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Concept: Diagnosis-related group

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Taking medications as prescribed is imperative for their effectiveness. In populations such as Medicare, where two thirds of Medicare beneficiaries have at least 2 or more chronic conditions requiring treatment with medications and account for more than 90% of Medicare health care spend, examining ways to improve medication adherence in patients with comorbidities is warranted.

Concepts: Health care, Pharmacology, Medicine, Health insurance, Pharmaceutical drug, Pharmacy, Health science, Diagnosis-related group

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BACKGROUND: The study of length of stay (LOS) outliers is important for the management and financing of hospitals. Our aim was to study variables associated with high LOS outliers and their evolution over time. METHODS: We used hospital administrative data from inpatient episodes in public acute care hospitals in the Portuguese National Health Service (NHS), with discharges between years 2000 and 2009, together with some hospital characteristics. The dependent variable, LOS outliers, was calculated for each diagnosis related group (DRG) using a trim point defined for each year by the geometric mean plus two standard deviations. Hospitals were classified on the basis of administrative, economic and teaching characteristics. We also studied the influence of comorbidities and readmissions. Logistic regression models, including a multivariable logistic regression, were used in the analysis. All the logistic regressions were fitted using generalized estimating equations (GEE). RESULTS: In near nine million inpatient episodes analysed we found a proportion of 3.9 % high LOS outliers, accounting for 19.2 % of total inpatient days. The number of hospital patient discharges increased between years 2000 and 2005 and slightly decreased after that. The proportion of outliers ranged between the lowest value of 3.6 % (in years 2001 and 2002) and the highest value of 4.3 % in 2009. Teaching hospitals with over 1,000 beds have significantly more outliers than other hospitals, even after adjustment to readmissions and several patient characteristics. CONCLUSIONS: In the last years both average LOS and high LOS outliers are increasing in Portuguese NHS hospitals. As high LOS outliers represent an important proportion in the total inpatient days, this should be seen as an important alert for the management of hospitals and for national health policies. As expected, age, type of admission, and hospital type were significantly associated with high LOS outliers. The proportion of high outliers does not seem to be related to their financial coverage; they should be studied in order to highlight areas for further investigation. The increasing complexity of both hospitals and patients may be the single most important determinant of high LOS outliers and must therefore be taken into account by health managers when considering hospital costs.

Concepts: Regression analysis, Logistic regression, Patient, Hospital, Arithmetic mean, National Health Service, Teaching hospital, Diagnosis-related group

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BACKGROUND: Accurate and comprehensive clinical documentation is crucial for effective ongoing patient care, follow-up and to optimize case mix based funding. Each Diagnostic Related Group (DRG) is assigned a “weight”: leading to Weighted Inlier Equivalent Separation (WIES), a system many public and private hospitals in Australia subscribe to. AIMS: To identify the top DRGs in a general medical inpatient service, the completeness of medical discharge documentation, commonly missed co-morbidities and system related issues and subsequent impact on DRG and WIES allocation. METHODS: 150 completed discharge summaries were randomly selected from the top ten medical DRGs in our health service. From a detailed review of the clinical documentation, principal diagnoses, associated comorbidities and complications, where appropriate, the DRG and WIES were modified. RESULTS: 72 (48%) of the 150 reviewed admissions resulted in a revision of DRG and WIES equivalent to an increase of AUD 142,000. Respiratory-based DRGs generated the largest revision of DRG and WIES, while ‘Cellulitis’ DRG had the largest relative change. 27% of summaries reviewed necessitated a change in coding with no subsequent change in DRG allocation or WIES. Acute renal failure, anaemia and electrolyte disturbances were the most commonly underrepresented entities in clinical discharge documentation. Seven patients had their WIES downgraded. CONCLUSION: Comprehensive documentation of principal diagnosis/diagnoses, co-morbidities and their complications is imperative to optimal DRG and WIES allocation. Regular meetings between clinical and coding staff improve the quality and timeliness of medical documentation, ensure adequate communication with general practitioners and lead to appropriate funding.

Concepts: Health care, Renal failure, Kidney, Medicine, Patient, Hospital, Physician, Diagnosis-related group

