High-cost Groups Research Articles

Lack of Association of Pediatric Surgical Patient Outcomes With Increased Disposable Operating Room Supply Costs

IntroductionThere is wide variation in the cost of disposable operating room supplies between surgeons performing the same operation at the same institution. The general relationship between variation in disposable supply cost and patient outcomes is unknown. We aimed to evaluate the relationship between disposable supply cost and patient outcomes for sixteen common operations. MethodsCost data were reviewed for the most common procedures performed by five surgical divisions at a single children's hospital over a six-month period in 2021. For procedure, the median disposable OR costs were calculated. Each operation performed was categorized as low cost (below the group median) or high cost (above the group median. We compared the rates of adverse events (clinic visit within 5 days, 30-day emergency department visit, unplanned reoperation, unplanned readmission, anesthesia complications, prolonged hospital length of stay, need for blood product transfusion, or death) between procedures with low and high disposable supply costs. Results1139 operations performed by 48 unique surgeons from five specialties were included; 596 (52%) were low-cost and 543 (48%) high-cost. The low and high-cost groups did not differ regarding most demographic characteristics. Overall, 21.9% of children suffered any adverse outcome; this rate did not differ between the low and high-cost groups when evaluated individually or in aggregate (20.5% vs 23.6%, p = 0.23). ConclusionOur data demonstrate that across a wide range of pediatric surgical procedures, the cost of disposable operating room supplies was not associated with the risk of adverse outcomes. Level of EvidenceLevel 3.

Hospitalization costs of road traffic injuries in Hunan, China: A quantile regression analysis

BackgroundHealthcare expenditure of road traffic injuries in China has not been adequately investigated so far. We aim to provide comprehensive information about the hospitalization costs of inpatients who suffered road traffic injuries, and explore the components and influencing factors of costs. MethodsWe extracted the data of all inpatients (n = 60535) with road traffic injuries during the year 2019 from Chinese National Health Statistics Network Reporting System database in Hunan, China. We calculated the components of hospitalization costs and analyzed the association between hospitalization costs and patient characteristics using quantile regression models. ResultsThe median hospitalization cost was $853.48, and the median length of hospital stay was 9 days. Vulnerable road users accounted for 84.9 % of all cases. Medicine cost is the first driver of hospitalization cost, accounting for 25.94 %. In the low- and medium-cost groups, hospitalization costs were highly concentrated on diagnosis, medicine, and medical services, while in the high-cost groups, consumable cost constituted the highest percentage. Male, a longer length of stay, more severe injuries, two or more comorbidities, surgical treatment, and admission to tertiary hospitals were significantly associated with higher hospitalization costs, and the regression coefficients increased with increasing of quartile points. Costs were lower in the 0–14 years group than in the other groups across all quartiles. At the median, occupants of heavy transport vehicle incurred the highest costs, $44.18 higher than pedestrians; injuries at lower extremities generated higher costs than those at any other site; and vascular injuries caused the greatest costs, $786.24 higher than superficial injuries. ConclusionsRoad traffic injuries cause huge healthcare costs for victims, most of whom are vulnerable road users. The total cost of hospitalization is incurred mainly for medicine, consumables, diagnosis, medical services, and treatment. Patients’ demographic factors (gender and age), clinical factors (injury severity, location, nature, and number of comorbidities), treatment factors (surgery, length of stay, and hospital level), and road user type are all significantly associated with hospitalization costs.

Addressing unanticipated interactions in risk equalization: A machine learning approach to modeling medical expenditure risk

Adverse selection harms market efficiency and access to essential services, particularly for disadvantaged groups. Risk equalization policies attempt to mitigate this by compensating agents for risk disparities, but often fall short of addressing interactions between risk factors. Using health insurance data from the Netherlands, we present a machine learning approach to capture unanticipated interactions that impact medical expenditure risk. We compare our novel approach to a state-of-the-art statistical model. We find that our approach explains an additional 1.5% of medical expenditure, equivalent to 571 million euros over all individuals in the Dutch market. In particular, this translates into better compensation for low- and high-cost groups that are especially vulnerable to adverse selection. These findings confirm the significance of risk factor interactions in explaining medical expenditure risk, and support the adoption of machine learning alongside statistical models to further mitigate selection incentives in risk equalization policies.

