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Many hospitals in Germany are facing escalating economic pressures. After several years of stagnation, the number of inpatient hospital treatments dropped by in 2020 compared to the previous year. This negative tendency can also be seen in operating theaters (OTs). Strategic management of the case mix in hospital OTs now necessitates a solid data foundation. The case mix and the case mix index have become central economic indicators in contemporary hospital operations. In this work, we develop a mathematical model for case mix optimization at Augsburg University Hospital in Germany, which is based on an extensive data analysis with descriptive methods. The optimization model is subject to rigorous testing and evaluation through an extensive series of scenario analyses. The primary objective is to calculate a revenue-maximizing patient mix while respecting the available scarce personnel resources in the OT and intensive care unit. This research marks a pioneering effort in delineating the practical integration of case mix planning into a hospital's routine operations using mathematical optimization. The analyses reveal a strong correlation between an upsurge in revenue and an increased number of cases. Furthermore, the results demonstrate that strategic planning of the patient mix has the potential to enhance revenue with existing resources. Even though the optimal patient mix may not be directly implementable in practice, the findings yield valuable insights for managerial decision-making. A critical examination of these results also fosters a nuanced discourse on the utilization of optimization models as decision support tools within hospital management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Chicago's homicide arrest rate dropped from 91% in 1965 to 57% in 1994 and dropped still lower in recent years. This pattern mirrors the trend in the national homicide clearance rate. A plausible explanation for this great decline is the trend in homicide case mix, which arguably has made it intrinsically more difficult to solve homicide cases. Our analysis describes the change in case mix for the period 1965 to 2020 and analyzes the effect on the arrest rate for the first 30 years of this period, all by use of a unique homicide case microdata set. We document the large changes in case mix: for example, the percentage of all homicides in which a male victim was shot outdoors increased from 18% (1965) to 69% (2020). But the change in case mix does not account for Chicago's great decline during the earlier decades, as we demonstrate by use of a novel arrest rate index. In fact, the arrest rates in each of the categories defined by location, sex, and weapon type exhibited similar declines through 1994. (Subsequent years of arrest data are unavailable for now.) Our preferred explanation for the great decline is that the operational standard for making an arrest increased during this period. That interpretation is well supported by evidence explaining the corresponding national trend, though direct evidence is lacking for Chicago. This interpretation challenges the use of the arrest rate as a police performance indicator and offers a positive interpretation of the great decline. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BackgroundPatient surveys are the primary tool to measure patient experiences of care. Caution must be taken when analyzing these data, as responses can be influenced by factors that do not reflect the quality of care received.ObjectivesTo provide a practical overview of adjusting patient experience survey results to address bias related to patient case-mix, extreme response tendency, and mode of survey administration.Research designWe discuss options for adjustment for biases in how people respond to patient experience surveys.ResultsCase-mix adjustment (CMA) aims to compare provider performance that would have been observed if all providers had treated the same set of patients by removing the effects of patient characteristics that vary across providers. Extreme response tendency can bias the measurement of the disparities in patient experiences even after typical CMAs, since differences in patients' use of extreme response options may affect patient experience scores when they have a skewed distribution. Survey mode may affect scores for the provider entity being evaluated (eg, hospital) more than CMA if survey mode differs at the provider level.ConclusionsIt is best practice to evaluate known source of bias when analyzing patient experience surveys. Failure to adjust for patient case-mix, extreme response tendency, and survey mode in patient experience surveys may lead to erroneous comparisons of providers.

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'. [ABSTRACT FROM AUTHOR]

Defining, maintaining, and improving quality is essential to surgical practice. This article utilizes a leading quality assessment model and explores its relevance to the surgical community. National initiatives are discussed with critique of their effectiveness in improving clinical outcomes. Maintaining quality of care during crisis is addressed. [ABSTRACT FROM AUTHOR]

In this study, the goal programming approach is discussed which is used in hospitals to allocate resources. Two linear goal-programming models are included in the process. One model fixes the ratio of different types of cases that doctors see each year, while the other translates case mix decisions into a set of practice changes for doctors. Decision-makers may use the models to find the optimal case mix that maximizes profitability for the institution while minimizing negative impacts on physician compensation and clinical workflow. [ABSTRACT FROM AUTHOR]

Introduction New Zealand general practice and primary care is currently facing significant challenges and opportunities following the impact of the coronavirus disease 2019 (COVID-19) pandemic and the introduction of health sector reform. For future sustainability, it is important to understand the workload associated with differing levels of patient case mix seen in general practice. Aim To assess levels of morbidity and concomitant levels of socio-economic deprivation among primary care practices within a large primary health organisation (PHO) and associated Māori provider network. Methods Routinely collected practice data from a PHO of 57 practices and a Māori provider (PHO) of five medical practices in the same geographical area were used to compare a number of population health indicators between practices that had a high proportion of high needs patients (HPHN) and practices with a low proportion of high needs patients (Non-HPHN). Results When practices in these PHOs are grouped in terms of ethnicity distribution and deprivation scores between the HPHN and Non-HPHN groups, there is significantly increased clustering of both long-term conditions and health outcome risk factors in the HPHN practices. Discussion In this study, population adverse health determinants and established co-morbidities are concentrated into the defined health provider grouping of HPHN practices. This ‘concentration of complexity’ raises questions about models of care and adequate resourcing for quality primary care in these settings. The findings also highlight the need to develop equitable and appropriate resourcing for all patients in primary care.

