ABSTRACT
Post-discharge coping difficulty presents a significant challenge for mothers of preterm infants. The readiness for hospital discharge and parenting self-efficacy are crucial factors influencing post-discharge coping difficulty. However, the pathways through which these factors impact post-discharge coping difficulty remain unclear. This study aims to investigate the impact of readiness for hospital discharge on post-discharge coping difficulty and the mediating role of parenting self-efficacy among mothers of preterm infants. A prospective study involving 462 mothers of preterm infants from six tertiary hospitals in Shandong Province was conducted. Mothers were evaluated on the day of discharge (using the Baseline characteristics and Readiness for Hospital Discharge Scale) and three weeks post-discharge (utilizing the Parenting Sense of Competence Scale-Efficacy subscale and Post-Discharge Coping Difficulty Scale). Structural equation modeling was employed to analyze the mediating effect. The results of this study revealed that readiness for hospital discharge significantly decreased post-discharge coping difficulty (ß = - 0.533, P < 0.001), and parenting self-efficacy also significantly reduced post-discharge coping difficulty (ß = - 0.419, P < 0.001). Furthermore, parenting self-efficacy partially mediated the relationship between readiness for hospital discharge and post-discharge coping difficulty, accounting for 25.35% of the total effect. Mothers reported a moderate level of post-discharge coping difficulty. In assisting mothers of premature infants to alleviate post-discharge coping difficulty, nurses could implement strategies focused on enhancing readiness for hospital discharge and parenting self-efficacy.
Subject(s)
Adaptation, Psychological , Infant, Premature , Mothers , Parenting , Patient Discharge , Self Efficacy , Humans , Female , Mothers/psychology , Adult , Parenting/psychology , Infant, Newborn , Prospective Studies , MaleABSTRACT
AIMS: Per-oral endoscopic myotomy (POEM) is a recognised treatment for achalasia, with the accepted approach involving admission for imaging and dietary progression. However, recent publications suggest same-day discharge (SDD) may be possible, which could be time and cost-saving. We sought to investigate the safety of SDD following POEM. METHODS: Fifty consecutive POEMs at two referral centres in New Zealand were performed between 2020-2023. All patients were planned for early dietary introduction and were eligible for SDD if symptoms were managed. Analgesia was available in recovery and supplied at discharge. Imaging and endoscopy were performed only if there were clinical concerns. Rates of discharge clearance, discharge, complications and re-admission were analysed. RESULTS: All 50 POEMs were technically successful. A total of 41/50 (82%) received clearance for SDD. Additionally, 35/50 (70%) achieved discharge and 6/50 (12%) were observed overnight for social reasons, including lack of transport to the referring domicile. Of the patients not cleared for SDD, 7/9 (78%) were discharged within 24 hours, and the others after 48 and 72 hours. Procedural complications were recorded in three patients (6%), with one requiring endoscopic assessment and clipping. There were two re-admissions (4%), both lt;24-hour hospital stays, and managed medically. CONCLUSIONS: The majority of patients achieved same-day discharge clearance (82%) and 96% required less than 24 hours hospital stay. Complication and re-admission rates were low overall. We have demonstrated that POEM can be an SDD procedure facilitated by early dietary introduction and liberal analgesia, without the need for routine imaging or endoscopy.
