Analysis of Clinical Criteria for Discharge Among Patients Hospitalized for COVID-19: Development and Validation of a Risk Prediction Model.

BACKGROUND: Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge. OBJECTIVE: To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19. DESIGN: Multicenter retrospective observational cohort study, 2020-2021, with 30-day follow-up. PARTICIPANTS: Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system. MAIN MEASURES: Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN). KEY RESULTS: The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model's AUC for the internal validation set was 0.73 (95% CI 0.71-0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set. CONCLUSIONS: This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve.

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.

Design and pilot implementation for the BETTER CARE-HF trial: A pragmatic cluster-randomized controlled trial comparing two targeted approaches to ambulatory clinical decision support for cardiologists.

BACKGROUND: Beart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown. METHODS AND RESULTS: We developed two targeted CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: (1) an alert that notifies cardiologists at the time of patient visit, and (2) an automated electronic message that allows for review between visits. We designed these tools using an established CDS framework and findings from semistructured interviews with cardiologists. We then pilot tested both CDS tools (n = 596 patients) and further enhanced them based on additional semistructured interviews (n = 11 cardiologists). The message was modified to reduce the number of patients listed, include future visits, and list date of next visit. The alert was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications. CONCLUSIONS: The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of the alert vs. the automated message vs. usual care on the primary outcome of MRA prescribing. To our knowledge, no study has directly compared the efficacy of these two different types of electronic CDS interventions. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states. (Trial registration: Clinicaltrials.gov NCT05275920).

Using Rapid Randomized Trials to Improve Health Care Systems.

Rapid randomized controlled trials have been surprisingly rare in health care quality improvement (QI) and systems interventions. Applying clinical trials methodology QI work brings two distinct fields together, applying the robustness of randomized controlled trials (RCTs) to the practical, operational learnings of the well-established QI field. Rapid trials also add a third element-speed-that enables health care systems to rapidly test multiple variations of an intervention in much the same way that A/B testing is done in the technology sector. When performed well, these rapid trials free researchers and health care systems from the requirement to be correct the first time (because it is low cost and quick to try something else) while offering a standard of evidence often absent in QI. Here we outline the historical underpinnings of this approach, provide guidance about how best to implement it, and describe lessons learned from running more than 20 randomized projects in the NYU Langone Health system.

"I Am Not the Same as I Was Before": A Qualitative Analysis of COVID-19 Survivors.

BACKGROUND: Little is known about the illness experience of patients' long-term emotional and physical recovery from severe COVID-19 infection. This study aimed to expand upon the recovery process of COVID-19 survivors up to 6 months after hospital discharge. METHODS: Qualitative analysis of free-response answers from a cohort study of 152 patients ≥ 18 years hospitalized with laboratory-confirmed SARS-CoV-2 surveyed at 1-month post hospital discharge and 6-months post hospital discharge. Responses were analyzed with a grounded theory approach to identify overarching themes. RESULTS: Participants described persistent complications, both physical and mental, that have affected their recovery from COVID-19. Five overarching themes of post-acute patient experiences were generated: (1) an increased awareness of a mind and body connection, (2) feelings of premature aging, (3) an overall decline in quality of life, (4) a continued fear of infection, and (5) methods of coping. CONCLUSIONS: Patients described lasting changes to their mental health and overall quality of life in connection to physical complications after severe COVID-19 infection. Patients' reports of their experience call for a greater awareness of the psychological aspects of COVID-19 recovery to provide both physical and psychological rehabilitation services. Additional resources such as education around re-infection and financial resources are needed.

Missed opportunities in medical therapy for patients with heart failure in an electronically-identified cohort.