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. [ABSTRACT FROM AUTHOR]

Introduction. New Zealand general practice and primary care is currently facing significant challenges and opportunities following the impact of the coronavirus disease 2019 (COVID-19) pandemic and the introduction of health sector reform. For future sustainability, it is important to understand the workload associated with differing levels of patient case mix seen in general practice. Aim. To assess levels of morbidity and concomitant levels of socio-economic deprivation among primary care practices within a large primary health organisation (PHO) and associated Māori provider network. Methods. Routinely collected practice data from a PHO of 57 practices and a Māori provider (PHO) of five medical practices in the same geographical area were used to compare a number of population health indicators between practices that had a high proportion of high needs patients (HPHN) and practices with a low proportion of high needs patients (Non-HPHN). Results. When practices in these PHOs are grouped in terms of ethnicity distribution and deprivation scores between the HPHN and Non-HPHN groups, there is significantly increased clustering of both long-term conditions and health outcome risk factors in the HPHN practices. Discussion. In this study, population adverse health determinants and established co-morbidities are concentrated into the defined health provider grouping of HPHN practices. This ‘concentration of complexity’ raises questions about models of care and adequate resourcing for quality primary care in these settings. The findings also highlight the need to develop equitable and appropriate resourcing for all patients in primary care. [ABSTRACT FROM AUTHOR]

Abstract Background The coronavirus disease-19 (COVID-19) pandemic had a relatively minimal direct impact on critical illness in children compared to adults. However, children and paediatric intensive care units (PICUs) were affected indirectly. We analysed the impact of the pandemic on PICU admission patterns and patient characteristics in the UK and Ireland. Methods We performed a retrospective cohort study of all admissions to PICUs in children

The diagnosis related group (DRG) was the most commonly used prospective hospital payment platform in developed countries. One of the major limitations of the DRG system is that the DRG grouping is not sufficiently homogeneous in benchmarking underlying resource needs. We developed a novel hospital payment and management system called Big Data Diagnosis & Intervention Packet (BD-DIP) by applying the similar case mix index (CMI) principles but the grouping is based on unique combination of ICD-10 and ICD-9 v3 codes. The initial prototype of BD-DIP was developed using hospital discharge records in Shanghai and then piloted in Guangzhou, China. The average coefficient of variation of the DB-DIP is about one-third smaller than the US DRG system. Results from the pilot evaluation showed that introduction of the BD-DIP lead to about 5% hospital budget savings and notable improvement in hospital care efficiency, including increased institutional CMI, lower admission rates, smaller variation in hospital charges, and lower patient cost-sharing burdens. The implementation of hospital monitoring tools resulted in identification of potential irregular practices to enable further auditing and investigation. The BD-DIP platform has a number of advantages over DRG-based payment models in terms of more homogeneous resource utilization within groups, design simplicity, dynamic in grouping, and reimbursement value in reflecting real-world treatment pathways and costs, and easy to implement.

Purpose: The study aims to examine the length of stay (LOS) outliers by analyzing the hospital administrative database. Material and Methods: The Turkish Ministry of Health DRG grouper database was utilized to obtain hospital administrative data on discharges for 15 training and research hospitals in 2012. For each diagnosis-related group (DRG), the geometric mean plus two standard deviations were calculated to identify the outliers. According to the recent data, demographic, clinical, and DRG-specific characteristics influence LOS outliers. Analyses were conducted using descriptive statistics and logistic regression using generalized estimating equations (GEE). Results: High LOS outliers were found to be 4.4 % of the cases. Even though they made up only 4.4 percent of the patients, outliers accounted for 24.50 percent of all discharge days. The top three specific DRGs with the highest LOS outliers were neonatal cases. According to the multivariate model, alcohol, drug use disorders, burns, and ear, nose, mouth, and throat diseases influenced high LOS outliers the most. Furthermore, DRG weight, DRG type, discharge type, and age groups were other significant factors related to high LOS outliers. Conclusion: A quarter of all inpatient days are made up of LOS outliers. Burns, neonate cases, and alcohol/drug use cases should be carefully evaluated. To improve clinical quality and effectively manage hospital resources, hospital administrators and health policymakers should consider the length of stay outliers. [ABSTRACT FROM AUTHOR]

Zhanglei Mu,1 Xiaojing Liu,2 Kun Li,1 Jianzhong Zhang1 1Department of Dermatology, Peking University People’s Hospital, Beijing, People’s Republic of China; 2Department of Dermatology, Shanghai Skin Disease Hospital, Tongji University, Shanghai, People’s Republic of ChinaCorrespondence: Jianzhong ZhangDepartment of Dermatology, Peking University People’s Hospital, No. 11 Xizhimen South St, Beijing, 100044, People’s Republic of ChinaTel +86-10-88325472Email rmzjz@126.comPurpose: During the COVID-19 pandemic, teledermatology service was increased rapidly. The purpose of our study was to analyze the characteristics of patients and common skin diseases via teledermatology during the COVID-19 pandemic in mainland China.Patients and Methods: During weekends between January 21 and April 4, 2020, the data of patients who used teledermatology service via a mobile application were collected, including gender, age, and diagnosis.Results: A total of 698 patients (315 men and 383 women), with a mean age of 26 years, used this service. The top ten common diseases in order of proportion were eczema (22%), acne (9%), atopic dermatitis (9%), urticaria (5%), contact dermatitis (5%), herpes zoster (3%), warts (3%), folliculitis (3%), prurigo (3%), and androgenetic alopecia (2%). When classified according to age groups, atopic dermatitis was the most common condition for patients in the first decade, acne was more prevalent in the second and third decades, and eczema was the most prevalent condition for all other age groups.Conclusion: The ten common diseases accounted for the majority of the evaluated cases and varied by age group, allowing individualizing teledermatology services.Keywords: teledermatology, COVID-19, China, clinical epidemiology, case mix, dermatology

