Risk factors for prolonged length of hospital stay following elective hip replacement surgery: a retrospective longitudinal observational study.

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.

Incidence and outcomes of delayed intracranial hemorrhage: a population-based cohort study.

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.

A predictive model for readmission within 1-year post-discharge in patients with schizophrenia.

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.

Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

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.

Health outcomes of COVID-19 patients from Wuhan, China 3-year after hospital discharge: a cohort study.

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.

Inpatient opioid prescribing patterns and their effect on rehospitalisations: a nested case-control study using data from a Swiss public acute hospital.

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.

Patient-Representing Population's Perceptions of GPT-Generated Versus Standard Emergency Department Discharge Instructions: Randomized Blind Survey Assessment.

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.

Decreased Long-Term Survival of Patients With Newly Diagnosed Cancer Discharged Home After Unplanned ICU Admission: A Prospective Observational Study.

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.

Factors Associated with Readmission within 30 Days of Hospital Discharge of Psychiatric Patients: Case-Control Study.

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.

Emergency Department Visits And Hospital Capacity In The US: Trends In The Medicare Population During The COVID-19 Pandemic.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25 percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2 percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

Postdischarge functional outcomes in older patients with acute heart failure in Japan: the Longevity Improvement & Fair Evidence study.

BACKGROUND: The association between care needs level (CNL) at hospitalisation and postdischarge outcomes in older patients with acute heart failure (aHF) has been insufficiently investigated. METHODS: This population-based cohort study was conducted using health insurance claims and CNL data of the Longevity Improvement & Fair Evidence study. Patients aged ≥65 years, discharged after hospitalisation for aHF between April 2014 and March 2022, were identified. CNLs at hospitalisation were classified as no care needs (NCN), support level (SL) and CNL1, CNL2-3 and CNL4-5 based on total estimated daily care time as defined by national standard criteria, and varied on an ordinal scale between SL&CNL1 (low level) to CNL4-5 (fully dependent). The primary outcomes were changes in CNL and death 1 year after discharge, assessed by CNL at hospitalisation using Cox proportional hazard models. RESULTS: Of the 17 724 patients included, 7540 (42.5%), 4818 (27.2%), 3267 (18.4%) and 2099 (11.8%) had NCN, SL&CNL1, CNL2-3 and CNL4-5, respectively, at hospitalisation. One year after discharge, 4808 (27.1%), 3243 (18.3%), 2968 (16.7%), 2505 (14.1%) and 4200 (23.7%) patients had NCN, SL&CNL1, CNL2-3, CNL4-5 and death, respectively. Almost all patients' CNLs worsened after discharge. Compared to patients with NCN at hospitalisation, patients with SL&CNL1, CNL2-3 and CNL4-5 had an increased risk of all-cause death 1 year after discharge (hazard ratio [95% confidence interval]: 1.19 [1.09-1.31], 1.88 [1.71-2.06] and 2.56 [2.31-2.84], respectively). CONCLUSIONS: Older patients with aHF and high CNL at hospitalisation had a high risk of all-cause mortality in the year following discharge.

Fulfilling last wishes: improving the compassionate discharge process.

BACKGROUND: Compassionate discharges (ComD), commonly known as rapid discharges, are urgent one-way discharges for critically ill hospitalised patients with death expected within hours or less than 7 days, to die at their place of choice-usually in their own home. Challenges abound in this time-sensitive setting when multiple parties must work together to prepare medically unstable patients for discharge, yet healthcare staff are largely unaware of the process, resulting in delays. METHODS: Process mapping, an Ishikawa diagram and a Pareto chart were used to identify barriers, which included timely acquisition of home equipment and medication and poor communication among stakeholders. In May 2020, the Quality Improvement (QI) team embarked on a pilot project to reduce family caregiver anxiety and delays in the ComD process while maintaining a success rate above 90% over a 12-month period. INTERVENTIONS: Three Plan-Do-Study-Act (PDSA) cycles were used to refine a ComD resource package that was developed; this consisted of a checklist, a kit and caregiver resources. This was to support nurses, doctors and families during this difficult and emotional transition. Items in the ComD resource package were revised iteratively based on user feedback, with further data collected to measure its usefulness. RESULTS: The 12-month ComD success rate over 3 PDSA cycles were 88.9%, 94.2% and 96.7%, respectively, after each cycle. There was a consistent reduction in the level of family anxiety before and after caregiver training and resources. Reasons for failed ComD included acute clinical deterioration or delays in obtaining home oxygen support. CONCLUSION: The ComD resource package allowed collaborative work across different disciplines, strengthening the safety and utility of ComD and allowing patients to die in their place of choice. These are ubiquitous across settings; this QI problem is thus relevant beyond our local institution.

