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Early Discharge Planning and Improved Care Transitions: Pre-Admission Assessment for Readmission Risk in an Elective Orthopedic and Cardiovascular Surgical Population.

ABSTRACT: BACKGROUND/METHODS:Readmission prevention is a marker of patient care quality and requires comprehensive, early discharge planning for safe hospital transitions. Effectively performed, this process supports patient satisfaction, efficient resource utilization, and care integration. This study developed/tested the utility of a predictive early discharge risk assessment with 366 elective orthopedic/cardiovascular surgery patients. Quality improvement cycles were undertaken for the design and to inform analytic plan. An 8-item questionnaire, which includes patient self-reported health, was integrated into care managers' telephonic pre-admission assessments during a 12-month period. RESULTS:Regression models found the questionnaire to be predictive of readmission (p ? .005; R(2) = .334) and length-of-stay (p ? .001; R(2) = .314). Independent variables of "lives-alone" and "self-rated health" were statistically significant for increased readmission odds, as was "self-rated health" for increased length-of-stay. Quality measures, patient experience and increased rates of discharges-to-home further supported the benefit of embedding these questions into the pro-active planning process. CONCLUSION:The pilot discharge risk assessment was predictive of readmission risk and length-of-stay for elective orthopedic/cardiovascular patients. Given the usability of the questionnaire in advance of elective admissions, it can facilitate pro-active discharge planning essential for producing quality outcomes and addressing new reimbursement methodologies for continuum-based episodes of care.

SUBMITTER: Ohta B

PROVIDER: S-EPMC5015549 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Early Discharge Planning and Improved Care Transitions: Pre-Admission Assessment for Readmission Risk in an Elective Orthopedic and Cardiovascular Surgical Population.

Ohta Brenda B Mola Ana A Rosenfeld Peri P Ford Shauna S

International journal of integrated care 20160524 2

<h4>Background/methods</h4>Readmission prevention is a marker of patient care quality and requires comprehensive, early discharge planning for safe hospital transitions. Effectively performed, this process supports patient satisfaction, efficient resource utilization, and care integration. This study developed/tested the utility of a predictive early discharge risk assessment with 366 elective orthopedic/cardiovascular surgery patients. Quality improvement cycles were undertaken for the design a ...[more]

PMID: 27616965

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Project description:Background Proximal humerus fractures (PHFs) are generally surgically treated with open reduction internal fixation (ORIF), hemiarthroplasty (HA), or total shoulder arthroplasty (TSA). Diverse fracture patterns and a high prevalence in the elderly population make it difficult to establish objective guidelines for the decision to undergo surgical treatment. The purpose of this study was to investigate risk factors associated with readmission, reoperation, and nonhome discharge following ORIF, HA, and TSA for PHFs. Methods Data on all patients who underwent ORIF, TSA, or HA for treatment of closed PHF between 2015 and 2017 were obtained by querying the American College of Surgeons National Surgical Quality Improvement database. Rates of postoperative readmission, nonhome discharge, and reoperation within 30 days were collected. Multivariate logistic regression was employed to identify predictors of readmission, nonhome discharge, and reoperation. Results A total of 2825 patients were included in this study: 1829 underwent ORIF, 707 underwent TSA, and 289 underwent HA. The significant predictors for readmission were having an American Society of Anesthesiologists class ≥ 3 (odds ratio [OR] 1.95, P = .003) and being of dependent functional status (OR 3.15, P < .001). The significant predictors for reoperation were male sex (OR 2.41, P < .001) and dependent functional status (OR 2.92, P = .006). The significant predictors for nonhome discharge were age 66-80 years (OR 7.00, P < .001), age ≥ 81 years (OR 16.31, P < .001), American Society of Anesthesiologists ≥3 (OR 2.34, P < .001), dependent functional status (OR 2.48, P < .001), and inpatient status (OR 3.32, P < .001). TSA showed slightly higher rates of nonhome discharge than HA and ORIF. Conclusion Significant risk factors for readmission, reoperation, and nonhome discharge within 30 days following surgical treatment for PHF were identified. Additionally, TSA was significantly associated with nonhome discharge compared with HA and ORIF.

