Discharge Planning For Patients Research Articles

Implementation and Evaluation of Discharge Planning for Patients Undergoing Umbilical Cord Blood Transplantation.

BACKGROUND Umbilical cord blood transplantation (UCBT) patients have high rates of unplanned readmissions and poor quality of life (QoL). The aim of this study was to evaluate the effects of discharge planning on unplanned readmissions, self-efficacy, QoL, and clinical outcomes. MATERIAL AND METHODS Patients who received their first UCBT from April 2022 to March 2023 were included. Participants (n=72) were assigned to a control group (CG: received usual care) or an intervention group (IG: received discharge planning from admission to 100 days after UCBT). The cumulative readmission rates 30 days after discharge and 100 days after UCBT were analyzed using the log-rank test. Self-efficacy and QoL were assessed at admission and 100 days after UCBT using the General Self-Efficacy Scale and FACT-BMT version 4, clinical outcomes derived from medical records. RESULTS Sixty-six patients completed the study. Discharge planning did not reduce readmission rates 30 days after discharge (20.59% vs 31.25%, P=0.376) or 100 days after UCBT (29.41% vs 34.38%, P=0.629). However, the IG showed significantly better self-efficacy (P<0.001), and except for social and emotional well-being, all the other dimensions and 3 total scores of FACT-BMT in the IG were higher than for the controls at 100 days after UCBT (P<0.05). CONCLUSIONS The discharge planning program can improve self-efficacy and QoL of UCBT recipients. The implementation of discharge planning for patients undergoing UCBT was necessary for successful hospital-to-home transitions.

NURSE PERFORMANCE INFLUENCES DOCUMENTATION SUCCESS IN IMPLEMENTING DISCHARGE PLANNING IN INPATIENT CARE AT THE HOSPITAL IN BANJARMASIN

Nurse performance is an activity in implementing authority, duties and responsibilities in order to achieve the main tasks of the profession and organization. Discharge planning is a stage that has the goal of preparing the patient during the post-treatment transition period so that he can return home. Purpose of this study in general was to determine the corre. lation between nurse performance and successful documentation in the implementation of discharge planning at inpatient care at Hospital, Banjarmasin. Method this research is quantitative by using infants and post-partum care, totaling 6,5 executive nurse. in taking the simple Random Sampling Technique. Result. Thus it can be concluded that there is a correlation between the performance variables of nurses and the implementation of discharge planning in the inpatient care of Hospital, Banjarmasin. Health services are expected to always improve the performance of good nurses for health services, especially nurses in implementing discharge planning for patients, nurse performance is very important in completing nursing tasks such as implementing discharge planning in hospitalization. In order to create good quality for the quality of health services in hospitals.

Missed nursing care and its associated factors in public hospitals of Bahir Dar City, Northwest Ethiopia: a cross-sectional study

ObjectivesThe aim of this study was to investigate the prevalence of missed nursing care and its associated factors among public hospitals in Bahir Dar City, Northwest Ethiopia.DesignAn institution-based cross-sectional study...

Reducing severe maternal morbidity for birthing persons with severe hypertension through a statewide quality improvement initiative