Determinants of High Facility Costs in Total Joint Arthroplasty Patients: A Time-driven Activity-based Costing Analysis.

Although several studies identify risk factors for high-cost patients in an episode of care for total hip (THA) and knee arthroplasty (TKA), few have looked at cost outliers from a facility perspective. The purpose of this study was to use time-driven activity-based costing (TDABC) to identify characteristics of high-cost patients. We reviewed a consecutive series of primary THA and TKA patients by 22 different surgeons at two hospitals between 2015 and 2020. Facility costs were calculated using a TDABC algorithm for their entire hospital stay. Patients in the top decile of costs were considered to be high-cost patients. Multivariate regression was done to identify independent patient factors that predicted high costs. Of the 8,647 patients we identified, 60.5% underwent THA and 39.5% underwent TKA. Implant purchase price accounted for 49.5% of total inpatient costs (mean $2,880), followed by intraoperative (15.9%, mean $925) and postoperative personnel costs (16.8%, mean $980). Implant price demonstrated the highest variation between high-cost and low-cost groups (4.4 times). Patient-related factors associated with high costs were female sex (OR = 1.332), Hispanic ethnicity (OR = 1.409), American Society of Anesthesiology score (OR = 1.658), need for transfusion (OR = 2.008), and lower preoperative HOOS/KOOS Jr (OR = 1.009). This study identifies several variables for patients at risk to have high facility costs after primary THA and TKA. From the hospital's perspective, efforts to reduce implant purchase prices may translate into substantial cost savings. At the patient level, multidisciplinary initiatives to optimize medical comorbidities, decrease transfusion risk, and control medication expenses in high-risk patients may narrow the existing variation in costs.

How to Predict Drug Expenditure: A Markov Model Approach with Risk Classes.

Although pharmaceutical expenditures have been rising for decades, the question of their drivers remains unclear, and long-term projections of pharmaceutical spending are still scarce. We use a Markov approach considering different cost-risk groups to show the possible range of future drug spending in Germany and illustrate the influence of various determinants on pharmaceutical expenditure. We compute different medium and long-term projections of pharmaceutical expenditure in Germany up to 2060 and compare extrapolations with constant shares, time-to-death scenarios, and Markov modeling based on transition probabilities. Our modeling is based on data from a large statutory sickness fund covering around four million insureds. We divide the population into six risk groups according to their share of total pharmaceutical expenditures, determine their cost growth rates, survival and transition probabilities, and compute different scenarios related to changes in life expectancy or spending trends in different cost-risk groups. If the spending trends in the high-cost groups continue, per-capita expenditure will increase by over 40% until 2040. By 2060, pharmaceutical expenditures could more than double, even if these groups would not benefit from rising life expectancy. By contrast, the isolated effect of demographic change would "only" lead to a long-term increase of around 15%. The long-term development of pharmaceutical spending in Germany will depend mainly on future expenditure and life expectancy trends of particularly high-cost patients. Thus, appropriate pricing of new expensive pharmaceuticals is essential for the sustainability of the German healthcare system.

A non-convex metafrontier DEA model with natural and managerial disposability for pollutant tax levels and environmental efficiencies analysis

Efficiency analysis is an important step in the environmental performance evaluation research, especially in the context of the implementation of the Environmental Protection Tax Law in China. In this paper, we first consider regional heterogeneity and divide regions into high, medium, and low-cost groups based on pollution tax levels. Then, we construct a non-convex metafrontier Data Envelopment Analysis (DEA) model that can solve the heterogeneity problem well and measure the environmental efficiency of each region. In addition to considering natural disposability, we further discuss managerial disposability here. The empirical results show the difference between the efficiency and the benchmark test based on the convex or non-convex metafrontier assumption. Besides, middle and low-cost groups are encouraged to increasing environmental protection expenditure under managerial disposability. Finally, we summarize the improvement methods of provincial administrative regions under different tax rates: high-cost groups develop a green environmental economy and control pollution emissions; middle-level groups optimize industrial structure and increase investment in technological innovation; the low-tax groups increase the tax on pollutants and strengthen government control.

