segregation, while Getting Care Quickly scores were lower with higher poverty and racial segregation. CONCLUSIONS: Efforts to reduce black-white disparities in patient experiences should focus on areas with low average patient experiences. Attempts to reduce disparities in timely access to health care should target primarily black, low-income, and racially and economically segregated areas. Positive associations of racial segregation with accessing needed care were unexpected.

Association of Medicare's Bundled Payments for Care Improvement initiative with patient-reported outcomes.

OBJECTIVE: To determine whether the Bundled Payments for Care Improvement (BPCI) initiative affected patient-reported measures of quality. DATA SOURCES: Surveys of Medicare fee-for-service beneficiaries discharged from acute care hospitals participating in BPCI Model 2 and comparison hospitals between October 2014 and June 2017. Variables from Medicare administrative data and the Provider of Services file were used for sampling and risk adjustment. STUDY DESIGN: We estimated risk-adjusted differences in patient-reported measures of care experience and changes in functional status, for beneficiaries treated by BPCI and comparison hospitals. DATA COLLECTION: We selected a stratified random sample of BPCI and matched comparison beneficiaries. We fielded nine waves of surveys using a mail and phone protocol, yielding 29 193 BPCI and 29 913 comparison respondents. PRINCIPAL FINDINGS: Most BPCI and comparison survey respondents reported a positive care experience and high satisfaction. BPCI respondents were slightly less likely than comparison respondents to report positive care experience or high satisfaction. Despite these differences in care experience, there was no difference between BPCI and comparison respondents in self-reported functional status approximately 90 days after hospital discharge. CONCLUSIONS: These findings reduce concerns that BPCI may have unintentionally harmed patient health but suggest room for improvement in patient care experience.

Modeling variation of clinical team processes with multiple sequence alignment.

Our objective was to model process variation of Emergency Medical Service teams responding to simulated pediatric emergencies and determine if sequence alignment distinguishes performance quality. We performed a retrospective process analysis by watching and coding activities in videos from standardized simulations of 42 Emergency Medical Service teams. Teams were classified into high- or low-performing groups based on the Clinical Teamwork Scale™. Activities were coded according to resuscitation tasks, performer, and times. We used ClustalG to align task sequences within and between groups, and measured similarity. Teams within and between performance levels had an average sequence similarity of 52 ± 7% and 50 ± 7%. Teams performed clinically appropriate tasks that varied in prioritization, for example, performing compressions or connecting the EKG monitor early. There was no statistical difference in gross similarity between groups but specific differences in prioritization may have had clinically meaningful implications. Alignment could improve by accounting for task duration and concurrency.

Quantifying Magnitude of Group-Level Differences in Patient Experiences with Health Care.

OBJECTIVE: Review approaches assessing magnitude of differences in patient experience scores between different providers. DATA SOURCES: 1990-2016 literature. STUDY DESIGN: Systematic literature review. DATA EXTRACTION METHODS: Of 812 articles mentioning "CAHPS," "patient experience," "patient satisfaction," "important(ce)," "difference," or "significance," we identified 79 possible articles, yielding 35 for data abstraction. We included 22 articles measuring magnitude of differences in patient experiences. PRINCIPAL FINDINGS: We identified three main ways of estimating magnitude of differences in patient experience scores: (1) by distribution/range of patient experience variable, (2) against external anchor, and (3) comparing a difference in patient experience on one covariate to differences in patient experience on other covariates. CONCLUSIONS: We suggest routine estimation of magnitude in patient experience research. More work is needed documenting magnitude of differences between providers to make patient experience data more interpretable and usable.

Patient, Physician, and Health-System Factors Influencing the Quality of Antidepressant and Sedative Prescribing for Older, Community-Dwelling Adults.

OBJECTIVE: To identify determinants of potentially inappropriate (PI) antidepressant and anxiolytic/sedative prescribing for older, community-dwelling adults. DATA SOURCES/STUDY SETTING: Office visits from the 2010 National Ambulatory Medical Care Survey. STUDY DESIGN: A cross-sectional study measuring associations between various patient and physician factors and prescribing of PI antidepressants, and PI sedatives among elderly, using Beers 2012/2015 criteria, a clinical decision model, and multivariate logistic regressions. DATA COLLECTION: Visits by older adults (≥65 years) involving medications were extracted to identify visits with antidepressants and sedatives. PRINCIPAL FINDINGS: Black race, asthma, depression, osteoporosis, payment type, consultation time, and computer systems with prescribing support were associated with reduced odds of PI antidepressant prescribing among users. Income, chronic renal failure, diabetes, and obesity were associated with reduced odds of PI sedative prescribing. Female sex, white race, depression, increasing number of medications, and physician specialty were associated with increased odds of PI sedative prescribing. CONCLUSIONS: Various patient and health-system factors influence the quality of antidepressant and sedative prescribing for older community-dwelling adults. Longer consultations and the use of computer systems with prescribing support may minimize potentially inappropriate antidepressant prescribing. As medication numbers increase, exposure to PI sedatives is more likely, requiring medication review and monitoring.

Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.

OBJECTIVE: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers. DATA SOURCES: Veterans Health Administration's Corporate Data Warehouse. STUDY DESIGN: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE. PRINCIPAL FINDINGS: Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent. CONCLUSIONS: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE.

Feasibility of Collecting Patient-Reported Outcomes for Inpatient Rehabilitation Quality Reporting.

