Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients.

BACKGROUND: Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system. METHODS: Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity. RESULTS: The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011-5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system. CONCLUSIONS: The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

Survival does not differ by annual center transplant volume-A Pediatric Heart Transplant Society Registry study.

BACKGROUND: There are conflicting data regarding the relationship between center volume and outcomes in pediatric heart transplantation. Previous studies have not fully accounted for differences in case mix, particularly in high-risk congenital heart disease (CHD) groups. We aimed to evaluate the relationship between center volume and outcomes using the Pediatric Heart Transplant Society (PHTS) Registry and explore how case mix may affect outcomes. METHODS: A retrospective cohort study of all pediatric patients in the PHTS Registry who received a heart transplant from 2009 to 2018 was performed. Centers were divided into 5 groups based on average yearly transplant volume. The primary outcome was time to death or graft loss and outcomes were compared using Kaplan-Meier analysis. RESULTS: There were 4583 cases among 55 centers included. There was no difference in time to death or graft loss by center volume in the entire cohort (p = .75), in patients with CHD (p = .79) or in patients with cardiomyopathy (p = .23). There was also no difference in time to death or graft loss by center size in patients undergoing transplant after Norwood, Glenn or Fontan (log rank p = .17, p = .31, and p = .10 respectively). There was a statistically significant difference in outcomes by center size in the positive crossmatch group (p < .0001), though no discernible pattern related to high or low center volume. CONCLUSIONS: Outcomes are similar among transplant centers of all sizes, including for high-risk patient groups with CHD. Future work is needed to understand how patient-specific risk factors may vary among centers of various sizes and whether this influences patient outcomes.

Healthcare-associated infection in a thoracic surgery ICU based on case mix index and relative weight of diagnosis-related groups / 中国感染控制杂志

Objective To explore the correlation between healthcare-associated infection(HAI)and partial inde-xes in the diagnosis-related groups(DRGs)of patients in thoracic surgery intensive care unit(ICU).Methods DRGs,case mix index(CMI),relative weight(RW),and HAI of patients in thoracic surgery ICU and four subspe-cialty departments(pulmonary surgery group,esophageal surgery group,mediastinum group[mainly thymic sur-gery],and trachea group)in a tertiary chest hospital in Shanghai from January to December 2022 were retrospec-tively analyzed and compared through DRGs index grouping.Results A total of 1 429 patients in the department of thoracic surgery ICU were analyzed,including 59 HAI cases,with a HAI rate of 4.13％.The incidences of HAI in pulmonary surgery group,esophageal surgery group,mediastinum group and trachea group were 3.74％(30/803),5.84％(25/428),1.27％(2/157)and 4.88％(2/41),respectively.There was no statistically significant differ-ence in the incidences of HAI among different subspecialty groups(P＞0.05).A total of 35 DRGs were involved,with CMI of 2.75,3.41,2.35 and 1.25 in pulmonary surgery group,esophageal surgery group,mediastinum group and trachea group,respectively,and RW ranged from 0.53 to 12.62.In the pulmonary surgery group,inci-dence of HAI in male patients was higher than that in female patients.Higher RW score level was associated with higher incidence of HAI.Differences were all statistically significant(all P 0.05).Among patients in the esophageal surgery group,the age of HAI group was higher than that of the non-HAI group(P＜0.05).Higher RW score level was associated with higher incidence of HAI(P＜0.05).Among patients in the mediastinum sur-gery group,the age of patients in the infected group was higher than that in the non-infected group(P＜0.05).Among the 59 HAI cases,31 were infected with MDROs.Conclusion Focusing on CMI and RW in the DRGs in-dex system,analyzing HAI from the perspectives of disease complexity and overall technical difficulties of medical services can provide reference for the precise management of HAI in the new era.

Construction and application of a nursing human resource allocation model based on the case mix index.