86Segmenting persistently high-cost individuals into actionable groups

Abstract Background Despite growing evidence describing high cost patients, decision-makers struggle to implement effective strategies to improve care and curb spending in this population. Using a multi-phased approach, we aimed to classify high cost patients into homogeneous subgroups amenable to targeted interventions. Methods We linked population-level administrative health data in Alberta, Canada from 2012-2017. We defined “persistently high-cost” as those in the top 1% of cumulative inpatient, outpatient and medication cost in at least two consecutive years. We used latent class analysis to separate this persistent high-cost population into potentially actionable subgroups. Results Of the 3,795,067 adults residing in Alberta, 21,361 were ‘persistently high-cost’. Latent class models identified 10 high-cost subgroups: individuals with CKD (19.3% of persistent high-cost individuals), those undergoing joint surgery/replacement and rehabilitation (18.6%), individuals with IBD (11.6%), patients receiving biologics for autoimmune conditions (11.3%), patients receiving high cost drugs for other conditions (11.1%), community-dwelling individuals with multimorbid chronic conditions (9.0%), individuals with schizophrenia (6.8%), individuals with other mental health issues (6.2%), rural individuals with COPD (3.4%), and frail elderly in institutional settings (2.7%). Conclusions Latent class analysis was able to identify 10 persistently high-cost groups based on meaningful differences in health care spending, demographics, and clinical diagnoses. Key messages This taxonomy will inform the identification of interventions shown to improve care and reduce cost for each subgroup in addition to consultation with key stakeholders to identify and reflect on key barriers and facilitators to implementing identified interventions within the local context.

A latent profile analysis of undergraduates’ achievement motivations and metacognitive behaviors, and their relations to achievement in science.

Achievement motivation theories propose that student motivation is composed of multiple factors. Models of self-regulated learning adopt this assumption and further articulate that multiple metacognitive processes—planning, monitoring learning, and self-evaluation—are essential to guide progress toward a learning goal. Learners’ motivations are theorized to influence these metacognitive processes, which in turn influence learning outcomes. Latent profile analyses (LPA) enable a person-centered approach and capture multiple dimensions of motivation as they co-occur when learners engage in a task. This study documents the emergent motivation profiles of 1326 undergraduate biology students comprising efficacy beliefs, achievement goals, and perceptions of the value and costs of an anatomy and physiology course. Traces obtained from the learning management system provide data on students’ use of tools designed to support metacognitive processes including planning, monitoring learning, and self-evaluation. Latent profiles document the emergence of motivation, and metacognition profiles and a 3-step process reveals how student demographics predict motivation profile membership, and how the motivation profiles are related to metacognition profiles. Four motivation profiles (High Cost, Moderately Motivated, High Goals and Values, Mastery-Driven) and 3 metacognitive learning profiles (Infrequent Metacognitive Processing; Planning and Self-Evaluation; Monitoring via Self-Assessment) emerged. Demographic information was found to predict motivation profile membership. Members of Mastery-Driven and High Cost groups were less likely to use tools that support metacognitive processing. Learners in High Goals and Values and Mastery-Driven groups outperformed those in other groups, and learners in Planning and Self-evaluation and Monitoring performance outperformed those with little metacognitive activity. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Prognostic factors for medical and productivity costs, and return to work after trauma.

