Patient-Reported Outcome-Based Performance Measures in Alternative Payment Models: Current Use, Implementation Barriers, and Principles to Succeed.

OBJECTIVES: Patient-reported outcome (PRO)-based performance measures (PRO-PMs) offer opportunities to aggregate survey data into a reliable and valid assessment of performance at the entity-level (eg, clinician, hospital, and accountable care organization). Our objective was to address the existing literature gap regarding the implementation barriers, current use, and principles for PRO-PMs to succeed. METHODS: As quality measurement experts, we first highlighted key principles of PRO-PMs and how alternative payment models (APMs) may be integral in promoting more widespread use. In May 2023, we reviewed the Centers for Medicare and Medicaid Services (CMS) Measures Inventory Tool for active PRO-PM usage within CMS programs. We finally present principles to prioritize as part PRO-PMs succeeding within APMs. RESULTS: We identified 5 implementation barriers to PRO-PM use: original development of instrument, response rate sufficiency, provider burden, hesitancy regarding fairness, and attribution of desired outcomes. There existed 54 instances of active PRO-PM usage across CMS programs, including 46 unique PRO-PMs within 14 CMS programs. Five principles to prioritize as part of greater PRO-PM development and incorporation within APMs include the following: (1) clinical salience, (2) adequate sample size, (3) meaningful range of performance among measured entities and the ability to detect performance change in a reasonable time frame, (4) equity focus, and (5) appropriate risk adjustment. CONCLUSIONS: Identified barriers and principles to prioritize should be considered during PRO-PM development and implementation phases to link available and novel measures to payment programs while ensuring provider and stakeholder engagement.

Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance.

Importance: Equity is an essential domain of health care quality. The Centers for Medicare & Medicaid Services (CMS) developed 2 Disparity Methods that together assess equity in clinical outcomes. Objectives: To define a measure of equitable readmissions; identify hospitals with equitable readmissions by insurance (dual eligible vs non-dual eligible) or patient race (Black vs White); and compare hospitals with and without equitable readmissions by hospital characteristics and performance on accountability measures (quality, cost, and value). Design, Setting, and Participants: Cross-sectional study of US hospitals eligible for the CMS Hospital-Wide Readmission measure using Medicare data from July 2018 through June 2019. Main Outcomes and Measures: We created a definition of equitable readmissions using CMS Disparity Methods, which evaluate hospitals on 2 methods: outcomes for populations at risk for disparities (across-hospital method); and disparities in care within hospitals' patient populations (within-a-single-hospital method). Exposures: Hospital patient demographics; hospital characteristics; and 3 measures of hospital performance-quality, cost, and value (quality relative to cost). Results: Of 4638 hospitals, 74% served a sufficient number of dual-eligible patients, and 42% served a sufficient number of Black patients to apply CMS Disparity Methods by insurance and race. Of eligible hospitals, 17% had equitable readmission rates by insurance and 30% by race. Hospitals with equitable readmissions by insurance or race cared for a lower percentage of Black patients (insurance, 1.9% [IQR, 0.2%-8.8%] vs 3.3% [IQR, 0.7%-10.8%], P < .01; race, 7.6% [IQR, 3.2%-16.6%] vs 9.3% [IQR, 4.0%-19.0%], P = .01), and differed from nonequitable hospitals in multiple domains (teaching status, geography, size; P < .01). In examining equity by insurance, hospitals with low costs were more likely to have equitable readmissions (odds ratio, 1.57 [95% CI, 1.38-1.77), and there was no relationship between quality and value, and equity. In examining equity by race, hospitals with high overall quality were more likely to have equitable readmissions (odds ratio, 1.14 [95% CI, 1.03-1.26]), and there was no relationship between cost and value, and equity. Conclusion and Relevance: A minority of hospitals achieved equitable readmissions. Notably, hospitals with equitable readmissions were characteristically different from those without. For example, hospitals with equitable readmissions served fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Implementation of an equitable readmission measure must consider unequal distribution of at-risk patients among hospitals.

Severe Fatigue and Persistent Symptoms at 3 Months Following Severe Acute Respiratory Syndrome Coronavirus 2 Infections During the Pre-Delta, Delta, and Omicron Time Periods: A Multicenter Prospective Cohort Study.