Timing of follow-up visits after hospital discharge for COPD: Application of a new method.

RATIONALE: A common strategy to reduce COPD readmissions is to encourage patient follow-up with a physician within 1 to 2 weeks of discharge, yet evidence confirming its benefit is lacking. We used a new study design called target randomized trial emulation to determine the impact of follow-up visit timing on patient outcomes. METHODS: All Ontario residents aged 35 or older discharged from a COPD hospitalization were identified using health administrative data and randomly assigned to those who received and did not receive physician visit follow-up by within seven days. They were followed to all-cause emergency department visits, readmissions or death. Targeted randomized trial emulation was used to adjust for differences between the groups. COPD emergency department visits, readmissions or death was also considered. RESULTS: There were 94,034 patients hospitalized with COPD, of whom 73.5% had a physician visit within 30 days of discharge. Adjusted hazard ratio for all-cause readmission, emergency department visits or death for people with a visit within seven days post discharge was 1.03 (95% Confidence Interval [CI]: 1.01-1.05) and remained around 1 for subsequent days; adjusted hazard ratio for the composite COPD events was 0.97 (95% CI 0.95-1.00) and remained significantly lower than 1 for subsequent days. CONCLUSION: While a physician visit after discharge was found to reduce COPD events, a specific time period when a physician visit was most beneficial was not found. This suggests that follow-up visits should not occur at a predetermined time but be based on factors such as anticipated medical need.

Nurse-Sensitive Indicators as Predictors of Trauma Patient Discharge Disposition.

BACKGROUND: About 3.5 million trauma patients are hospitalized every year, but 35%-40% require further care after discharge. Nurses' ability to affect discharge disposition by minimizing the occurrence of nurse-sensitive indicators (catheter-associated urinary tract infection [CAUTI], central line-associated bloodstream infection [CLABSI], and hospital-acquired pressure injury [HAPI]) is unknown. These indicators may serve as surrogate measures of quality nursing care. OBJECTIVE: The purpose of this study was to determine whether nursing care, as represented by three nurse-sensitive indicators (CAUTI, CLABSI, and HAPI), predicts discharge disposition in trauma patients. METHODS: This study was a secondary analysis of the 2021 National Trauma Data Bank. We performed logistic regression analyses to determine the predictive effects of CAUTI, CLABSI, and HAPI on discharge disposition, controlling for participant characteristics. RESULTS: A total of n = 29,642 patients were included, of which n = 21,469 (72%) were male, n = 16,404 (64%) were White, with a mean (SD) age of 44 (14.5) and mean (SD) Injury Severity Score of 23.2 (12.5). We created four models to test nurse-sensitive indicators, both individually and compositely, as predictors. While CAUTI and HAPI increased the odds of discharge to further care by 1.4-1.5 and 2.1 times, respectively, CLABSI was not a statistically significant predictor. CONCLUSIONS: Both CAUTI and HAPI are statistically significant predictors of discharge to further care for patients after traumatic injury. High-quality nursing care to prevent iatrogenic complications can improve trauma patients' long-term outcomes.

Unfulfilled home health referrals lead to higher mortality among Medicare Advantage members.

OBJECTIVES: Medicare Advantage (MA) members referred to home health after inpatient hospitalization may or may not receive these services for a variety of member- and health care system-related reasons. Our objective was to compare outcomes among MA members referred to home health following hospitalization who receive home health services vs those who do not. STUDY DESIGN: Retrospective quasi-experimental study. METHODS: Following acute hospitalization, members with discharge orders to receive home health services between January 2021 and October 2022 were identified in a medical claims database consisting of MA beneficiaries. Members who received services within 30 days of discharge were balanced using inverse propensity score weighting on member- and admission-related covariates with a comparator group of members who did not receive services. Primary outcomes included mortality and readmissions in the ensuing 30, 90, and 180 days. Secondary outcomes included emergency department visits, primary care visits, and per-member per-month costs. RESULTS: The home health-treated group consisted of 2115 discharges, and the untreated group consisted of 761 discharges. The treated group experienced lower mortality at 30 days (2% vs 3%, respectively; OR, 0.58; 95% CI, 0.36-0.92), 90 days (8% vs 10%; OR, 0.77; 95% CI, 0.60-0.98), and 180 days (11% vs 14%; OR, 0.81; 95% CI, 0.65-0.99). The treated group also experienced higher readmissions at 30 days (13% vs 10%; OR, 1.26; 95% CI, 1.01-1.60), 90 days (24% vs 16%; OR, 1.69; 95% CI, 1.39-2.05), and 180 days (33% vs 24%; OR, 1.52; 95% CI, 1.29-1.79). CONCLUSION: MA members referred to home health after acute hospitalization who did not receive home health services had higher mortality.