BackgroundHypertensive disorders of pregnancy are a leading preventable cause of severe maternal morbidity and maternal mortality worldwide. ObjectiveTo assess the improvement in hospital care processes and patient outcomes associated with hypertensive disorders of pregnancy after introduction of a statewide Severe Maternal Hypertension quality improvement initiative. Study DesignA prospective cohort design comparing outcomes before and after introduction of the Illinois Perinatal Quality Collaborative statewide hypertension quality improvement initiative among 108 hospitals across Illinois. Participating hospitals recorded data for all cases of new-onset severe hypertension (>160 systolic or >110 diastolic) during pregnancy through 6 weeks postpartum from May 2016 to December 2017. Introduction of the statewide quality improvement initiative included implementation of severe maternal hypertension protocols, standardized patient education and discharge planning, rapid access to medications and standardized treatment order sets, and provider and nurse education. The main outcome measure was the reduction of severe maternal morbidity for pregnant/postpartum patients with severe hypertension. Key process measures include time-to-treatment of severe hypertension, frequency of provider/nurse debriefs, appropriate patient education, and early postpartum follow up. ResultsData were reported for 8,073 cases of severe maternal hypertension. The frequency of patients with new-onset severe hypertension treated within 60 minutes increased from 41% baseline to 87% (p<0.001) at the end of the initiative. The initiative was associated with increased proportion of patients receiving preeclampsia education at discharge (41% to 89%, p<0.001), scheduling follow-up appointments within 10 days of discharge (68% to 83%, p<0.001), and having a care team debrief after severe hypertension was diagnosed (17% to 59%, p<0.001). Conversely, severe maternal morbidity was reduced from 11.5% baseline to 8.4% (p<0.002) at the end of the study period. Illinois hospitals were able to achieve time-to-treatment goal regardless of hospital characteristics including geography, birth volume, and patient mix. ConclusionsIntroduction of a statewide quality improvement effort was associated with improved time-to-treatment of severe hypertension, as well as increased frequency of provider/nurse debriefs, appropriate patient education, and early postpartum follow up scheduled at discharge, and reduced severe maternal morbidity.

Nomogram for Predicting Infectious Complications Following Curative Gastrectomy Using Clinical and Laboratory Parameters.

Postoperative infectious complications are prevalent and significantly impact the prognosis and hospital stay duration after curative gastrectomy. This study aimed to identify predictive factors and develop a nomogram for predicting infectious complications prior to patient discharge. Between April 2019 and December 2023, clinicopathological data of 237 patients with gastric cancer who underwent curative gastrectomy at the Eunpyeong St. Mary's Hospital were retrospectively reviewed. C-reactive protein (CRP), white blood cell (WBC) count, neutrophil-lymphocyte ratio (NLR), and procalcitonin (PCT) levels were analyzed. Overall, 58 patients experienced postoperative complications, with 33 patients developing infectious complications. Univariate analysis revealed that the open approach, esophagus involving resection, advanced stage, and operation time were risk factors for infectious complications among clinicopathologic characteristics. Significant associations with laboratory parameters and body temperature (BT) were observed from postoperative day (POD) 1 to 5, with the highest area under the curve (AUC) observed for POD 5 data in receiver operating characteristic (ROC) analysis. Multivariate analysis incorporating clinicopathologic features and laboratory parameters on POD 5 identified age (OR=2.98), approach (OR=4.05), operation time (OR=2.74), WBC count (OR=4.09), NLR (OR=9.74), and CRP (OR=2.62) as selected factors. The developed nomogram stratified patients into low-risk (<10%), intermediate-risk (10~50%), and high-risk (≥50%) groups, corresponding to actual infectious complication rates of 1.84%, 28.3%, and 71.43%, respectively. This study presents a novel estimating model for infectious complications following curative gastrectomy. The utilization of this model in patient discharge planning can aid in identifying individuals who require additional treatment, thereby minimizing unexpected readmissions.

Healthcare Professionals Perspectives on Discharge Planning for Patients with Mental Illness

Introduction: Discharge planning is transferring a patient to a different level of care to improve continuity of care by bridging the gap between hospital and community care. This study aimed to explore the perspectives of healthcare professionals involved in discharge planning for patients with mental illness. Method: This research used qualitative methods with a phenomenological approach. Ten participants were selected using a purposive sampling method, with inclusion criteria healthcare professionals involved in the discharge planning process for patients in psychiatric hospitals, had worked in the service for at least 5 years, and provided direct or indirect services to patients. Data were collected through in-depth interviews using interview guides, recording devices, and field notes. Data were analyzed using thematic analysis. Results: The study identified four themes related to discharge planning preparation for patients with mental illness perceptions of roles and experiences in discharge planning, internal and external barriers in discharge planning, internal and external support in discharge planning and expectations for the continuity of discharge planning. Conclusions: The findings indicate that there are still significant disparities in the way that discharge planning for patients with mental illness is implemented in different mental health service settings. These study findings are intended to serve as a further source of information to develop a discharge planning system that integrates patients with mental illnesses from the hospital into the community.