Subject(s)
Esophageal Achalasia , Feasibility Studies , Patient Discharge , Humans , Male , Esophageal Achalasia/surgery , Female , Middle Aged , New Zealand , Adult , Aged , Patient Readmission/statistics & numerical data , Natural Orifice Endoscopic Surgery/methods , Postoperative Complications/epidemiology , Myotomy/methods , Length of Stay/statistics & numerical dataABSTRACT
OBJECTIVES: Our aim was to identify which patients are likely to stay in hospital longer following total hip replacement surgery. DESIGN: Longitudinal, observational study used routinely collected data. SETTING: Data were collected from an NHS Trust in South-West England between 2016 and 2019. PARTICIPANTS: 2352 hip replacement patients had complete data and were included in analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Three measures of length of stay were used: a count measure of number of days spent in hospital, a binary measure of ≤7 days/>7 days in hospital and a binary measure of remaining in hospital when medically fit for discharge. RESULTS: The mean length of stay was 5.4 days following surgery, with 18% in hospital for more than 7 days, and 11% staying in hospital when medically fit for discharge. Longer hospital stay was associated with older age (OR=1.06, 95% CI 1.05 to 1.08), being female (OR=1.42, 95% CI 1.12 to 1.81) and more comorbidities (OR=3.52, 95% CI 1.45 to 8.55) and shorter length of stay with not having had a recent hospital admission (OR=0.44, 95% CI 0.32 to 0.60). Results were similar for remaining in hospital when medically fit for discharge, with the addition of an association with highest socioeconomic deprivation (OR=2.08, 95% CI 1.37 to 3.16). CONCLUSIONS: Older, female patients with more comorbidities and from more socioeconomically deprived areas are likely to remain in hospital for longer following surgery. This study produced regression models demonstrating consistent results across three measures of prolonged hospital stay following hip replacement surgery. These findings could be used to inform surgery planning and when supporting patient discharge following surgery.
Subject(s)
Arthroplasty, Replacement, Hip , Elective Surgical Procedures , Length of Stay , Humans , Arthroplasty, Replacement, Hip/statistics & numerical data , Length of Stay/statistics & numerical data , Female , Male , Aged , Longitudinal Studies , Retrospective Studies , Risk Factors , Middle Aged , Elective Surgical Procedures/statistics & numerical data , England , Patient Discharge/statistics & numerical data , Aged, 80 and over , Age Factors , ComorbidityABSTRACT
BACKGROUND: Low socio-economic status can lead to poor patient outcomes, exacerbated by lack of integration between health and social care and there is a demand for developing new models of working. AIM: To improve connections between patients, local services and their communities to reduce unscheduled admissions. DESIGN AND SETTING: A primary care cluster with areas of high deprivation, consisting of 11 general practices serving over 74,000 people. METHOD: A multi-disciplinary team with representatives from healthcare, local council and the third sector was formed to provide support for people with complex or social needs. A discharge liaison hub contacted patients following hospital discharge offering support, while cluster pharmacists led medicine reviews. Wellbeing Connectors were commissioned to act as a link to local wellbeing and social resources. Advance Care Planning was implemented to support personalised decision making. RESULTS: Unscheduled admissions in the over 75 age group decreased following the changes, equating to over 800 avoided monthly referrals to assessment units for the cluster. Over 2,500 patients have been reviewed by the MDT since its inception with referrals to social prescribing groups, physiotherapy and mental health teams; these patients are 20% less likely to contact their GP after their case is discussed. An improved sense of wellbeing was reported by 80% of patients supported by wellbeing connectors. Staff feel better able to meet patient needs and reported an increased joy in working. CONCLUSION: Improved integration between health, social care and third sector has led to a reduction in admissions, improved patient wellbeing and has improved job satisfaction amongst staff.
Subject(s)
Emergency Service, Hospital , Humans , Aged , Male , Female , Emergency Service, Hospital/statistics & numerical data , Middle Aged , Adult , Referral and Consultation , Primary Health Care/organization & administration , Patient Admission , Advance Care Planning/organization & administration , Aged, 80 and over , Patient DischargeABSTRACT
Head trauma is a common reason for emergency department (ED) visits. Delayed intracranial hemorrhage (ICH) in patients with minor head trauma is a major concern, but controversies exist regarding the incidence of delayed ICH and discharge planning at the ED. This study aimed to determine the incidence of delayed ICH in adults who developed ICH after a negative initial brain computed tomography (CT) at the ED and investigate the clinical outcomes for delayed ICH. This nationwide population cohort study used data from the National Health Insurance Service of Korea from 2013 to 2019. Adult patients who presented to an ED due to trauma and were discharged after a negative brain CT examination were selected. The main outcomes were the incidence of ICH within 14 days after a negative brain CT at initial ED visit and the clinical outcomes of patients with and without delayed ICH. The study patients were followed up to 1 year after the initial ED discharge. Cox proportional hazard regression analysis was used to estimate the hazard ratio for all-cause 1-year mortality of delayed ICH. During the 7-year study period, we identified 626,695 adult patients aged 20 years or older who underwent brain CT at the ED due to minor head trauma, and 2666 (0.4%) were diagnosed with delayed ICH within 14 days after the first visit. Approximately two-thirds of patients (64.3%) were diagnosed with delayed ICH within 3 days, and 84.5% were diagnosed within 7 days. Among the patients with delayed ICH, 71 (2.7%) underwent neurosurgical intervention. After adjustment for age, sex, Charlson Comorbidity Index, and insurance type, delayed ICH (adjusted hazard ratio, 2.15; 95% confidence interval, 1.86-2.48; p < 0.001) was significantly associated with 1-year mortality. The incidence of delayed ICH was 0.4% in the general population, with the majority diagnosed within 7 days. These findings suggest that patient discharge education for close observation for a week may be a feasible strategy for the general population.
