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Background and aims: Acute cholecystitis is a fairly common inpatient diagnosis among the gastrointestinal disorders. The aim of this study was to use a national database of US hospitals to evaluate the incidence and costs of hospital admissions associated with acute cholecystitis. Method: We analyzed the National Inpatient Sample Database (NIS) for all patients in which acute cholecystitis (ICD-9 codes: 574.00, 574.01, 574.30, 574.31, 574.60, 574.61 or 575.0) was the principal discharge diagnosis from 1997 to 2012. The NIS is the largest all-payer inpatient database in the United States and contains data from approximately 8 million hospital stays each year. The statistical significance of the difference in the number of hospital discharges, lengths of stay and associated hospital costs over the study period was determined by using the Chi-square test for trends. Results: In 1997, there were 149 661 hospital admissions with a principal discharge diagnosis of acute cholecystitis, which increased to 215 995 in 2012 ( P < 0.001). The mean length of stay for acute cholecystitis decreased by 17% between 1997 and 2012 (i.e. from 4.7 days to 3.9 days; (P < 0.05). During the same time period, however, mean hospital charges have increased by 195.4 % from US$14 608 per patient in 1997 to US$43 152 per patient in 2012 ( P < 0.001). Conclusion: The number of inpatient discharges related to acute cholecystitis has increased significantly in the United States over the last 16 years, along with a great increase in the associated hospital charges. However, there has been a gradual decline in the mean length of stay. Inpatient costs associated with acute cholecystitis contribute significantly to the total healthcare bill. Further research on cost-effective evaluation and management of acute cholecystitis is required.

The emergency department inpatient admission process is a key contributor to throughput delays and can indicate facility-wide inefficiencies. The purpose of this study was to examine the effectiveness of an enhanced goal-driven timed target emergency department inpatient workflow process, with a specific focus on timing, successful communication, and escalation. The researchers used a 9-month time series quasi-experimental design to examine 7478 emergency department inpatient admissions data from October 2022 to June 2023; a new workflow was introduced in November 2022. A Kruskal-Wallis H analysis showed that over 9 months, there was a drop in median duration for all 4 phases of the workflow: Bed requested/assigned (113.50-75.00 minutes), bed assigned/ready (127.00-81.00 minutes), overall bed assigned/transport off the emergency department (63.00-59.00 minutes); however, data showed an increase in bed ready/nurse report (31.00-32.00 minutes), χ2(8) = 205.99, P < .001. These results subsequently contributed to an overall decrease in emergency department length of stay (8.90-7.70 hours). A hierarchical multiple regression model of month, duration of time of bed requested/assigned, bed assigned/ready, bed ready/nurse report, and overall bed assigned/ transport off the emergency department duration was also statistically significant, F (4, 6751) = 1073.57, P < .001, adjusted R2 = 0.39. The full model showed that bed ready/nurse report accounted for 38.8% of the observed variation in results. A goal-driven timed target workflow standardization focusing on timing, direct communication, and escalation holds significant promise in increasing efficiency and ultimately improving emergency department inpatient admission.

Emergency inpatient admissions of children and adolescents are more difficult if the patient is admitted involuntarily and/or the caregivers or custodians of institutional care are absent. The present study aimed to clinically characterize involuntary versus voluntary admissions by examining the reasons for presentation and associated factors. We retrospectively analyzed patients who presented to the emergency department of a hospital for child and adolescent psychiatry in Bavaria, Germany, and were admitted as inpatients for crisis intervention in the 4th quarter of 2014–2018. Reasons for presentation, clinical and sociodemographic characteristics, and type of admission (voluntary versus involuntary) were analyzed for 431 emergency inpatient admissions. A total of 106 (24.6%) patients were involuntarily admitted. In a binominal logistic regression, presentation due to alcohol consumption, deviant social behavior, and psychosocial burden was positively associated, whereas difficulties at school and depression were negatively associated, with the likelihood of involuntary admission. 58.5% of the 123 unaccompanied patients were admitted involuntarily. Reasons for the presentation of unaccompanied and voluntary inpatient admissions were suicidal thoughts, psychosocial burden, and externalized aggression. A substantial number of child and adolescent psychiatric admissions represent emergency admissions. Involuntarily admitted patients and unaccompanied children/adolescents represent a non-negligible proportion of clinical routine and the clinical and legal background factors need to be further clarified in future studies. This study is registered in the German Clinical Trials Register (24 September 2019, DRKS00017689).

