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Objective: To develop a casemix classification to underpin a new funding model for residential aged care in Australia., Design, Setting: Cross-sectional study of resident characteristics in thirty non-government residential aged care facilities in Melbourne, the Hunter region of New South Wales, and northern Queensland, March 2018 - June 2018., Participants: 1877 aged care residents and 1600 residential aged care staff., Main Outcome Measures: The Australian National Aged Care Classification (AN-ACC), a casemix classification for residential aged care based on the attributes of aged care residents that best predict their need for care: frailty, mobility, motor function, cognition, behaviour, and technical nursing needs., Results: The AN-ACC comprises 13 aged care resident classes reflecting differences in resource use. Apart from the class that included palliative care patients, the primary branches were defined by the capacity for mobility; further classification is based on physical capacity, cognitive function, mental health problems, and behaviour. The statistical performance of the AN-ACC was good, as measured by the reduction in variation statistic (RIV; 0.52) and class-specific coefficients of variation. The statistical performance and clinical acceptability of AN-ACC compare favourably with overseas casemix models, and it is better than the current Australian aged care funding model, the Aged Care Funding Instrument (64 classes; RIV, 0.20)., Conclusions: The care burden associated with frailty, mobility, function, cognition, behaviour and technical nursing needs drives residential aged care resource use. The AN-ACC is sufficiently robust for estimating the funding and staffing requirements of residential aged care facilities in Australia., (© 2020 AMPCo Pty Ltd.)

In health insurance, a reimbursement mechanism refers to a method of third-party repayment to offset the use of medical services and equipment. This systematic review aimed to identify challenges and adverse outcomes generated by the implementation of reimbursement mechanisms based on the diagnosis-related group (DRG) classification system. All articles published between 1983 and 2017 and indexed in various databases were reviewed. Of the 1,475 articles identified, 36 were relevant and were included in the analysis. Overall, the most frequent challenges were increased costs (especially for severe diseases and specialised services), a lack of adequate supervision and technical infrastructure and the complexity of the method. Adverse outcomes included reduced length of patient stay, early patient discharge, decreased admissions, increased re-admissions and reduced services. Moreover, DRG-based reimbursement mechanisms often resulted in the referral of patients to other institutions, thus transferring costs to other sectors., Competing Interests: CONFLICT OF INTEREST The authors declare no conflicts of interest., (© Copyright 2020, Sultan Qaboos University Medical Journal, All Rights Reserved.)

Background: In activity based funding systems, the misclassification of inpatient episode Diagnostic Related Groups (DRGs) can have significant impacts on the revenue of health care providers. Weakly informative Bayesian models can be used to estimate an episode's probability of DRG misclassification., Methods: This study proposes a new, Hybrid prior approach which utilises guesses that are elicited from a clinical coding auditor, switching to non-informative priors where this information is inadequate. This model's ability to detect DRG revision is compared to benchmark weakly informative Bayesian models and maximum likelihood estimates., Results: Based on repeated 5-fold cross-validation, classification performance was greatest for the Hybrid prior model, which achieved best classification accuracy in 14 out of 20 trials, significantly outperforming benchmark models., Conclusions: The incorporation of elicited expert guesses via a Hybrid prior produced a significant improvement in DRG error detection; hence, it has the ability to enhance the efficiency of clinical coding audits when put into practice at a health care provider., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Coded data are the basis of information systems in all countries that rely on Diagnosis Related Groups in order to reimburse/finance hospitals, including both administrative and clinical data. To identify the problems and barriers that affect the quality of the coded data is paramount to improve data quality as well as to enhance its usability and outcomes. This study aims to explore problems and possible solutions associated with the clinical coding process. Problems were identified according to the perspective of ten medical coders, as the result of four focus groups sessions. This convenience sample was sourced from four public hospitals in Portugal. Questions relating to problems with the coding process were developed from the literature and authors' expertise. Focus groups sessions were taped, transcribed and analyzed to elicit themes. Variability in the documents used for coding, illegibility of hand writing when coding on paper, increase of errors due to an extra actor in the coding process when transcribed from paper, difficulties in the diagnoses' coding, coding delay and unavailability of resources and tools designed to help coders, were some of the problems identified. Some problems were identified and solutions such as the standardization of the documents used for coding an episode, the adoption of the electronic coding, the development of tools to help coding and audits, and the recognition of the importance of coding by the management were described as relevant factors for the improvement of the quality of data.