Subject(s)
Intracranial Hemorrhages , Tomography, X-Ray Computed , Humans , Male , Female , Middle Aged , Intracranial Hemorrhages/epidemiology , Intracranial Hemorrhages/mortality , Intracranial Hemorrhages/etiology , Incidence , Adult , Aged , Republic of Korea/epidemiology , Cohort Studies , Emergency Service, Hospital/statistics & numerical data , Craniocerebral Trauma/complications , Craniocerebral Trauma/epidemiology , Young Adult , Patient Discharge/statistics & numerical data , Time FactorsABSTRACT
BACKGROUND: Schizophrenia is a pervasive and severe mental disorder characterized by significant disability and high rates of recurrence. The persistently high rates of readmission after discharge present a serious challenge and source of stress in treating this population. Early identification of this risk is critical for implementing targeted interventions. The present study aimed to develop an easy-to-use predictive instrument for identifying the risk of readmission within 1-year post-discharge among schizophrenia patients in China. METHODS: A prediction model, based on static factors, was developed using data from 247 schizophrenia inpatients admitted to the Mental Health Center in Wuxi, China, from July 1 to December 31, 2020. For internal validation, an additional 106 patients were included. Multivariate Cox regression was applied to identify independent predictors and to create a nomogram for predicting the likelihood of readmission within 1-year post-discharge. The model's performance in terms of discrimination and calibration was evaluated using bootstrapping with 1000 resamples. RESULTS: Multivariate cox regression demonstrated that involuntary admission (adjusted hazard ratio [aHR] 4.35, 95% confidence interval [CI] 2.13-8.86), repeat admissions (aHR 3.49, 95% CI 2.08-5.85), the prescription of antipsychotic polypharmacy (aHR 2.16, 95% CI 1.34-3.48), and a course of disease ≥ 20 years (aHR 1.80, 95% CI 1.04-3.12) were independent predictors for the readmission of schizophrenia patients within 1-year post-discharge. The area under the curve (AUC) and concordance index (C-index) of the nomogram constructed from these four factors were 0.820 and 0.780 in the training set, and 0.846 and 0.796 for the validation set, respectively. Furthermore, the calibration curves of the nomogram for both the training and validation sets closely approximated the ideal diagonal line. Additionally, decision curve analyses (DCAs) demonstrated a significantly better net benefit with this model. CONCLUSIONS: A nomogram, developed using pre-discharge static factors, was designed to predict the likelihood of readmission within 1-year post-discharge for patients with schizophrenia. This tool may offer clinicians an accurate and effective way for the timely prediction and early management of psychiatric readmissions.
Subject(s)
Nomograms , Patient Readmission , Schizophrenia , Humans , Schizophrenia/drug therapy , Patient Readmission/statistics & numerical data , Male , Female , Adult , China , Middle Aged , Patient Discharge/statistics & numerical data , Risk Assessment/methods , Antipsychotic Agents/therapeutic use , Proportional Hazards Models , Risk FactorsABSTRACT
Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.
Subject(s)
Climate Change , Forecasting , Costa Rica/epidemiology , Humans , Patient Discharge/statistics & numerical data , Respiratory Tract Diseases/epidemiology , Climate , Models, Statistical , Seasons , Hospitals , Air Pollution/analysis , Hospitalization/statistics & numerical data , Machine Learning , AlgorithmsABSTRACT
Adding continuous monitoring to usual care at an acute admission ward did not have an effect on the proportion of patients safely discharged. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed.
