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Structure and predictors of in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia in Japan: A cross-sectional study.

ABSTRACT: Schizophrenia is a disorder characterized by psychotic relapses. Globally, about 15%-30% of patients with schizophrenia discharged from inpatient psychiatric admissions are readmitted within 90 days due to exacerbation of symptoms that leads to self-harm, harm to others, or self-neglect. The purpose of this study was to investigate the structure and predictors of in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia. A new questionnaire was developed to assess the extent to which respondents delivered in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia. This study adopted a cross-sectional research design. The survey was conducted with the new questionnaires. The participants were registered nurses working in psychiatric wards. Item analyses and exploratory factor analyses were performed using the new questionnaires to investigate the structure of in-hospital nursing care leading to reduction in early readmission. Stepwise regression analyses were conducted to examine the factors predicting in-hospital nursing care leading to reduction in early readmission. Data were collected from 724 registered nurses in Japan. In-hospital nursing care leading to reduction in early readmission was found to consist of five factors: promoting cognitive functioning and self-care, identifying reasons for readmission, establishing cooperative systems within the community, sharing goals about community life, and creating restful spaces. In-hospital nursing care leading to reduction in early readmission was predicted by the following variables: the score on the nursing excellence scale in clinical practice, the score on therapeutic hold, and the participation of community care providers in pre-discharge conferences. Japanese psychiatric nurses provide nursing care based on these five factors leading to reduction in early readmission. Such nursing care would be facilitated by not only nurses' excellence but also nurses' environmental factors, especially the therapeutic climate of the ward and the participation of community care providers in pre-discharge conferences.

SUBMITTER: Maki S

PROVIDER: S-EPMC8087037 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo examine the association of hospital volume (a marker of quality of care) with hospital readmission rates.DesignRetrospective cross-sectional study.Setting4651 US acute care hospitals.Study data6,916,644 adult discharges, excluding patients receiving psychiatric or medical cancer treatment.Main outcome measuresWe used Medicare fee-for-service data from 1 July 2011 to 30 June 2012 to calculate observed-to-expected, unplanned, 30 day, standardized readmission rates for hospitals and for specialty cohorts medicine, surgery/gynecology, cardiorespiratory, cardiovascular, and neurology. We assessed the association of hospital volume by quintiles with 30 day, standardized readmission rates, with and without adjustment for hospital characteristics (safety net status, teaching status, geographic region, urban/rural status, nurse to bed ratio, ownership, and cardiac procedure capability. We also examined associations with the composite outcome of 30 day, standardized readmission or mortality rates.ResultsMean 30 day, standardized readmission rate among the fifth of hospitals with the lowest volume was 14.7 (standard deviation 5.3) compared with 15.9 (1.7) among the fifth of hospitals with the highest volume (P<0.001). We observed the same pattern of lower readmission rates in the lowest versus highest volume hospitals in the specialty cohorts for medicine (16.6 v 17.4, P<0.001), cardiorespiratory (18.5 v 20.5, P<0.001), and neurology (13.2 v 14.0, p=0.01) cohorts; the cardiovascular cohort, however, had an inverse association (14.6 v 13.7, P<0.001). These associations remained after adjustment for hospital characteristics except in the cardiovascular cohort, which became non-significant, and the surgery/gynecology cohort, in which the lowest volume fifth of hospitals had significantly higher standardized readmission rates than the highest volume fifth (difference 0.63 percentage points (95% confidence interval 0.10 to 1.17), P=0.02). Mean 30 day, standardized mortality or readmission rate was not significantly different between highest and lowest volume fifths (20.4 v 20.2, P=0.19) and was highest in the middle fifth of hospitals (range 20.6-20.8).ConclusionsStandardized readmission rates are lowest in the lowest volume hospitals-opposite from the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics and was only partially attenuated by examining mortality and readmission together. Our findings suggest that readmissions are associated with different aspects of care than mortality or complications.