BACKGROUND: The case mix index (CMI) may reflect the severity of disease and the difficulty of care objectively, and is expected to be an ideal indicator for assessing the nursing workload. The purpose of this study was to explore the quantitative relationship between daily nursing worktime (DNW) and CMI to provide a method for the rational allocation of nursing human resources. METHODS: Two hundred and seventy-one inpatients and 36 nurses of the department of hepatobiliary surgery were prospectively included consecutively from August to September 2022. The DNW of each patient were accurately measured, and the CMI data of each patient were extracted. Among 10 curve estimations, the optimal quantitative model was selected for constructing the nursing human resource allocation model. Finally, the applicability of the allocation model was preliminarily assessed by analyzing the relationship between the relative gap in nursing human resources and patient satisfaction, as well as the incidence of adverse events in 17 clinical departments. RESULTS: The median (P25, P75) CMI of the 271 inpatients was 2.62 (0.92, 4.07), which varied by disease type (F = 3028.456, P < 0.001), but not by patient gender (F = 0.481, P = 0.488), age (F = 2.922, P = 0.089), or level of care (F = 0.096, P = 0.757). The median (P25, P75) direct and indirect DNW were 76.07 (57.98, 98.85) min and 43.42 (39.42, 46.72) min, respectively. Among the 10 bivariate models, the quadratic model established the optimal quantitative relationship between CMI and DNW; DNW = 92.3 + 4.8*CMI + 2.4*CMI2 (R2 = 0.627, F = 225.1, p < 0.001). The relative gap between theoretical and actual nurse staffing in the 17 clinical departments were linearly associated with both patient satisfaction (r = 0.653, P = 0.006) and incidence of adverse events (r = - 0.567, P = 0.021). However, after adjusting for other factors, it was partially correlated only with patient satisfaction (rpartial = 0.636, P = 0.026). CONCLUSION: The DNW derived from CMI can be used to allocate nursing human resources in a rational and convenient way, improving patient satisfaction while ensuring quality and safety.

Resource Utilization Groups in transitional home care: validating the RUG-III/HC case-mix system in hospital-to-home care programs.

BACKGROUND: Transitional hospital-to-home care programs support safe and timely transition from acute care settings back into the community. Case-mix systems that classify transitional care clients into groups based on their resource utilization can assist with care planning, calculating reimbursement rates in bundled care funding models, and predicting health human resource needs. This study evaluated the fit and relevance of the Resource Utilization Groups version III for Home Care (RUG-III/HC) case-mix classification system in transitional care programs in Ontario, Canada. METHODS: We conducted a retrospective analysis of clinical assessment data and administrative billing records from a cohort of clients (n = 1,680 care episodes) in transitional home care programs in Ontario. We classified care episodes into established RUG-III/HC groups based on clients' clinical and functional characteristics and calculated four case-mix indices to describe care relative resource utilization in the study sample. Using these indices in linear regression models, we evaluated the degree to which the RUG-III/HC system can be used to predict care resource utilization. RESULTS: A majority of transitional home care clients are classified as being Clinically complex (41.6%) and having Reduced physical functions (37.8%). The RUG-III/HC groups that account for the largest share of clients are those with the lowest hierarchical ranking, indicating low Activities of Daily Living limitations but a range of Instrumental Activities of Daily Living limitations. There is notable heterogeneity in the distribution of clients in RUG-III/HC groups across transitional care programs. The case-mix indices reflect decreasing hierarchical resource use within but not across RUG-III/HC categories. The RUG-III/HC predicts 23.34% of the variance in resource utilization of combined paid and unpaid care time. CONCLUSIONS: The distribution of clients across RUG-III/HC groups in transitional home care programs is remarkably different from clients in long-stay home care settings. Transitional care programs have a higher proportion of Clinically complex clients and a lower proportion of clients with Reduced physical function. This study contributes to the development of a case-mix system for clients in transitional home care programs which can be used by care managers to inform planning, costing, and resource allocation in these programs.

Case-mix adjustments for patient reported experience and outcome measures in primary care: an empirical approach to identify patient characteristics as case-mix adjusters based on a secondary analysis of an international survey among patients and their general practitioners in 34 countries.

BACKGROUND: Case-mix adjustment of patient reported experiences (PREMs) and outcomes (PROMs) of care are meant to enable fair comparison between units (e.g. care providers or countries) and to show where improvement is possible. It is important to distinguish between fair comparison and improvement potential, as case-mix adjustment may mask improvement potential. Case-mix adjustment takes into account the effect of patient characteristics that are related to the PREMs and PROMs studied, but are outside the sphere of influence of the units being compared. We developed an approach to assess which patient characteristics would qualify as case-mix adjusters, using data from an international primary care study. RESULTS: We used multilevel analysis, with patients nested in general practices nested in countries. Case-mix adjustment is indicated under the following conditions: there is a main effect of the potential case-mix adjuster on the PREM/PROM; this effect does not vary between units; and the distribution of the potential case-mix adjuster differs between units. Random slope models were used to assess whether the impact of a potential case-mix adjuster varied between units. To assess whether a slope variance is big enough to decide that case-mix adjustment is not indicated, we compared the variances in the categories of a potential case-mix adjuster. Significance of the slope variance is not enough, because small variances may be significantly different from zero when numbers are large. We therefore need an additional criterion to consider a slope variance as important. Borrowing from the idea of a minimum clinically important difference (MCID) we proposed a difference between the variances of 0.25*variance (equivalent to a medium effect size). We applied this approach to data from the QUALICOPC (Quality and costs of primary care in Europe) study. CONCLUSIONS: Our approach provides guidance to decide whether or not patient characteristics should be considered as case-mix adjusters. The criterion of a difference between variances of 0.25*variance works well for continuous PREMs and PROMs, but seems to be too strict for binary PREMs and PROMs. Without additional information, it is not possible to decide whether important slope variation is the result of either differences in performance between general practices or countries, or cultural differences.