Background: The value of medical registries strongly depends on the quality of the data collected. This must be objectively measured before large clinical databases can be promoted for observational research, quality improvement, and clinical trials. We aimed to evaluate the quality of a multinational intensive care unit (ICU) network of registries of critically ill patients established in seven Asian low- and middle-income countries (LMICs). Methods: The Critical Care Asia federated registry platform enables ICUs to collect clinical, outcome and process data for aggregate and unit-level analysis. The evaluation used the standardised criteria of the Directory of Clinical Databases (DoCDat) and a framework for data quality assurance in medical registries. Six reviewers assessed structure, coverage, reliability and validity of the ICU registry data. Case mix and process measures on patient episodes from June to December 2020 were analysed. Results: Data on 20,507 consecutive patient episodes from 97 ICUs in Afghanistan, Bangladesh, India, Malaysia, Nepal, Pakistan and Vietnam were included. The quality level achieved according to the ten prespecified DoCDat criteria was high (average score 3.4 out of 4) as was the structural and organizational performance -- comparable to ICU registries in high-income countries. Identified strengths were types of variables included, reliability of coding, data completeness and validation. Potential improvements included extension of national coverage, optimization of recruitment completeness validation in all centers and the use of interobserver reliability checks. Conclusions: The Critical Care Asia platform evaluates well using standardised frameworks for data quality and equally to registries in resource-rich settings.

Background: The Americas Hepato-Pancreato-Biliary Association (AHPBA) Education and Training Committee standardized a Hepatopancreatobiliary (HPB) Surgery Fellowship certification process in 2010. Several classes of fellows have since graduated from HPB, combined Society of Surgical Oncology/AHPBA, and combined American Society of Transplant Surgeons/AHPBA fellowships, but there is little information on their career outcomes. We seek to offer long-term data on the careers of HPB fellowship graduates.Methods: A 26-question anonymous survey was distributed among graduates of accredited programs for the last 10 years. We generated descriptive statistics from the responses.Results: The respondents were evenly distributed in terms of graduation years between 2010 and 2019. Fifty-eight percent of fellows had completed a prior fellowship, 82% received 1 to 3 job offers during the fellowship, and 75% of respondents were still at their first job. The majority of graduates (>60%) were able to secure a job with a >50% HPB practice and >40 HPB cases per year within 3 years of graduation. Overall, >90% candidates rated their satisfaction with fellowship training greater than 8 out of 10.Discussion: This survey helps shed light on the early formative years in the practices of HPB fellowship graduates. These data show that HPB fellowship training is essential and effective in providing job security and helps fellowship graduates develop a gratifying practice. [ABSTRACT FROM AUTHOR]

Background: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes., Objective: This study proposes the development of a data-driven decision-support system incorporating interactions between clinical factors and SDoH into an algorithm for prioritizing who receives case management services. The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system., Methods: Our study setting is a large, tertiary care academic medical center in the Deep South of the United States, where SDoH contribute to disparities in diabetes-specific hospitalizations and emergency department (ED) visits. This project will develop an interpretable artificial intelligence model for a population with diabetes using SDoH and clinical data to identify which posthospitalization cases have a higher likelihood of subsequent ED use. The electronic health record data collected for the study include demographics, SDoH, comorbidities, hospitalization-related factors, laboratory test results, and medication use to predict posthospitalization ED visits. Subsequently, a mixed methods approach will be used to validate prediction outcomes and develop an implementation strategy from insights into patient outcomes from case managers, clinicians, and quality and patient safety experts., Results: As of December 2023, we had abstracted data on 174,871 inpatient encounters between January 2018 and September 2023, involving 89,355 unique inpatients meeting inclusion criteria. Both clinical and SDoH data items were included for these patient encounters. In total, 85% of the inpatient visits (N=148,640) will be used for training (learning from the data) and the remaining 26,231 inpatient visits will be used for mixed-methods validation (testing)., Conclusions: By integrating a critical suite of SDoH with clinical data related to diabetes, the proposed data-driven risk stratification model can enable individualized risk estimation and inform health professionals (eg, case managers) about the risk of patients' upcoming ED use. The prediction outcome could potentially automate case management referrals, helping to better prioritize services. By taking a mixed methods approach, we aim to align the model with the hospital's specific quality and patient safety considerations for the quality of patient care and the optimization of case management resource allocation., International Registered Report Identifier (irrid): DERR1-10.2196/56049., (©Seung-Yup Lee, Leslie W Hayes, Bunyamin Ozaydin, Steven Howard, Alison M Garretson, Heather M Bradley, Andrew M Land, Erin W DeLaney, Amy O Pritchett, Amanda L Furr, Ashleigh Allgood, Matthew C Wyatt, Allyson G Hall, Jane C Banaszak-Holl. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 25.09.2024.)

Background: The coronavirus disease-19 (COVID-19) pandemic had a relatively minimal direct impact on critical illness in children compared to adults. However, children and paediatric intensive care units (PICUs) were affected indirectly. We analysed the impact of the pandemic on PICU admission patterns and patient characteristics in the UK and Ireland.Methods: We performed a retrospective cohort study of all admissions to PICUs in children < 18 years during Jan-Dec 2020, using data collected from 32 PICUs via a central database (PICANet). Admission patterns, case-mix, resource use, and outcomes were compared with the four preceding years (2016-2019) based on the date of admission.Results: There were 16,941 admissions in 2020 compared to an annual average of 20,643 (range 20,340-20,868) from 2016 to 2019. During 2020, there was a reduction in all PICU admissions (18%), unplanned admissions (20%), planned admissions (15%), and bed days (25%). There was a 41% reduction in respiratory admissions, and a 60% reduction in children admitted with bronchiolitis but an 84% increase in admissions for diabetic ketoacidosis during 2020 compared to the previous years. There were 420 admissions (2.4%) with either PIMS-TS or COVID-19 during 2020. Age and sex adjusted prevalence of unplanned PICU admission reduced from 79.7 (2016-2019) to 63.1 per 100,000 in 2020. Median probability of death [1.2 (0.5-3.4) vs. 1.2 (0.5-3.4) %], length of stay [2.3 (1.0-5.5) vs. 2.4 (1.0-5.7) days] and mortality rates [3.4 vs. 3.6%, (risk-adjusted OR 1.00 [0.91-1.11, p = 0.93])] were similar between 2016-2019 and 2020. There were 106 fewer in-PICU deaths in 2020 (n = 605) compared with 2016-2019 (n = 711).Conclusions: The use of a high-quality international database allowed robust comparisons between admission data prior to and during the COVID-19 pandemic. A significant reduction in prevalence of unplanned admissions, respiratory diseases, and fewer child deaths in PICU observed may be related to the targeted COVID-19 public health interventions during the pandemic. However, analysis of wider and longer-term societal impact of the pandemic and public health interventions on physical and mental health of children is required. [ABSTRACT FROM AUTHOR]