Readmission Rates After Expedited Postpartum Discharge.

OBJECTIVE: To characterize national trends in expedited postpartum discharge and, secondarily, to identify predictors of expedited postpartum discharge and assess whether expedited postpartum discharge was associated with postpartum readmissions within 60 days of delivery hospitalization discharge. METHODS: Birth hospitalizations and subsequent 60-day postpartum readmissions were extracted from the 2016-2020 Nationwide Readmissions Database for this retrospective cohort study. Postpartum discharge was categorized as expedited (less than 2 days after vaginal birth or less than 3 days after cesarean birth), routine (2 days after vaginal birth or 3 days after cesarean birth), or prolonged (more than 2 days after vaginal birth or more than 3 days after cesarean birth). Trends in expedited discharge were assessed over the study period with joinpoint regression. Unadjusted and adjusted logistic regression models were performed to assess clinical, hospital, and demographic predictors of expedited postpartum discharge. Sixty-day postpartum readmission risk was calculated, and adjusted regression models were performed to evaluate the association between expedited postpartum discharge and readmission. RESULTS: Of 17.9 million birth hospitalizations, 32.9% had expedited postpartum discharge. The overall 60-day postpartum readmission rate after delivery hospitalization discharge was 1.7% for all patients, 1.4% for expedited postpartum discharge, 1.6% for routine discharge, and 3.3% for prolonged discharge. Rates of expedited postpartum increased from 29.1% in 2016 to 31.4% in 2019 and to 43.8% in 2020. This trend was not significant (average annual percent change: 9.9%, 95% CI, -1.6% to 23.7%), although rates of expedited discharge were significantly higher in 2020 than in 2016-2019 ( P <.01). Younger and older age, chronic comorbid conditions, mental health conditions, and obstetric complications (eg, transfusion, chorioamnionitis or endometritis) were associated with lower likelihood of expedited postpartum discharge. Expedited postpartum discharge was associated with 14% lower adjusted odds of 60-day postpartum readmission compared with routine discharge (adjusted odds ratio 0.86, 95% CI, 0.85-0.88). CONCLUSION: Rates of expedited postpartum discharge increased significantly in 2020 compared with 2016-2019 and were not associated with 60-day postpartum readmission. These findings suggest that broader use of expedited postpartum discharge has not resulted in increased risk of postpartum readmissions.

Discharge against medical advice in trauma patients: Trends, risk factors, and implications for health care management strategies.

OBJECTIVE: Given the nonelective nature of most trauma admissions, patients who experience trauma are at a particular risk of discharge against medical advice. Despite the risk of unplanned readmission and financial burden on the health care system, discharge against medical advice among hospitalized patients continues to rise. The present study aimed to assess evolving trends and outcomes associated in patients with discharge against medical advice among patients hospitalized for traumatic injury. METHODS: The 2016-2020 Nationwide Readmissions Database was queried to identify all hospitalizations for traumatic injuries. The patient cohort was stratified into those who had discharge against medical advice and those who did not. Temporal trends of discharge against medical advice and associated costs over time were evaluated using nonparametric tests. Multivariable regression models were developed to assess factors associated with discharge against medical advice. Associations of discharge against medical advice with length of stay, hospitalization costs, and unplanned 30-day readmission were subsequently evaluated. RESULTS: Of an estimated 4,969,717 patients, 65,354 (1.3%) had discharge against medical advice after hospitalization for traumatic injury. Over the study period, the incidence of discharge against medical advice increased (nptrend <0.001). After risk adjustment, older age (adjusted odds ratio, 0.98/per year; 95% confidence interval, 0.97-0.98), female sex (adjusted odds ratio, 0.65; 95% confidence interval, 0.64-0.67), and management at high-volume trauma center (adjusted odds ratio, 0.71; 95% confidence interval, 0.69-0.74) were associated with lower odds of discharge against medical advice. Compared with others, discharge against medical advice was associated with decrements in length of stay by 1.3 days (95% confidence interval, 1.1-1.5 days) and index hospitalization costs by $2,200 (5% confidence interval, $1,600-2,900), while having a greater risk of unplanned 30-day readmission (adjusted odds ratio, 2.21; 95% confidence interval, 2.06-2.36). CONCLUSION: The incidence of discharge against medical advice and its associated cost burden have increased in recent years. Community-level interventions and institutional efforts to mitigate discharge against medical advice may improve the quality of care and resource allocation for patients with traumatic injuries.