Subject(s)
Patient Discharge , Wearable Electronic Devices , Humans , Male , Female , Monitoring, Ambulatory/instrumentation , Monitoring, Ambulatory/methods , Patient Admission , Aged , Middle Aged , Monitoring, Physiologic/instrumentationABSTRACT
This paper focuses on defining a framework to allow individual patients to track their own health related data. We propose a Patient Centered Information Framework (PCIF) allowing patient to manage their own data by using discharge letters. Discharge letters summarize information from a hospital stay, such as medical history, diagnoses, treatments and follow up, needed for continuity of care. It enables patients to share data with different organizations ensuring personal data protection, even when moving from different places and countries. A record of clinical management may thus be guaranteed when moving among different health structures as well as simplifying obtaining medications. We propose an approach to allow citizens to manage their health related data in a cross borders fashion. We compare the regulation of discharge letters among a sample of countries. We propose a management protocol for using a commonly adopted patient discharge letter framework within a PCIF.
Subject(s)
Patient-Centered Care , Humans , Patient Discharge , Electronic Health Records , Health Records, Personal , ConfidentialityABSTRACT
This study introduces a novel approach for generating machine-generated instruction datasets for fine-tuning medical-specialized language models using MIMIC-IV discharge records. The study created a large-scale text dataset comprising instructions, cropped discharge notes as inputs, and outputs in JSONL format. The dataset was generated through three main stages, generating instruction and output using seed tasks provided by medical experts, followed by invalid data filtering. The generated dataset consisted of 51,385 sets, with mean ROUGE between seed tasks of 0.185. Evaluation of the generated dataset were promising, with high validity rates determined by both GPT-3.5 and a human annotator (88.0% and 88.5% respectively). The study highlights the potential of automating dataset creation for NLP tasks in the medical domain.
Subject(s)
Electronic Health Records , Natural Language Processing , Humans , Patient Discharge , Patient Discharge SummariesABSTRACT
The overwhelming volume of patients in emergency departments (EDs) is a significant problem that hinders the delivery of high quality healthcare. Despite their great value, triage protocols are challenging to put into practice. This paper examines the utility of prediction models as a tool for clinical decision support, with a focus on medium-severity patients as defined by the ESI algorithm. 689 cases of medium-risk patients were gathered from the AHEPA hospital, evaluated, and their data fed three classifiers: XGBoost (XGB), Random Forest (RF) and Logistic Regression (LR), with the prediction goal being the outcome of their visit, i.e. admission or discharge. Essential features for the prediction task were determined using feature importance and distribution analysis. Despite having many missing values or high sparsity datasets, several symptoms and metrics are recommended as crucial for outcome prediction. When fed the patients' vital signs, XGB achieved an accuracy score of 91.30%. Several chief complaints were also proven beneficial. Prediction models can, in general, not only lessen the drawbacks of triage implementation, but also enhance its delivery.
Subject(s)
Decision Support Systems, Clinical , Emergency Service, Hospital , Triage , Humans , Artificial Intelligence , Algorithms , Patient DischargeABSTRACT
OBJECTIVES: To evaluate changes in health outcomes between years 2 and 3 after discharge following COVID-19 and to identify risk factors for poor health 3-year post-discharge. DESIGN: This is a multicentre observational cohort study. SETTING: This study was conducted in two centres from Wuhan, China. PARTICIPANTS: Eligibility screening has been performed in 3988 discharged laboratory-confirmed adult COVID-19 patients. Exclusion criteria were refusal to participate, inability to contact and death before follow-up. The WHO COVID-19 guidelines on defining disease severity were adopted. RESULTS: 1594 patients participated in the 1-year, 2-year and 3-year follow-ups, including 796 (49.9%) male patients, and 422 (26.5%) patients were classified in the severe disease group. 3 years after discharge, 182 (11.4%) patients still complained of at least one symptom. The most common symptoms were fatigue, myalgia, chest tightness, cough, anxiety, shortness of breath and expectoration. Fatigue or myalgia, the most common symptom cluster, frequently coexisted with chest symptoms and anxiety. Symptom persistence between years 2 and 3 was reported in 70 patients (4.4%) for which intensive care unit (ICU) admission was a risk factor (p=0.038). Of the 1586 patients who completed the chronic obstructive pulmonary disease assessment test (CAT), 97 (6.1%) scored ≥10, with older age being associated with CAT ≥10 (p=0.007). CONCLUSIONS: Between years 2 and 3 after SARS-CoV-2 infection, most patients returned to an asymptomatic state, and only a few were still symptomatic. ICU admission was a risk factor for symptom persistence.