Background: The value of medical registries strongly depends on the quality of the data collected. This must be objectively measured before large clinical databases can be promoted for observational research, quality improvement, and clinical trials. We aimed to evaluate the quality of a multinational intensive care unit (ICU) network of registries of critically ill patients established in seven Asian low- and middle-income countries (LMICs). Methods: The Critical Care Asia federated registry platform enables ICUs to collect clinical, outcome and process data for aggregate and unit-level analysis. The evaluation used the standardised criteria of the Directory of Clinical Databases (DoCDat) and a framework for data quality assurance in medical registries. Six reviewers assessed structure, coverage, reliability and validity of the ICU registry data. Case mix and process measures on patient episodes from June to December 2020 were analysed. Results: Data on 20,507 consecutive patient episodes from 97 ICUs in Afghanistan, Bangladesh, India, Malaysia, Nepal, Pakistan and Vietnam were included. The quality level achieved according to the ten prespecified DoCDat criteria was high (average score 3.4 out of 4) as was the structural and organizational performance -- comparable to ICU registries in high-income countries. Identified strengths were types of variables included, reliability of coding, data completeness and validation. Potential improvements included extension of national coverage, optimization of recruitment completeness validation in all centers and the use of interobserver reliability checks. Conclusions: The Critical Care Asia platform evaluates well using standardised frameworks for data quality and equally to registries in resource-rich settings.

Objective: Aboriginal and Torres Strait Islander patients are overrepresented in Australian EDs. The present study aimed to assess their characteristics in utilising ED services at a national level. Methods: This exploratory, quantitative study used 2016–2017 de‐identified data from the National Non‐admitted Patient Emergency Department Care Database to assess the proportions (with 95% confidence interval) of Indigenous and non‐Indigenous Australians across various aspects of ED presentations, including mode of arrival, triage scale, diagnosis information, episode end status and ED length of stay. Episode level ED data were compared by Indigenous status and geographical remoteness of EDs. Results: Of 7.4 million presentations, 6.58% were Indigenous presentations, with over two‐thirds occurring in regional and remote EDs. Indigenous patients were more likely than non‐Indigenous patients to arrive to EDs by ambulance and police/correctional services vehicle across all remoteness areas. Additionally, they were more likely to present with respiratory system illness, illness of the skin/subcutaneous tissue/breast and mental/behavioural disorders. Indigenous Australians were more likely to leave EDs before being seen or care complete (odds ratio 1.73, 95% confidence interval 1.71–1.74), and this was observed for patients classified across all levels of triage scale. Conclusions: This is the first national study looking at the characteristics of and reasons for presenting to Australian EDs for Indigenous and non‐Indigenous patients. Our findings provide important insight into the potential factors affecting Indigenous patient care, and an impetus for ongoing research and advocacy work to improve the quality of emergency care provided to Indigenous Australians. [ABSTRACT FROM AUTHOR]

Introduction: Community hospitals (CHs) could address the emerging complex care needs of patients. We investigated which characteristics of patients’ and CHs affect patient outcomes, in order to identify who could benefit the most from CH care and the best skill mix to deliver this care. Methods: We analysed all elderly patients discharged from the CHs of Emilia-Romagna, Italy. CH skill mix and care processes were collected with an ad hoc survey. The primary outcome was improvement in the Barthel index (BI) on discharge. Hierarchical regression analysis was performed to test the associations under study. Results: 53% of the patients had a BI improvement ≥10. After adjusting for the diverse case mix of the patients, no significant association was found between CH characteristics and BI improvement. Patient characteristics explained only a portion of the variability in CH performance. Discussion: Heterogeneity in case mix reflects the nature of CHs, which play context-specific roles as integrators between primary care services and hospitals. Residual variability in BI improvement rates across CHs might be attributed to aspects of care not detected in our survey. Conclusions: More research is needed to study the impact of CH skill mix and care processes on patient outcomes.