Bringing Delirium to Light: Impact of CAM-ICU Tool to Improve Care Coordination.

Delirium is a serious complication in patients in the critical care unit (CCU) that may lead to prolonged hospitalization if left undetected. The CCU at our hospital does not have a framework for determining delirium that could affect patient outcomes and discharge planning. CCU in a community hospital. A posttest-only design was used for this study. We established a framework for the early assessment of delirium, educated and trained nurses to detect delirium, collaborated with the informatics department, intensivist, nursing, respiratory therapy and worked with case management to deploy the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). We used a one-tailed independent t test to determine the impact of CAM-ICU on length of stay (LOS). Cross-tabulation and chi-square tests were used to examine the impact of CAM-ICU tool on home care utilization between the intervention and comparison groups. There was a 3.12% reduction in LOS after implementing the CAM-ICU tool. Also, a reduction in home care service utilization demonstrated statistical significance ( p = .001) between the intervention group (62.5%; n = 177) and the comparison group (37.5%; n = 106). Case managers are essential in improving care transitions. Case managers need to become competent in understanding the implications of the CAM-ICU tool because of their relevant role in the multidisciplinary rounds as advocates to improve care transitions across the continuum of care. Case managers need to have an understanding on how to escalate when changes in the Richmond Agitation-Sedation Scale scores occur during the multidisciplinary rounds because it can affect care coordination throughout the hospital. Implementing the CAM-ICU decreased LOS, and reduced health care utilization. The early identification of patients with delirium can affect the outcomes of critically ill patients and entails multidisciplinary collaboration.

Machine learning decision support model for discharge planning in stroke patients.

Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission. Prospective observational study. A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions. In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia. The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making. This study illustrates the characteristics and risk factors of non-home discharge in patients with stroke, potentially contributing to the improvement of the discharge process. STROBE guidelines.

Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study.

Reducing preventable hospitalization for congestive heart failure (CHF) patients is a challenge for health systems worldwide. CHF patients who also have a recent or ongoing mental disorder may have worse health outcomes compared with CHF patients with no mental disorders. This study examined the impact of mental disorders on 28day unplanned readmissions of CHF patients. This retrospective cohort study used population-level linked public and private hospitalization and death data of adults aged ≥18years who had a CHF admission in New South Wales, Australia, between 1 January 2014 and 31 December 2020. Individuals' mental disorder diagnosis and Charlson comorbidity and hospital frailty index scores were derived from admission records. Competing risk and cause-specific risk analyses were conducted to examine the impact of having a mental disorder diagnosis on all-cause hospital readmission. Of the 65861 adults with index CHF admission discharged alive (mean age: 78.6±12.1; 48% female), 19.2% (12675) had at least one unplanned readmission within 28days following discharge. Adults with CHF with a mental disorder diagnosis within 12months had a higher risk of 28day all-cause unplanned readmission [hazard ratio (HR): 1.21, 95% confidence interval (CI): 1.15-1.27, P-value<0.001], particularly those with anxiety disorder (HR: 1.49, 95% CI: 1.35-1.65, P-value<0.001). CHF patients aged ≥85years (HR: 1.19, 95% CI: 1.11-1.28), having ≥3 other comorbidities (HR: 1.35, 95% CI: 1.25-1.46), and having an intermediate (HR: 1.34, 95% CI: 1.28-1.40) or high (HR: 1.37, 95% CI: 1.27-1.47) frailty score on admission had a higher risk of unplanned readmission. CHF patients with a mental disorder who have ≥3 other comorbidities and an intermediate frailty score had the highest probability of unplanned readmission (29.84%, 95% CI: 24.68-35.73%) after considering other patient-level factors and competing events. CHF patients who had a mental disorder diagnosis in the past 12months are more likely to be readmitted compared with those without a mental disorder diagnosis. CHF patients with frailty and a mental disorder have the highest probability of readmission. Addressing mental health care services in CHF patient's discharge plan could potentially assist reduce unplanned readmissions.