Background: Health records are the basis of clinical coding. In Portugal, relevant diagnoses and procedures are abstracted and categorised using an internationally accepted classification system and the resulting codes, together with the administrative data, are then grouped into diagnosis-related groups (DRGs). Hospital reimbursement is partially calculated from the DRGs. Moreover, the administrative database generated with these data is widely used in research and epidemiology, among other purposes., Objective: To explore the perceptions of medical coders (medical doctors) regarding possible problems with health records that may affect the quality of coded data., Method: A qualitative design using four focus groups sessions with 10 medical coders was undertaken between October and November 2017. The convenience sample was obtained from four public hospitals in Portugal. Questions related to problems with the coding process were developed from the literature and authors' expertise. The focus groups sessions were taped, transcribed and analysed to elicit themes., Results: There are several problems, identified by the focus groups, in health records that influence the coded data: the lack of or unclear documented information; the variability in diagnosis description; "copy & paste"; and the lack of solutions to solve these problems., Conclusion and Implications: The use of standards in health records, audits and physician awareness could increase the quality of health records, contributing to improvements in the quality of coded data, and in the fulfilment of its purposes (e.g. more accurate payments and more reliable research).

Background: The Korean Diagnosis-Related Groups (KDRG) was revised in 2003, modifying the complexity adjustment mechanism of the Australian Refined Diagnosis-Related Groups (AR-DRGs). In 2014, the Complication and Comorbidity Level (CCL) of the existing AR-DRG system was found to have very little correlation with cost., Objective: Based on the Australian experience, the CCL for KDRG version 3.4 was reviewed., Method: Inpatient claim data for 2011 were used in this study. About 5,731,551 episodes, which had one or no complication and comorbidity (CC) and met the inclusion criteria, were selected. The differences of average hospital charges by the CCL were analysed in each Adjacent Diagnosis-Related Group (ADRG) using analysis of variance followed by Duncan's test. The patterns of differences were presented with R 2 in three patterns: The CCL reflected the complexity well (VALID); the average charge of CCL 2, 3, 4 was greater than CCL 0 (PARTIALLY VALID); the CCL did not reflect the complexity (NOT VALID)., Results: A total of 114 (19.03%), 190 (31.72%) and 295 (49.25%) ADRGs were included in VALID, PARTIALLY VALID and NOT VALID, respectively. The average R 2 for hospital charge of CCL was 4.94%. The average R 2 in VALID, PARTIALLY VALID and NOT VALID was 4.54%, 5.21%, and 4.93%, respectively., Conclusion: The CCL, the first step of complexity adjustment using secondary diagnoses, exhibited low performance. If highly accurate coding data and cost data become available, the performance of secondary diagnosis as a variable to reflect the case complexity should be re-evaluated., Implications: Lack of reviewing the complexity adjustment mechanism of the KDRG since 2003 has resulted in outdated CC lists and levels that no longer reflect the current Korean healthcare system. Reliable cost data (vs. charge) and accurate coding are essential for accuracy of reimbursement.

Background: The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all relevant diagnoses, namely the patient's underlying co-morbidities, is a key factor for ensuring that SOI determination will be adequate., Objective: In this study, we aimed to characterise the individual impact of co-morbidities on APR-DRG classification and hospital funding in the context of respiratory and cardiovascular diseases., Methods: Using 6 years of coded clinical data from a nationwide Portuguese inpatient database and support vector machine (SVM) models, we simulated and explored the APR-DRG classification to understand its response to individual removal of Charlson and Elixhauser co-morbidities. We also estimated the amount of hospital payments that could have been lost when co-morbidities are under-reported., Results: In our scenario, most Charlson and Elixhauser co-morbidities did considerably influence SOI determination but had little impact on base APR-DRG assignment. The degree of influence of each co-morbidity on SOI was, however, quite specific to the base APR-DRG. Under-coding of all studied co-morbidities led to losses in hospital payments. Furthermore, our results based on the SVM models were consistent with overall APR-DRG grouping logics., Conclusion and Implications: Comprehensive reporting of pre-existing or newly acquired co-morbidities should be encouraged in hospitals as they have an important influence on SOI assignment and thus on hospital funding. Furthermore, we recommend that future guidelines to be used by medical coders should include specific rules concerning coding of co-morbidities.