Subject(s)
COVID-19 , Patient Discharge , SARS-CoV-2 , Humans , COVID-19/epidemiology , Male , Female , China/epidemiology , Middle Aged , Patient Discharge/statistics & numerical data , Adult , Aged , Risk Factors , Cohort Studies , Severity of Illness Index , Intensive Care Units/statistics & numerical dataABSTRACT
BACKGROUND: The remarkable advancements in surgical techniques over recent years have shifted the clinical focus from merely reducing mortality to enhancing the quality of postoperative recovery. The duration of a patient's hospital stay serves as a crucial indicator in evaluating postoperative recovery and surgical outcomes. This study aims to identify predictors of the length of hospital stay for children who have undergone corrective surgery for Ebstein Anomaly (EA). METHODS: We conducted a retrospective cohort study on children (under 18 years of age) diagnosed with EA who were admitted for corrective surgery between January 2009 and November 2021 at Fuwai Hospital. The primary outcome was the Time to Hospital Discharge (THD). Cox proportional hazard models were utilized to identify predictors of THD. In the context of time-to-event analysis, discharge was considered an event. In cases where death occurred before discharge, it was defined as an extended THD, input as 100 days (exceeding the longest observed THD), and considered as a non-event. RESULTS: A total of 270 children were included in this study, out of which three died in the hospital. Following the Cox proportional hazard analysis, six predictors of THD were identified. The hazard ratios and corresponding 95% confidence intervals were as follows: age, 1.030(1.005,1.055); C/R > 0.65, 0.507(0.364,0.707); Carpentier type C or D, 0.578(0.429,0.779); CPB time, 0.995(0.991,0.998); dexamethasone, 1.373(1.051,1.795); and transfusion, 0.680(0.529,0.875). The children were categorized into three groups based on the quartile of THD. Compared to children in the ≤ 6 days group, those in the ≥ 11 days group were associated with a higher incidence of adverse outcomes. Additionally, the duration of mechanical ventilation and ICU stay, as well as hospital costs, were significantly higher in this group. CONCLUSION: We identified six predictors of THD for children undergoing corrective surgery for EA. Clinicians can utilize these variables to optimize perioperative management strategies, reduce adverse complications, improve postoperative recovery, and reduce unnecessary medical expenses.
Subject(s)
Ebstein Anomaly , Length of Stay , Humans , Retrospective Studies , Length of Stay/statistics & numerical data , Female , Male , Ebstein Anomaly/surgery , Child, Preschool , Infant , Child , Proportional Hazards Models , Adolescent , Risk Factors , Patient DischargeABSTRACT
AIMS OF THE STUDY: Opioid prescriptions have increased in Switzerland, even though current guidelines warn of their harms. If opioids for postoperative analgesia are not tapered before hospital discharge, patients are at risk of adverse events such as constipation, drowsiness, dependence, tolerance and withdrawal. The aim of this study was to investigate and quantify the potential association between opioids prescribed at discharge from hospital and rehospitalisation. METHODS: We conducted a nested case-control study using routinely collected electronic health records from a Swiss public acute hospital. Cases were patients aged 65 years or older admitted between November 2014 and December 2018, with documented opioid administration on the day of discharge and rehospitalisation within 18 or 30 days after discharge. Each case was matched to five controls for age, sex, year of hospitalisation and Charlson Comorbidity Index. We calculated odds ratios for 18-day and 30-day rehospitalisation based on exposure to opioids using a conditional logistic regression adjusted for potential confounders. Secondary analyses included stratifications into morphine-equivalent doses of <50 mg, 50-89 mg and ≥90 mg, and co-prescriptions of gabapentinoids and benzodiazepines. RESULTS: Of 22,471 included patients, 3144 rehospitalisations were identified, of which 1698 were 18-day rehospitalisations and 1446 were 30-day rehospitalisations. Documented opioid administration on the day of discharge was associated with 30-day rehospitalisation after adjustment for confounders (adjusted odds ratio 1.48; 95% CI 1.25-1.75, p <0.001), while no difference was observed in the likelihood of 18-day rehospitalisation. The combined prescription of opioids with benzodiazepines or gabapentinoids and morphine-equivalent doses >50 mg were rare. CONCLUSIONS: Patients receiving opioids on the day of discharge were 48% more likely to be readmitted to hospital within 30 days. Clinicians should aim to discontinue opioids started in hospital before discharge if possible. Patients receiving an opioid prescription should be educated and monitored as part of opioid stewardship programmes.