Seamless Discharge Planning and Care Transition in a Tertiary Hospital

Background: As Singapore's health care landscape evolves to cope with the rapidly ageing society, growing burden of chronic disease and rising health care cost, there is an impetus need to review the hospital's discharge planning process, so as to facilitate timely discharge and safe care transition from hospital to community. In SGH, the role of the Patient Navigator (PN) is to facilitate care coordination, discharge planning and care transition from hospital to community. However, in order to meet the increasing demand and workload, there is a need to look into capability building and empowering all nurses on the essential skills to perform a comprehensive discharge planning. Nurses play a pivotal role in patient/ caregiver's care needs assessment. Having the insight and knowledge will help them facilitate safe, smooth and timely discharge. Patient's discharge planning process is a critical point in care continuity, but it is often complex and challenging. A successful discharge planning involves the proactive identification of care needs and development of individualized care plans.&#x0D; Methods: A pre-survey was done to understand the nurses' knowledge on discharge planning and care transition. 463 nurses responded to this survey. 56% of the nurses rated their knowledge on discharge planning as fair (4 to 7) from a Likert scale of 0 (no knowledge) to 10 (very knowledgeable) (9% rated as poor and 34% rated as good).&#x0D; Hence, this study aims to increase the nurses' knowledge on discharge planning and care coordination, from baseline 34% (good) to 54% (20% improvement) within 6 months.&#x0D; The team used various Quality Improvement tools and techniques, such as the 5-Why diagram to identify the root cause/s to the problem. The final root cause identified was “nurses lack the information and skills” to initiate a comprehensive discharge planning.&#x0D; Intervention: The team then conducted a survey to gather the nurses (stakeholder) feedback on what areas/topics they would like to learn. From the survey results, the team then brainstormed on the delivery mode. Due to the Covid-19 restrictions, the team opted for online e-learning via Wizlearn. The e-learning invitation was then emailed to all Registered Nurses (RNs) in SGH to complete and their feedback was gathered for continued improvement. &#x0D; Results: Post e-learning, the nurses would rate their knowledge gained using a Likert Scale of 0 to 10 (with 0 being no knowledge gained). As of 16 Dec 2021, 504 nurses completed the wizlearn and post-survey. From the pre-post results, there was an increase in ‘Good’ by 27%.&#x0D; P value was statistically significant for “Fair” and “Good” outcomes, p=0.011117. Intangible results include the good feedback received from the nurses in wizlearn stating their appreciation for the information (staff satisfaction). &#x0D; Sustainability plans: The e-learning module is incorporated into the Singhealth e-learning portal and is made available to all staff. New nurses are encouraged to register for this course. The next phase of the project’s sustainability plans includes measuring how the knowledge translates to actual clinical outcome, such as patient’s discharge care plan are documented timely as part of the nurse's assessment.

Factors Predicting Discharge Disposition Following Head and Neck Free Flap Reconstruction.

Patients undergoing head and neck free flap reconstruction (HNFFR) may have significant change to their baseline functional status requiring inpatient rehabilitation (IPR) after discharge. We sought to identify patient/procedure characteristics predictive of discharge destination. Patients undergoing elective HNFFR between July 2017 and July 2022 were reviewed for discharge destination. Those discharged to IPR versus home were compared across patient/procedure characteristics and physical/occupational therapy metrics. Significance was assessed via bivariate and multivariable analyses. Of the 531 patients, 102 (19.2%) required IPR postoperatively. Patients discharged to IPR versus home were significantly older (70.1 [11.6] vs. 64.1 [13.1] years; p < 0.001) and more likely to lack family assistance (26.5% vs. 8.6%; p < 0.001), require baseline assistance for activities of daily living (ADLs) (31.4% vs. 9.8%; p < 0.001), have baseline cognitive dysfunction (15.7% vs. 6.1%; p = 0.001), were more likely to have neoplasm as the surgical indication for HNFFR (89.2% vs. 80.0%; p = 0.033) and more likely to have a tracheostomy postop (62.7% vs. 51.7%), and had a significantly longer length of stay (11.2 [8.0] vs. 6.8 [8.3] days; p < 0.001). There was no significant difference in gender, donor site, use of tube feeds, and use of assistive devices between the two groups. Following logistic regression, the strongest predictors of discharge to IPR include lack of family assistance (OR = 3.8; p < 0.001) and baseline assistance for ADLs (OR = 4.0, p < 0.001). Certain patient factors predict the need for discharge to rehab after HNFFR. Perioperative identification of these factors may facilitate patient counseling and discharge planning with potential to reduce hospital length of stay and further optimize patient care. III Laryngoscope, 134:2721-2725, 2024.