BACKGROUND: Most research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants focuses on initial symptomatology with limited longer-term data. We characterized prevalences of prolonged symptoms 3 months post-SARS-CoV-2 infection across 3 variant time-periods (pre-Delta, Delta, and Omicron). METHODS: This multicenter prospective cohort study of adults with acute illness tested for SARS-CoV-2 compared fatigue severity, fatigue symptoms, organ system-based symptoms, and ≥3 symptoms across variants among participants with a positive ("COVID-positive") or negative SARS-CoV-2 test ("COVID-negative") at 3 months after SARS-CoV-2 testing. Variant periods were defined by dates with ≥50% dominant strain. We performed multivariable logistic regression modeling to estimate independent effects of variants adjusting for sociodemographics, baseline health, and vaccine status. RESULTS: The study included 2402 COVID-positive and 821 COVID-negative participants. Among COVID-positives, 463 (19.3%) were pre-Delta, 1198 (49.9%) Delta, and 741 (30.8%) Omicron. The pre-Delta COVID-positive cohort exhibited more prolonged severe fatigue (16.7% vs 11.5% vs 12.3%; P = .017) and presence of ≥3 prolonged symptoms (28.4% vs 21.7% vs 16.0%; P < .001) compared with the Delta and Omicron cohorts. No differences were seen in the COVID-negatives across time-periods. In multivariable models adjusted for vaccination, severe fatigue and odds of having ≥3 symptoms were no longer significant across variants. CONCLUSIONS: Prolonged symptoms following SARS-CoV-2 infection were more common among participants infected during pre-Delta than with Delta and Omicron; however, these differences were no longer significant after adjusting for vaccination status, suggesting a beneficial effect of vaccination on risk of long-term symptoms. Clinical Trials Registration. NCT04610515.

Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data.

Clinical treatment outcomes are the quality and cost targets that health-care providers aim to improve. Most existing outcome analysis focuses on a single disease or all diseases combined. Motivated by the success of molecular and phenotypic human disease networks (HDNs), this article develops a clinical treatment network that describes the interconnections among diseases in terms of inpatient length of stay (LOS) and readmission. Here one node represents one disease, and two nodes are linked with an edge if their LOS and number of readmissions are conditionally dependent. This is the very first HDN that jointly analyzes multiple clinical treatment outcomes at the pan-disease level. To accommodate the unique data characteristics, we propose a modeling approach based on two-part generalized linear models and estimation based on penalized integrative analysis. Analysis is conducted on the Medicare inpatient data of 100,000 randomly selected subjects for the period of January 2010 to December 2018. The resulted network has 1008 edges for 106 nodes. We analyze key network properties including connectivity, module/hub, and temporal variation. The findings are biomedically sensible. For example, high connectivity and hub conditions, such as disorders of lipid metabolism and essential hypertension, are identified. There are also findings that are less/not investigated in the literature. Overall, this study can provide additional insight into diseases' properties and their interconnections and assist more efficient disease management and health-care resources allocation.

Variation in Risk-standardized Rates and Causes of Unplanned Hospital Visits Within 7 Days of Hospital Outpatient Surgery.

OBJECTIVES: The objectives of this study were to compare risk-standardized hospital visit ratios of the predicted to expected number of unplanned hospital visits within 7 days of same-day surgeries performed at US hospital outpatient departments (HOPDs) and to describe the causes of hospital visits. SUMMARY OF BACKGROUND DATA: More than half of procedures in the US are performed in outpatient settings, yet little is known about facility-level variation in short-term safety outcomes. METHODS: The study cohort included 1,135,441 outpatient surgeries performed at 4058 hospitals between October 1, 2015 and September 30, 2016 among Medicare Fee-for-Service beneficiaries aged ≥65 years. Hospital-level, risk-standardized measure scores of unplanned hospital visits (emergency department visits, observation stays, and unplanned inpatient admissions) within 7 days of hospital outpatient surgery were calculated using hierarchical logistic regression modeling that adjusted for age, clinical comorbidities, and surgical procedural complexity. RESULTS: Overall, 7.8% of hospital outpatient surgeries were followed by an unplanned hospital visit within 7 days. Many of the leading reasons for unplanned visits were for potentially preventable conditions, such as urinary retention, infection, and pain. We found considerable variation in the risk-standardized ratio score across hospitals. The 203 best-performing HOPDs, at or below the 5th percentile, had at least 22% fewer unplanned hospital visits than expected, whereas the 202 worst-performing HOPDs, at or above the 95th percentile, had at least 29% more post-surgical visits than expected, given their case and surgical procedure mix. CONCLUSIONS: Many patients experience an unplanned hospital visit within 7 days of hospital outpatient surgery, often for potentially preventable reasons. The observed variation in performance across hospitals suggests opportunities for quality improvement.

