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This review aims to quantify the effect of minority status on analgesia use for acute pain management in US Emergency Department (ED) settings. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to perform a review of studies from 1990 to 2018 comparing racial and ethnic differences in the administration of analgesia for acute pain. Studies were included if they measured analgesia use in white patients compared to a racial minority in the ED and studies were excluded if they focused primarily on chronic pain, case reports and survey studies. Following data abstraction, a meta-analysis was performed using fixed and random-effect models to determine primary outcome of analgesia administration stratified by racial and ethnic classification. 763 articles were screened for eligibility and fourteen studies met inclusion criteria for qualitative synthesis. The total study population included 7070 non-Hispanic White patients, 1538 Hispanic, 3125 Black, and 50.3% female. Black patients were less likely than white to receive analgesia for acute pain: OR 0.60 [95%-CI, 0.43–0.83, random effects model]. Hispanics were also less likely to receive analgesia: OR 0.75 [95%-CI, 0.52–1.09]. This study demonstrates the presence of racial disparities in analgesia use for the management of acute pain in US EDs. Further research is needed to examine patient reported outcomes in addition to the presence of disparities in other groups besides Black and Hispanic. Trial registration: Registration number CRD42018104697 in PROSPERO.

The Centers for Medicare and Medicaid Services (CMS) has promoted bundled payment programs nationwide as one of its flagship value-based payment reforms. Under bundled payment, providers assume accountability for the quality and costs of care delivered during an episode of care. We performed a systematic review of the impact of three CMS bundled payment programs on spending, utilization, and quality outcomes. The three programs were the Acute Care Episode Demonstration, the voluntary Bundled Payments for Care Improvement initiative, and the mandatory Comprehensive Care for Joint Replacement model. Twenty studies that we identified through search and screening processes showed that bundled payment maintains or improves quality while lowering costs for lower extremity joint replacement, but not for other conditions or procedures. Our review also suggests that policy makers should account for patient-level heterogeneity and include risk stratification for specific conditions in emerging bundled payment programs.

Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Private equity firms have increased their participation in the US health care system, raising questions about incentive alignment and downstream effects on patients. However, there is a lack of systematic characterization of private equity acquisition of short-term acute care hospitals. We present an overview of the scope of private equity-backed hospital acquisitions over the course of 2003-17, comparing the financial and operational differences between those hospitals and hospitals that remained unacquired through 2017. A total of 42 private equity deals occurred, involving 282 unique hospitals across 36 states. In unadjusted analyses, hospitals that were acquired had larger bed sizes, more discharges, and more full-time-equivalent staff positions in 2003 relative to nonacquired hospitals; private equity-acquired hospitals also had higher charge-to-cost ratios and higher operating margins, and this gap widened during our study period. These findings motivate evaluations by policy makers and researchers on the impact, if any, of private equity acquisition on health care access, spending, and risk-adjusted outcomes.

Medicare's Hospital Readmissions Reduction Program (HRRP) has been credited with lowering risk-adjusted readmission rates for targeted conditions at general acute care hospitals. However, these reductions appear to be illusory or overstated. This is because a concurrent change in electronic transaction standards allowed hospitals to document a larger number of diagnoses per claim, which had the effect of reducing risk-adjusted patient readmission rates. Prior studies of the HRRP relied upon control groups' having lower baseline readmission rates, which could falsely create the appearance that readmission rates are changing more in the treatment than in the control group. Accounting for the revised standards reduced the decline in risk-adjusted readmission rates for targeted conditions by 48 percent. After further adjusting for differences in pre-HRRP readmission rates across samples, we found that declines for targeted conditions at general acute care hospitals were statistically indistinguishable from declines in two control samples. Either the HRRP had no effect on readmissions, or it led to a systemwide reduction in readmissions that was roughly half as large as prior estimates have suggested.

Interventions for dementia caregivers should be scalable and available at the point of care. CommunityRx‐Dementia (CRxD) is an IT‐based intervention to improve caregiver outcomes by providing information about local community resources and connection to a resource navigator and online resource finder. We compared self‐reported unmet needs and resource knowledge among 343 caregivers enrolled in a randomized trial of CRxD vs usual care (UC). At baseline,1 and 3 months, caregivers’ reported their knowledge of and need for any of 14 resource types (e.g., respite care, end‐of‐life planning, food) and, at 12 months, they reported their use of acute care. For the outcomes of knowledge and needs, mixed‐effects regression models were fit with treatment arm, time, treatment arm by time interaction and baseline knowledge or needs as predictors. For acute care utilization outcomes, negative binomial regression models were fit with treatment group and baseline utilization as predictors. Incidence rate ratios (IRR) and corresponding 95% CIs were calculated. Participants (78% women, 81% non‐Hispanic Black, 49% aged 50‐64, 64% income ≥$50k/year) included both newer (22% 6 months‐2 years) and more experienced caregivers (44% > 5 years). At baseline, caregivers in both study arms reported an average of 4 unmet needs (87% ≥1, 65% ≥3, most often [61%] for caregiver education), and knew of an average of 6 resources. At 3 months, the number of known resources was greater in the CRxD than UC group (6.5 vs 5.2; β: 1.0, 95% CI 0.3, 1.7) and the number of unmet needs was lower (2.8 vs 3.6; β: ‐0.5, 95% CI ‐1.1, 0.0). Over 12 months, caregivers in CRxD had lower ED visits rates than UC participants (0.3 vs 0.5; IRR: 0.6, 95% CI 0.3, 0.9) but similar hospitalization rates (0.1 vs 0.2; IRR: 0.7, 95% CI 0.3, 1.2). This low‐intensity social care intervention for dementia caregivers may improve knowledge of available resources, decrease unmet needs, and reduce caregivers’ use of emergency care.