Development of Discharge Planning for Stroke Patients

Highlights: 1. Given the frequent long-term complications of stroke, this study identified the ideal approach to discharge planning to improve the quality of life of patients and hospital care.2. This study provides evidence supporting the effectiveness of a conventional discharge planning approach that incorporates specific educational intervention with interactive learning through audiovisual media.3. The approaches presented in this study may offer valuable perspectives on enhancing health service provisions, particularly regarding the discharge planning process for nurses. Abstract The quality improvement of discharge planning is essential throughout the development of discharge planning, which guarantees a seamless transition of care for stroke patients and family preparedness. This systematic review aimed to analyze the development of discharge planning and its impact on stroke patients and their families. The research was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible pieces of literature were compiled from seven electronic databases, i.e., ScienceDirect, Scopus, PubMed, EBSCOhost, ProQuest, SAGE Journals, and Google Scholar. The literature search was performed using predetermined search terms, with specific criteria that included papers exclusively published in English and studies conducted in 2018–2022. The development of discharge planning showed a significant impact on stroke patients, as it could influence various aspects of their quality of life. This included improvements in physiological function, enhanced cognitive knowledge, increased satisfaction and self-efficacy, reduced stress levels and care burden, and the opportunity for families to adequately prepare for home-based patient care. This study concluded that integrating conventional with technology-based media is effective for developing discharge plans for stroke patients. The implementation of this novel approach in a health system can improve patient outcomes, family preparedness when providing care, and the quality of hospital care.

Depressive Symptoms and Missed Nursing Care among Clinical Nurses: A Cross-Sectional Survey

Purpose: This study aimed to compare missed nursing care between nurses with and without depressive symptoms. Methods: A cross-sectional survey was conducted with shift-working nurses at a general hospital in South Korea. Data from 184 nurses were analyzed using the independent t-test, the &lt;i&gt;x&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; test and the Fisher’s exact test. Results: The most frequently missed nursing care items reported by hospital nurses were, in descending order, monitoring intake/output, assessing the effectiveness of medications, bathing and skin care, washing hands. The order of missed nursing care items was similar between groups with and without depressive symptoms, but there were statistically significant differences between the two groups in the mean scores for vital signs, bedside glucose monitoring, turning patients every 2 hours, providing emotional support to patients, skin/wound care, patient discharge planning, response to the call light, and pro re nata (PRN) medication requests acted on. Conclusion: Nurses’ mental health needs to be monitored and managed more closely, as it is correlated to missed nursing care. Health institutions and nursing departments should support nurses through strategies that can quickly detect and manage nurses’ depressive symptoms. Systematic resources incorporating social support among nurse colleagues could also be used as strategies to reduce depressive symptoms and missed nursing care.

Implementation and Evaluation of Discharge Planning for Patients Undergoing Umbilical Cord Blood Transplantation

Purpose The purpose of this study was to implement discharge planning for patients undergoing umbilical cord blood transplantation (UCBT) and evaluate the effects on length of stay (LOS), hospital readmission, discharge readiness, self-efficacy, and quality of life (QoL). Methods Participants (n =72) were assigned to a control group (CG: received usual care) or an intervention group (IG: received discharge planning from admission to 100 days after UCBT). LOS and the rate of hospital readmission within 30 days after discharge were collected. Discharge readiness was measured by the Readiness for Hospital Discharge Scale, self-efficacy and QoL were assessed at 100 days after UCBT using General Self-Efficacy Scale and FACT-BMT version 4. Results Sixty-six patients completed the study, the IG group showed significantly better discharge readiness (p&amp;lt;0.05), improvement in self-efficacy (p&amp;lt;0.001). Except for social and emotional well-being, all the other dimensions and three total scores of FACT-BMT in the IG were higher than the controls (p&amp;lt;0.05). The LOS was less in the IG (50.12±8.80 vs. 53.28±11.28, p=0.207), the rate of hospital readmission within 30 days after discharge was lower (20.59% vs. 31.25%, p=0.376), however, the differences were not significant. Conclusion This study provided the evidences on the implementation of discharge planning for patients undergoing UCBT, and suggested that nurse-led discharge planning should be incorporated into usual care for successful hospital-to-home transitions.

