Inpatient Days Research Articles

Complications, Costs, and Health Care Resource Use with Tissue Biopsy Followed by Liquid Biopsy Versus Tissue Re-biopsy in Patients With Newly Diagnosed Metastatic Nonsmall-cell Lung Cancer.

We compared complications, costs, and health care resource utilization (HCRU) of patients with newly diagnosed metastatic nonsmall-cell lung cancer (mNSCLC) who had a tissue biopsy followed by either liquid biopsy (TFLB) (identified with a novel algorithm) or tissue re-biopsy (TRB). This claims-based retrospective analysis included commercial and Medicare Advantage members in the Optum Research Database with mNSCLC (January 2017 to June 2021) and ≥2 tissue biopsy claims (7 to 90d apart) (TRB) or ≥1 tissue and ≥1 liquid biopsy claim within 90 days (TFLB). Patients in the TFLB group were matched 1:1 to patients in the TRB group using propensity score matching. Surgical biopsy-related complications and complication-related and all-cause medical costs and HCRU during the 6-month follow-up were compared. Both groups had 235 patients post-match. During the follow-up, the surgical biopsy-related complication rate was lower in the TFLB group than the TRB group (65.1% [153/235] vs. 84.7% [199/235], P<0.001). Mean complication-related medical costs were significantly lower with TFLB ($8494 vs. $19,741, P<0.001) during the follow-up; mean (SD) duration of complication-related inpatient stays was significantly lower with TFLB (3.5 [7.0] vs. 6.6 [13.3]d, P=0.002). Mean all-cause medical costs were not significantly different between the groups; the TFLB group had fewer all-cause inpatient stays, inpatient days, and outpatient visits. Multiple tissue biopsy procedures may be associated with significantly higher biopsy complication rates, higher complication-related medical costs, and longer complication-related inpatient stays than TFLB. All-cause medical costs were similar between groups.

Preterm infant attachment is independent of breast milk intake during neonatal care

Background Following preterm birth, breast milk feeding is recommended because it confers broad health benefits to preterm infants. Breast milk has been suggested to promote secure attachment in infancy, but this is uncertain. Elucidating the relationship between breast milk intake and infant attachment is crucial for supporting parents of preterm infants to make feeding choices and providing accurate counselling when breast milk is not available. We aimed to investigate if breast milk exposure during neonatal care associates with attachment outcomes derived from infants’ responses to the Still-Face Paradigm (SFP) at nine months of age. Methods We studied 77 preterm (&lt; 33 weeks’ gestation) and 71 term infants. Preterm infant feeding data were collected from birth until hospital discharge. Infant responses to the SFP were videocoded at nine months corrected age. Infants’ distress, fretfulness, attentiveness to caregivers and attachment classification were compared between preterm infants who received exclusive breast milk for ≥ 75% of inpatient days, those who received exclusive breast milk for &lt; 75% of inpatient days, and term infants. Milk intake thresholds were chosen based on our previous work demonstrating positive associations between breast milk intake and MRI markers of brain maturation. Results There were no significant differences in infant distress, fretfulness, attentiveness to caregivers (p-values &gt; 0.19), or in the odds of secure versus insecure attachment classification (relative risk ratio = 0.57–0.89, p-values = 1.00) between preterm infants with high or low breast milk exposure, or term comparators. Conclusions Attachment outcomes at nine months were similar between term and preterm infants, and breast milk exposure did not associate with attachment in the preterm group. Although breast milk feeding has clear benefits, these findings may alleviate some of the pressures experienced by parents who are unable to provide breast milk for infants cared for in the neonatal unit.