AimThe aim of this study was to determine prognostic factors for medical and productivity costs, and return to work (RTW) during the first two years after trauma in a clinical trauma population.MethodsThis prospective multicentre observational study followed all adult trauma patients (≥18 years) admitted to a hospital in Noord-Brabant, the Netherlands from August 2015 through November 2016. Health care consumption, productivity loss and return to work were measured in questionnaires at 1 week, 1, 3, 6, 12 and 24 months after injury. Data was linked with hospital registries. Prognostic factors for medical costs and productivity costs were analysed with log-linked gamma generalized linear models. Prognostic factors for RTW were assessed with Cox proportional hazards model. The predictive ability of the models was assessed with McFadden R2 (explained variance) and c-statistics (discrimination).ResultsA total of 3785 trauma patients (39% of total study population) responded to at least one follow-up questionnaire. Mean medical costs per patient (€9,710) and mean productivity costs per patient (€9,000) varied widely. Prognostic factors for high medical costs were higher age, female gender, spine injury, lower extremity injury, severe head injury, high injury severity, comorbidities, and pre-injury health status. Productivity costs were highest in males, and in patients with spinal cord injury, high injury severity, longer length of stay at the hospital and patients admitted to the ICU. Prognostic factors for RTW were high educational level, male gender, low injury severity, shorter length of stay at the hospital and absence of comorbidity.ConclusionsProductivity costs and RTW should be considered when assessing the economic impact of injury in addition to medical costs. Prognostic factors may assist in identifying high cost groups with potentially modifiable factors for targeted preventive interventions, hence reducing costs and increasing RTW rates.

Characteristics of heart failure patients incurring high medical costs via matching specific health examination results and medical claim data: a cross-sectional study

ObjectiveWe describe the characteristics of patients with high medical costs by matching specific annual medical examination results and medical claim data. Clarifying the relationships between examination items and high medical...

QOLP-22. DRIVERS OF COST OF SURGERY IN NEWLY-DIAGNOSED GLIOBLASTOMA PATIENTS

Abstract INTRODUCTION Glioblastoma carries a high economic burden for patients and caregivers. We investigated drivers of hospital costs of surgery for newly-diagnosed glioblastoma. METHODS Retrospective review of GBM patients undergoing first resection at UCSF (2010–2015) and corresponding hospital charges. Our cohort was divided into low (LC) and high cost (HC) groups for total surgical cost. Multivariate regression was used to identify factors driving cost of surgery. RESULTS Of 242 patients, 36.7% (n=86) were females (median age=62 years). The mean total hospital cost for surgery among our patient cohort was $40,384. When comparing the LC and HC groups, mean total hospital cost for surgery for HC patients was almost twice as much as LC group ($51,744 vs $29,023, p< 0.001). Kaplan-Meier analysis revealed that having higher cost of surgery worsened patient prognosis, with a 21% longer overall survival in the LC cohort versus the HC cohort (14.7 vs 17.9 months, p=0.02; HR=1.41 [1.05–1.91], p= 0.023). Tumor diameter at diagnosis was largest for HC group (4.7 cm) versus LC patients (3.9 cm, p=0.002). Multivariate analysis revealed longer hospital stay (F-ratio=8.87; p=0.01), longer ICU stay (F-ratio= 12.34, p< 0.001), younger age at surgery (F-ratio=6.71, p=0.02) and multifocal disease (F-ratio=6.26, p=0.02) to be independent predictors of higher cost of surgery, while having PCP at diagnosis (F-ratio=6.92, p=0.02), health insurance coverage (F-ratio= 4.23, p=0.03) and being married (F-ratio=3.71, p=0.04) were independent predictors of lower cost of surgery. CONCLUSIONS Higher costs of surgery correlate with worse survival outcomes in glioblastoma patients. Beyond the anticipated finding that greater disease burden drives some of this inverse correlation between cost and survival, correctable socioeconomic factors such as PCP and insurance status also drive higher hospital charges for GBM surgery. Manipulation of these factors is necessary to minimize the economic burden of disease and adopt cost-effective surgical treatments for GBM patients.