Preoperative predictors of nonhome discharge after fenestrated-branched endovascular repair of complex abdominal and thoracoabdominal aortic aneurysms

BackgroundNonhome discharge (NHD) has significant implications for patient counseling and discharge planning and is frequently required following fenestrated-branched endovascular aortic repair (FB-EVAR) of complex abdominal aortic aneurysms (CAAA) and thoracoabdominal aortic aneurysms (TAAA). We aimed to identify preoperative predictors of NHD after elective FB-EVAR for CAAA and TAAA and develop a risk calculator able to predict NHD. MethodsA retrospective review of prospectively collected data on all patients undergoing FB-EVAR between January 2007 and December 2021 at a single institution was performed. Exclusion criteria were admission from a nonhome setting, emergency and repeat FB-EVAR, and discharge to an unknown destination. The cohort was randomly split into separate development (70% of patients) and validation (30%) cohorts to develop a predictive calculator for NHD. Independent variables associated with NHD were assessed in a series of logistic regression analyses from 100 bootstrapped samples of the development set, and a model was developed using the most predictive variables. Resulting parameter estimates were applied to data in the validation set to assess model discrimination and calibration. ResultsFrom the initial cohort of 712 FB-EVAR patients, 644 were included in the study (74% male; mean age, 75.4 ± 7.6 years), including 452 with CAAA (70%) and 192 with TAAA (30%). Early mortality occurred in eight patients (1.2%; 5 in CAAA and 3 in TAAA) and the median hospital stay was 5 days (4 for CAAA and 7 for TAAA). Ninety-seven patients (15%) had a NHD. On multivariable analysis, older age (per year, odds ratio [OR], 1.08; P < .001), female gender (OR, 3.03; P < .001), smoking (OR, 2.86; P = .01), congestive heart failure (OR, 3.05; P = .004), peripheral artery disease (OR, 1.81; P = .07), and extent I (OR, 3.17), II (OR, 2.84), and III (OR, 2.52; all P = .08) TAAAs were associated with an increased likelihood of NHD in the development set. Based on these factors, the risk calculator was developed which accurately predicts NHD in the validation set with an area under the curve of 0.7. ConclusionsOlder, female smokers with congestive heart failure and peripheral artery disease and more extensive aneurysms are at highest risk of NHD after FB-EVAR. Using only preoperative factors, our risk calculator can predict accurately who will have a NHD, allowing enhanced preoperative patient counselling and accelerated hospital discharge.

Evaluating Inpatient Care Team Models in Hematology and Oncology Fellowship Programs in the United States