Clinical characteristics of megaloblastic anemia with pancytopenia

ABSTRACT Objectives In this study, we aimed to explore the clinical characteristics of patients with megaloblastic anemia and pancytopenia. Methods Data on patient characteristics, laboratory examinations, clinical manifestations, and associated diseases were collected from Changzhou Hospital of Traditional Chinese Medicine. The participants were divided into two groups according to routine blood test results: megaloblastic anemia patients with pancytopenia (Group A) and megaloblastic anemia patients with simple anemia (Group B). Results The number of patients with fever and bleeding in Group A was much higher than that in Group B. Inpatient days and hospitalization costs in Group A were higher than those in Group B. White blood cell count, red blood cell count, haemoglobin level, and platelet count in Group A were lower than those in Group B. There were no significant differences in mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), and serum levels of lactate dehydrogenase (LDH), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), albumin (ALB), vitamin B12, folic acid, and ferritin between the two groups. The incidence of associated diseases, particularly pulmonary infections, was significantly higher in Group A than in Group B. There was no significant difference in the degree of bone marrow hyperplasia between the two groups. Conclusion Patients with pancytopenia and megaloblastic anemia often developed infection and bleeding, which caused more severe anemia, higher hospitalization costs, and longer inpatient days. Early diagnosis and active treatment are required to reduce patient complications and costs.

Machine-learning based prediction for high health care utilizers using a multi-institution registry

Abstract Background Rising healthcare costs worldwide have spurred interest in using machine learning to pinpoint major healthcare utilizers for population health efforts. Previous studies have focused on individuals who impose the highest financial burden, such as targeting high-need high-cost utilizers, a small group with limited potential for cost reduction. Therefore, we developed models to predict future healthcare utilization across various thresholds. Methods We leveraged data from a multi-institutional diabetes database to develop binary classification models capable of predicting healthcare utilization in the following year. The models predicted six different outcomes: patients with an annual total length of stay of ≥ 7, ≥14, and ≥30 days, and emergency department (ED) attendance of ≥ 3, ≥5, and ≥10 visits. To mitigate class imbalance, we applied random and synthetic minority oversampling techniques and compared against models trained without oversampling. Models were trained with 2019 data and tested on unseen data from 2020 and 2021. Results Models trained with random oversampling, including logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron, consistently demonstrated high AUC (&amp;gt;0.80) and sensitivity (&amp;gt;0.60) across both training-validation and test datasets. Addressing class imbalance was found to be crucial, as models without oversampling displayed satisfactory AUC (&amp;gt;0.80) but significantly lower sensitivity (&amp;lt;0.10). Key predictors identified included age, number of ED visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean HbA1c levels. Conclusions Our machine learning models successfully predict high service level utilization with strong performance, offering valuable tools for policymakers and health planners to develop targeted health initiatives based on patient utilization patterns. Key messages • Machine learning models can be leveraged to predict future healthcare utilization and facilitate interventional program development. • Annual total length of stay and total emergency department visits are useful service level indicators.

IEHT vs. Regular Treatment - Are there Differences between the Two forms of Treatment in the Retrospective Course with regard to Individual Objective and Subjective Outcome Variables?

This retrospective follow-up study over one year (after discharge from the index treatment), compares the effects of inpatient-equivalent home treatment (IEHT) and regular psychiatric treatment using subjective and objective quality indicators. Using a 1:1 matching of cohorts (diagnosis, age, gender, number of previous treatments and severity of illness), 732 IEHT patients were assigned to a comparison group. Outcome data was taken from the routine data of the clinic database and the in-house basic documentation. In the subjective quality measure, patients and practitioners in regular psychiatric treatment reported a significantly higher improvement in condition. In terms of the objective quality measure, the number of further inpatient treatment days in the observation period, the two groups did not differ significantly, which can be interpreted as encouragement for the use of this new, increasingly established form of treatment.

The influence of Muscle Wasting on Patient Outcomes among Burn Patients: A Burn Care Quality Platform Study.

The hypermetabolism that occurs after large burns leads to muscle wasting that can result in weakness, difficulty fighting infections, and other challenges to healing. Overall, rates of muscle wasting are not well established across burn centers. The Burn Care Quality Platform (BCQP), the largest available dataset on burn patients globally, was used to explore the rate of Muscle Wasting using data collected from more than 100 burn centers. "Muscle Wasting" was defined from relevant ICD codes (sarcopenia, muscle cachexia, protein malnutrition). Binomial and logistic regression were used to analyze the role of Muscle Wasting in burn mortality and other outcomes after controlling for demographic and comorbid characteristics. In total, the BCQP provided data from 84,438 adult and pediatric burn patients injured between 2000-2018. Only 2.6% (N=2,159) of the patients in the BCQP were diagnosed with having Muscle Wasting at some point during their admission. While Muscle Wasting was not associated with a statistically significant impact on mortality, it remained an independent predictor of inability to discharge to independent living, longer inpatient days, as well as an average of 10 additional surgical procedures (P<0.001 for all) as compared to patients who did not have Muscle Wasting. Muscle Wasting is an independent risk factor for several adverse outcomes in burn patients, but not mortality in the BCQP. Confidence in these findings would be improved with more accurate data collection, as the diagnosis of Muscle Wasting is likely under-reported, under-diagnosed, or both.