Project description:ImportanceMedicare is experimenting with numerous concurrent reforms aimed at improving quality and value for hospitals. It is unclear if these myriad reforms are mutually reinforcing or in conflict with each other.ObjectiveTo evaluate whether hospital participation in voluntary value-based reforms was associated with greater improvement under Medicare's Hospital Readmission Reduction Program (HRRP).Design, setting, and participantsRetrospective, longitudinal study using publicly available national data from Hospital Compare on hospital readmissions for 2837 hospitals from 2008 to 2015. We assessed hospital participation in 3 voluntary value-based reforms: Meaningful Use of Electronic Health Records; the Bundled Payment for Care Initiative episode-based payment program (BPCI); and Medicare's Pioneer and Shared Savings accountable care organization (ACO) programs. We used an interrupted time series design to test whether hospitals' time-varying participation in these value-based reforms was associated with greater improvement in Medicare's HRRP.Main outcomes and measuresThirty-day risk standardized readmission rates for acute myocardial infarction (AMI), heart failure, and pneumonia.ResultsAmong the 2837 hospitals in this study, participation in value-based reforms varied considerably over the study period. In 2010, no hospitals were participating in the meaningful use, ACO, or BPCI programs. By 2015, only 56 hospitals were not participating in at least 1 of these programs. Among hospitals that did not participate in any voluntary reforms, the association between the HRRP and 30-day readmission was -0.76 percentage points for AMI (95% CI, -0.93 to -0.60), -1.30 percentage points for heart failure (95% CI, -1.47 to -1.13), and -0.82 percentage points for pneumonia (95% CI, -0.97 to -0.67). Participation in the meaningful use program alone was associated with an additional change in 30-day readmissions of -0.78 percentage points for AMI (95% CI, -0.89 to -0.67), -0.97 percentage points for heart failure (95% CI, -1.08 to -0.86), and -0.56 percentage points for pneumonia (95% CI, -0.65 to -0.47). Participation in ACO programs alone was associated with an additional change in 30-day readmissions of -0.94 percentage points for AMI (95% CI, -1.29 to -0.59), -0.83 percentage points for heart failure (95% CI, -1.26 to -0.41), and -0.59 percentage points for pneumonia (95% CI, -1.00 to -0.18). Participation in multiple reforms led to greater improvement: participation in all 3 programs was associated with an additional change in 30-day readmissions of -1.27 percentage points for AMI (95% CI, -1.58 to -0.97), -1.64 percentage points for heart failure (95% CI, -2.02 to -1.26), and -1.05 percentage points for pneumonia (95% CI, -1.32 to -0.78).Conclusions and relevanceHospital participation in voluntary value-based reforms was associated with greater reductions in readmissions. Our findings lend support for Medicare's multipronged strategy to improve hospital quality and value.

Project description:Readmission rates declined after announcement of the Hospital Readmission Reduction Program (HRRP), which penalizes hospitals for excess readmissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.To compare trends in readmission rates for target and nontarget conditions, stratified by hospital penalty status.Retrospective cohort study of Medicare fee-for-service beneficiaries older than 64 years discharged between January 1, 2008, and June 30, 2015, from 2214 penalty hospitals and 1283 nonpenalty hospitals. Difference-interrupted time-series models were used to compare trends in readmission rates by condition and penalty status.Hospital penalty status or target condition under the HRRP.Thirty-day risk adjusted, all-cause unplanned readmission rates for target and nontarget conditions.The study included 48?137?102 hospitalizations of 20?351?161 Medicare beneficiaries. In January 2008, the mean readmission rates for AMI, HF, pneumonia, and nontarget conditions were 21.9%, 27.5%, 20.1%, and 18.4%, respectively, at hospitals later subject to financial penalties and 18.7%, 24.2%, 17.4%, and 15.7% at hospitals not subject to penalties. Between January 2008 and March 2010, prior to HRRP announcement, readmission rates were stable across hospitals (except AMI at nonpenalty hospitals). Following announcement of HRRP (March 2010), readmission rates for both target and nontarget conditions declined significantly faster for patients at hospitals later subject to financial penalties compared with those at nonpenalized hospitals (for AMI, additional decrease of -1.24 [95% CI, -1.84 to -0.65] percentage points per year relative to nonpenalty discharges; for HF, -1.25 [95% CI, -1.64 to -0.86]; for pneumonia, -1.37 [95% CI, -1.80 to -0.95]; and for nontarget conditions, -0.27 [95% CI, -0.38 to -0.17]; P?<?.001 for all). For penalty hospitals, readmission rates for target conditions declined significantly faster compared with nontarget conditions (for AMI, additional decline of -0.49 [95% CI, -0.81 to -0.16] percentage points per year relative to nontarget conditions [P?=?.004]; for HF, -0.90 [95% CI, -1.18 to -0.62; P?<?.001]; and for pneumonia, -0.57 [95% CI, -0.92 to -0.23; P?<?.001]). In contrast, among nonpenalty hospitals, readmissions for target conditions declined similarly or more slowly compared with nontarget conditions (for AMI, additional increase of 0.48 [95% CI, 0.01-0.95] percentage points per year [P?=?.05]; for HF, 0.08 [95% CI, -0.30 to 0.46; P?=?.67]; for pneumonia, 0.53 [95% CI, 0.13-0.93; P?=?.01]). After HRRP implementation in October 2012, the rate of change for readmission rates plateaued (P?<?.05 for all except pneumonia at nonpenalty hospitals), with the greatest relative change observed among hospitals subject to financial penalty.Medicare fee-for-service patients at hospitals subject to penalties under the HRRP had greater reductions in readmission rates compared with those at nonpenalized hospitals. Changes were greater for target vs nontarget conditions for patients at the penalized hospitals but not at the other hospitals.