BACKGROUND: National registries reveal significant gaps in medical therapy for patients with heart failure and reduced ejection fraction (HFrEF), but may not accurately (or fully) characterize the population eligible for therapy. OBJECTIVE: We developed an automated, electronic health record-based algorithm to identify HFrEF patients eligible for evidence-based therapy, and extracted treatment data to assess gaps in therapy in a large, diverse health system. METHODS: In this cross-sectional study of all NYU Langone Health outpatients with EF ≤ 40% on echocardiogram and an outpatient visit from 3/1/2019 to 2/29/2020, we assessed prescription of the following therapies: beta-blocker (BB), angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), and mineralocorticoid receptor antagonist (MRA). Our algorithm accounted for contraindications such as medication allergy, bradycardia, hypotension, renal dysfunction, and hyperkalemia. RESULTS: We electronically identified 2732 patients meeting inclusion criteria. Among those eligible for each medication class, 84.8% and 79.7% were appropriately prescribed BB and ACE-I/ARB/ARNI, respectively, while only 23.9% and 22.7% were appropriately prescribed MRA and ARNI, respectively. In adjusted models, younger age, cardiology visit and lower EF were associated with increased prescribing of medications. Private insurance and Medicaid were associated with increased prescribing of ARNI (OR = 1.40, 95% CI = 1.02-2.00; and OR = 1.70, 95% CI = 1.07-2.67). CONCLUSIONS: We observed substantial shortfalls in prescribing of MRA and ARNI therapy to ambulatory HFrEF patients. Subspecialty care setting, and Medicaid insurance were associated with higher rates of ARNI prescribing. Further studies are warranted to prospectively evaluate provider- and policy-level interventions to improve prescribing of these evidence-based therapies.

Identifying drivers of health care value: a scoping review of the literature.

BACKGROUND: As health care spending reaches unsustainable levels, improving value has become an increasingly important policy priority. Relatively little research has explored factors driving value. As a first step towards filling this gap, we performed a scoping review of the literature to identify potential drivers of health care value. METHODS: Searches of PubMed, Embase, Google Scholar, Policy File, and SCOPUS were conducted between February and March 2020. Empirical studies that explored associations between any range of factors and value (loosely defined as quality or outcomes relative to cost) were eligible for inclusion. We created a template in Microsoft Excel for data extraction and evaluated the quality of included articles using the Critical Appraisal Skills Programme (CASP) quality appraisal tool. Data was synthesized using narrative methods. RESULTS: Twenty-two studies were included in analyses, of which 20 focused on low value service utilization. Independent variables represented a range of system-, hospital-, provider-, and patient-level characteristics. Although results were mixed, several consistent findings emerged. First, insurance incentive structures may affect value. For example, patients in Accountable Care Organizations had reduced rates of low value care utilization compared to patients in traditionally structured insurance plans. Second, higher intensity of care was associated with higher rates of low value care. Third, culture is likely to contribute to value. This was suggested by findings that recent medical school graduation and allopathic training were associated with reduced low value service utilization and that provider organizations had larger effects on value than did individual physicians. CONCLUSIONS: System, hospital, provider, and community characteristics influence low value care provision. To improve health care value, strategies aiming to reduce utilization of low value services and promote high value care across various levels will be essential.

Who is Responsible for Discharge Education of Patients? A Multi-Institutional Survey of Internal Medicine Residents.

BACKGROUND: Safely and effectively discharging a patient from the hospital requires working within a multidisciplinary team. However, little is known about how perceptions of responsibility among the team impact discharge communication practices. OBJECTIVE: Our study attempts to understand residents' perceptions of who is primarily responsible for discharge education, how these perceptions affect their own reported communication with patients, and how residents envision improving multidisciplinary communication around discharges. DESIGN: A multi-institutional cross-sectional survey. PARTICIPANTS: Internal medicine (IM) residents from seven US residency programs at academic medical centers were invited to participate between March and May 2019, via email of an electronic link to the survey. MAIN MEASURES: Data collected included resident perception of who on the multidisciplinary team is primarily responsible for discharge communication, their own reported discharge communication practices, and open-ended comments on ways discharge multidisciplinary team communication could be improved. KEY RESULTS: Of the 613 resident responses (63% response rate), 35% reported they were unsure which member of the multidisciplinary team is primarily responsible for discharge education. Residents who believed it was either the intern's or the resident's primary responsibility had 4.28 (95% CI, 2.51-7.30) and 3.01 (95% CI, 1.66-5.71) times the odds, respectively, of reporting doing discharge communication practices frequently compared to those who were not sure who was primarily responsible. To improve multidisciplinary discharge communication, residents called for the following among team members: (1) clarifying roles and responsibilities for communication with patients, (2) setting expectations for communication among multidisciplinary team members, and (3) redefining culture around discharges. CONCLUSIONS: Residents report a lack of understanding of who is responsible for discharge education. This diffusion of ownership impacts how much residents invest in patient education, with more perceived responsibility associated with more frequent discharge communication.