Machine Learning-Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation.

The cost of health care in many countries is increasing rapidly. There is a growing interest in using machine learning for predicting high health care utilizers for population health initiatives. Previous studies have focused on individuals who contribute to the highest financial burden. However, this group is small and represents a limited opportunity for long-term cost reduction. We developed a collection of models that predict future health care utilization at various thresholds. We utilized data from a multi-institutional diabetes database from the year 2019 to develop binary classification models. These models predict health care utilization in the subsequent year across 6 different outcomes: patients having a length of stay of ≥7, ≥14, and ≥30 days and emergency department attendance of ≥3, ≥5, and ≥10 visits. To address class imbalance, random and synthetic minority oversampling techniques were employed. The models were then applied to unseen data from 2020 and 2021 to predict health care utilization in the following year. A portfolio of performance metrics, with priority on area under the receiver operating characteristic curve, sensitivity, and positive predictive value, was used for comparison. Explainability analyses were conducted on the best performing models. When trained with random oversampling, 4 models, that is, logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron consistently achieved high area under the receiver operating characteristic curve (>0.80) and sensitivity (>0.60) across training-validation and test data sets. Correcting for class imbalance proved critical for model performance. Important predictors for all outcomes included age, number of emergency department visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean hemoglobin A1c levels. Explainability analyses using partial dependence plots demonstrated that for the best performing models, the learned patterns were consistent with real-world knowledge, thereby supporting the validity of the models. We successfully developed machine learning models capable of predicting high service level utilization with strong performance and valid explainability. These models can be integrated into wider diabetes-related population health initiatives.

Cardiovascular risk factors as potential biomarkers of length of hospital stay and rehospitalization in mood disorders – a retrospective study

ObjectiveCardiovascular diseases are often comorbid with mood disorders, increasing the burden on patients. Few studies explore the role of cardiovascular disease on psychiatric inpatient stays. This retrospective study investigated the association of cardiovascular risk factors during an index stay on psychiatric length of stays and rehospitalization in the subsequent 10 years in patients with mood disorders. MethodsPatients with a first inpatient stay in two German psychiatric hospitals, diagnosed with F31 (bipolar affective disorder), F32 (depressive episode) or F33 (recurrent depressive disorder) were investigated (n=930). The association of hypertension, hypercholesterolemia, and type 2 diabetes mellitus at the index stay with total length of stays, average length of stay and rehospitalization in the next 10 years was investigated in bivariate, and multivariate regression analyses, adjusting for sex, age, BMI, smoking, taking antidepressants and antipsychotics. ResultsPatients averaged 99.27 (± 134.94) inpatient days within 10 years, patients with hypertension 138.09 (± 162.85) days. Controlling for confounders, hypertension associated with longer total length of stays (β=0.105, p=0.044), hypertension (β=0.146, p=0.004), hypercholesterolemia (β=0.104, p=0.034) and diabetes (β=0.103, p=0.015) linked to more rehospitalizations and not having hypercholesterolemia (β=0.101, p=0.041) related to longer average stays. Sex mediated the relationship between hypercholesterolemia and both rehospitalization (F=11.60, p<0.001) and total length of stays (F=7.21, p=0.007), with males with hypercholesterolemia showing more rehospitalization and longer total length of stays than females. ConclusionCardiovascular risk factors were associated with duration and frequency of psychiatric inpatient stays in patients with mood disorders. Cardiovascular risk factors as potential predictive biomarkers for the course of disease of mood disorders should be monitored and managed.

First Pregnancy Abortion or Natural Pregnancy Loss: A Cohort Study of Mental Health Services Utilization.