Introduction: Community hospitals (CHs) could address the emerging complex care needs of patients. We investigated which characteristics of patients' and CHs affect patient outcomes, in order to identify who could benefit the most from CH care and the best skill mix to deliver this care. Methods: We analysed all elderly patients discharged from the CHs of Emilia-Romagna, Italy. CH skill mix and care processes were collected with an ad hoc survey. The primary outcome was improvement in the Barthel index (BI) on discharge. Hierarchical regression analysis was performed to test the associations under study. Results: 53% of the patients had a BI improvement =10. After adjusting for the diverse case mix of the patients, no significant association was found between CH characteristics and BI improvement. Patient characteristics explained only a portion of the variability in CH performance. Discussion: Heterogeneity in case mix reflects the nature of CHs, which play contextspecific roles as integrators between primary care services and hospitals. Residual variability in BI improvement rates across CHs might be attributed to aspects of care not detected in our survey. Conclusions: More research is needed to study the impact of CH skill mix and care processes on patient outcomes. [ABSTRACT FROM AUTHOR]

It is useful to know the experience of Bulgaria in terms of case-mix and DRG in view of guidelines for possible future activities on pilot implementation of DRG for a new payment and budgeting system. Since the mid-1990s, DRG research has been conducted in the country and a number of projects have been implemented. At the end of 1993 Robert Fetter, one of the people who developed DRGs in the United States, was invited to Bulgaria. He was the first to present to experts from the Bulgarian health institutions the main provisions of the case-mix approach and DRG. The overview of the current state of the information systems for health management, information technologies and the infrastructure for the management of the information systems of the hospitals, as well as the information systems of NCPHA, NHIF and MH, regarding the implementation of DRG, show the following: coding of clinical morbidity; - the current state of the IT infrastructure and systems of the ISMS, the hospitals, the NHIF, the NHIF and the Ministry of Health, as well as their capacity to implement DRGs. There are two issues that need to be addressed before implementing a DRG-based payment system: - procedures and diagnoses in hospital systems and XML files should be encoded with AKMP 7.0 to eliminate the need to use mapping tables .; - special processing of the lists of diagnoses when exporting codes to the DRG group and removal of unusual coding rules for CP during the annual CP update.

Objective: To understand differences in hospital length of stay and costs associated with the presence of a comorbid psychiatric disorder among physically ill inpatients within a publicly funded pediatric hospital.Method: This was a retrospective observational design using administrative data on physically ill inpatients 2 to 18 years old who were admitted over a 5-year period (n = 54,316 admissions). Records with (n = 4,953) and without (n = 49,363) documented comorbid psychiatric disorder were compared for differences in baseline characteristics. To optimize the balance of measured covariates, individuals with comorbid psychiatric disorders were matched on propensity score, case mix group, and Elixhauser comorbidities, resulting in 4,371 pairs of inpatients with and without a comorbid psychiatric disorder. Differences in length of stay and total hospital costs were assessed using generalized estimating equation models on matched patients.Results: Unmatched analyses demonstrated that inpatient admissions with comorbid psychiatric disorders were associated with higher occurrence of previous hospitalizations (69.2% versus 55.0%), unscheduled admissions (66.9% versus 60.9%), medical admissions (75.6% versus 52.7%), urgent admissions (62.5% versus 56.2%), and Elixhauser comorbidities (69.0% versus 39.0%), with standardized differences > |0.1|. Matched analyses demonstrated a 9.6% longer length of stay (95% CI = 5.7-13.7; p < .001) and 9.6% higher costs per admission (95% CI = 5.9-13.4; p < .001) in inpatients with comorbid psychiatric disorders compared to those without.Conclusion: The complexity of inpatients with a comorbid psychiatric disorder, in conjunction with the approximate 10% increase in hospital resource use, highlights the need for innovative models of clinical care and research directed at improving patient outcomes and reducing hospital costs. [ABSTRACT FROM AUTHOR]

To investigate case mix in relation to capsule complication, possible associations between case mix and operation volume, and change in case mix over time. Register-based study. Swedish patients who underwent cataract surgery between 2007 and 2016. Demographics and data on ocular comorbidity, intraoperative difficulties, and capsule complications were registered from 2007 to 2016 and analyzed retrospectively in relation to coded data on individual surgeons' operation volume. Single factor analysis and logistic regression were performed, and a composite risk score was created. Risk of capsule complication, given as adjusted and composite odds ratio in relation to cataract surgery volume. Preoperative and intraoperative variables significantly associated with capsule complications were best-corrected visual acuity (BCVA) ≤0.1 (decimal, adjusted odds ratio [aOR], 1.82; P < 0.001); pseudoexfoliation (PEX) (aOR, 1.53; P < 0.001); sight-threatening ocular comorbidity other than age-related macular degeneration (AMD), diabetic retinopathy, glaucoma, or cornea guttata (aOR, 1.35; P = 0.006); use of Trypan blue (aOR, 1.76; P < 0.001); mechanical pupil dilation (aOR, 1.36; P = 0.024); and iris hooks at the rhexis margin (aOR, 6.99; P < 0.001). The composite risk score was 3.09 ± 6.40 (mean ± standard deviation) for patients with capsule complication and 1.28 ± 1.66 for uncomplicated procedures (P < 0.001). High-volume cataract surgeons (≥500 procedures yearly) had a significantly lower composite risk score (mean risk score ≤1.28; range, 1.01–2.02) compared with low- and medium-volume cataract surgeons (1.34 ± 0.56; range, 1.00–4.55 and 1.49 ± 0.58; range, 1.01–5.19), respectively. During the period 2007–2016, the proportion of patients aged >88 years, patients with BCVA ≤0.1, and patients with intraoperative difficulties decreased. Case mix, as calculated from a composite risk score based on preoperative and intraoperative parameters registered in the National Cataract Register (NCR), may contribute to the decrease in capsule complications from 2007 to 2016 and the lower complication rate observed in cases managed by high-volume cataract surgeons. [ABSTRACT FROM AUTHOR]

Background: There is little information available on the range of conditions presenting to generalist run rural district hospital emergency departments (EDs) which are the first point of acute care for many South Africans. Aim: This study aims to assess the range of acute presentations as well as the types of procedures required by patients in a rural district hospital context. Setting: Zithulele is a 147-bed district hospital in rural Eastern Cape. Methods: This is a cross-sectional study assessing all patients presenting to the Zithulele hospital emergency department from 01 October 2015 to 31 December 2015. Data collected included the triage acuity using the South African Triage Scale system, patient demographics, diagnosis, outcome and procedures performed. Diagnoses were coded retrospectively according to the international statistical classification of diseases and related health problems version 10 (ICD 10). Results: Of the 4 002 patients presenting to the ED during the study period, 2% were triaged as emergencies and 45% as non-urgent. The most common diagnostic categories were injuries, infections and respiratory illnesses respectively. Diagnoses from all broad categories of the ICD-10 were represented. 67% of patients required no procedure. Diagnostic procedures (n = 877) were more prevalent than therapeutic procedures (n = 377). Only 2.4% of patients were transferred to a referral centre acutely. Conclusion: Patients with conditions from all categories of the ICD-10 present for management at rural district hospitals. Healthcare professionals working in this setting need to independently diagnose and manage a wide range of ED presentations and execute an assortment of procedures.