Efficiency and Resource Allocation in Government Hospitals in Saudi Arabi: A Casemix Index Approach.

In Saudi Arabia, the evaluation of healthcare institutions' performance and efficiency is gaining prominence to ensure effective resource utilization. This study aims to assess the efficiency of government hospitals in Saudi Arabia using the case mix index (CMI) approach. Comprehensive data from 67 MoH hospitals were collected and analyzed. The CMI was calculated by assigning weights to different patient groups based on case complexity and resource requirements, facilitating comparisons of hospital performance in terms of resource utilization and patient outcomes. The findings reveal variations in the CMI across hospitals in relation to size and type. The average CMI was 1.26, with the highest recorded at 1.67 and the lowest at 1.02. Medical cities demonstrated the highest CMI (1.47), followed by specialized hospitals (1.32), and general hospitals (1.21). The study highlights opportunities for enhancing productivity and efficiency, particularly in hospitals with lower CMI, by benchmarking against peer institutions with similar capacities and patient case mix. These findings have significant implications for hospital operations and resource allocation policies, supporting ongoing efforts to improve the efficiency of government hospitals in Saudi Arabia. By incorporating these insights into healthcare strategies, policymakers can work towards enhancing the overall performance and effectiveness of the healthcare system.

Is Age the Most Important Risk Factor in COVID-19 Patients? The Relevance of Comorbidity Burden: A Retrospective Analysis of 10,551 Hospitalizations.

Purpose: To assess the contribution of age and comorbidity to the risk of critical illness in hospitalized COVID-19 patients using increasingly exhaustive tools for measuring comorbidity burden. Patients and Methods: We assessed the effect of age and comorbidity burden in a retrospective, multicenter cohort of patients hospitalized due to COVID-19 in Catalonia (North-East Spain) between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses. The primary outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death. Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission extracted from three indices: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis. Results: The primary analysis included 10,551 hospitalizations due to COVID-19; of them, 3632 (34.4%) experienced critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 98.2% (95% CI 84.1-117.1%) of the observed effect of age on critical illness. Conclusion: Comorbidity burden (when measured exhaustively) explains better than chronological age the increased risk of critical illness observed in patients hospitalized with COVID-19.

Case mix-based changes in health status: A prospective study of elective surgery patients in Vancouver, Canada.

INTRODUCTION: Hospital activity is often measured using diagnosis-related groups, or case mix groups, but this information does not represent important aspects of patients' health outcomes. This study reports on case mix-based changes in health status of elective (planned) surgery patients in Vancouver, Canada. DATA AND METHODS: We used a prospectively recruited cohort of consecutive patients scheduled for planned inpatient or outpatient surgery in six acute care hospitals in Vancouver. All participants completed the EQ-5D(5L) preoperatively and 6 months postoperatively, collected from October 2015 to September 2020 and linked with hospital discharge data. The main outcome was whether patients' self-reported health status improved among different inpatient and outpatient case mix groups. RESULTS: The study included 1665 participants with completed EQ-5D(5L) preoperatively and postoperatively, representing a 44.8% participation rate across eight inpatient and outpatient surgical case mix categories. All case mix categories were associated with a statistically significant gain in health status (p < .01 or lower) as measured by the utility value and visual analogue scale score. Foot and ankle surgery patients had the lowest preoperative health status (mean utility value: 0.6103), while bariatric surgery patients reported the largest improvements in health status (mean gain in utility value: 0.1515). CONCLUSIONS: This study provides evidence that it was feasible to compare patient-reported outcomes across case mix categories of surgical patients in a consistent manner across a system of hospitals in one province in Canada. Reporting changes in health status of operative case mix categories identifies characteristics of patients more likely to experience significant gains in health.