While both induced abortion and natural pregnancy loss have been associated with subsequent mental health problems, population-based studies directly comparing these two pregnancy outcomes are rare. We sought to compare mental health morbidity after an induced abortion or natural loss. Continuously eligible Medicaid beneficiaries age 16 in 1999 were assigned to two cohorts based upon the first pregnancy outcome: abortion (n = 1,331) or natural loss (n = 605). Outcomes were mental health outpatient visits, inpatient hospital admissions and hospital days of stay per patient per year. Average exposure periods before and after the first pregnancy outcome for each cohort were used to adjust the mental health service rates. Prior to the first pregnancy outcome, all three utilization rates were significantly higher for the natural loss cohort compared to the abortion cohort. For the abortion cohort, the per-patient per-year increase from the pre- to post-pregnancy periods was significant for all three rates: 2.04 times for outpatient visits (p < 0.0001), 3.04 times for inpatient admissions (p = 0.0003), and 3.01 times for hospital days of stay (p = 0.0112). None of the pre-to-post rate increases were significant for the natural loss cohort. Higher pre-pregnancy use rates for women who experience a natural pregnancy loss indicate that increased mental health services use following abortion cannot be solely attributed to pre-existing mental illness. Only the abortion cohort, but not the natural loss cohort, experienced significant increases in mental health services use following the first pregnancy outcome.

Technical efficiency analysis of advanced medical centers in Burkina Faso

IntroductionBurkina Faso faces many challenges in the health domain, with no real opportunity for an increase in public health expenditures. In Burkina Faso, as in all low-income countries, health spending efficiency is crucial. The objective of this paper is to assess the efficiency of Advanced Medical Centers (AMCs)—which correspond to district hospitals—in Burkina Faso over the 2017–2020 period and identify the factors that promote—or, on the contrary, limit—the efficiency of these health structures.MethodWe first assessed the efficiency level of the 45 AMCs running in the country between 2017 and 2020 using a bootstrap Data Envelopment Analysis (DEA) methodology. Inputs include the number of doctors, nurses, other medical staff, non-medical staff, and beds, while output variables correspond to the number of inpatients, surgeries, outpatients, and inpatient days. In a second step, determinants of AMC’s efficiency levels were explored using a double-bootstrap procedure. The roles of AMCs’ internal and environmental factors were both considered.ResultsWe found a mean efficiency score of 0.51 over the study period, indicating that AMCs could have almost doubled their healthcare production without needing additional resources. The size, education level, and health status of the covered population and the density of the healthcare supply in the district appeared to be the driving factors of AMCs’ efficiency.ConclusionOur results indicate that improving the efficiency of AMCs should be a high-level priority for the Burkinabe health policy. Resources could be reallocated across AMCs to increase the overall efficiency of the health system.

Racial, Ethnic, and Socioeconomic Disparities in Pediatric Aquaporin-4-Positive Neuromyelitis Optica Spectrum Disorder

BackgroundOnly 5% of aquaporin-4-positive neuromyelitis optica spectrum disorder (AQP4+ NMOSD) cases emerge during childhood. Poorer outcomes have been suggested in black/African American (AA) adults with NMOSD; however, conflicting and limited data exist for pediatric-onset NMOSD. This study evaluates racial, ethnic, and socioeconomic disparities in pediatric AQP4+ NMOSD outcomes. MethodsThirty-eight pediatric patients with AQP4+ NMOSD cared for at three pediatric tertiary care centers between 2009 and 2021 were identified. Patient addresses connected to socioeconomic measures available from the US Census. Demographic characteristics, pertinent clinical outcomes, and health care utilization in the two years following diagnosis were captured. ResultsCompared with non-Hispanic White children, Black/AA children had a significantly higher Expanded Disability Status Scale (EDSS) (2.46 vs 0.33, P = 0.003), 2.37 more hospital admissions (P = 0.002), and 28.40 additional inpatient days (P = 0.002) in the two years following their NMOSD diagnosis. Additionally, children with public insurance had higher relapse rates than those with private insurance (P = 0.046). At two years and at the most recent follow-up, a significantly higher EDSS was correlated with children living in census tracts with a lower median income, higher deprivation index, and higher proportion of population on assisted income, in poverty, and with vacant housing (all P < 0.05). ConclusionsWe identified racial, ethnic, and socioeconomic disparities in clinical outcomes and health care utilization in pediatric AQP4+ NMOSD. Further prospective and household-level data are needed to dissect the interplay of genetics, structural racism, and social determinants of health so that interventions to optimize care and outcomes for this population may be developed.