Subject(s)
Analgesics, Opioid , Pain, Postoperative , Patient Readmission , Practice Patterns, Physicians' , Humans , Analgesics, Opioid/therapeutic use , Switzerland , Case-Control Studies , Male , Female , Aged , Patient Readmission/statistics & numerical data , Pain, Postoperative/drug therapy , Practice Patterns, Physicians'/statistics & numerical data , Aged, 80 and over , Patient Discharge/statistics & numerical data , Hospitals, Public/statistics & numerical data , Inpatients/statistics & numerical data , Drug Prescriptions/statistics & numerical dataABSTRACT
BACKGROUND: Discharge instructions are a key form of documentation and patient communication in the time of transition from the emergency department (ED) to home. Discharge instructions are time-consuming and often underprioritized, especially in the ED, leading to discharge delays and possibly impersonal patient instructions. Generative artificial intelligence and large language models (LLMs) offer promising methods of creating high-quality and personalized discharge instructions; however, there exists a gap in understanding patient perspectives of LLM-generated discharge instructions. OBJECTIVE: We aimed to assess the use of LLMs such as ChatGPT in synthesizing accurate and patient-accessible discharge instructions in the ED. METHODS: We synthesized 5 unique, fictional ED encounters to emulate real ED encounters that included a diverse set of clinician history, physical notes, and nursing notes. These were passed to GPT-4 in Azure OpenAI Service (Microsoft) to generate LLM-generated discharge instructions. Standard discharge instructions were also generated for each of the 5 unique ED encounters. All GPT-generated and standard discharge instructions were then formatted into standardized after-visit summary documents. These after-visit summaries containing either GPT-generated or standard discharge instructions were randomly and blindly administered to Amazon MTurk respondents representing patient populations through Amazon MTurk Survey Distribution. Discharge instructions were assessed based on metrics of interpretability of significance, understandability, and satisfaction. RESULTS: Our findings revealed that survey respondents' perspectives regarding GPT-generated and standard discharge instructions were significantly (P=.01) more favorable toward GPT-generated return precautions, and all other sections were considered noninferior to standard discharge instructions. Of the 156 survey respondents, GPT-generated discharge instructions were assigned favorable ratings, "agree" and "strongly agree," more frequently along the metric of interpretability of significance in discharge instruction subsections regarding diagnosis, procedures, treatment, post-ED medications or any changes to medications, and return precautions. Survey respondents found GPT-generated instructions to be more understandable when rating procedures, treatment, post-ED medications or medication changes, post-ED follow-up, and return precautions. Satisfaction with GPT-generated discharge instruction subsections was the most favorable in procedures, treatment, post-ED medications or medication changes, and return precautions. Wilcoxon rank-sum test of Likert responses revealed significant differences (P=.01) in the interpretability of significant return precautions in GPT-generated discharge instructions compared to standard discharge instructions but not for other evaluation metrics and discharge instruction subsections. CONCLUSIONS: This study demonstrates the potential for LLMs such as ChatGPT to act as a method of augmenting current documentation workflows in the ED to reduce the documentation burden of physicians. The ability of LLMs to provide tailored instructions for patients by improving readability and making instructions more applicable to patients could improve upon the methods of communication that currently exist.