Abstract Background In Japan, several large healthcare databases have become available for research since the early 2000’s. However, validation studies to examine the accuracy of these databases remain scarce. We conducted a validation study in order to estimate the positive predictive value (PPV) of local or ICD-10 codes for acute myocardial infarction (AMI) in Japanese claims. In particular, we examined whether the PPV differs between claims in the Diagnosis Procedure Combination case mix scheme (DPC claims) and in non-DPC claims. Methods We selected a random sample of 200 patients from all patients hospitalized at a large tertiary-care university hospital between January 1, 2009 and December 31, 2011 who had an inpatient claim assigned a local or ICD-10 code for AMI. We used a standardized data abstraction form to collect the relevant information from an electronic medical records system. Abstracted information was then categorized by a single cardiologist as being either definite or not having AMI. Results In a random sample of 200 patients, the average age was 67.7 years and the proportion of males was 78.0%. The PPV of the local or ICD-10 code for AMI was 82.5% in this sample of 200 patients. Further, of 178 patients who had an ICD-10 code for AMI based on any of the 7 types of condition codes in the DPC claims, the PPV was 89.3%, whereas of the 161 patients who had an ICD-10 code for AMI based on any of 3 major types of condition codes in the DPC claims, the PPV was 93.8%. Conclusion The PPV of the local or ICD-10 code for AMI was high for inpatient claims in Japan. The PPV was even higher for the ICD-10 code for AMI for those patients who received AMI care through the DPC case mix scheme. The current study was conducted in a single center, suggesting that a multi-center study involving different types of hospitals is needed in the future. The accuracy of condition codes for DPC claims in Japan may also be worth examining for conditions other than AMI such as stroke.

Department of Veterans Affairs (VA) health services researchers often adjust for the differing risk profiles of selected patient populations for a variety of purposes. This paper explains the major reasons to conduct risk adjustment and provides a high level overview of what risk adjustment actually does and how the results of risk adjustment can be used in different ways for different purposes. The paper also discusses choosing a diagnostic classification system and describes some of the systems commonly used in risk adjustment along with comorbidity/severity indices and individual disease taxonomies. The factors influencing the choice of diagnostic classification systems and other commonly used risk adjustors are also presented along with a discussion of data requirements. Statistical approaches to risk adjustment are also briefly discussed. The paper concludes with some recommendations concerning risk adjustment that should be considering when developing research proposals. [ABSTRACT FROM AUTHOR]

Defining and maintaining quality is essential to surgical practice. It is only through structured approaches to assessing outcomes that we can ensure that optimal care is delivered. This article will define quality in healthcare and discuss assessment models with reference to pertinent surgical literature. National initiatives are discussed with a critical appraisal of their role and effectiveness. We discuss the aim of quality improvement initiatives and comment on reporting of outcomes. The difficult question of how to maintain quality during a crisis, such as an infectious disease pandemic, is addressed. [ABSTRACT FROM AUTHOR]

Bariatric and metabolic surgery are being performed in India for 2 decades. Aim of this paper is to evaluate the changing clinical trends over the last 5 years and to present the other aspects helmed by Obesity and Metabolic Surgery Society of India (OSSI) to aid the growth of research, education, data management and registry, quality control, insurance-related issues and policy change. OSSI conducts an annual survey to collect data pertaining to numbers of surgical procedures. With the approval of the executive committee, data collected from 2014 to 2018 were retrieved and analysed. 20,242 surgical procedures were performed in 2018 which is an 86.7% increase from 2014. Laparoscopic sleeve gastrectomy continued to remain the most popular procedure, it's percent share saw a steady decline from 68 to 48%. One anastomosis gastric bypass showed an unprecedented growth from 14 to 34%. Numbers of laparoscopic Roux en y gastric bypass remained constant at 15–16%. OSSI has also initiated a COE program along with training fellowships and focus on registry and inclusion in insurance coverage. National trends over the past 5 years in bariatric surgery have shown emergence of newer procedures like OAGB, although LSG continues to be the most popular procedure performed These trends give an insight on how the field is evolving and the implications for any distinctive requirements unique to this region These will lay out important directives for not only ensuring good treatment outcomes but also increasing awareness about the disease on the whole. [ABSTRACT FROM AUTHOR]

Background: Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS). Aim: To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation. Method: We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression. Results: The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix. Conclusion: The conceptually-derived classification merits further testing and development in the context of collaborative decision making. [ABSTRACT FROM AUTHOR]