The Ambulatory Diabetes Outreach Program (ADOP): Rigorous Evaluation of a Pharmacist and Nurse-Led Care Model.

Although several systematic reviews found that ambulatory diabetes mellitus (DM) interventions involving pharmacists generally yielded better outcomes than the ones that did not, existing studies have limitations in rigor and study design. To examine the intention-to-treat effects of the Ambulatory Diabetes Outreach Program (ADOP) on participants' A1c values and healthcare utilization over a 52-month follow-up period. Difference-in-differences with staggered adoption. Specifically, we employed the Callaway and Sant'Anna's "group-time average treatment effect" estimator using not-yet treated as controls adjusting for patient's age, BMI, sex, race, comorbidity, payor, and socio-economic status. All patients with at least one ADOP treatment encounter from July 2017 to October 2021, regardless of program completion or length of exposure to the program. ADOP, a collaborative population health program led by pharmacists and nurse specialists to provide individualized type 2 DM management and education within a large and diverse health system. Patients' A1c values and healthcare utilization, including inpatient admission, inpatient days, and numbers of visits to the emergency department, urgent care, and primary care in recent 6months. ADOP participation was associated with an overall average reduction of 1.04 percentage points (95%CI - 1.12, - 0.95) in A1c level. Similar A1c reductions were also observed in the subgroups by sex and race/ethnicity. An average of 2months were required to reach the overall average effect, which persisted over 4years. Compared to the respective utilization levels pre-intervention, participants also had average reductions in inpatient admissions by 32.4%, inpatient days by 81.6%, visits to the emergency department by 21.6%, and primary care by 17.9%. The results suggest that a collaborative model of pharmacist and nurse-led type 2 DM intervention was effective in improving A1c outcomes and reducing healthcare utilization in the long term.

Time toxicity faced by patients with advanced cancer across primary cancer sites.

268 Background: Time toxicity, defined as the burden of time for patients interacting with the health care systems, is particularly important for patients with limited life expectancy. Little is known about the relative impact of time toxicity among different cancer types at the end of life. This study examines median contact days for inpatient, outpatient, and home health services among different primary cancer types for patients with advanced cancer with limited prognosis. Methods: A retrospective cohort analysis was performed on patients with advanced (stage 4) cancer who died within one year of diagnosis at Kaiser Permanente Northern California between January 1, 2015, and June 30, 2023. Time toxicity was defined as comprehensive contact days with healthcare system over the course of patients’ survival period between cancer diagnosis and death (i.e., contact days divided by survival or contact %). Types of contact included institutional stay (emergency department visit, hospitalization, skilled nursing facility, rehabilitation); outpatient visits (ambulatory care, labs, imaging); and in-home care (home-health, home hospice, telephone and virtual visit). The study quantified overall median contact % and contact % by specific care settings, stratified by cancer primary site. Results: The study included 10102 patients. The median age at diagnosis was 74 years with 47% female. Patients were predominantly white (65%), followed by Asian (12%), Hispanic (12%), and black (8%). The median Elixhauser comorbidity score was 4. With the exception of head and neck cancers, most cancers had higher contact % near diagnosis and at end of life (U-shaped curve). Head and neck cancers had the highest percent of patients undergoing surgery (36%) and radiation (52%), which may explain its sinuosity-like curve with a rapid increase of weekly contact % after diagnosis (a small peak), followed by rapid decrease and another increase by the end of their life. Survival days were comparatively long for head and neck cancers (158 days, IQR: 84-265) and short for thoracic (80 days, IQR: 35-166) and upper gastrointestinal cancers (80 days, IQR: 38-173). Malignant hematology patients had notably higher comprehensive contact % (74%, IQR: 47-100) which was driven primarily by emergency room department visits and hospital stays (p&lt;0.001 for all contact types). Conclusions: The study highlights the high burden of time toxicity in patients with advanced cancer with limited prognosis. The significant variations in time toxicity among different cancer types, especially in malignant hematology and head and neck patients, emphasize the need to tailor goals of care discussions for patients with cancer at the end of life. By focusing on reducing unwanted time toxicity, healthcare providers can enhance the quality of life for patients with cancer, ensuring more efficient and patient-centered care delivery.