Introduction. In response to a growing and increasingly older population with complex medical needs, the hematology and oncology (HO) care system has integrated the hospitalist model into inpatient systems alongside HO trainees and faculty. The inclusive workforce is comprised of hospitalists and advanced practice providers (APPs) who co-manage patients with specialists or form separate non-trainee (NT) teams. Despite multiple advantages of the hospitalist model, the optimal care team structure and the impact on HO fellowship training have not been previously explored. We aimed to survey HO fellowship programs in the United States to identify the structure of inpatient HO staffing models implemented at their institution, and collect data on perceptions of the NT model within the fellowship programs. Methods. We constructed and distributed two national web-based surveys targeting all adult HO fellowship program directors (PDs) and fellows from Accreditation Council for Graduate Medical Education (ACGME)-accredited programs. The surveys were designed to assess various aspects of HO inpatient services, including team compositions, individual responsibilities, and availability of NT services. We also assessed the PDs' and fellows' perception on the impact of NT teams on their training and education. The surveys were emailed weekly between 6/20/23-7/29/23. Results.We identified 147 HO training programs.A total of 35 PDs and 101 fellows responded our survey (Table 1). The majority of PDs (83%) were from university training programs in the Midwest (43%) and Northeast (33%), with fellow class sizes of 6-12 (47%). Most (67%) programs had both trainee (T) and NT teams included in their inpatient medicine models (Figure 1). The most commonly reported NT team was the bone marrow transplant and cellular therapy service (BMT) (57%), supported primarily by APPs (55%). Only 33% of respondents reported a dedicated sickle cell disease (SCD) inpatient team within their institution. Hospitalists were included in the care model of the inpatient oncology (27%), inpatient malignant hematology (23%) and SCD (13%) teams. Most PDs agreed that the fellows are primarily responsible for most inpatient tasks and over 50% agreed that APPs are responsible for direct patient care, admissions and discharge planning. 83% of PDs reported that their program had an appropriate balance between inpatient and outpatient care. Half (50%) of PDs reported that fellows had a positive perception of NT teams at their institution, and 35% were neutral. The fellows who responded were predominantly second-year fellows (43%) in Northeastern (43%) university hospitals (92%) from programs with 6-12 fellows (63%). Most (71%) respondents had both T and NT teams included in their inpatient model. Inpatient malignant hematology (53%) and inpatient solid oncology (53%) teams were the most commonly reported NT teams, followed by BMT (40%) (Figure 1). Fellows reported their primary responsibilities to include procedures and tumor board discussions (94%), communication with outpatient teams (91%) and resident education and supervision (89%). APPs were primarily responsible for discharge planning (63%), admissions (59%) and direct patient care (58%). Most (85%) fellows agreed that their program had an appropriate balance between inpatient and outpatient care. Most fellows (69%) reported the impact of NT teams on fellows' inpatient and training experience to be neutral. Fellows from institutions without NT teams responded that the addition on NT team would have a positive impact in their training experience (69%). Most PDs and fellow respondents perceived a financial barrier to the implementation of NT teams at their institutions. Conclusions.Our findings highlight the existing heterogeneity in the distribution and composition of services among HO programs nationwide. PD respondents and fellows without NT teams perceived these teams as a beneficial addition to the program, in contrast to fellows in programs with existing NT teams who reported a neutral impact. Further investigation of the reasons for this discrepancy is essential to identify areas of improvement. Additional insights from the ongoing survey will be presented during the meeting.

Evaluasi Pelaksanaan Discharge Planning pada Pasien Diabetes Melitus

The aim of this review is to map the discharge planning implementation model and its effects. The research method uses a literature review by searching for appropriate articles in scientific databases, namely PubMed, EBSCO, ScienceDirect and Garuda using keywords based on the Population method: Diabetes mellitus; Concept: Discharge Planning and Context: Hospital. As a result of the research, after searching for articles, 44 articles were found that matched the research questions and resulted in 20 articles to be included in this review. Evaluation of the discharge planning implementation model used a research design of 7 RCT studies, 5 quasi-experimental studies, 3 cross-sectional studies, 2 retrospective studies, 1 qualitative study, 1 cohort study, and 1 comparative descriptive study. We identified several models for evaluating the implementation of discharge planning in DM patients. In conclusion, the evaluation model for implementing discharge planning in the form of training and education can increase knowledge and compliance with treatment as well as glycemic control.&#x0D; &#x0D; Keywords: Discharge Planning, DM Patients, Hospital