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Few studies have examined rates and causes of short-term readmissions among adults across age and insurance types. We compared rates, characteristics, and costs of 30-day readmission after all-cause hospitalizations across insurance types in the US. We retrospectively evaluated alive patients ≥18 years old, discharged for any cause, 1/1/13-11/31/13, 2006 non-federal hospitals in 21 states in the Nationwide Readmissions Database. The primary stratification variable of interest was primary insurance. Comorbid conditions were assessed based on Elixhauser comorbidities, as defined by administrative billing codes. Additional measures included diagnoses for index hospitalizations leading to rehospitalization. Hierarchical multivariable logistic regression models, with hospital site as a random effect, were used to calculate the adjusted odds of 30-day readmissions by age group and insurance categories. Cost and discharge estimates were weighted per NRD procedures to reflect a nationally representative sample. Diagnoses for index hospitalizations leading to rehospitalization were determined. Among 12,533,551 discharges, 1,818,093 (14.5%) resulted in readmission within 30 days. Medicaid insurance was associated with the highest adjusted odds ratio (AOR) for readmission both in those ≥65 years old (AOR 1.12, 95%CI 1.10-1.14; p <0.001), and 45-64 (AOR 1.67, 95% CI 1.66-1.69; p < 0.001), and Medicare in the 18-44 group (Medicare vs. private insurance: AOR 1.99, 95% CI 1.96-2.01; p <0.001). Discharges for psychiatric or substance abuse disorders, septicemia, and heart failure accounted for the largest numbers of readmissions, with readmission rates of 24.0%, 17.9%, 22.9% respectively. Total costs for readmissions were 50.7 billion USD, highest for Medicare (29.6 billion USD), with non-Medicare costs exceeding 21 billion USD. While Medicare readmissions account for more than half of the total burden of readmissions, costs of non-Medicare readmissions are nonetheless substantial. Medicaid patients have the highest odds of readmission in individuals older than age 44, commonly due to hospitalizations for psychiatric illness and substance abuse disorders. Medicaid patients represent a population at uniquely high risk for readmission.

Concepts: Logit, Hospital, United States, Odds ratio, Mental disorder, Comorbidity, Health insurance in the United States, Diagnosis-related group

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BACKGROUND: There is increasing concern regarding the financial burden of care on cancer patients and their families. Medicare beneficiaries often have extensive comorbidities and limited financial resources, and may face substantial cost sharing even with supplemental coverage. In the current study, the authors examined out-of-pocket (OOP) spending and burden relative to income for Medicare beneficiaries with cancer. METHODS: This retrospective, observational study pooled data for 1997 through 2007 from the Medicare Current Beneficiary Survey linked to Medicare claims. Medicare beneficiaries with newly diagnosed cancer were selected using claims-based diagnoses. Generalized linear models were used to estimate OOP spending. Logistic regression models identified factors associated with a high OOP burden, defined as spending > 20% of one’s income during the cancer diagnosis and subsequent year. RESULTS: The cohort included 1868 beneficiaries with and 10,047 without cancer. Compared with the noncancer cohort, cancer patients were older, had more comorbidities, and were more likely to lack supplemental coverage. The mean OOP spending for cancer patients was $4727. Cancer patients faced an adjusted $976 (P < .01) incremental OOP spending. Greater than one-quarter (28%) of beneficiaries with cancer experienced a high OOP burden compared with 16% of beneficiaries without cancer (P < .001). Supplemental insurance and higher income were found to be protective against a high OOP burden, whereas assets, comorbidity, and receipt of cancer-directed radiation and antineoplastic therapy were associated with a higher OOP burden. CONCLUSIONS: Medicare beneficiaries with cancer face a higher OOP burden than their counterparts without cancer; some of the higher burden was explained by the higher comorbidity burden and lack of supplemental insurance noted among these patients. Financial pressures may discourage some elderly patients from pursuing treatment. Cancer 2012. © 2012 American Cancer Society.

Concepts: Regression analysis, Logistic regression, Linear regression, Cancer, Actuarial science, Generalized linear model, American Cancer Society, Diagnosis-related group

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IMPORTANCE Providing high-quality care while containing cost is essential for the economic stability of our health care system. The United States is experiencing a rapidly growing elderly population. The Acute Care for Elders (ACE) unit interdisciplinary team model of care has been shown to improve outcomes in hospitalized older adults. The University of Alabama at Birmingham ACE unit incorporates evidence-based care processes. We hypothesized that the ACE model would also reduce costs. OBJECTIVE To examine variable direct costs from an interdisciplinary ACE compared with a multidisciplinary usual care (UC) unit. DESIGN Retrospective cohort study. SETTING Tertiary care academic medical center. PARTICIPANTS Hospitalists' patients aged 70 years or older spending the entirety of their hospitalization in either the ACE or UC unit in fiscal year 2010. MAIN OUTCOME MEASURES Using administrative data, we analyzed variable direct costs for ACE and UC patients. We also conducted a subset analysis restricted to the 25 most common diagnosis related groups (DRGs) shared by ACE and UC patients. Generalized linear regression was used to estimate cost ratios and 95% confidence intervals adjusted for age, sex, comorbidity score, and case mix index (CMI). RESULTS A total of 818 hospitalists' patients met inclusion criteria: 428 from the ACE and 390 from the UC unit. For this study group (all DRGs), the mean (SD) variable direct cost per patient was $2109 ($1870) for ACE and $2480 ($2113) for UC (P = .009). Adjusted cost ratios revealed significant cost savings for patients with low (0.82; 95% CI, 0.72-0.94) or moderate (0.74; 95% CI, 0.62-0.89) CMI scores; care was cost neutral for patients with high CMI scores (1.13; 95% CI, 0.93-1.37). Significantly fewer ACE patients than UC patients were readmitted within 30 days of discharge (7.9% vs 12.8%; P = .02). Subset analysis of the 25 most common DRGs revealed a significantly reduced mean (SD) variable direct cost per patient for ACE compared with UC patients ($1693 [$1063] vs $2138 [$1431]; P < .001); cost ratios for total (0.78; 95% CI, 0.70-0.87) and daily (0.89; 95% CI, 0.85-0.94) variable direct costs remained significant after adjustment. CONCLUSIONS AND RELEVANCE The ACE unit team model reduces costs and 30-day readmissions. In an era when improving care processes while reducing costs is a vital objective for the Medicare program and our nation as a whole, the ACE model meets these goals.