Multimodal surveillance of Care-related infections by Alert microorganisms in Ospedali Riuniti di Vittoria e Comiso ASP Ragusa

Background: monitoring the incidence and surveillance of alert microorganism infections detected in inpatients of the Ospedali Riuniti di Vittoria and Comiso in 2023. Objectives: early identification and implementation of shared pathways aimed at prevention and control of care-related infections. the spread of colonization and infections; promotion and implementation of effective risk assessment programs. Methods: a multicenter study was conducted on 79 patients with infections with Alert microorganisms, in ordinary hospitalization, day service, and day hospital, of the "R. Guzzardi" hospitals of Vittoria and "R. Margherita" of Comiso. Sample stratification was performed according to gender, females 51.9% and males 48%, and the four age groups: 18-40 (4.8%), 41-60 (9.5%), 61-80 (45.2%), &gt;80 (40.5%), x̄ 51.90. The calculated mortality rate was n.16 (20.25%). The types, number and frequency of use of invasive devices were described (n.68, 86%, including 64.5% urinary bladder catheter). Results: the operating unit in which there is significant positivity to Alert infections is the UOC of Internal Medicine (40.5%), followed by RSA (16.46%) and ICU (8.86%), table1. The most representative Alert is Acinetobacter baumannii (24.05%), other microorganisms isolated are Klebsiella P. (22.78%), Escherichia c. (21.52%), and Staphylococcus A. (18.99%). In addition, a significant correlation between microorganisms and sample type is confirmed (p=0.019). Inpatient days are proportional to the care complexity index (p=0.0021). In addition, there is no correlation between inpatient days and microorganism type. Conclusions: the reduction of inpatient time and the move toward territorial continuity of care will promote the community spread of selected antibiotic-resistant bacteria in the hospital. Surveillance measures include early identification of patients positive to Alerts, active follow-up of colonized patients, isolation of colonized/infected patients, and adoption of contact precautions in the management of such patients.

OUTREACH: phase 2 study of lisocabtagene maraleucel as outpatient or inpatient treatment at community sites for R/R LBCL

AbstractLisocabtagene maraleucel (liso-cel) is an autologous, CD19-directed, 4-1BB chimeric antigen receptor (CAR) T-cell product approved for relapsed/refractory (R/R) large B-cell lymphoma (LBCL). We present the OUTREACH primary analysis, evaluating the safety and efficacy of outpatient monitoring after liso-cel treatment at community sites in the United States. Adults with R/R LBCL after ≥2 prior lines of therapy received liso-cel. Outpatient vs inpatient monitoring was per investigator discretion. The primary end points were incidence of grade ≥3 cytokine release syndrome (CRS), neurological events (NEs), prolonged cytopenia, and infections. Efficacy was a secondary end point. Eighty-two patients received liso-cel (outpatient monitored, 70%; inpatient monitored, 30%). The median follow-up was 10.6 months (range, 1.0-24.5). In outpatients and inpatients, grade ≥3 CRS occurred in 0% and 0%, NEs in 12% and 4%, infections in 12% and 8%, and prolonged cytopenia in 33% and 32%, respectively. Among outpatients, 25% were never hospitalized after infusion, and 32% were hospitalized ≤72 hours after the day of infusion; the median time to hospitalization was 5.0 days (range, 2-310). The median initial hospitalization duration after liso-cel was 6.0 days (range, 1-28) for outpatients and 15.0 days (range, 3-31) for inpatients. Objective response rate was 80%, complete response rate was 54%, and the median duration of response was 14.75 months (95% confidence interval, 5.0 to not reached). OUTREACH is, to our knowledge, the first and largest study to prospectively assess CAR T-cell therapy with outpatient monitoring in community-based medical centers. Liso-cel demonstrated meaningful efficacy with favorable safety in patients with R/R LBCL. Data support the feasibility of liso-cel administration at community sites with outpatient monitoring. This trial was registered at www.ClinicalTrials.gov as #NCT03744676.