Disparities in Excess Mortality Associated with COVID-19 - United States, 2020.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged <25 years (-2.9 to 14.1). Among persons aged <65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of >1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates.

Human disease clinical treatment network for the elderly: The analysis of medicare inpatient length of stay data.

Disease clinical treatment measures, such as inpatient length of stay (LOS), have been examined for most if not all diseases. Such analysis has important implications for the management and planning of health care, financial, and human resources. In addition, clinical treatment measures can also informatively reflect intrinsic disease properties such as severity. The existing studies mostly focus on either a single disease (or a few pre-selected and closely related diseases) or all diseases combined. In this study, we take a new and innovative perspective, examine the interconnections in length of stay (LOS) among diseases, and construct the very first disease clinical treatment network on LOS. To accommodate uniquely challenging data distributions, a new conditional network construction approach is developed. Based on the constructed network, the analysis of important network properties is conducted. The Medicare data on 100 000 randomly selected subjects for the period of January 2008 to December 2018 is analyzed. The network structure and key properties are found to have sensible biomedical interpretations. Being the very first of its kind, this study can be informative to disease clinical management, advance our understanding of disease interconnections, and foster complex network analysis.

Unscheduled Care Access in the United States-A Tale of Two Emergency Departments.

BACKGROUND: Rural communities face challenges in accessing healthcare services due to physician shortages and limited unscheduled care capabilities in office settings. As a result, rural hospital-based Emergency Departments (ED) may disproportionately provide acute, unscheduled care needs. We sought to examine differences in ED utilization and the relative role of the ED in providing access to unscheduled care between rural and urban communities. METHODS: Using a 20% sample of the 2012 Medicare Chronic Condition Warehouse, we studied the overall ED visit rate and the unscheduled care rate by geography using the Dartmouth Atlas' hospital referral regions (HRR). We calculated HRR urbanicity as the proportion of beneficiaries residing in an urban zip code within each HRR. We report descriptive statistics and utilize K-means clustering based on the ED visit rates and unscheduled care rates. RESULTS: We found rural ED use is more common and disproportionately the site of unscheduled care delivery when compared to urban communities. The ED visit and. unscheduled care proportions were negatively correlated with increased urbanicity (r =. -0.48, p < 0.001; r = -0.58, p < 0.001). CONCLUSION: The use and role of EDs by Medicare beneficiaries appears to be substantially different between urban and rural areas. This suggests that the ED may play a distinct role within the healthcare delivery system of rural communities that face disproportionate barriers to care access.

Temporal relationship of computed and structured diagnoses in electronic health record data.

BACKGROUND: The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). METHODS: We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. RESULTS: We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. CONCLUSIONS: We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects.