Concepts: Cohort study, Medicine, Hospital, Costs, Cost, Interdisciplinarity, Diagnosis-related group

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OBJECTIVE: Depression is a significant comorbidity in patients with chronic obstructive pulmonary disease (COPD). Although comorbid depression is associated with low use and poor adherence to medications treating other chronic conditions, evidence of the relationship between depression and COPD management is limited. This study estimated the association between depression and COPD maintenance medication (MM) adherence among patients with COPD. METHODS: This cross-sectional study used a 5% random sample of 2006-2007 Chronic Condition Warehouse data. Medicare beneficiaries enrolled in Parts A, B, and D plans with diagnosed COPD who survived through 2006 were included (n = 74,863). COPD MM adherence was measured as medication discontinuation and proportion of days covered (PDC). Depression was identified through the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes. Multivariable models with modified generalized estimating equations were used to estimate adjusted association between depression diagnosis and medication adherence, controlling for sociodemographics, comorbidities, and disease severity. RESULTS: Among the sample, about one third (33.6%) had diagnosed depression. More than half (61.8%) of beneficiaries with COPD filled at least one COPD MM prescription. Depressed beneficiaries had a higher likelihood of using COPD MM than non-depressed beneficiaries (adjusted prevalence ratios [PR] = 1.02; 95% confidence intervals [CI] = 1.01, 1.03). Among COPD MM users, depressed beneficiaries were more likely to discontinue medications (PR = 1.09; 95% CI = 1.04, 1.14) and less likely to exhibit PDC ≥ 0.80 (PR = 0.89; 95% CI = 0.86, 0.92) than non-depressed beneficiaries. CONCLUSIONS: Depression is prevalent in Medicare beneficiaries with COPD and independently associated with lower COPD MM adherence. Interventions to improve medication adherence for COPD patients may consider management of comorbidities such as depression. Copyright © 2013 John Wiley & Sons, Ltd.

Concepts: Medicine, Asthma, Pneumonia, Chronic obstructive pulmonary disease, Major depressive disorder, Estimation, Comorbidity, Diagnosis-related group

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Clinical documentation can be an underappreciated. Trauma Centers (TCs) are now routinely evaluated for quality performance. TCs with poor documentation may not accurately reflect actual injury burden or comorbidities and can impact accuracy of mortality measures. Markers exist to adjust crude death rates for injury severity: observed over expected deaths (O/E) adjust for injury; Case Mix Index (CMI) reflects disease burden, and Severity of Illness (SOI) measures organ dysfunction. We aim to evaluate the impact of implementing a Clinical Documentation Improvement Program (CDIP) on reported outcomes.

Concepts: Epidemiology, Death, Mortality rate, Medical statistics, Demography, Physical trauma, Diagnosis-related group, Medical manuals

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We aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabetes within the Veterans Health Administration (VHA) system.

Concepts: Medical terms, Heart failure, Demography, Validation, United States Department of Veterans Affairs, Veterans Health Administration, Diagnosis-related group, Acute decompensated heart failure

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The All Patient Refined Diagnosis Related Group (APR-DRG) is an inpatient visit classification system that assigns a diagnostic related group, a Risk of Mortality (ROM) subclass and a Severity of Illness (SOI) subclass. While extensively used for cost adjustment, no study has compared the APR-DRG subclass modifiers to the popular Charlson Comorbidity Index as a measure of comorbidity severity in models for perioperative in-hospital mortality. In this study we attempt to validate the use of these subclasses to predict mortality in a cohort of surgical patients. We analyzed all adult (age over 18 years) inpatient non-cardiac surgery at our institution between December 2005 and July 2013. After exclusions, we split the cohort into training and validation sets. We created prediction models of inpatient mortality using the Charlson Comorbidity Index, ROM only, SOI only, and ROM with SOI. Models were compared by receiver-operator characteristic (ROC) curve, area under the ROC curve (AUC), and Brier score. After exclusions, we analyzed 63,681 patient-visits. Overall in-hospital mortality was 1.3%. The median number of ICD-9-CM diagnosis codes was 6 (Q1-Q3 4-10). The median Charlson Comorbidity Index was 0 (Q1-Q3 0-2). When the model was applied to the validation set, the c-statistic for Charlson was 0.865, c-statistic for ROM was 0.975, and for ROM and SOI combined the c-statistic was 0.977. The scaled Brier score for Charlson was 0.044, Brier for ROM only was 0.230, and Brier for ROM and SOI was 0.257. The APR-DRG ROM or SOI subclasses are better predictors than the Charlson Comorbidity Index of in-hospital mortality among surgical patients.

Concepts: Medical terms, Prediction, Receiver operating characteristic, Comorbidity, Diagnosis-related group, Medical classification, Medical manuals, Diagnosis classification