PCV28 CHARACTERIZATION OF COSTS AND PATIENTS WITH INCIDENT HEART FAILURE (HF) INCURRING THE HIGHEST HEALTHCARE COSTS FROM SECONDARY CARE: A RETROSPECTIVE, POPULATION-BASED COHORT STUDY IN SWEDEN

This retrospective study aimed to identify the healthcare costs from secondary care (HCsc) of newly diagnosed patients with HF and characterize the cost drivers of patients with incident HF incurring the highest cost in Sweden. Electronic Medical Records from both primary and secondary care in the Uppsala and Västerbotten counties were used to identify adult patients with incident HF recorded between 2010 and 2012 (considering look-back period from 01/01/2005) and at least two HF diagnoses during the analysis period. Patients were followed until 31/03/2015, transfer out or death, whichever occurred first. 5,205 patients were eligible. In the first year after first HF diagnosis (index), total all-cause HCsc were €12,890/patient/year. Inpatient care constituted more than 90% of total HCsc/patient at 1-year after diagnosis for all-cause (€11,805), CVD-related (€11,141) and HF-related (€8,704) events. Of the total patient population, 10% (n=520) incurred almost 40% of total HCsc. Compared with the overall population these patients were younger (70.4 vs. 76.8 years) and were more predominantly men (60.8% vs. 54%). A logistic regression using top 10% most expensive patients as dependent variable showed that factors (OR [95% CI]) with significant impact on costs included: age (reference category [rc]: 18-54 years, 75-84 years (0.39 [0.27-0.57]) and 85+ years (0.21 [0.13-0.33]); NT-proBNP levels (ng/L) (rc: 0-1000, 1001-3000 (1.48 [1.09-2.01]), >3001 (1.93 [1.45-2.57]); no ACEi/ARB treatment (rc: ACEi treatment, 1.63 [1.31-2.03]); AF (0.77 [0.61-0.97]); anemia (1.63 [1.24-2.14]); aortic regurgitation (2.54 [1.92-3.37]); dementia (0.53 [0.29-0.99]); and mitral insufficiency (2.58 [1.74-3.81]). Patients newly diagnosed with HF incur high HCsc, mainly driven by inpatient costs. Factors associated with higher costs during first year were younger age, anemia, aortic regurgitation, mitral insufficiency, no ACEi/ARB treatment and higher NT-proBNP levels. Understanding the cost drivers and profile of high cost groups of patients with HF is important from both healthcare systems and payer perspective.

Impact of Critical Illness Insurance on the Burden of High-Cost Rural Residents in Central China: An Interrupted Time Series Study.

Critical illness insurance (CII) in China was introduced to protect high-cost groups from health expenditure shocks for the purpose of mutual aid. This study aimed to evaluate the impact of CII on the burden of high-cost groups in central rural China. Data were extracted from the basic medical insurance (BMI) hospitalization database of Xiantao City from January 2010 to December 2016. A total of 77,757 hospitalization records were included in our analysis. The out-of-pocket (OOP) expenses and reimbursement ratio (RR) were the two main outcome variables. Interrupted time series analysis with a segmented regression approach was adopted. Level and slope changes were reported to reflect short- and long-term effects, respectively. Results indicated that the number of high-cost inpatient visits, the average monthly hospitalization expenses, and OOP expenses per high-cost inpatient visit were increased after CII introduction. By contrast, the RR from BMI and non-reimbursable expenses ratio were decreased. The OOP expenses and RR covered by CII were higher than those uncovered. We estimated a significant level decrease in OOP expenses (p < 0.01) and rise in RR (p < 0.01), whereas the slope decreases of OOP expenses (p = 0.19) and rise of RR (p = 0.11) after the CII were non-significant. We concluded that the short-term effect of the CII policy is significant and contributes to decreasing OOP expenses and raising RR for high-cost groups, whereas the long-term effect is non-significant. These findings can be explained by increasing hospitalization expenses, many non-reimbursable expenses, low coverage for high-cost groups, and the unsustainability of the financing methods.

Characteristics and outcome of high-cost ICU patients.