Patient and family perceptions of a discharge bedside board

ObjectiveTo explore patient and family perspectives of a discharge bedside board for supporting engagement in patient care and discharge planning to inform tool revision. MethodsThis qualitative descriptive study included 45 semi-structured interviews with a purposeful sample of English-speaking patients (n = 44; mean age 58.5 years) and their family members (n = 5) across seven adult inpatient units at a tertiary acute care hospital in mid-western Canada. Thematic (interviews), content (board, organization procedure document), and framework-guided integrated (all data) analyses were performed. ResultsFour themes were generated from interview data: understanding the board, included essential information to guide care, balancing information on the board, and maintaining a sense of connection. Despite application inconsistencies, documented standard procedures aligned with recommended board (re)orientation, timely patient-friendly content, attention to privacy, and patient-provider engagement strategies. ConclusionFindings indicate the tool supported consultation and some involvement level engagement in patient care and discharge. Board information was usually valued, however, perceived procedural gaps in tool education, privacy, and the quality of tool-related communication offer opportunities to strengthen patients' and families' tool experience. InnovationNovel application of a continuum engagement framework in the exploration of multiple data sources generated significant insights to guide tool revision.

Predictors of Length of Stay, Rehospitalization and Mortality in Community-Acquired Pneumonia Patients: A Retrospective Cohort Study.

Community-acquired pneumonia (CAP) represents one of the leading causes of hospitalization and has a substantial impact on the financial burden of healthcare. The aim of this study was to identify factors associated with the length of hospital stay (LOHS), rehospitalization and mortality of patients admitted for CAP. A retrospective cohort study was conducted with patients presenting to a Swiss public hospital between January 2019 and December 2019. Zero-truncated negative binomial and multivariable logistic regression analyses were performed to assess risk factors. A total of 300 patients were analyzed (median 78 years, IQR [67.56, 85.50] and 53% males) with an average LOHS of 7 days (IQR [5.00, 9.00]). Of the 300 patients, 31.6% (97/300) were re-hospitalized within 6 months, 2.7% (8/300) died within 30 days and 11.7% (35/300) died within 1 year. The results showed that sex (IRR = 0.877, 95% CI = 0.776-0.992, p-value = 0.036), age (IRR = 1.007, 95% CI = 1.002-1.012, p-value = 0.003), qSOFA score (IRR = 1.143, 95% CI = 1.049-1.246, p-value = 0.002) and atypical pneumonia (IRR = 1.357, 95% CI = 1.012-1.819, p-value = 0.04) were predictive of LOHS. Diabetes (OR = 2.149, 95% CI = 1.104-4.172, p-value = 0.024), a higher qSOFA score (OR = 1.958, 95% CI = 1.295-3.002, p-value = 0.002) and rehabilitation after discharge (OR = 2.222, 95% CI = 1.017-4.855, p-value = 0.044) were associated with a higher chance of being re-hospitalized within 6 months, whereas mortality within 30 days and within one year were both associated with older age (OR = 1.248, 95% CI = 1.056-1.562, p-value = 0.026 and OR = 1.073, 95% CI = 1.025-1.132, p-value = 0.005, respectively) and the presence of a cancer diagnosis (OR = 32.671, 95% CI = 4.787-369.1, p-value = 0.001 and OR = 4.408, 95% CI = 1.680-11.43, p-value = 0.002, respectively). This study identified routinely available predictors for LOHS, rehospitalization and mortality in patients with CAP, which may further advance our understanding of CAP and thereby improve patient management, discharge planning and hospital costs.

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort. The ACS-NSQIP database was queried to acquire 267,966 TKA cases from 2013 to 2020. Four machine learning models-artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor were trained and tested on the dataset for the prediction of prolonged LOS (LOS exceeded the 75th of all values in the cohort). The model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility. ANN delivered the best performance among the four models. ANN distinguished prolonged LOS in the study cohort with an AUC of 0.71 and accurately predicted the probability of prolonged LOS for individual patients (calibration slope: 0.82; calibration intercept: 0.03; Brier score: 0.089). All models demonstrated clinical utility by generating positive net benefits in decision curve analyses. Operation time, pre-operative transfusion, pre-operative laboratory tests (hematocrit, platelet count, and white blood cell count), and BMI were the strongest predictors of prolonged LOS. ANN demonstrated modest discrimination capacity and excellent performance in calibration and clinical utility for the prediction of prolonged LOS following TKA. Clinical application of the machine learning models has the potential to improve care coordination and discharge planning for patients at high risk of extended hospitalization after surgery. Incorporating more relevant patient factors may further increase the models' prediction strength.