Six-Month Outcomes in Patients Hospitalized with Severe COVID-19.

BACKGROUND: Previous work has demonstrated that patients experience functional decline at 1-3 months post-discharge after COVID-19 hospitalization. OBJECTIVE: To determine whether symptoms persist further or improve over time, we followed patients discharged after hospitalization for severe COVID-19 to characterize their overall health status and their physical and mental health at 6 months post-hospital discharge. DESIGN: Prospective observational cohort study. PARTICIPANTS: Patients ≥ 18 years hospitalized for COVID-19 at a single health system, who required at minimum 6 l of supplemental oxygen during admission, had intact baseline functional status, and were discharged alive. MAIN MEASURES: Overall health status, physical health, mental health, and dyspnea were assessed with validated surveys: the PROMIS® Global Health-10 and PROMIS® Dyspnea Characteristics instruments. KEY RESULTS: Of 152 patients who completed the 1 month post-discharge survey, 126 (83%) completed the 6-month survey. Median age of 6-month respondents was 62; 40% were female. Ninety-three (74%) patients reported that their health had not returned to baseline at 6 months, and endorsed a mean of 7.1 symptoms. Participants' summary t-scores in both the physical health and mental health domains at 6 months (45.2, standard deviation [SD] 9.8; 47.4, SD 9.8, respectively) remained lower than their baseline (physical health 53.7, SD 9.4; mental health 54.2, SD 8.0; p<0.001). Overall, 79 (63%) patients reported shortness of breath within the prior week (median score 2 out of 10 (interquartile range [IQR] 0-5), vs 42 (33%) pre-COVID-19 infection (0, IQR 0-1)). A total of 11/124 (9%) patients without pre-COVID oxygen requirements still needed oxygen 6 months post-hospital discharge. One hundred and seven (85%) were still experiencing fatigue at 6 months post-discharge. CONCLUSIONS: Even 6 months after hospital discharge, the majority of patients report that their health has not returned to normal. Support and treatments to return these patients back to their pre-COVID baseline are urgently needed.

Post-Discharge Health Status and Symptoms in Patients with Severe COVID-19.

BACKGROUND: Little is known about long-term recovery from severe COVID-19 disease. Here, we characterize overall health, physical health, and mental health of patients 1 month after discharge for severe COVID-19. METHODS: This was a prospective single health system observational cohort study of patients ≥ 18 years hospitalized with laboratory-confirmed COVID-19 disease who required at least 6 l of oxygen during admission, had intact baseline cognitive and functional status, and were discharged alive. Participants were enrolled between 30 and 40 days after discharge. Outcomes were elicited through validated survey instruments: the PROMIS® Dyspnea Characteristics and PROMIS® Global Health-10. RESULTS: A total of 161 patients (40.6% of eligible) were enrolled; 152 (38.3%) completed the survey. Median age was 62 years (interquartile range [IQR], 50-67); 57 (37%) were female. Overall, 113/152 (74%) participants reported shortness of breath within the prior week (median score 3 out of 10 [IQR 0-5]), vs 47/152 (31%) pre-COVID-19 infection (0, IQR 0-1), p < 0.001. Participants also rated their physical health and mental health as worse in their post-COVID state (43.8, standard deviation 9.3; mental health 47.3, SD 9.3) compared to their pre-COVID state, (54.3, SD 9.3; 54.3, SD 7.8, respectively), both p < 0.001. Physical and mental health means in the general US population are 50 (SD 10). A total of 52/148 (35.1%) patients without pre-COVID oxygen requirements needed home oxygen after hospital discharge; 20/148 (13.5%) reported still using oxygen at time of survey. CONCLUSIONS: Patients with severe COVID-19 disease typically experience sequelae affecting their respiratory status, physical health, and mental health for at least several weeks after hospital discharge.