PurposeCare at ICUs is expensive and variable depending on the type of care that the patients received. Knowing the characteristics of the patient and his or her disease is always useful for improving health services and cost containment.Patients and methodsAn observational study was conducted at four different intensive care units of an academic medical institution. Demographic characteristics, disease-management casemix information, cost and outcome of the high costing decile, and the rest of the cases were compared.ResultsA total of 3,220 discharges were included in the study. The high-cost group contributed 35.4% of the ICU stays and 38.8% of the total ICU expenditure. Diseases of the central nervous system had higher odds to be in the top decile of costly patients whereas the cardiovascular system was more likely to be in the non-high cost category. The high-cost patients were more likely to have death as an outcome (19.2% vs 9.3%; p<0.001). The most common conditions that were in the high-cost groups were craniotomy, other ear, nose, mouth, and throat operations, simple respiratory system operations, complex intestinal operations, and septicemia. These five diagnostic groups made up 43% of the high-cost decile.ConclusionHigh-cost patients utilized almost 40% of the ICU cost although they were only 10% of the ICU patients. The chances of admission to the ICU increased with older age and severity level of the disease. Central nervous system diseases were the major problem of patients aged 46–69 years old. In addition to cost reduction strategies at the treatment level, detailed analysis of these cases was needed to explore and identify pre-event stage prevention strategies.

Medical expenditure clustering and determinants of the annual medical expenditures of residents: a population-based retrospective study from rural China

ObjectiveTo identify the characteristics of high-cost (HC) patients and the determinants of the annual medical expenditures of Chinese rural residents.MethodsMedical expenditure clustering was performed by Lorentz curve and Gini index....

Comparative effectiveness of novel nonmonetary incentives to promote HIV testing.

To assess the comparative effectiveness of alternative incentive-based interventions to promote HIV testing among men. Randomized clinical trial. We enumerated four Ugandan parishes and enrolled men at least 18 years. Participants were randomized to six groups that received incentives of varying type and amount for HIV testing at a 13-day community health campaign. Incentive types were: gain-framed (control): participants were told they would receive a prize for testing; loss-framed: participants were told they had won a prize, shown several prizes, asked to select one, then told they would lose the prize if they did not test; lotteries: those who tested had a chance to win larger prizes. Each incentive type had a low and high amount (∼US$1 and US$5/participant). The primary outcome was HIV-testing uptake at the community health campaign. Of 2532 participants, 1924 (76%) tested for HIV; 7.6% of those tested were HIV-positive. There was no significant difference in testing uptake in the two lottery groups (78%; P = 0.076) or two loss-framed groups (77%; P = 0.235) vs. two gain-framed groups (74%). Across incentive types, testing did not differ significantly in high-cost (76%) vs. low-cost (75%; P = 0.416) groups. Within low-cost groups, testing uptake was significantly higher in the lottery (80%) vs. gain-framed (72%; P = 0.009) group. Overall, neither offering incentives via lotteries nor framing incentives as losses resulted in significant increases in HIV testing compared with standard gain-framed incentives. However, when offering low-cost incentives to promote HIV testing, providing lottery-based rewards may be a better strategy than gain-framed incentives.

Factors associated with hospitalisation costs in patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital admissions, which can result in a significant financial burden. To determine hospitalisation costs and factors associated with higher costs in patients with acute exacerbations of COPD (AE-COPD). Patients hospitalised for a whole year formed the study cohort. Demographic features, clinical data and hospitalisation bills were evaluated retrospectively. Student's t-test or the Mann-Whitney U-test were used to compare the mean values of variables between high-cost and low-cost groups. Logistic regression analysis was used to study the relationship between hospitalisation costs with clinical factors. A total of 188 patients were evaluated. The mean length of stay in hospital (LOSH) was 8.5 days. The mean cost of AE-COPD was US$1722.0. Costs were significantly associated with LOSH and the per cent predicted value of forced expiratory volume in one second. Age, sex, smoking index, partial oxygen pressure, partial carbon dioxide pressure, haemoglobin concentration and white blood cell counts were not associated with hospitalisation costs. Medications and laboratory services are the main drivers of hospitalisation costs in AE-COPD. Longer LOSH and reduced pulmonary function determine the high costs in hospitalised patients with AE-COPD admitted to a general ward. To reduce hospitalisation costs, more emphasis should be placed on shortening LOSH and preventing the worsening of pulmonary function.