We study the design of provider incentives in the post-acute care setting—a highstakes but under-studied segment of the healthcare system. We focus on long-term care hospitals (LTCHs) and the large (approximately $13,500) jump in Medicare payments they receive when a patient's stay reaches a threshold number of days. Discharges increase substantially after the threshold, with the marginal discharged patient in relatively better health. Despite the large financial incentives and behavioral response in a high mortality population, we are unable to detect any compelling evidence of an impact on patient mortality. To assess provider behavior under counterfactual payment schedules, we estimate a simple dynamic discrete choice model of LTCH discharge decisions. When we conservatively limit ourselves to alternative contracts that hold the LTCH harmless, we find that an alternative contract can generate Medicare savings of about $2,100 per admission, or about 5% of total payments. More aggressive payment reforms can generate substantially greater savings, but the accompanying reduction in LTCH profits has potential out-of-sample consequences. Our results highlight how improved financial incentives may be able to reduce healthcare spending, without negative consequences for industry profits or patient health.

Because of workforce needs and demographic and chronic disease trends, nurse practitioners (NPs) and physician assistants (PAs) are taking a larger role in the primary care of medically complex patients with chronic conditions. Research shows good quality outcomes, but concerns persist that NPs' and PAs' care of vulnerable populations could increase care costs compared to the traditional physician-dominated system. We used 2012-13 Veterans Affairs data on a cohort of medically complex patients with diabetes to compare health services use and costs depending on whether the primary care provider was a physician, NP, or PA. Case-mix-adjusted total care costs were 6-7 percent lower for NP and PA patients than for physician patients, driven by more use of emergency and inpatient services by the latter. We found that use of NPs and PAs as primary care providers for complex patients with diabetes was associated with less use of acute care services and lower total costs.

Background. Leaving the hospital against medical advice is an increasing problem in acute care settings and is associated with an array of negative health consequences that may lead to readmission for a worsened health outcome or mortality. Leaving the hospital against medical advice is particularly common among people who use illicit drugs (PWUD) and has been linked to a number of complex issues; however, few studies have focused specifically on this population beyond identifying them as being at an increased risk of leaving the hospital prematurely. Furthermore, programs and interventions for reducing the rate of leaving the hospital against medical advice among PWUD in acute care settings have not been well studied. Objectives. We systematically assessed the literature examining hospital discharge against medical advice from acute care among this population and identified potential methods to minimize the occurrence of this phenomenon. Search methods. We searched 5 electronic databases (from database inception to August 2014) and article reference lists for articles investigating hospital discharge from acute care against medical advice among PWUD. Search terms consistent across databases included “patient discharge,” “hospital discharge,” “against medical advice,” “drug user,” “substance-related disorders,” and “intravenous substance abuse.” Selection criteria. Studies were eligible for inclusion if they were published in a peer-reviewed journal as an original research article in English. We excluded gray literature, case reports, case series, reviews, and editorials. We retained original studies that reported illicit drug use as a predictor of leaving the hospital against medical advice and studies of discharge against medical advice that included PWUD as a population of interest, and we assessed significance through appropriate statistical tests. We excluded studies that reported patients leaving the hospital against medical advice from psychiatric hospitals, drug treatment centers and emergency departments, and studies that discussed misuse of alcohol but not illicit drugs. Data collection and analysis. We created an electronic database that included study abstracts and relevant information matching the keywords and search criteria. We reviewed potentially eligible articles independently by scanning the titles, abstracts, and full texts of articles after removing duplicates. We identified studies for which eligibility was unclear and decided which studies to include after thoroughly reviewing and discussing them. Results. Of the 1649 studies that matched the search criteria, 17 met our inclusion criteria. Thirteen studies identified substance misuse as a significant predictor of leaving the hospital against medical advice. Three studies assessed the prevalence and predictors of leaving the hospital against medical advice among people who inject drugs and found that this phenomenon was commonly reported (prevalence range = 25%–30%). Factors positively associated with leaving the hospital against medical advice included recent injection drug use, Aboriginal ancestry, leaving on weekends and welfare check day. In-hospital methadone use, social support, older age, and admission to a community-based model of care were negatively associated with the outcome. Conclusions. To better understand risk factors associated with leaving the hospital against medical advice among PWUD, future research should consider the effect of individual, social, and structural characteristics on leaving the hospital against medical advice among PWUD. The development and evaluation of novel methods to address interventions to reduce the rate of leaving the hospital prematurely is necessary.