OBJECTIVE: To evaluate rehabilitation inpatients' willingness and ability to complete patient-reported outcomes (PROs) and the burden of completion on patients and staff. DATA SOURCES/STUDY SETTING: Two inpatient rehabilitation facilities. STUDY DESIGN: Patients with neurological disorders were assigned randomly to receive a nominal monetary incentive during or 1 month after the stay. DATA COLLECTION: Patients responded using a tablet computer or paper. PRINCIPAL FINDINGS: Of the 1,055 admissions, 74 percent were eligible, and 51 percent of eligible patients completed the survey. Most answered without assistance. A majority completed the survey 1 month after discharge; incentive timing was unrelated to postdischarge completion. Half of the 285 follow-up respondents required at least two reminder calls. CONCLUSIONS: Collection of PROs from rehabilitation patients is feasible. Results inform policy makers regarding feasibility of PRO data in evaluating rehabilitation quality.

Development of a Caregiver-Reported Experience Measure for Pediatric Hospital-to-Home Transitions.

OBJECTIVE: To develop and test a caregiver-reported experience measure for pediatric hospital-to-home transitions. DATA SOURCES/STUDY SETTING: Primary data were collected between 07/2014 and 05/2015 from caregivers within 2-8 weeks of their child's discharge from a tertiary care children's hospital. STUDY DESIGN/DATA COLLECTION: We used a step-wise approach to developing the measure that included drafting de novo survey items based on caregiver interviews (n = 18), pretesting items using cognitive interviews (n = 18), and pilot testing revised items among an independent sample of caregivers (n = 500). Item reduction statistics and confirmatory factor analysis (CFA) were performed on a test sample of the pilot data to refine the measure, followed by CFA on the validation sample to test the final measure model fit. PRINCIPAL FINDINGS: Of 46 initial survey items, 19 were removed after pretesting and 19 were removed after conducting item statistics and CFA. This resulted in an eight-item measure with two domains: transition preparation (four items) and transition support (four items). Survey items assess the quality of discharge instructions, access to needed support and resources, care coordination, and follow-up care. Practical fit indices demonstrated an acceptable model fit: χ2  = 28.3 (df = 19); root-mean-square error of approximation = 0.04; comparative fit index = 0.99; and Tucker-Lewis index = 0.98. CONCLUSIONS: An eight-item caregiver-reported experience measure to evaluate hospital-to-home transition outcomes in pediatric populations demonstrated acceptable content validity and psychometric properties.

Do Reduced Hospital Mortality Rates Lead to Increased Utilization of Inpatient Emergency Care? A Population-Based Cohort Study.

OBJECTIVES: To measure the impact of the improvement in hospital survival rates on patients' subsequent utilization of unplanned (emergency) admissions. DATA SOURCES/STUDY SETTING: Unplanned admissions occurring in all acute hospitals of the National Health Service in England between 2000 and 2009, including 286,027 hip fractures, 375,880 AMI, 387,761 strokes, and 9,966,246 any cause admissions. STUDY DESIGN: Population-based retrospective cohort study. Unplanned admissions experienced by patients within 28 days, 1 year, and 2 years of discharge from the index admission are modeled as a function of hospital risk-adjusted survival rates using patient-level probit and negative binomial models. Identification is also supported by an instrumental variable approach and placebo test. PRINCIPAL FINDINGS: The improvement in hospital survival rates that occurred between 2000 and 2009 explains 37.3 percent of the total increment in unplanned admissions observed over the same period. One extra patient surviving increases the expected number of subsequent admissions occurring within 1 year from discharge by 1.9 admissions for every 100 index admissions (0.019 per admission, 95% CI, 0.016-0.022). Similar results in hip fracture (0.006[0.004-0.007]), AMI (0.006[0.04-0.007]), and stroke (0.004(0.003-0.005)). CONCLUSIONS: The success of hospitals in improving survival from unplanned admissions can be an important contributory factor to the increase in subsequent admissions.

Comparison of Approaches for Aggregating Quality Measures in Population-based Payment Models.

OBJECTIVE: To assess the impact of alternative methods of aggregating individual quality measures on Accountable Care Organization (ACO) overall scores. DATA SOURCE: 2014 quality scores for Medicare ACOs. STUDY DESIGN: We compare ACO overall scores derived using CMS' aggregation approach to those derived using alternative approaches to grouping and weighting measures. PRINCIPAL FINDINGS: Alternative grouping and weighting methods based on statistical criteria produced overall quality scores similar to those produced using CMS' approach (κ = 0.80 to 0.95). Scores derived from giving specific domains greater weight were less similar (κ = 0.51 to 0.93). CONCLUSIONS: How measures are grouped into domains and how these domains are weighted to generate overall scores can have important implications for ACO's shared savings payments.

A Two-Step Method to Identify Positive Deviant Physician Organizations of Accountable Care Organizations with Robust Performance Management Systems.

OBJECTIVE: To identify positive deviant (PD) physician organizations of Accountable Care Organizations (ACOs) with robust performance management systems (PMSYS). DATA SOURCE: Third National Survey of Physician Organizations (NSPO3, n = 1,398). STUDY DESIGN: Organizational and external factors from NSPO3 were analyzed. DATA COLLECTION/EXTRACTION METHODS: Linear regression estimated the association of internal and contextual factors on PMSYS. Two cutpoints (75th/90th percentiles) identified PDs with the largest residuals and highest PMSYS scores. PRINCIPAL FINDINGS: A total of 65 and 41 PDs were identified using 75th and 90th percentiles cutpoints, respectively. The 90th percentile more strongly differentiated PDs from non-PDs. Having a high proportion of vulnerable patients appears to constrain PMSYS development. CONCLUSIONS: Our PD identification method increases the likelihood that PD organizations selected for in-depth inquiry are high-performing organizations that exceed expectations.