BACKGROUND: To isolate hospital effects on risk-standardized hospital-readmission rates, we examined readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one hospital within a given year. METHODS: We divided the Centers for Medicare and Medicaid Services hospital-wide readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized readmission rate within 30 days for each hospital, and we classified hospitals into performance quartiles, with a lower readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed readmission rates among patients who had been admitted to hospitals in different performance quartiles. RESULTS: In the performance-classification sample, the median risk-standardized readmission rate was 15.5% (interquartile range, 15.3 to 15.8). The study sample included 37,508 patients who had two admissions for similar diagnoses at a total of 4272 different hospitals. The observed readmission rate was consistently higher among patients admitted to hospitals in a worse-performing quartile than among those admitted to hospitals in a better-performing quartile, but the only significant difference was observed when the patients were admitted to hospitals in which one was in the best-performing quartile and the other was in the worst-performing quartile (absolute difference in readmission rate, 2.0 percentage points; 95% confidence interval, 0.4 to 3.5; P=0.001). CONCLUSIONS: When the same patients were admitted with similar diagnoses to hospitals in the best-performing quartile as compared with the worst-performing quartile of hospital readmission performance, there was a significant difference in rates of readmission within 30 days. The findings suggest that hospital quality contributes in part to readmission rates independent of factors involving patients. (Funded by Yale-New Haven Hospital Center for Outcomes Research and Evaluation and others.).

EPIDEMIOLOGIC CHARACTERISTICS AND RISK FACTORS FOR CONGENITAL HYPOTHYROIDISM FROM 2009 TO 2018 IN XIAMEN, CHINA.

Objective: Early diagnosis and treatment of children with congenital hypothyroidism (CH) through newborn screening can effectively prevent delayed development. This study was designed to investigate the pathogenesis and factors that influence CH in urban areas of China between 2009 and 2018. Methods: A retrospective analysis of newborn screening data and diagnosis and treatment information for CH diagnosed in the information database of the neonatal disease screening center in one of China's five special economic zones from 2009 to 2018. Results: Of the 947,258 newborns screened between 2009 and 2018, 829 (406 girls) were diagnosed with CH at birth (1 diagnosis/1,136 births). Among the 608 cases of CH diagnosed at birth and re-evaluated at the age of 3 years, 487 were permanent congenital hypothyroidism (PCH, 1/1,429), and 121 were transient congenital hypothyroidism (TCH, 1/5,882). A total of 83.2% of infants with PCH (405/487) underwent thyroid imaging in the neonatal period, of which thyroid dysgenesis accounted for 28.64% (116/405) and functional defects accounted for 71.36% (289/405). The incidence of CH changed significantly in infants with initial serum thyroid-stimulating hormone concentrations of 41 to 100 mIU/L and ≥100 mIU/L, whereas the incidence of mild CH showed a slight increase. The incidence of CH was significantly higher in postterm infants (1/63) and low-birth-weight infants (1/370). Conclusion: In the past decade, the incidence of CH has increased, mainly due to the increase in the incidence of PCH and TCH. The incidence of mild CH has increased slightly. Postterm birth and low birth weight are important factors affecting the incidence of CH. Abbreviations: CH = congenital hypothyroidism; FT4 = free thyroxine; L-T4 = levothyroxine sodium; PCH = permanent congenital hypothyroidism; TCH = transient congenital hypothyroidism; TSH = thyroid-stimulating hormone; TT4 = total thyroxine.

Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data.

BACKGROUND: To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. METHODS: The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays. RESULTS: These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia. CONCLUSIONS: This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

Admission diagnoses among patients with heart failure: Variation by ACO performance on a measure of risk-standardized acute admission rates.

BACKGROUND: A key quality metric for Accountable Care Organizations (ACOs) is the rate of hospitalization among patients with heart failure (HF). Among this patient population, non-HF-related hospitalizations account for a substantial proportion of admissions. Understanding the types of admissions and the distribution of admission types across ACOs of varying performance may provide important insights for lowering admission rates. METHODS: We examined admission diagnoses among 220 Medicare Shared Savings Program ACOs in 2013. ACOs were stratified into quartiles by their performance on a measure of unplanned risk-standardized acute admission rates (RSAARs) among patients with HF. Using a previously validated algorithm, we categorized admissions by principal discharge diagnosis into: HF, cardiovascular/non-HF, and noncardiovascular. We compared the mean admission rates by admission type as well as the proportion of admission types across RSAAR quartiles (Q1-Q4). RESULTS: Among 220 ACOs caring for 227,356 patients with HF, the median (IQR) RSAARs per 100 person-years ranged from 64.5 (61.7-67.7) in Q1 (best performers) to 94.0 (90.1-99.9) in Q4 (worst performers). The mean admission rates by admission types for ACOs in Q1 compared with Q4 were as follows: HF admissions: 9.8 (2.2) vs 14.6 (2.8) per 100 person years (P < .0001); cardiovascular/non-HF admissions: 11.1 (1.6) vs 15.9 (2.6) per 100 person-years (P < .0001); and noncardiovascular admissions: 42.7 (5.4) vs 69.6 (11.3) per 100 person-years (P < .0001). The proportion of admission due to HF, cardiovascular/non-HF, and noncardiovascular conditions was 15.4%, 17.5%, and 67.1% in Q1 compared with 14.6%, 15.9%, and 69.4% in Q4 (P < .007). CONCLUSIONS: Although ACOs with the best performance on a measure of all-cause admission rates among people with HF tended to have fewer admissions for HF, cardiovascular/non-HF, and noncardiovascular conditions compared with ACOs with the worst performance (highest admission rates), the largest difference in admission rates were for noncardiovascular admission types. Across all ACOs, two-thirds of admissions of patients with HF were for noncardiovascular causes. These findings suggest that comprehensive approaches are needed to reduce the diverse admission types for which HF patients are at risk.