Subject(s)
Emergency Service, Hospital , Patient Discharge , Humans , Emergency Service, Hospital/statistics & numerical data , Patient Discharge/statistics & numerical data , Female , Male , Surveys and Questionnaires , Adult , Middle Aged , Artificial IntelligenceABSTRACT
IMPORTANCE AND OBJECTIVES: To compare the 18-month survival between patients with newly diagnosed cancer discharged home after early unplanned ICU admission and those without early unplanned ICU admission; we also evaluated the frequency and risk factors for early unplanned ICU admission. DESIGN: Observational study with prospectively collected data from September 2019 to June 2021 and 18 months follow-up. SETTING: Single dedicated cancer center in São Paulo, Brazil. PARTICIPANTS: We screened consecutive adults with suspected cancer and included those with histologically proven cancer from among 20 highly prevalent cancers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The exposure was early unplanned ICU admission, defined as admission for medical reasons or urgent surgery during the first 6 months after cancer diagnosis. The main outcome was 18-month survival after cancer diagnosis, and the main analysis was Cox's proportional hazards model adjusted for confounders and immortal time bias. Propensity score matching was used in the sensitivity analysis. We screened 4738 consecutive adults with suspected cancer and included 3348 patients. Three hundred twelve (9.3%) had early unplanned ICU admission, which was associated with decreased 18-month survival both in the unadjusted (hazard ratio, 4.03; 95% CI, 2.89-5.62) and adjusted (hazard ratio, 1.84; 95% CI, 1.29-2.64) models. The sensitivity analysis confirmed the results because the groups were balanced after matching, and the 18-month survival of patients with early ICU admission was lower compared with patients without early ICU admission (87.0% vs. 93.9%; p = 0.01 log-rank test). Risk factors for early unplanned ICU admission were advanced age, comorbidities, worse performance status, socioeconomic deprivation, metastatic tumors, and hematologic malignancies. CONCLUSIONS: Patients with newly diagnosed cancer discharged home after early unplanned ICU admission have decreased 18-month survival compared with patients without early unplanned ICU admission.
Subject(s)
Intensive Care Units , Neoplasms , Patient Discharge , Humans , Male , Female , Prospective Studies , Intensive Care Units/statistics & numerical data , Middle Aged , Neoplasms/mortality , Neoplasms/diagnosis , Neoplasms/therapy , Patient Discharge/statistics & numerical data , Aged , Brazil/epidemiology , Risk Factors , Adult , Proportional Hazards Models , Patient Admission/statistics & numerical data , Survival AnalysisABSTRACT
AIMS: Patients undergoing catheter ablation (CA) of ventricular arrhythmias (VAs) are generally observed overnight in the hospital given the concern for complications. To evaluate the efficacy and safety of same-day discharge (SDD) of patients undergoing elective CA of premature ventricular complexes (PVCs). METHODS AND RESULTS: A retrospective evaluation of all patients undergoing elective VA ablation at Ascension St Vincent Hospital from 1 January 2018 to 31 December 2019 was undertaken. Of those, the patients undergoing PVC ablation were divided into SDD and non-SDD. Patients underwent SDD at the discretion of the operator. The primary safety outcome was the 30-day incidence of complications and death. The primary efficacy outcome was procedural success. Among 188 patients who underwent VA ablation, 98 (52.1%) were PVC ablations, and of those, 55 (56.1%) were SDD. There was no difference in age, gender, comorbidities, or ejection fraction between the two groups. Patients that were non-SDD were more likely to be on chronic anticoagulation (P = 0.03), have ablation in the LV (P = 0.04), have retrograde access (P = 0.03), and receive heparin during the procedure (P = 0.01). There were no complications in the SDD group compared with one (2.3%) in the non-SDD group. There was no difference in primary efficacy between the two groups with a 90.9% acute success in the SDD and 88.4% in the non-SDD (P = 0.68). CONCLUSION: Same-day discharge for CA of PVCs is feasible and could lower healthcare resource utilization without compromising outcomes in this unique population.