Background Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. Methods We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. Results For the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. Conclusions Hybrid model does not necessarily outperform its constituents’ performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.

Background People who have been given a diagnosis of a ‘personality disorder’ need access to good quality mental healthcare when in crisis, but the evidence underpinning crisis services for this group is limited. We synthesised quantitative studies reporting outcomes for people with a ‘personality disorder’ diagnosis using crisis and acute mental health services. Methods We searched OVID Medline, PsycInfo, PsycExtra, Web of Science, HMIC, CINAHL Plus, Clinical Trials and Cochrane CENTRAL for randomised controlled trials (RCTs) and observational studies that reported at least one clinical or social outcome following use of crisis and acute care for people given a ‘personality disorder’ diagnosis. We performed a narrative synthesis of evidence for each model of care found. Results We screened 16,953 records resulting in 35 studies included in the review. Studies were published between 1987–2022 and conducted in 13 countries. Six studies were RCTs, the remainder were non randomised controlled studies or cohort studies reporting change over time. Studies were found reporting outcomes for crisis teams, acute hospital admission, acute day units, brief admission, crisis-focused psychotherapies in a number of settings, Mother and Baby units, an early intervention service and joint crisis planning. The evidence for all models of care except brief admission and outpatient-based psychotherapies was assessed as low or very low certainty. Conclusion The literature found was sparse and of low quality. There were no high-quality studies that investigated outcomes following use of crisis team or hospital admission for this group. Studies investigating crisis-focused psychological interventions showed potentially promising results.

Self-harm and suicidality are common presentations in children and adolescents requiring a mental health inpatient admission. Although there are several studies on adolescents, there is relatively limited research into childhood self-harm and suicidality during such admissions. A retrospective electronic file review was conducted on all children discharged from a national mental health inpatient children's unit over a 6-year period. Several independent variables were compared between self-harm/suicidal and non-self-harm/non-suicidal children. Separate analyses investigated changes in self-harm/suicidality, functional outcomes, and risk assessment ratings between admission and discharge. A total of 105 children were included in this study. During admission, 65.7% of them reported self-harm thoughts, 61% engaged in self-harm, 50.5% expressed suicidal thoughts, and 14.3% engaged in suicidal behavior. Thoughts and acts of self-harm were associated with previous self-harm, longer admissions, and Attention Deficit Hyperactivity Disorder. Suicidality overlapped with self-harm and was strongly predicted by previous self-harm. The prevalence of self-harm and suicidal thoughts and acts significantly decreased during admission. Children in the non-self-harm group had marginally better functional outcomes upon discharge compared to those in the self-harm group. Children and parents/caregivers were similarly satisfied with treatment, irrespective of children's self-harm/suicidality. Self-harm and suicidality were widespread among children admitted to hospital. Better understanding of the mechanisms and factors related to self-harm and suicidality in this age group could help mitigate associated risks and improve existing safety strategies.

Background The incremental hospital cost and length of stay (LOS) associated with adverse events (AEs) has not been well characterized for planned and unplanned inpatient spine, hip, and knee surgeries. Methods Retrospective cohort study of hip, knee, and spine surgeries at an academic hospital in 2011–2012. Adverse events were prospectively collected for 3,063 inpatient cases using the Orthopaedic Surgical AdVerse Event Severity (OrthoSAVES) reporting tool. Case costs were retrospectively obtained and inflated to equivalent 2021 CAD values. Propensity score methodology was used to assess the cost and LOS attributable to AEs, controlling for a variety of patient and procedure factors. Results The sample was 55% female and average age was 64; 79% of admissions were planned. 30% of cases had one or more AEs (82% had low-severity AEs at worst). The incremental cost and LOS attributable to AEs were $8,500 (95% confidence interval [CI]: 5100–11,800) and 4.7 days (95% CI: 3.4–5.9) per admission. This corresponded to a cumulative $7.8 M (14% of total cohort cost) and 4,290 bed-days (19% of cohort bed-days) attributable to AEs. Incremental estimates varied substantially by (1) admission type (planned: $4,700/2.4 days; unplanned: $20,700/11.5 days), (2) AE severity (low: $4,000/3.1 days; high: $29,500/11.9 days), and (3) anatomical region (spine: $19,800/9 days; hip: $4,900/3.8 days; knee: $1,900/1.5 days). Despite only 21% of admissions being unplanned, adverse events in these admissions cumulatively accounted for 59% of costs and 62% of bed-days attributable to AEs. Conclusions This study comprehensively demonstrates the considerable cost and LOS attributable to AEs in orthopaedic and spine admissions. In particular, the incremental cost and LOS attributable to AEs per admission were almost five times as high among unplanned admissions compared to planned admissions. Mitigation strategies focused on unplanned surgeries may result in significant quality improvement and cost savings in the healthcare system.