Risk Trajectories of Readmission and Death in the First Year after Hospitalization for Chronic Obstructive Pulmonary Disease.

RATIONALE: Characterization of the dynamic nature of posthospital risk in chronic obstructive pulmonary disease (COPD) is needed to provide counseling and plan clinical services. OBJECTIVES: To analyze risk of readmission and death after discharge for COPD among Medicare beneficiaries aged 65 years and older and to determine the association between ventilator support and risk trajectory. METHODS: We computed daily absolute risks of hospital readmission and death for 1 year after discharge for COPD, stratified by ventilator support. We determined the time required for risks to decline by 50% from maximum daily values after discharge and for daily risks to plateau. We compared risks with those found in the general elderly population. MEASUREMENTS AND MAIN RESULTS: Among 2,340,637 hospitalizations, the readmission rate at 1 year was 64.2%, including 63.5%, 66.0%, and 64.1% among those receiving invasive, noninvasive, and no ventilation, respectively. Among 1,283,069 hospitalizations, mortality at 1 year was 26.2%, including 45.7%, 41.8%, and 24.4% among those same respective groups. Daily risk of readmission declined by 50% within 28, 39, and 43 days and plateaued at 46, 54, and 61 days among those receiving invasive, noninvasive, and no ventilation, respectively. Risk of death declined by 50% by 3, 4, and 17 days and plateaued by 21, 18, and 24 days in the same respective groups. Risks of hospitalization and death were significantly higher after discharge for COPD than among the general Medicare population. CONCLUSIONS: Discharge from the hospital is associated with prolonged risks of readmission and death that vary with need for ventilator support. Interventions limited to the first month after discharge may be insufficient to improve longitudinal outcomes.

Hospital Characteristics Associated With Postdischarge Hospital Readmission, Observation, and Emergency Department Utilization.

BACKGROUND: Whether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown. DESIGN: Cross-sectional analysis. SUBJECTS: Nonfederal United States acute care hospitals. MEASURES: Using methodology established by the Centers for Medicare & Medicaid Services, we calculated each hospital's "excess days in acute care" for fee-for-service (FFS) Medicare beneficiaries aged over 65 years discharged after hospitalization for acute myocardial infarction, heart failure (HF), or pneumonia, representing the mean difference between predicted and expected total days of acute care utilization in the 30 days following hospital discharge, per 100 discharges. We assessed the multivariable association of 8 hospital characteristics with excess days in acute care and the proportion of hospitals with each characteristic that were statistical outliers (95% credible interval estimate does not include 0). RESULTS: We included 2184 hospitals for acute myocardial infarction [228 (10.4%) better than expected, 549 (25.1%) worse than expected], 3720 hospitals for HF [484 (13.0%) better and 840 (22.6%) worse], and 4195 hospitals for pneumonia [673 (16.0%) better, 1005 (24.0%) worse]. Results for all conditions were similar. Worse than expected outliers for pneumonia included: 18.8% of safety net hospitals versus 26.1% of nonsafety net hospitals; 16.7% of public hospitals versus 33.1% of for-profit hospitals; 19.5% of nonteaching hospitals versus 52.2% of major teaching hospitals; 7.9% of rural hospitals versus 42.1% of large urban hospitals; 5.9% of hospitals with 24-<50 beds versus 58% of hospitals with >500 beds; and 29.0% of hospitals with nurse-to-bed ratios >1.0-1.5 versus 21.7% of hospitals with ratios >2.0. CONCLUSIONS: Including emergency department and observation stays in measures of postdischarge utilization produces similar results as measuring only readmissions in that major teaching, urban and for-profit hospitals still perform disproportionately poorly versus nonteaching or public hospitals. However, it enables identification of more outliers and a more granular assessment of the association of hospital factors and outcomes.