Home care is an important service for persons with neurological conditions, but little is known about factors affecting health care costs in this setting. Using administrative data collected with the Resident Assessment Instrument for Home Care (RAI-HC), this study identified factors associated with home care costs for recipients of home care services with Alzheimer disease or related dementias, multiple sclerosis, and/or amyotrophic lateral sclerosis. As part of this study, the effectiveness of the Resource Utilization Groups for Home Care (RUG-III/HC), a case-mix classification system developed for the RAI-HC, in predicting care costs for this population, was also tested. Clinical characteristics indicative of greater disease severity had high levels of significance in predicting home care costs. In particular, the RUG-III/HC was highly predictive of home care costs for 3 neurological conditions, indicating the validity of this case-mix system for this population. With the increasing prevalence of neurological conditions and demand for home care services, future studies should continue to focus on identifying specific predictors care costs for those with neurological conditions in this care setting. [ABSTRACT FROM AUTHOR]

Background: There is little information available on the range of conditions presenting to generalist run rural district hospital emergency departments (EDs) which are the first point of acute care for many South Africans. Aim: This study aims to assess the range of acute presentations as well as the types of procedures required by patients in a rural district hospital context. Setting: Zithulele is a 147-bed district hospital in rural Eastern Cape. Methods: This is a cross-sectional study assessing all patients presenting to the Zithulele hospital emergency department from 01 October 2015 to 31 December 2015. Data collected included the triage acuity using the South African Triage Scale system, patient demographics, diagnosis, outcome and procedures performed. Diagnoses were coded retrospectively according to the international statistical classification of diseases and related health problems version 10 (ICD 10). Results: Of the 4 002 patients presenting to the ED during the study period, 2% were triaged as emergencies and 45% as non-urgent. The most common diagnostic categories were injuries, infections and respiratory illnesses respectively. Diagnoses from all broad categories of the ICD-10 were represented. 67% of patients required no procedure. Diagnostic procedures (n = 877) were more prevalent than therapeutic procedures (n = 377). Only 2.4% of patients were transferred to a referral centre acutely. Conclusion: Patients with conditions from all categories of the ICD-10 present for management at rural district hospitals. Healthcare professionals working in this setting need to independently diagnose and manage a wide range of ED presentations and execute an assortment of procedures. [ABSTRACT FROM AUTHOR]

This study examined the effects of observation‐based supervision Building Outcomes with Observation‐Based Supervision of Therapy (BOOST therapists = 26, families = 105), versus supervision as usual (SAU therapists = 21, families = 59) on (a) youth externalizing behavior problems and (b) the moderating effects of changes in family functioning on youth externalizing behaviors for adolescents receiving Functional Family Therapy (FFT). Exploratory analyses examined the impact of supervision conditions on youth internalizing problems. In 8 community agencies, experienced FFT therapists (M = 1.4 years) received either BOOST or SAU supervision in a quasi‐experimental design. Male (59%) or female (41%) adolescents were referred for the treatment of behavior problems (e.g., delinquency, substance use). Clients were Hispanic (62%), African American (19%), Non‐Hispanic White (12%), or Other (7%) ethnic/racial origins. Therapists (female, 77%) were Hispanic 45%, African American (19%), White Non‐Hispanic (30%), or other (4%) ethnic/racial backgrounds. Analyses controlled for the presence or absence of clinically elevated symptoms on outcome variables. Clinical outcomes were measured at baseline, 5 months, and 12 months after treatment initiation. Clients with externalizing behavior above clinical thresholds had significantly greater reductions in problem behaviors in the BOOST versus the SAU conditions. Clients below thresholds did not respond differentially to conditions. Supervisors in BOOST had more experience with the FFT model; as such, the observed results may be a result of supervisor experience. The BOOST supervision was associated with improved outcomes on problem behaviors that were above clinical thresholds. The findings demonstrate the importance of addressing client case mix in implementation studies in natural environments. [ABSTRACT FROM AUTHOR]

Aim: The purpose of the present study was to identify the relationship between staff mix in nursing homes and quality of care by level of case mix in Korea. Methods: Data used in the present study came from Long‐Term Care Insurance claims data with basic information of nursing homes with >29 beds (n = 1137) and quality evaluation reports. Staff mix was calculated as the number of nursing staff, social workers and care workers per total staff number. Results: In multinomial logistic regression analyses, institutions with a higher ratio of social workers were classified as top‐quality class institutes after controlling ownership, location, size and percentage of high level of care needs residents. In analyzing the higher case mix nursing homes, institutions with a high ratio of nursing staff and social workers were more likely to be classified as top‐quality class than the lowest class institutions. However, there was no significant association between quality of care and ratio of staff mix in the lower case mix nursing homes. Conclusions: A higher staff mix was positively related to nursing home quality of care, but the relationship was affected by case mix of residents' care demand. Therefore, the current minimum staffing standard for personnel in nursing homes should be modified considering the acuity of the residents. Geriatr Gerontol Int 2019; 19: 438–443. [ABSTRACT FROM AUTHOR]

Background The Italian National Health Service (NHS) provides universal coverage to all citizens, granting primary and hospital care with a copayment system for outpatient and drug services. Financing of Local Health Trusts (LHTs) is based on a capitation system adjusted only for age, gender and area of residence. We applied a risk-adjustment system (Johns Hopkins Adjusted Clinical Groups System, ACG® System) in order to explain health care costs using routinely collected administrative data in the Veneto Region (North-eastern Italy). Methods All residents in the Veneto Region were included in the study. The ACG system was applied to classify the regional population based on the following information sources for the year 2015: Hospital Discharges, Emergency Room visits, Chronic disease registry for copayment exemptions, ambulatory visits, medications, the Home care database, and drug prescriptions. Simple linear regressions were used to contrast an age-gender model to models incorporating more comprehensive risk measures aimed at predicting health care costs. Results A simple age-gender model explained only 8% of the variance of 2015 total costs. Adding diagnoses-related variables provided a 23% increase, while pharmacy based variables provided an additional 17% increase in explained variance. The adjusted R-squared of the comprehensive model was 6 times that of the simple age-gender model. Conclusions ACG System provides substantial improvement in predicting health care costs when compared to simple age-gender adjustments. Aging itself is not the main determinant of the increase of health care costs, which is better explained by the accumulation of chronic conditions and the resulting multimorbidity. [ABSTRACT FROM AUTHOR]