Long Term Care As An Outcome Of Community Integrated Care Team Intervention: A Review

Abstract Background Over 65’s occupy over 55% of inpatient hospital bed days. Assessment of average length of stay for patients awaiting long term care (LTC) in the acute hospital setting was found to be 122 days. My aim is to assess the number of patients transferred to long term care (LTC) from the community post referral to an Integrated Care for Older Persons (ICPOP) service, therefore bypassing inpatient admission. Methods Retrospective analysis of patients notes managed by the ICPOP team from January 2023- January 2024. Comprehensive geriatric assessment data as to frailty markers, plus duration of engagement with service and over all discharge destination were assessed. Patients were identified based on unique identifying number but also using date of birth and name to avoid missing cases. Results 59 patients were discharged to a nursing home from the ICPOP service, mean age 80. Sex differentiation 56% female. On average at least 3 out of 8 possible frailty indicators were identified per patient. Most prevalent -67% polypharmacy, 47% memory impairment and 45% carer stress were identified as key concerns on initial assessment. 96% of cases were initially referred for complex case assessment/admission avoidance. Average length of engagement with service 5 weeks. Conclusion The ICPOP service is highly effective at managing complex cases in the community. The multidisciplinary nature of the team facilitates comprehensive assessment and patient centred care which enables a smooth transition through supportive strategies available to the patient and their family. In total 7198 bed days were saved due to direct transfer to nursing home from the community setting. If the patients care needs warrant LTC the ICPOP service is an invaluable tool which could further reduce pressure on hospitals and facilitate appropriate transfers to nursing home care.

Clinical Outcomes and Cost Implications of a Community Psychosocial Rehabilitation Service for Severe and Persistent Mental Illness in Nova Scotia, Canada.

Introduction: Individuals with severe and persistent mental illness (SPMI) present distinct challenges in mental healthcare due to the chronic and complex nature of their conditions. This study was conducted to assess the clinical efficacy and potential cost-effectiveness of a multidisciplinary community-based psychosocial rehabilitation team serving individuals with SPMI in Nova Scotia, Canada. Method: This study was conducted to evaluate the effects of a community-based psychosocial rehabilitation program on individuals with severe and persistent mental illness (SPMI) in Nova Scotia, Canada. This research focused on clinical outcomes and potential cost savings following a one-year intervention, offering critical insights into the benefits of community-based care for this population. A cohort of 137 clients accepted into the community rehabilitation service (Connections Dartmouth) between September 2016 and September 2020 was analyzed. Each participant received one year of community rehabilitation intervention. Using data from the Canadian Medical Service Insurance (MSI) billing system, this research compared the use of inpatient services and Emergency Department visits in the year prior to and the year following the intervention. The findings provide valuable evidence on the role of community rehabilitation in reducing healthcare utilization for individuals with SPMI. Results: The results demonstrated a statistically significant reduction in mean admission rates and length of inpatient admissions in the year following rehabilitation compared to the pre-rehabilitation year. A substantial percentage of patients experienced no inpatient admissions (88% vs. 60%) or Emergency Department visits (82% vs. 67%) in the post-rehabilitation year, compared to the pre-rehabilitation year. There was a significant reduction in inpatient days by 90%, translating into substantial cost savings. The findings highlight the potential economic benefits of community rehabilitation for people with SPMI. Conclusions: This uncontrolled study suggests that community rehabilitation is associated with positive clinical outcomes for individuals with SPMI in terms of reduced inpatient service use and associated costs. Further research, including controlled studies and cost-effectiveness studies, into the community psychosocial rehabilitation services in the Canadian setting is needed.

Technical efficiency of ghanaian health facilities before and during the COVID-19 pandemic