Project description:BACKGROUND:Discharge planning is a routine feature of health systems in many countries. The aim of discharge planning is to reduce hospital length of stay and unplanned readmission to hospital, and to improve the co-ordination of services following discharge from hospital.This is the third update of the original review. OBJECTIVES:To assess the effectiveness of planning the discharge of individual patients moving from hospital. SEARCH METHODS:We updated the review using the Cochrane Central Register of Controlled Trials (CENTRAL) (2015, Issue 9), MEDLINE, EMBASE, CINAHL, the Social Science Citation Index (last searched in October 2015), and the US National Institutes of Health trial register (ClinicalTrials.gov). SELECTION CRITERIA:Randomised controlled trials (RCTs) that compared an individualised discharge plan with routine discharge care that was not tailored to individual participants. Participants were hospital inpatients. DATA COLLECTION AND ANALYSIS:Two authors independently undertook data analysis and quality assessment using a pre-designed data extraction sheet. We grouped studies according to patient groups (elderly medical patients, patients recovering from surgery, and those with a mix of conditions) and by outcome. We performed our statistical analysis according to the intention-to-treat principle, calculating risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous data using fixed-effect meta-analysis. When combining outcome data was not possible because of differences in the reporting of outcomes, we summarised the reported data in the text. MAIN RESULTS:We included 30 trials (11,964 participants), including six identified in this update. Twenty-one trials recruited older participants with a medical condition, five recruited participants with a mix of medical and surgical conditions, one recruited participants from a psychiatric hospital, one from both a psychiatric hospital and from a general hospital, and two trials recruited participants admitted to hospital following a fall. Hospital length of stay and readmissions to hospital were reduced for participants admitted to hospital with a medical diagnosis and who were allocated to discharge planning (length of stay MD - 0.73, 95% CI - 1.33 to - 0.12, 12 trials, moderate certainty evidence; readmission rates RR 0.87, 95% CI 0.79 to 0.97, 15 trials, moderate certainty evidence). It is uncertain whether discharge planning reduces readmission rates for patients admitted to hospital following a fall (RR 1.36, 95% CI 0.46 to 4.01, 2 trials, very low certainty evidence). For elderly patients with a medical condition, there was little or no difference between groups for mortality (RR 0.99, 95% CI 0.79 to 1.24, moderate certainty). There was also little evidence regarding mortality for participants recovering from surgery or who had a mix of medical and surgical conditions. Discharge planning may lead to increased satisfaction for patients and healthcare professionals (low certainty evidence, six trials). It is uncertain whether there is any difference in the cost of care when discharge planning is implemented with patients who have a medical condition (very low certainty evidence, five trials). AUTHORS' CONCLUSIONS:A discharge plan tailored to the individual patient probably brings about a small reduction in hospital length of stay and reduces the risk of readmission to hospital at three months follow-up for older people with a medical condition. Discharge planning may lead to increased satisfaction with healthcare for patients and professionals. There is little evidence that discharge planning reduces costs to the health service.

Project description:BackgroundDischarge planning is a routine feature of health systems in many countries that aims to reduce delayed discharge from hospital, and improve the co-ordination of services following discharge from hospital and reduce the risk of hospital readmission. This is the fifth update of the original review.ObjectivesTo assess the effectiveness of planning the discharge of individual patients moving from hospital.Search methodsWe searched CENTRAL, MEDLINE, Embase and two trials registers on 20 April 2021. We searched two other databases up to 31 March 2020. We also conducted reference checking, citation searching and contact with study authors to identify additional studies.Selection criteriaRandomised trials that compared an individualised discharge plan with routine discharge that was not tailored to individual participants. Participants were hospital inpatients.Data collection and analysisTwo review authors independently undertook data analysis and quality assessment using a pre-designed data extraction sheet. We grouped studies by older people with a medical condition, people recovering from surgery, and studies that recruited participants with a mix of conditions. We calculated risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous data using fixed-effect meta-analysis. When combining outcome data it was not possible because of differences in the reporting of outcomes, we summarised the reported results for each trial in the text.Main resultsWe included 33 trials (12,242 participants), four new trials included in this update. The majority of trials (N = 30) recruited participants with a medical diagnosis, average age range 60 to 84 years; four of these trials also recruited participants who were in hospital for a surgical procedure. Participants allocated to discharge planning and who were in hospital for a medical condition had a small reduction in the initial hospital length of stay (MD - 0.73, 95% confidence interval (CI) - 1.33 to - 0.12; 11 trials, 2113 participants; moderate-certainty evidence), and a relative reduction in readmission to hospital over an average of three months follow-up (RR 0.89, 95% CI 0.81 to 0.97; 17 trials, 5126 participants; moderate-certainty evidence). There was little or no difference in participant's health status (mortality at three- to nine-month follow-up: RR 1.05, 95% CI 0.85 to 1.29; 8 trials, 2721 participants; moderate certainty) functional status and psychological health measured by a range of measures, 12 studies, 2927 participants; low certainty evidence). There was some evidence that satisfaction might be increased for patients (7 trials), caregivers (1 trial) or healthcare professionals (2 trials) (very low certainty evidence). The cost of a structured discharge plan compared with routine discharge is uncertain (7 trials recruiting 7873 participants with a medical condition; very low certainty evidence).Authors' conclusionsA structured discharge plan that is tailored to the individual patient probably brings about a small reduction in the initial hospital length of stay and readmissions to hospital for older people with a medical condition, may slightly increase patient satisfaction with healthcare received. The impact on patient health status and healthcare resource use or cost to the health service is uncertain.