Patient characteristics and associative value with admission rates for HS Characteristic Non-admitted [N (%)] Admitted [N (%)] OR (95% CI)a P value Age (years) 0–17 4478 (8.3) 170 (3.4) 1 (Reference) 1 (Reference) 18–44 40,744 (75.3) 3093 (61.9) 1.89 [1.30, 2.75] 0.001 45–64 8371 (15.5) 1478 (29.6) 2.02 [1.34, 3.04] 0.001 65+ 512 (0.9) 253 (5.1) 2.85 [1.59, 5.12] <0.001 Sex Male 16,082 (29.7) 2366 (47.4) 1 (Reference) 1 (Reference) Female 38,024 (70.3) 2629 (52.6) 0.47 [0.40, 0.55] <0.001 Payer status Medicare 4963 (9.2) 1375 (27.5) 1 (Reference) 1 (Reference) Medicaid 23,799 (44.0) 1999 (40.0) 0.57 [0.45, 0.73] <0.001 Private insurance 11,407 (21.1) 1004 (20.1) 0.59 [0.45, 0.77] <0.001 Self-pay 12,261 (22.7) 462 (9.2) 0.24 [0.18, 0.33] <0.001 No charge 450 (0.8) 73 (1.5) 0.89 [0.43, 1.85] 0.752 Otherb 1226 (2.3) 81 (1.6) 0.41 [0.22, 0.80] 0.008 Median household income quartile of ZIP code 1st quartile 26,567 (49.1) 2291 (45.9) 1 (Reference) 1 (Reference) 2nd quartile 14,120 (26.1) 1260 (25.2) 1.01 [0.82, 1.25] 0.922 3rd quartile 8724 (16.1) 856 (17.1) 1.09 [0.86, 1.40] 0.474 4th quartile 4695 (8.7) 588 (11.8) 1.28 [1.00, 1.66] 0.054 Modified Charlson Comorbidity Index 0 45,405 (83.9) 2533 (50.7) 1 (Reference) 1 (Reference) 1 7085 (13.1) 1162 (23.2) 2.61 [2.20, 3.10] <0.001 2 1133 (2.1) 597 (12.0) 6.75 [5.01, 9.09] <0.001 3+ 483 (0.9) 702 (14.1) 17.5 [12.92, 23.73] <0.001 Statistically significant P values are in bold (P<0.05) Multiple regression model controlled for age, sex, payer status, median household income based on ZIP code, modified Charlson Comorbidity Index, hospital region, hospital trauma status, and hospital teaching status bIncludes worker’s compensation, CHAMPUS, CHAMPVA, and other government programs Compared to Northeastern hospitals, patients with HS presenting to Midwestern hospitals reported lower odds of inpatient admission. Hospital characteristics and associative value with admission rates for HS Characteristic Non-admitted [N (%)] Admitted [N (%)] OR (95% CI)a P value Hospital region Northeast 8929 (16.5) 1142 (22.9) 1 (Reference) 1 (Reference) Midwest 11,623 (21.5) 926 (18.5) 0.69 [0.49, 0.98] 0.037 South 27,463 (50.8) 2489 (49.8) 1.10 [0.83, 1.45] 0.511 West 6091 (11.3) 437 (8.8) 0.67 [0.43, 1.04] 0.071 Hospital trauma statusb Non-trauma 29,796 (55.8) 2374 (48.0) 1 (Reference) 1 (Reference) Level I 10,414 (19.5) 1438 (29.1) 1.31 [0.99, 1.73] 0.062 Level II 7056 (13.2) 643 (13.0) 0.84 [0.62, 1.13] 0.256 Level III 6089 (11.4) 491 (9.9) 0.96 [0.69, 1.31] 0.778 Hospital teaching status Metro, non-teaching 12,744 (23.6) 779 (15.6) 1 (Reference) 1 (Reference) Metro, teaching 33,740 (62.4) 3912 (78.3) 1.82 [1.41, 2.35] <0.001 Non-metropolitan 7622 (14.1) 303 (6.1) 0.67 [0.43, 1.05] 0.083 Statistically significant P values are in bold (P<0.05) aMultiple regression model controlled for age, sex, payer status, median household income based on ZIP code, modified Charlson Comorbidity Index, hospital region, hospital trauma status, and hospital teaching status bCollapsed categories (i.e., Level I + Level II trauma centers) were not included We found that adult age, comorbidity, male sex, and Medicare insurance status were positively associated with higher inpatient admission rates among HS patients. Another cross-sectional analysis of event-based ambulatory surveys (2002–2010) found that adult patients with HS comprised 92.8% (standard error, 2.6%) of the HS population and were more likely to have public health care insurance than were all adults in this age range [5]. Taken together, these data suggest that interventions targeting these demographics may reduce ED utilization and hospital admissions among HS patients.