PurposeUnderstanding the technical efficiency of health facilities is essential for an optimal allocation of scarce resources to primary health sectors. The COVID-19 pandemic may have further undermined levels of efficiency in low-resource settings. This study takes advantage of 2019 and 2020 data on characteristics of health facilities, health services inputs and output to examine the levels and changes in efficiency of Ghanaian health facilities. The current study by using a panel dataset contributes to existing evidence, which is mostly based on pre-COVID-19 and single-period data.MethodsThe analysis is based on a panel dataset including 151 Ghanaian health facilities. Data Envelopement Analysis (DEA) technique was used to estimate the level and changes in efficiency of health facilities across two years..ResultsThe results show a net increase of 26% in inputs, influenced mostly by increases in temporary non-clinical staff (131%) and attrition of temporary clinical staff and permanent non-clinical staff, 40% and 54% respectively. There was also a net reduction in output of 34%, driven by a reduction in in-patient days (37%), immunization (11%), outpatients visits and laboratory test of 9%. Nowithstanding the COVID-19 pandemic, the results indicate that 59 (39%) of sampled health facilities in 2020 were efficient, compared to 48 (32%) in 2019. The results also indicate that smaller-sized health facilities were less likely to be efficient compared to relatively bigger health facilities.ConclusionBased on the findings, it will be essential to examine factors that accounted for efficiency improvements in some health facilities, to enable health facilities lagging behind to learn from those on the efficiency frontier. In addition, the findings emphasise the need for CHAG to work with health facility managers to optimise inputs allocation through a redistribution of staff. Most importantly, the findings are suggestive of the resilience of CHAG health facilities in responding to a health shock such as the COVID-19 pandemic.

WP3.14 - A Single Unit Service Review in Northern Ireland: The Ever-increasing Acute Surgical Take and the Need for Service Adaptation

Abstract Aims Referrals to the on-call general surgeons in the unit has increased significantly over the past year. Staff levels and hospital resources have not changed to reflect this increasing demand on the service. Our aim is to evaluate the increase in numbers and develop a strategic plan for resolution. Methods A database of referrals to the on-call surgical team was developed using the daily handover records. Two periods of data collection were undertaken, each lasting a duration of 30 days, taking place approximately one year apart. Patient demographics and clinical information was gathered using our electronic care record and electronic notes record. Data gathered included diagnosis, treatment, investigations, interventions, length of stay, suitability for ambulation and follow up. Results The total number of patients referred to the on-call team during cycle one was 233, compared with 405 in cycle two; this is a 74% increase in referrals. During the windows of data collection, 75% of patients with an unscheduled admission did not undergo any intervention and 80% were deemed suitable for ambulation. Conclusion Increasing demands on the NHS require a forward-thinking approach to how we provide an acute surgical service. This study demonstrates that a large proportion of patients are suitable for an ambulatory model of care and development of this service would be a valuable adjunct to the unit. To propose an ambulatory service providing urgent surgical assessment would be an asset to the Trust; potential outcomes would include improving the patient experience, reducing inpatient admissions and bed days, with the associated cost-saving.

Prediction of primary admission total charges following cervical disc arthroplasty utilizing machine learning

BACKGROUND CONTEXTCervical disc arthroplasty (CDA) has become an increasingly popular alternative to anterior cervical discectomy and fusion, offering benefits such as motion preservation and reduced risk of adjacent segment disease. Despite its advantages, understanding the economic implications associated with varying patient and hospital factors remains critical. PURPOSETo evaluate how hospital size, geographic region, and patient-specific variables influence charges associated with the primary admission period following CDA. STUDY DESIGNA retrospective analysis using machine learning models to predict and analyze charge factors associated with CDA. PATIENT SAMPLEData from the National Inpatient Sample (NIS) database from 2016 to 2020 was used, focusing on patients undergoing CDA. OUTCOME MEASURESThe primary outcome was total charge associated with the primary admission for CDA, analyzed against patient demographics, hospital characteristics, and regional economic conditions. METHODSMultivariate linear regression and machine learning algorithms including logistic regression, random forest, and gradient boosting trees were employed to assess their predictive power on charge outcomes. Statistical significance was set at the 0.003 level after applying a Bonferroni correction. RESULTSThe analysis included 3,772 eligible CDA cases. Major predictors of charge identified were hospital size and ownership type, with large and privately owned hospitals associated with higher charges (p<.001). The Western region of the U.S. also showed significantly higher charges compared to the Northeast (p<.001). The gradient boosting trees model showed the highest accuracy (AUC=85.6%). Length of stay and wage index were significant charge drivers, with each additional inpatient day increasing charges significantly (p<.001) and higher wage index regions correlating with increased charges (p<.001). CONCLUSIONSHospital size, geographic region, and specific patient demographics significantly influence the charges of CDA. Machine learning models proved effective in predicting these charges, suggesting that they could be instrumental in guiding economic decision-making in spine surgery. Future efforts should aim to incorporate these models into broader clinical practice to optimize healthcare spending and enhance patient care outcomes.