Defining Multiple Chronic Conditions for Quality Measurement.

BACKGROUND/OBJECTIVE: Patients with multiple chronic conditions (MCCs) are a critical but undefined group for quality measurement. We present a generally applicable systematic approach to defining an MCC cohort of Medicare fee-for-service beneficiaries that we developed for a national quality measure, risk-standardized rates of unplanned admissions for Accountable Care Organizations. RESEARCH DESIGN: To define the MCC cohort we: (1) identified potential chronic conditions; (2) set criteria for cohort conditions based on MCC framework and measure concept; (3) applied the criteria informed by empirical analysis, experts, and the public; (4) described "broader" and "narrower" cohorts; and (5) selected final cohort with stakeholder input. SUBJECTS: Subjects were patients with chronic conditions. Participants included 21.8 million Medicare fee-for-service beneficiaries in 2012 aged 65 years and above with ≥1 of 27 Medicare Chronic Condition Warehouse condition(s). RESULTS: In total, 10 chronic conditions were identified based on our criteria; 8 of these 10 were associated with notably increased admission risk when co-occurring. A broader cohort (2+ of the 8 conditions) included 4.9 million beneficiaries (23% of total cohort) with an admission rate of 70 per 100 person-years. It captured 53% of total admissions. The narrower cohort (3+ conditions) had 2.2 million beneficiaries (10%) with 100 admissions per 100 person-years and captured 32% of admissions. Most stakeholders viewed the broader cohort as best aligned with the measure concept. CONCLUSIONS: By systematically narrowing chronic conditions to those most relevant to the outcome and incorporating stakeholder input, we defined an MCC admission measure cohort supported by stakeholders. This approach can be used as a model for other MCC outcome measures.

The influence of sociodemographic factors on operative decision-making in small bowel obstruction.

BACKGROUND: Current guidelines for small bowel obstruction (SBO) recommend a limited trial of nonoperative management of no more than 3-5 d. For patients requiring surgery, it is uncertain if sociodemographic factors are associated with disparities in the duration of the trial of nonoperative therapy. METHODS: The Healthcare Cost and Utilization Project National Inpatient Sample from 2012 to 2014 was queried for discharges with a primary diagnosis of SBO. Primary outcomes of interest were the effects of sociodemographic factors, including race, insurance status, and income on the rate of receiving any operative management for SBO, and subsequently, among patients managed surgically, the risk of operative delay, defined as operative management ≥ 5 d after admission. We did this by using logistic hierarchical generalized linear models, accounting for hospital clustering and adjusted for sex, age, comorbidity, and hospital factors. RESULTS: Of the 589,850 admissions for SBO between 2012 and 2014, 22.0% underwent operations. Overall, 26.2% were non-White, including 12.2% Black and 8.6% Hispanic patients, and the majority (56.0%) had Medicare insurance coverage. Income quartiles were evenly distributed across the overall study population. In adjusted logistic regression, operative delay was associated with increased odds of in-hospital mortality (odds ratio 1.30 95% confidence interval [1.10, 1.54]). Adjusted for patient and hospital factors, Black patients were significantly more likely to receive operations for SBO, whereas Medicaid and Medicare patients were significantly less likely. However, Black, Medicaid, and Medicare patients who were managed operatively were significantly more likely to have an operative delay of 5 or more d. There was no significant association between income and operative management in adjusted regression models. CONCLUSIONS: Significant disparities in the operative management were based on race and insurance status. Further research is warranted to understand the causes of, and solutions to, these sociodemographic disparities in care.