Project description:ObjectiveTo determine the benefits and harms of pre-admission interventions (prehabilitation) on postoperative outcomes in patients undergoing major elective surgery.DesignSystematic review and meta-analysis of randomised controlled trials (RCTs) (published or unpublished). We searched Medline, Embase, CENTRAL, DARE, HTA and NHS EED, The Cochrane Library, CINAHL, PsychINFO and ISI Web of Science (June 2020).SettingSecondary care.ParticipantsPatients (≥18 years) undergoing major elective surgery (curative or palliative).InterventionsAny intervention administered in the preoperative period with the aim of improving postoperative outcomes.Outcomes and measuresPrimary outcomes were 30-day mortality, hospital length of stay (LoS) and postoperative complications. Secondary outcomes included LoS in intensive care unit or high dependency unit, perioperative morbidity, hospital readmission, postoperative pain, heath-related quality of life, outcomes specific to the intervention, intervention-specific adverse events and resource use.Review methodsTwo authors independently extracted data from eligible RCTs and assessed risk of bias and the certainty of evidence using Grading of Recommendations, Assessment, Development and Evaluation. Random-effects meta-analyses were used to pool data across trials.Results178 RCTs including eight types of intervention were included. Inspiratory muscle training (IMT), immunonutrition and multimodal interventions reduced hospital LoS (mean difference vs usual care: -1.81 days, 95% CI -2.31 to -1.31; -2.11 days, 95% CI -3.07 to -1.15; -1.67 days, 95% CI -2.31 to -1.03, respectively). Immunonutrition reduced infective complications (risk ratio (RR) 0.64 95% CI 0.40 to 1.01) and IMT, and exercise reduced postoperative pulmonary complications (RR 0.55, 95% CI 0.38 to 0.80, and RR 0.54, 95% CI 0.39 to 0.75, respectively). Smoking cessation interventions reduced wound infections (RR 0.28, 95% CI 0.12 to 0.64).ConclusionsSome prehabilitation interventions may reduce postoperative LoS and complications but the quality of the evidence was low.Prospero registration numberCRD42015019191.

Project description:BackgroundTo develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.MethodData were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four secondary analyses were conducted to improve the interpretability of the results.ResultsA total of 2702 (2.0%) patients were admitted to the intensive care unit postoperatively. The gradient boosting machine model attained the highest area under the receiver operating characteristic curve of 0.90. The machine learning models predicted intensive care unit admission better than the American Society of Anesthesiologists Physical Status (area under the receiver operating characteristic curve: 0.68). The gradient boosting machine recognized several features as highly significant predictors for postoperatively intensive care unit admission. By applying subgroup analysis and secondary analysis, we found that patients with operations on the digestive, respiratory, and vascular systems had higher probabilities for intensive care unit admission.ConclusionCompared with conventional American Society of Anesthesiologists Physical Status and logistic regression model, the gradient boosting machine could improve the performance in the prediction of intensive care unit admission. Machine learning models could be used to improve the discrimination and identify the need for intensive care unit admission after surgery in elective noncardiac surgical patients, which could help manage the surgical risk.

Dataset Information

Early Discharge Planning and Improved Care Transitions: Pre-Admission Assessment for Readmission Risk in an Elective Orthopedic and Cardiovascular Surgical Population.

Publications

Early Discharge Planning and Improved Care Transitions: Pre-Admission Assessment for Readmission Risk in an Elective Orthopedic and Cardiovascular Surgical Population.

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