Patient-level predictors of temporal regularity of primary care visits.

BACKGROUND: Patients with chronic diseases should meet with their primary care doctor regularly to facilitate proactive care. Little is known about what factors are associated with more regular follow-up. METHODS: We studied 70,095 patients age 40 + with one of three chronic conditions (diabetes mellitus, heart failure, chronic obstructive pulmonary disease), cared for by Leumit Health Services, an Israeli health maintenance organization. Patients were divided into the quintile with the least temporally regular care (i.e., the most irregular intervals between visits) vs. the other four quintiles. We examined patient-level predictors of being in the least-temporally-regular quintile. We calculated the risk-adjusted regularity of care at 239 LHS clinics with at least 30 patients. For each clinic, compared the number of patients with the least temporally regular care with the number predicted to be in this group based on patient characteristics. RESULTS: Compared to older patients, younger patients (age 40-49), were more likely to be in the least-temporally-regular group. For example, age 70-79 had an adjusted odds ratio (AOR) of 0.82 compared to age 40-49 (p < 0.001 for all findings discussed here). Males were more likely to be in the least-regular group (AOR 1.18). Patients with previous myocardial infarction (AOR 1.07), atrial fibrillation (AOR 1.08), and current smokers (AOR 1.12) were more likely to have an irregular pattern of care. In contrast, patients with diabetes (AOR 0.79) or osteoporosis (AOR 0.86) were less likely to have an irregular pattern of care. Clinic-level number of patients with irregular care, compared with the predicted number, ranged from 0.36 (fewer patients with temporally irregular care) to 1.71 (more patients). CONCLUSIONS: Some patient characteristics are associated with more or less temporally regular patterns of primary care visits. Clinics vary widely on the number of patients with a temporally irregular pattern of care, after adjusting for patient characteristics. Health systems can use the patient-level model to identify patients at high risk for temporally irregular patterns of primary care. The next step is to examine which strategies are employed by clinics that achieve the most temporally regular care, since these strategies may be possible to emulate elsewhere.

Validity of Early Outcomes as Indicators for Comparing Hospitals on Quality of Stroke Care.

Background Insight into outcome variation between hospitals could help to improve quality of care. We aimed to assess the validity of early outcomes as quality indicators for acute ischemic stroke care for patients treated with endovascular therapy (EVT). Methods and Results We used data from the MR CLEAN (Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry, a large multicenter prospective cohort study including 3279 patients with acute ischemic stroke undergoing EVT. Random effect linear and proportional odds regression were used to analyze the effect of case mix on between-hospital differences in 2 early outcomes: the National Institutes of Health Stroke Scale (NIHSS) score at 24 to 48 hours and the expanded thrombolysis in cerebral infarction score. Between-hospital variation in outcomes was assessed using the variance of random hospital effects (tau2). In addition, we estimated the correlation between hospitals' EVT-patient volume and (case-mix-adjusted) outcomes. Both early outcomes and case-mix characteristics varied significantly across hospitals. Between-hospital variation in the expanded thrombolysis in cerebral infarction score was not influenced by case-mix adjustment (tau 2=0.17 in both models). In contrast, for the NIHSS score at 24 to 48 hours, case-mix adjustment led to a decrease in variation between hospitals (tau 2 decreases from 0.19 to 0.17). Hospitals' EVT-patient volume was strongly correlated with higher expanded thrombolysis in cerebral infarction scores (r=0.48) and weakly with lower NIHSS score at 24 to 48 hours (r=0.15). Conclusions Between-hospital variation in NIHSS score at 24 to 48 hours is significantly influenced by case-mix but not by patient volume. In contrast, between-hospital variation in expanded thrombolysis in cerebral infarction score is strongly influenced by EVT-patient volume but not by case-mix. Both outcomes may be suitable for comparing hospitals on quality of care, provided that adequate adjustment for case-mix is applied for NIHSS score.

Surgeon-level versus hospital-level quality variance in kidney cancer surgery.