Improving risk equalization for individuals with persistently high costs: Experiences from the Netherlands

BackgroundRisk-equalization (RE) models in competitive health insurance markets have become increasingly sophisticated. However, these models still have important imperfections. A specific problem in the Netherlands is that insurers are insufficiently compensated for individuals who can persistently be found in the right-end tail of the cost distribution. ObjectivesThe goal of this study is to explore and evaluate options for improving compensation for persistently high-cost individuals in the Dutch basic health insurance. MethodsPrescription drugs claims (2012) and administrative data on costs and risk-characteristics (2010–2013) for the entire Dutch population are used to identify high-cost individuals and evaluate improvement options. These options – including new risk-classes and a form of risk-sharing – are evaluated in terms of insurers’ incentives for risk-selection and efficiency. ResultsThree significantly undercompensated high-cost groups are identified: users of specific expensive drugs for rare diseases, hemophilia-patients, and individuals whose costs are in the top-0.50% in 3 prior years. The improvement options effectively remove the undercompensations for these groups and lead to a considerable improvement in individual-level model fit. However, the options differ in terms of their (potential) effects on insurers’ efficiency incentives. ConclusionsAlthough this study provides useful insights in the possibilities for improving RE-models for persistently high-cost individuals, improving compensation remains challenging and dependent on the ongoing debate regarding coverage and reimbursement of expensive drugs.

The Effects of Training Institution Practice Costs, Quality, and Other Characteristics on Future Practice.

Medicare beneficiary spending patterns reflect those of the 306 Hospital Referral Regions where physicians train, but whether this holds true for smaller areas or for quality is uncertain. This study assesses whether cost and quality imprinting can be detected within the 3,436 Hospital Service Areas (HSAs), 82.4 percent of which have only 1 teaching hospital, and whether sponsoring institution characteristics are associated. We conducted a secondary, multi-level, multivariable analysis of 2011 Medicare claims and American Medical Association Masterfile data for a random, nationally representative sample of family physicians and general internists who completed residency between 1992 and 2010 and had more than 40 Medicare patients (3,075 physicians providing care to 503,109 beneficiaries). Practice and training locations were matched with Dartmouth Atlas HSAs and categorized into low-, average-, and high-cost spending groups. Practice and training HSAs were assessed for differences in 4 diabetes quality measures. Institutional characteristics included training volume and percentage of graduates in rural practice and primary care. The unadjusted, annual, per-beneficiary spending difference between physicians trained in high- and low-cost HSAs was $1,644 (95% CI, $1,253-$2,034), and the difference remained significant after controlling for patient and physician characteristics. No significant relationship was found for diabetes quality measures. General internists were significantly more likely than family physicians to train in high-cost HSAs. Institutions with more graduates in rural practice and primary care produced lower-spending physicians. The "imprint" of training spending patterns on physicians is strong and enduring, without discernible quality effects, and, along with identified institutional features, supports measures and policy options for improved graduate medical education outcomes.

Comprehensive population-based determination of pediatric multiple sclerosis health care costs.

Objective:To determine the health care costs associated with pediatric multiple sclerosis (MS).Methods:We performed a retrospective analysis of all patients with MS 18 years of age or younger who were diagnosed or treated between 2002 and 2012 in a population-based cohort. Demographics and health care costs were extracted from the Intermountain Healthcare Enterprise Data Warehouse. Patients were divided into high-cost (>84th percentile) and low-cost groups and differences in health care utilization between the groups were analyzed.Results:Fifty-seven pediatric patients with MS were identified. Health care costs for the cohort totaled more than $1.5 million over the 10-year period, with the top 16th percentile of patients contributing nearly two-thirds. Outpatient visits represented the majority of health care encounters and expenditures, accounting for 83.1% of total costs. Costs per encounter were highest for inpatient stays, averaging $2,924 per stay.Conclusions:The burden of health care expenses for pediatric patients with MS is significant. Expenditures related to outpatient visits were the largest contributor to costs, but inpatient stays were the most costly per encounter. A small proportion of patients incurred the bulk of costs and spent significantly more time receiving care compared to the majority of patients. Avoidance of inpatient treatment and efficient outpatient management are potential areas for health care cost reduction and improvement in care.