Background There is no convincing data on the trends of hospitalizations, mortality, cost, and demographic variations associated with inpatient admissions for gastric cancer in the USA. The aim of this study was to use a national database of US hospitals to evaluate the trends associated with gastric cancer. Methods We analyzed the National Inpatient Sample (NIS) database for all patients in whom gastric cancer (ICD-9 code: 151.0, 151.1, 151.2, 151.3, 151.4, 151.5, 151.6, 151.8, 151.9) was the principal discharge diagnosis during the period, 2003–2014. The NIS is the largest publicly available all-payer inpatient care database in the US. It contains data from approximately eight million hospital stays each year. The statistical significance of the difference in the number of hospital discharges, length of stay, and hospital costs over the study period was determined by regression analysis. Results In 2003, there were 23,921 admissions with a principal discharge diagnosis of gastric cancer as compared to 21,540 in 2014 ( P  < 0.01). The mean length of stay for gastric cancer decreased by 17% between 2003 and 2014 from 10.9 days to 8.95 days ( P  < 0.01). However, during this period, the mean hospital charges increased significantly by 21% from $ 75,341 per patient in 2003 to $ 91,385 per patient in 2014 ( P  < 0.001). There was a more significant reduction in mortality over a period of 11 years from 2428 (10.15%) in 2003 to 1345 (6.24%) in 2014 ( P  < 0.01). The aggregate charges (i.e., “national bill”) for gastric cancer increased significantly from 1.79 bn $ to 1. 96 bn $ ( P  < 0.001), despite decrease in hospitalization (inflation adjusted). Conclusion Although the number of inpatient admissions for gastric cancer have decreased over the past decade, the healthcare burden and cost related to it has increased significantly. Inpatient mortality is decreasing which is consistent with overall decrease in gastric cancer-related deaths. Cost increase associated with gastric cancer contributed significantly to the national healthcare bill.

Background An ongoing challenge with rare diseases is limited data and, consequently, limited knowledge about the collective prevalence and impact of these conditions on individuals, families, and the health system, particularly in rural and regional areas. Using existing datasets, this project aimed to examine the epidemiology of and hospital activity for Tasmanians with rare diseases. Methods Rare diseases were defined as non-infectious diseases with a prevalence of less than 1 in 2000. An initial resource set of 1028 ICD-10-AM diagnostic codes was used to identify a cohort of Tasmanians with rare diseases in Tasmanian Health datasets (1 January 2007 until 31 December 2020). Validating the resource set using a small group with known rare diseases revealed limitations in ascertainment, and so an expanded set of 1940 ICD-10-AM diagnostic codes was developed by cross-referencing ICD-10-AM codes with Orphanet data. Cohort hospital activity and admission costs were compared to statewide data for the final year of the study, 01 January 2020 to 31 December 2020. Results Using the resource set of 1028 ICD-10-AM diagnostic codes, the period prevalence of rare diseases in Tasmania across all age groups was estimated at 3.5%, with a point prevalence of 1.5% in December 2020. In 2020, 3384 individuals within the Tasmanian rare disease cohort, representing 0.6% of the Tasmanian population, accessed the public hospital system and accounted for 5.6% of all admissions. The mean length of stay for rare disease-related hospital admissions was 5.0 days, compared to 3.3 days for non-rare disease-related admissions. The mean cost per admission for the rare disease cohort was AUD$11,310, compared to AUD$6475 for all admissions statewide. In 2020, using the expanded resource set, the total cost of public hospital admissions in Tasmania was estimated to be AUD$979 million, with rare disease-related hospital admissions accounting for 9.1% of this cost, increasing to 19.0% when the costs for all admissions for the rare disease patients were included. Conclusions Patients with rare diseases had more admissions, longer length of stay, and a higher average cost per admission. Patients with rare diseases have a disproportionate impact on statewide hospital activity and costs in Tasmania.