PURPOSE: To determine whether variance in kidney cancer surgery quality indicators (QIs) is most impacted by surgeon-level or hospital-level factors in order to inform quality improvement initiatives. MATERIALS AND METHODS: The ICES and Veterans Affairs (VA) databases were queried for patients undergoing surgery for localized kidney cancer. Kidney cancer surgery QIs were defined within each cohort. Quality of care was benchmarked at a surgeon- vs. hospital-level to identify statistical outliers, using available clinicopathological data to adjust for differences in case-mix. Variance between surgeons and hospitals was calculated for each QI using a random-effects model. RESULTS: The QI with the greatest amount of variance explained by hospital and surgeon-level factors was proportion of cases performed with minimally invasive surgery (MIS). The majority of this variance was due to surgeon-level factors for both the VA and ICES cohorts. The proportion of cases performed using an MIS approach was also the QI with the greatest number of outlier hospitals and surgeons compared to the average performance. The proportion of partial nephrectomies performed for patients at risk of chronic kidney disease was the QI with the greatest amount of variance due to hospital-level factors for the ICES cohort. CONCLUSIONS: The proportion of localized kidney cancer cases performed using an MIS approach is the QI requiring the greatest attention. Quality improvement initiatives should focus on surgeon-level factors to increase the number of MIS cases being performed for patients with localized renal masses.

Enlightenment of the DRG payment reform in the United States on the reform of China′s medical insurance payment methods / 中华医院管理杂志

In order to curb the excessive growth of medical expenses, the United States has initiated payment reform of diagnosis-related groups (DRG) since 1983, and developed a series of complementary measures to address issues such as overcoding and declining healthcare service quality which were exposed during the reform. The authors discussed the implementation of DRG payment reform in the United States, namely the case-mix specialization of medical institutions and the reduction of costs, as well as the relationship between the two. On this basis, the authors suggested that when implementing reforms to the medical insurance payment system in China, it is imperative to avoid such loopholes as overcoding by medical institutions and excessive pursuit of efficiency at the expense of quality control, as well as the decline of comprehensive rescue capability and quality of care incurred by the exacerbated specialization.

Efficiency analysis and management strategies of hospital beds based on diagnosis-related groups / 中华医院管理杂志

Objective:To improve the evaluation method of hospital beds efficiency based on diagnosis-related groups (DRG), and to provide a basis for hospitals to allocate beds reasonably and improve bed efficiency.Methods:Taking a tertiary hospital in Beijing as the research object, the types of beds were evaluated by the beds utilization matrix with the time consumption index as the X-axis and the bed utilization rate as the Y-axis. The types of beds in the department were divided into efficiency type, pressure type, turnover type, and idle type. The efficiency of medical services and the level of diagnosis and treatment were evaluated by the weight of DRG per bed. The calculation method of theoretical number of beds was improved by incorporating hospital case mix index as a risk adjustment factor into the formula to evaluate the status of beds allocation. Combining the bed type, DRG weight per bed, and bed allocation status, the improvement emphasis and management strategy of bed utilization could be comprehensively analyzed.Results:Among the 24 departments in the hospital, there were 5, 9, 1 and 9 departments being efficiency type, pressure type, turnover type and idle type, respectively. The weight per bed of 11 departments was higher than the average level of the hospital. There were 16, 5, and 3 departments with appropriate, fewer, and excessive beds, respectively.Conclusions:The comprehensive analysis of beds utilization type, allocation status and weight of each bed based on DRG is an effective method to evaluate the efficiency of hospital beds, and can provide decision-making basis for hospital bed resource allocation, hospital operation focus adjustment, and subject development planning.

Predictors of amounts of child and adolescent mental health service use.

The aim of this study was to build evidence about how to tailor services to meet the individual needs of young people by identifying predictors of amounts of child and adolescent mental health service use. We conducted a secondary analysis of a large administrative dataset from services in England was conducted using the Mental Health Services Data Set (years 2016-17 and 2017-18). The final sample included N = 27,362 episodes of care (periods of service use consisting of at least two attended care contacts and less than 180 days between care contacts) from 39 services. There were 50-10,855 episodes per service. The descriptive statistics for episodes of care were: Mage = 13 years, SDage = 4.71, range = 0-25 years; 13,785 or 50% male. Overall, there were high levels of heterogeneity in number of care contacts within episodes of care: M = 11.12, SD = 28.28, range = 2-1529. Certain characteristics predicted differential patterns of service use. For example, young people with substance use (beta = 6.29, 95% CI = 5.06-7.53) or eating disorders (beta = 4.30, 95% CI = 3.29-5.30) were particularly more likely to have higher levels of service use. To build on this, evidence is needed about predictors of child and adolescent mental health treatment outcome and whether the same characteristics predict levels of improvement as well as levels of service use.