Project description:Importance:The strongest evidence for the effectiveness of Medicare's Hospital Readmissions Reduction Program (HRRP) involves greater reductions in readmissions for hospitals receiving penalties compared with those not receiving penalties. However, the HRRP penalty is an imperfect measure of hospitals' marginal incentive to avoid a readmission for HRRP-targeted diagnoses. Objectives:To assess the association between hospitals' condition-specific incentives and readmission performance and to examine the responsiveness of hospitals to condition-specific incentives compared with aggregate penalty amounts. Design, Setting, and Participants:This retrospective cohort analysis used Medicare readmissions data from 2823 US short-term acute care hospitals participating in HRRP to compare 3-year (fiscal years 2016-2019) follow-up readmission performance according to tertiles of hospitals' baseline (2016) marginal incentives for each of 5 HRRP-targeted conditions (acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, pneumonia, and hip and/or knee surgery). Main Outcomes and Measures:Linear regression models were used to estimate mean change in follow-up readmission performance, measured using the excess readmissions ratio, with baseline condition-specific incentives and aggregate penalty amounts. Results:Of 2823 hospitals that participated in the HRRP from baseline to follow-up, 2280 (81%) had more than 1 excess readmission for 1 or more applicable condition and 543 (19%) did not have any excess readmissions. The mean (SD) financial incentive to reduce readmissions for incentivized hospitals ranged from $8762 ($3699) to $58?158 ($26?198) per 1 avoided readmission. Hospitals with greater incentives for readmission avoidance had greater decreases in readmissions compared with hospitals with smaller incentives (45% greater for pneumonia, 172% greater for acute myocardial infarction, 40% greater for hip and/or knee surgery, 32% greater for chronic obstructive pulmonary disease, and 13% greater for heart failure), whereas hospitals with no incentives had increases in excess readmissions of 4% to 7% (median, 4% [percentage change for nonincentivized hospitals was 3.7% for pneumonia, 4.2% for acute myocardial infarction, 7.1% for hip and/or knee surgery, 3.7% for chronic obstructive pulmonary disease, and 3.7% for heart failure]; P?<?.001). During the study period, each additional $5000 in the incentive amount was associated with a 0.6- to 1.3-percentage point decrease, or up to a 26% decrease, in excess readmissions (P?<?.001). Regression to the mean explained approximately one-third of the results depending on the condition examined. Conclusions and Relevance:The findings suggest that improvements in readmission avoidance are more strongly associated with incentives from the HRRP than with aggregate penalty amounts, suggesting that the program has elicited sizeable changes. Worsened performance among hospitals with small or no incentives may indicate the need for reconsideration of the program's lack of financial rewards for high-performing hospitals.

Project description:OBJECTIVES:Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. SETTING:A single academic, tertiary care hospital in the UK. PARTICIPANTS:A set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ?16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained. PRIMARY AND SECONDARY OUTCOME MEASURES:Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48?hours of first ICU discharge. RESULTS:In 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital's data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test). CONCLUSIONS:Despite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.

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Structure and predictors of in-hospital nursing care leading to reduction in early readmission among patients with schizophrenia in Japan: A cross-sectional study.

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