Prevalence and Outcomes of D-Dimer Elevation in Hospitalized Patients With COVID-19.

OBJECTIVE: To determine the prevalence of D-dimer elevation in coronavirus disease 2019 (COVID-19) hospitalization, trajectory of D-dimer levels during hospitalization, and its association with clinical outcomes. Approach and Results: Consecutive adults admitted to a large New York City hospital system with a positive polymerase chain reaction test for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) between March 1, 2020 and April 8, 2020 were identified. Elevated D-dimer was defined by the laboratory-specific upper limit of normal (>230 ng/mL). Outcomes included critical illness (intensive care, mechanical ventilation, discharge to hospice, or death), thrombotic events, acute kidney injury, and death during admission. Among 2377 adults hospitalized with COVID-19 and ≥1 D-dimer measurement, 1823 (76%) had elevated D-dimer at presentation. Patients with elevated presenting baseline D-dimer were more likely than those with normal D-dimer to have critical illness (43.9% versus 18.5%; adjusted odds ratio, 2.4 [95% CI, 1.9-3.1]; P<0.001), any thrombotic event (19.4% versus 10.2%; adjusted odds ratio, 1.9 [95% CI, 1.4-2.6]; P<0.001), acute kidney injury (42.4% versus 19.0%; adjusted odds ratio, 2.4 [95% CI, 1.9-3.1]; P<0.001), and death (29.9% versus 10.8%; adjusted odds ratio, 2.1 [95% CI, 1.6-2.9]; P<0.001). Rates of adverse events increased with the magnitude of D-dimer elevation; individuals with presenting D-dimer >2000 ng/mL had the highest risk of critical illness (66%), thrombotic event (37.8%), acute kidney injury (58.3%), and death (47%). CONCLUSIONS: Abnormal D-dimer was frequently observed at admission with COVID-19 and was associated with higher incidence of critical illness, thrombotic events, acute kidney injury, and death. The optimal management of patients with elevated D-dimer in COVID-19 requires further study.

Cardiovascular disease and cumulative incidence of cognitive impairment in the Health and Retirement Study.

BACKGROUND: We sought to examine whether people with a diagnosis of cardiovascular disease (CVD) experienced a greater incidence of subsequent cognitive impairment (CI) compared to people without CVD, as suggested by prior studies, using a large longitudinal cohort. METHODS: We employed Health and Retirement Study (HRS) data collected biennially from 1998 to 2014 in 1305 U.S. adults age ≥ 65 newly diagnosed with CVD vs. 2610 age- and gender-matched controls. Diagnosis of CVD was adjudicated with an established HRS methodology and included self-reported coronary heart disease, angina, heart failure, myocardial infarction, or other heart conditions. CI was defined as a score < 11 on the 27-point modified Telephone Interview for Cognitive Status. We examined incidence of CI over an 8-year period using a cumulative incidence function accounting for the competing risk of death. RESULTS: Mean age at study entry was 73 years, 55% were female, and 13% were non-white. Cognitive impairment developed in 1029 participants over 8 years. The probability of death over the study period was greater in the CVD group (19.8% vs. 13.8%, absolute difference 6.0, 95% confidence interval 2.2 to 9.7%). The cumulative incidence analysis, which adjusted for the competing risk of death, showed no significant difference in likelihood of cognitive impairment between the CVD and control groups (29.7% vs. 30.6%, absolute difference - 0.9, 95% confidence interval - 5.6 to 3.7%). This finding did not change after adjusting for relevant demographic and clinical characteristics using a proportional subdistribution hazard regression model. CONCLUSIONS: Overall, we found no increased risk of subsequent CI among participants with CVD (compared with no CVD), despite previous studies indicating that incident CVD accelerates cognitive decline.