Differences in Colonoscopy Quality Among Facilities: Development of a Post-Colonoscopy Risk-Standardized Rate of Unplanned Hospital Visits.

BACKGROUND & AIMS: Colonoscopy is a common procedure, yet little is known about variations in colonoscopy quality among outpatient facilities. We developed an outcome measure to profile outpatient facilities by estimating risk-standardized rates of unplanned hospital visits within 7 days of colonoscopy. METHODS: We used a 20% sample of 2010 Medicare outpatient colonoscopy claims (331,880 colonoscopies performed at 8140 facilities) from patients ≥65 years or older, and developed a patient-level logistic regression model to estimate the risk of unplanned hospital visits (ie, emergency department visits, observation stays, and inpatient admissions) within 7 days of colonoscopy. We then used the patient-level risk model variables and hierarchical logistic regression to estimate facility rates of risk-standardized unplanned hospital visits using data from the Healthcare Cost and Utilization Project (325,811 colonoscopies at 992 facilities), from 4 states containing 100% of colonoscopies per facility. RESULTS: Outpatient colonoscopies were followed by 5412 unplanned hospital visits within 7 days (16.3/1000 colonoscopies). Hemorrhage, abdominal pain, and perforation were the most common causes of unplanned hospital visits. Fifteen variables were independently associated with unplanned hospital visits (c = 0.67). A history of fluid and electrolyte imbalance (odds ratio [OR] = 1.43; 95% confidence interval [CI]: 1.29-1.58), psychiatric disorders (OR = 1.34; 95% CI: 1.22-1.46), and, in the absence of prior arrhythmia, increasing age past 65 years (aged >85 years vs 65-69 years: OR = 1.87; 95% CI: 1.54-2.28) were most strongly associated. The facility risk-standardized unplanned hospital visits calculated using Healthcare Cost and Utilization Project data showed significant variation (median 12.3/1000; 5th-95th percentile, 10.5-14.6/1000). Median risk-standardized unplanned hospital visits were comparable between ambulatory surgery centers and hospital outpatient departments (each was 10.2/1000), and ranged from 16.1/1000 in the Northeast to 17.2/1000 in the Midwest. CONCLUSIONS: We calculated a risk-adjusted measure of outpatient colonoscopy quality, which shows important variation in quality among outpatient facilities. This measure can make transparent the extent to which patients require follow-up hospital care, help inform patient choices, and assist in quality-improvement efforts.

Hospital Characteristics Associated With Risk-standardized Readmission Rates.

BACKGROUND: Safety-net and teaching hospitals are somewhat more likely to be penalized for excess readmissions, but the association of other hospital characteristics with readmission rates is uncertain and may have relevance for hospital-centered interventions. OBJECTIVE: To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR). DESIGN: This is a retrospective cross-sectional multivariable analysis. SUBJECTS: US hospitals. MEASURES: Centers for Medicare and Medicaid Services specification of hospital-wide RSRR from July 1, 2013 through June 30, 2014 with race and Medicaid dual-eligibility added. RESULTS: We included 6,789,839 admissions to 4474 hospitals of Medicare fee-for-service beneficiaries aged over 64 years. In multivariable analyses, there was regional variation: hospitals in the mid-Atlantic region had the highest RSRRs [0.98 percentage points higher than hospitals in the Mountain region; 95% confidence interval (CI), 0.84-1.12]. For-profit hospitals had an average RSRR 0.38 percentage points (95% CI, 0.24-0.53) higher than public hospitals. Both urban and rural hospitals had higher RSRRs than those in medium metropolitan areas. Hospitals without advanced cardiac surgery capability had an average RSRR 0.27 percentage points (95% CI, 0.18-0.36) higher than those with. The ratio of registered nurses per hospital bed was not associated with RSRR. Variability in RSRRs among hospitals of similar type was much larger than aggregate differences between types of hospitals. CONCLUSIONS: Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence. Disproportionately high readmission rates at for-profit hospitals may highlight the role of financial incentives favoring utilization.