Subject(s)
Catheter Ablation , Patient Discharge , Ventricular Premature Complexes , Humans , Ventricular Premature Complexes/surgery , Ventricular Premature Complexes/physiopathology , Ventricular Premature Complexes/diagnosis , Male , Female , Retrospective Studies , Middle Aged , Catheter Ablation/adverse effects , Catheter Ablation/methods , Treatment Outcome , Aged , Postoperative Complications/epidemiology , Time FactorsABSTRACT
BACKGROUND: Readmission, defined as any admission after discharge from the same hospital, has negative implications for health outcomes. This study aims to identify the sociodemographic and clinical factors associated with hospital readmission among psychiatric patients. METHODOLOGY: This case-control study analyzed 202 clinical records of patients admitted to a psychiatric hospital between 2019-2021. The sample was selected using simple random sampling. Qualitative variables were presented using frequencies, percentages, and chi-square tests for association. Quantitative variables were described using central tendency measures and dispersion of data, investigated with the Kolmogorov-Smirnov test, Student's t-test or Wilcoxon test as appropriate. Regression analysis was conducted to determine factors linked to readmission. p < 0.05 was considered. RESULTS: Women accounted for a higher readmission rate (59%). Patients diagnosed with schizophrenia had a higher readmission rate (63%), experienced longer transfer times to the hospital during readmissions, and had shorter hospital stays. Polypharmacy and pharmacological interactions were associated with readmission. Olanzapine treatment was identified as a risk factor for readmission (ExpB = 3.203, 95% CI 1.405-7.306, p = 0.006). CONCLUSIONS: The findings suggest avoiding polypharmacy and medications with high side effect profiles to reduce readmissions. This study offers valuable insights for clinical decision-making from admission to discharge planning, aiming to enhance the quality of care.
Subject(s)
Mental Disorders , Patient Discharge , Patient Readmission , Humans , Patient Readmission/statistics & numerical data , Case-Control Studies , Female , Male , Patient Discharge/statistics & numerical data , Middle Aged , Adult , Risk Factors , Mental Disorders/therapy , Mental Disorders/drug therapy , Length of Stay/statistics & numerical data , Hospitals, Psychiatric/statistics & numerical data , Time Factors , Schizophrenia/drug therapy , Schizophrenia/therapy , Polypharmacy , Olanzapine/therapeutic use , Antipsychotic Agents/therapeutic use , AgedABSTRACT
BACKGROUND: Longer length of hospital stay (LOS) negatively affects the organizational efficiency of public health systems and both clinical and functional aspects of older patients. Data on the effects of transitional care programs based on multicomponent interventions to reduce LOS of older patients are scarce and controversial. AIMS: The PRO-HOME study aimed to assess the efficacy in reducing LOS of a transitional care program involving a multicomponent intervention inside a technologically monitored in-hospital discharge facility. METHODS: This is a Randomized Clinical Trial on 60 patients (≥65 years), deemed stable and dischargeable from the Acute Geriatrics Unit, equally assigned to the Control Group (CG) or Intervention Group (IG). The latter underwent a multicomponent intervention including lifestyle educational program, cognitive and physical training. At baseline, multidimensional frailty according to the Multidimensional Prognostic Index (MPI), and Health-Related Quality of Life (HRQOL) were assessed in both groups, along with physical capacities for the IG. Enrolled subjects were evaluated after 6 months of follow-up to assess multidimensional frailty, HRQOL, and re-hospitalization, institutionalization, and death rates. RESULTS: The IG showed a significant 2-day reduction in LOS (median days IG = 2 (2-3) vs. CG = 4 (3-6); p < 0.001) and an improvement in multidimensional frailty at 6 months compared to CG (median score IG = 0.25(0.25-0.36) vs. CG = 0.38(0.31-0.45); p = 0.040). No differences were found between the two groups in HRQOL, and re-hospitalization, institutionalization, and death rates. DISCUSSION: Multidimensional frailty is a reversible condition that can be improved by reduced LOS. CONCLUSIONS: The PRO-HOME transitional care program reduces LOS and multidimensional frailty in hospitalized older patients. TRIAL REGISTRATION: ClinicalTrials.gov n. NCT06227923 (retrospectively registered on 29/01/2024).