Defining value in health care: a scoping review of the literature.

BACKGROUND: As health-care spending rises internationally, policymakers have increasingly begun to look to improve health-care value. However, the precise definition of health-care value remains ambiguous. METHODS: We conducted a scoping review of the literature to understand how value has been defined in the context of health care. We searched PubMed, Embase, Google Scholar, PolicyFile and Scopus between February and March 2020 to identify articles eligible for inclusion. Publications that defined value (including high or low value) using an element of cost and an element of outcomes were included in this review. No restrictions were placed on the date of publication. Articles were limited to those published in English. RESULTS: Out of 1750 publications screened, 46 met inclusion criteria. Among the 46 included articles, 22 focused on overall value, 19 on low value and 5 on high value. We developed a framework to categorize definitions based on three core domains: components, perspective and scope. Differences across these three domains contributed to significant variations in definitions of value. CONCLUSIONS: How value is defined has the potential to influence measurement and intervention strategies in meaningful ways. To effectively improve value in health-care systems, we must understand what is meant by value and the merits of different definitions.

Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials.

BACKGROUND: Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. OBJECTIVE: This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. METHODS: A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. RESULTS: To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. CONCLUSIONS: These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. TRIAL REGISTRATION: Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.

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.).

Perceptions of Radiologists and Emergency Medicine Providers Regarding the Quality, Value, and Challenges of Outside Image Sharing in the Emergency Department Setting.

OBJECTIVE. The purpose of this study is to assess the perceptions of radiologists and emergency medicine (EM) providers regarding the quality, value, and challenges associated with using outside imaging (i.e., images obtained at facilities other than their own institution). MATERIALS AND METHODS. We surveyed radiologists and EM providers at a large academic medical center regarding their perceptions of the availability and utility of outside imaging. RESULTS. Thirty-four of 101 radiologists (33.6%) and 38 of 197 EM providers (19.3%) responded. A total of 32.4% of radiologists and 55.3% of EM providers had confidence in the quality of images from outside community facilities; 20.6% and 44.7%, respectively, had confidence in the interpretations of radiologists from these outside facilities. Only 23.5% of radiologists and 5.3% of EM physicians were confident in their ability to efficiently access reports (for outside images, 47.1% and 5.3%). Very few radiologists and EM providers had accessed imaging reports from outside facilities through an available stand-alone portal. A total of 40.6% of radiologists thought that outside reports always or frequently reduced additional imaging recommendations (62.5% for outside images); 15.6% thought that reports changed interpretations of new examinations (37.5% for outside images); and 43.8% thought that reports increased confidence in interpretations of new examinations (75.0% for outside images). A total of 29.4% of EM providers thought that access to reports from outside facilities reduced repeat imaging (64.7% for outside images), 41.2% thought that they changed diagnostic or management plans (50.0% for outside images), and 50.0% thought they increased clinical confidence (67.6% for outside images). CONCLUSION. Radiologists and EM providers perceive high value in sharing images from outside facilities, despite quality concerns. Substantial challenges exist in accessing these images and reports from outside facilities, and providers are unlikely to do so using separate systems. However, even if information technology solutions for seamless image integration are adopted, providers' lack of confidence in outside studies may remain an important barrier.

Changes in Hospital Referral Patterns to Skilled Nursing Facilities Under the Hospital Readmissions Reduction Program.