Surgeons are increasingly under pressure to measure and improve their quality. While there is broad consensus that we ought to track surgical quality, there is far less agreement about which metrics matter most. This article reviews the important statistical concepts of case mix and chance as they apply to understanding the observed wide variation in surgical quality. We then discuss the benefits and drawbacks of current measurement strategies through the framework of structure, process, and outcomes approaches. Finally, we describe emerging new metrics, such as video evaluation and network optimization, that are likely to take on an increasingly important role in the future of measuring surgical quality. [ABSTRACT FROM AUTHOR]

Purpose Multiple models have been developed to predict pathologic complete response ( pCR) in locally advanced rectal cancer patients. Unfortunately, validation of these models normally omit the implications of cohort differences on prediction model performance. In this work, we will perform a prospective validation of three pCR models, including information whether this validation will target transferability or reproducibility (cohort differences) of the given models. Methods We applied a novel methodology, the cohort differences model, to predict whether a patient belongs to the training or to the validation cohort. If the cohort differences model performs well, it would suggest a large difference in cohort characteristics meaning we would validate the transferability of the model rather than reproducibility. We tested our method in a prospective validation of three existing models for pCR prediction in 154 patients. Results Our results showed a large difference between training and validation cohort for one of the three tested models [Area under the Receiver Operating Curve ( AUC) cohort differences model: 0.85], signaling the validation leans towards transferability. Two out of three models had a lower AUC for validation (0.66 and 0.58), one model showed a higher AUC in the validation cohort (0.70). Discussion We have successfully applied a new methodology in the validation of three prediction models, which allows us to indicate if a validation targeted transferability (large differences between training/validation cohort) or reproducibility (small cohort differences). [ABSTRACT FROM AUTHOR]

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Aim: A meaningful comparison of clinical outcomes of mental health care providers requires risk adjustment of case mix variables that affect treatment outcome. The first goal of the present study was to describe the risk adjustment method we applied to routine outcome monitoring data in The Netherlands. Second, the consequences of risk adjustment for the ranking of providers were investigated. Subject and methods: Using an observational study design, pre- and posttest self-report assessments of symptoms were obtained for 31,849 adults with common mental health problems, such as mood and anxiety disorders. Regression analyses were performed to predict the posttest symptom level using socio-demographics and clinical characteristics as predictors. Results: Results showed that the posttest level of symptoms was best predicted by the symptom level at baseline, followed by the baseline level of functioning, age, socioeconomic status and some diagnoses. For the majority of the providers, risk adjustment did not markedly change their ranking. Conclusion: The baseline level of symptoms is the best predictor of the posttest level. The other investigated case mix variables only have a modest influence on the performance of providers. Risk adjustment is particularly necessary when consequences are related to the performance of an individual provider. These results contribute to the limited literature on risk adjustment in mental health care by using a large national data set. [ABSTRACT FROM AUTHOR]

Background: Despite increasing research on activity-based funding (ABF), there is no empirical evidence on the accuracy of outpatient service data for payment. Objective: This study aimed to identify data entry errors affecting ABF in two drug and alcohol outpatient clinic services in Australia. Methods: An audit was carried out on healthcare workers' (doctors, nurses, psychologists, social workers, counsellors, and aboriginal health education officers) data entry errors in an outpatient electronic documentation system. Results: Of the 6919 data entries in the electronic documentation system, 7.5% (518) had errors, 68.7% of the errors were related to a wrong primary activity, 14.5% were due to a wrong activity category, 14.5% were as a result of a wrong combination of primary activity andmodality of care, 1.9% were due to inaccurate information on a client's presence during service delivery and 0.4% were related to a wrong modality of care. Conclusion: Data entry errors may affect the amount of funding received by a healthcare organisation, which in turn may affect the quality of treatment provided to clients due to the possibility of underfunding the organisation. To reduce errors or achieve an errorfree environment, there is a need to improve the naming convention of data elements, their descriptions and alignment with the national standard classification of outpatient services. It is also important to support healthcare workers in their data entry by embedding safeguards in the electronic documentation system such as flags for inaccurate data elements. [ABSTRACT FROM AUTHOR]

Background Research regarding return on investment for electronic health records (EHRs) is sparse. Objective To extend previously established research and examine rigorously whether increasing the adoption of computer-based provider/prescriber order entry (CPOE) leads to a decrease in length of stay (LOS), and to demonstrate that the two are inversely and bidirectionally proportional even while other efforts to decrease LOS are in place. Method The study assessed CPOE, LOS and case mix index (CMI) data in a community hospital in the United States, using a mature and nearly fully deployed vendor product EHR. CPOE rates and LOS over 7 years were determined on a per-patient, per-visit and per-discipline basis and compared with concomitant CMI data. Results An inverse relationship of CPOE to LOS was correlated for 13 disciplines out of 19, and organisation wide for all disciplines combined during the first 5 years of study. During the subsequent 2 years, both CPOE and LOS plateaued, except in eight disciplines where CPOE rates at first declined and LOS concurrently rose slightly, and then returned to the baseline plateau levels. CMI increased during the entire period of evaluation. An inflection point at approximately 60% CPOE adoption predicted the greatest improvement in lowering of LOS. Conclusions Rising and falling rates of CPOE correlated with reductions and rises in LOS, respectively. CPOE appeared statistically to be an independent factor in affecting LOS, over and above other efforts to shorten LOS, thus contributing to lower costs and improved efficiency outcomes as measured by LOS, even as CMI rises.