Impact of case-mix adjustment on observed variation in the healing of diabetic foot ulcers at 12 weeks using data from the National Diabetes Foot Care Audit of England and Wales: A cohort study.

AIM: This cohort study investigates the extent to which variation in ulcer healing between services can be explained by demographic and clinical characteristics. METHODS: The National Diabetes Foot Care Audit collated data on people with diabetic foot ulcers presenting to specialist services in England and Wales between July 2014 and March 2018. Logistic regression models were created to describe associations between risk factors and a person being alive and ulcer-free 12 weeks from presentation, and to investigate whether variation between 120 participating services persisted after risk factor adjustment. RESULTS: Of 27,030 people with valid outcome data, 12,925 (47.8%) were alive and ulcer-free at 12 weeks, 13,745 (50.9%) had an unhealed ulcer and 360 had died (1.3%). Factors associated with worse outcome were male sex, more severe ulcers, history of cardiac or renal disease and a longer time between first presentation to a non-specialist healthcare professional and first expert assessment. After adjustment for these factors, four services (3.3%) were more than 3SD above and seven services (5.8%) were more than 3SD below the national mean for proportions that were alive and ulcer-free at follow-up. CONCLUSIONS/INTERPRETATIONS: Variation in the healing of diabetic foot ulcers between specialist services in England and Wales persisted after adjusting for demographic characteristics, ulcer severity, smoking, body mass index and co-morbidities. We conclude that other factors contribute to variation in healing of diabetic foot ulcers and include the time to specialist assessment.

Special needs dentistry in multidisciplinary head and neck cancer management - characterizing scope and complexity of patient care.

AIMS: To describe the involvement and need for specialists in special needs dentistry (SND) in the multidisciplinary management of patients and to characterize the complexity of patients with special health care needs. METHODS: Referrals for SND review from the Fiona Stanley Hospital head and neck cancer (HNC) multidisciplinary team between January 1 and December 31, 2019 were screened. Data recorded included demographic information, medical history, dental status, and dental treatment provided prior to HNC management. RESULTS: In total 127 patients were referred to the SND clinic in 2019 of which 89.0% were males and 74.8% were aged 50-79. The mean waiting time for SND review was 19.6 days. On average patients had a decayed, missing, filled tooth (DMFT) score of 20.3. During the study period 407 extractions and 30 restorations were completed. Seventeen patients (13.4%) had all their teeth removed prior to HNC management. The majority of patients (79.6%) were categorized as either moderate or severe complexity. Increasing complexity was seen in older age groups. CONCLUSIONS: Significant oral disease is seen in patients with HNC often requiring extensive dental treatment. Complexity is seen in patients requiring multidisciplinary care however characteristics that constitute complexity are varied given the spectrum encompassing 'special needs'.

The association of demographics, frailty and multiple health conditions with outcomes from acute medical admissions to hospitals in England: exploratory analysis of an administrative dataset.

Emergency and acute hospital services in England are under increasing pressure. The aim of this study was to investigate the association between key case-mix indicators and outcomes for adults admitted to hospital with an acute medical condition in England. All patients aged ≥16 years admitted to hospital in England as an acute unselected medical admission and who survived to discharge during the financial year 2021-2022 were included. Length of hospital stay was the primary outcome of interest. Data were available for 1,586,168 unique patients. A case-mix index was developed with a score that ranged from 0 to 12. Frailty was the most important variable in the index, followed by multiple health conditions and patient age. The mean case-mix score across hospital trusts in England ranged from 5.3 to 7.8. The case-mix index will support initiatives to better understand factors contributing to outcomes from acute medical admissions to hospital.

Case-Mix Moderation of the Relationship Between OR Performance Metrics and Utilization.

We investigated the impact of the case-mix ratio of inpatients to outpatients on the relationships between OR utilization and late starts, turnover time, delays, cancellations, and idle time at an academic medical center in the southeastern United States. After extracting 55 months of data from the surgical repository, we used simple and multiple linear regression models to analyze the data and determine the strength and direction of the relationships among the variables. We compared models comprising proportionally more inpatients to models comprising proportionally more outpatients for each metric to ascertain the effects of case mix on OR utilization. Idle time had the greatest effect on OR utilization, followed by late starts and turnover time. Case mix moderated the relationship between OR utilization and the metrics of cancellations and turnover time. Perioperative leaders may enhance OR utilization by monitoring and addressing idle time and late starts and scheduling an appropriate mix of inpatients and outpatients.