BACKGROUND: The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals for higher-than-expected readmission rates. Almost 20% of Medicare fee-for-service (FFS) patients receive postacute care in skilled nursing facilities (SNFs) after hospitalization. SNF patients have high readmission rates. OBJECTIVE: The objective of this study was to investigate the association between changes in hospital referral patterns to SNFs and HRRP penalty pressure. DESIGN: We examined changes in the relationship between penalty pressure and outcomes before versus after HRRP announcement among 2698 hospitals serving 6,936,393 Medicare FFS patients admitted for target conditions: acute myocardial infarction, heart failure, or pneumonia. Hospital-level penalty pressure was the expected penalty rate in the first year of the HRRP multiplied by Medicare discharge share. OUTCOMES: Informal integration measured by the percentage of referrals to hospitals' most referred SNF; formal integration measured by SNF acquisition; readmission-based quality index of the SNFs to which a hospital referred discharged patients; referral rate to any SNF. RESULTS: Hospitals facing the median level of penalty pressure had modest differential increases of 0.3 percentage points in the proportion of referrals to the most referred SNF and a 0.006 SD increase in the average quality index of SNFs referred to. There were no statistically significant differential increases in formal acquisition of SNFs or referral rate to SNF. CONCLUSIONS: HRRP did not prompt substantial changes in hospital referral patterns to SNFs, although readmissions for patients referred to SNF differentially decreased more than for other patients, warranting investigation of other mechanisms underlying readmissions reduction.

Patterns and Costs of 90-Day Readmission for Surgical and Medical Complications Following Total Hip and Knee Arthroplasty.

BACKGROUND: Unplanned readmissions following elective total hip (THA) and knee (TKA) arthroplasty as a result of surgical complications likely have different quality improvement targets and cost implications than those for nonsurgical readmissions. We compared payments, timing, and location of unplanned readmissions with Center for Medicare and Medicaid Services (CMS)-defined surgical complications to readmissions without such complications. METHODS: We performed a retrospective analysis on unplanned readmissions within 90 days of discharge following elective primary THA/TKA among Medicare patients discharged between April 2013 and March 2016. We categorized unplanned readmissions into groups with and without CMS-defined complications. We compared the location, timing, and payments for unplanned readmissions between both readmission categories. RESULTS: Among THA (N = 23,231) and TKA (N = 43,655) patients with unplanned 90-day readmissions, 27.1% (n = 6307) and 16.4% (n = 7173) had CMS-defined surgical complications, respectively. These readmissions with surgical complications were most commonly at the hospital of index procedure (THA: 84%; TKA: 80%) and within 30 days postdischarge (THA: 73%; TKA: 77%). In comparison, it was significantly less likely for patients without CMS-defined surgical complications to be rehospitalized at the index hospital (THA: 63%; TKA: 63%; P < .001) or within 30 days of discharge (THA: 58%; TKA: 59%; P < .001). Generally, payments associated with 90-day readmissions were higher for THA and TKA patients with CMS-defined complications than without (P < .001 for all). CONCLUSION: Readmissions associated with surgical complications following THA and TKA are more likely to occur at the hospital of index surgery, within 30 days of discharge, and cost more than readmissions without CMS-defined surgical complications, yet they account for only 1 in 5 readmissions.

Early Identification of Patients With Acute Decompensated Heart Failure.

BACKGROUND: Interventions to reduce readmissions after acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) with the use of data derived from the electronic health record. METHODS AND RESULTS: We included 37,229 hospitalizations of adult patients at a single hospital during 2013-2015. We developed 4 algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of 1 of 3 clinical characteristics, 2) logistic regression of 31 structured data elements, 3) machine learning with unstructured data, and 4) machine learning with the use of both structured and unstructured data. In data validation, algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96, and both machine learning algorithms had AUCs of 0.99. Based on a brief survey of 3 providers who perform chart review for ADHF, we estimated that providers spent 8.6 minutes per chart review; using this this parameter, we estimated that providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening were done with algorithms 1, 2, 3, and 4, respectively. CONCLUSIONS: Machine learning algorithms with unstructured notes had the best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.

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.