Background The Diagnosis Related Groups (DRG) constitute a method of classifying hospital discharges. Aim To report its development and implementation in a Chilean University Hospital and global results of 10 years Material and Methods We included 231,600 discharges from 2007 to 2016. In the development we considered the physical plant, clinical record flow, progressively incorporated human resources and computer equipment for coding and analysis to obtain results. The parameters used were: average stay, average DRG weight, mean of diagnosis and codified procedures, behavior of upper outliers, hospital mortality, distribution by severity and its relationship with other variables. Results The global complexity index was 0.9929. The average of diagnoses coded was 4.35 and of procedures was 7.21. The average stay was 4.56 days, with a downward trend. The top outliers corresponded to 2.25%, with stable hospital days and average DRG weight. The median of hospital mortality was 1.65% with a tendency to decrease and stable DRG mean weight. Seventy two percent had a grade 1 severity, with low median hospital stay. They occupied 40% of bed days. Nine percent had a grade 3 severity with high median hospital stay and accounting for 31.5% of bed days. Conclusions DRG methodology is a valuable information tool for decision making and result assessment in hospital management.

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Objective: To compare the performance in terms of goodness of fit and explanatory power of 2morbidity groupers in primary care (PC): adjusted morbidity groups (AMG) and clinical risk groups (CRG)., Design: Cross-sectional study., Location: PC in the Catalan Institute for the Health (CIH), Catalonia, Spain., Participants: Population allocated in primary care centers of the CIH for the year 2014., Main Measurements: Three indicators of interest are analyzed such as urgent hospitalization, number of visits and spending in pharmacy. A stratified analysis by centers is applied adjusting generalized lineal models from the variables age, sex and morbidity grouping to explain each one of the 3variables of interest. The statistical measures to analyze the performance of the different models applied are the Akaike index, the Bayes index and the pseudo-variability explained by deviance change., Results: The results show that in the area of the primary care the explanatory power of the AMGs is higher to that offered by the CRGs, especially for the case of the visits and the pharmacy., Conclusions: The performance of GMAs in the area of the CIH PC is higher than that shown by the CRGs., (Copyright © 2018 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)

Background: The routine application of a primary care classification system to patients' medical records in general practice/primary care is rare in the African region. Reliable data are crucial to understanding the domain of primary care in Nigeria, and this may be actualized through the use of a locally validated primary care classification system such as the International Classification of Primary Care, 2nd edition (ICPC-2). Although a few studies from Europe and Australia have reported that ICPC is a reliable and feasible tool for classifying data in primary care, the reliability and validity of the revised version (ICPC-2) is yet to be objectively determined particularly in Africa., Objectives: (i) To determine the convergent validity of ICPC-2 diagnoses codes when correlated with International Statistical Classification of Diseases (ICD)-10 codes, (ii) to determine the inter-coder reliability among local and foreign ICPC-2 experts and (iii) to ascertain the level of accuracy when ICPC-2 is engaged by coders without previous training., Methods: Psychometric analysis was carried out on ICPC-2 and ICD-10 coded data that were generated from physicians' diagnoses, which were randomly selected from general outpatients' clinic attendance registers, using a systematic sampling technique. Participants comprised two groups of coders (ICPC-2 coders and ICD-10 coders) who coded independently a total of 220 diagnoses/health problems with ICPC-2 and/or ICD-10, respectively., Results: Two hundred and twenty diagnoses/health problems were considered and were found to cut across all 17 chapters of the ICPC-2. The dataset revealed a strong positive correlation between selected ICPC-2 codes and ICD-10 codes (r ≈ 0.7) at a sensitivity of 86.8%. Mean percentage agreement among the ICPC-2 coders was 97.9% at the chapter level and 95.6% at the rubric level. Similarly, Cohen's kappa coefficients were very good (κ > 0.81) and were higher at chapter level (0.94-0.97) than rubric level (0.90-0.93) between sets of pairs of ICPC-2 coders. An accuracy of 74.5% was achieved by ICD-10 coders who had no previous experience or prior training on ICPC-2 usage., Conclusion: Findings support the utility of ICPC-2 as a valid and reliable coding tool that may be adopted for routine data collection in the African primary care context. The level of accuracy achieved without training lends credence to the proposition that it is a simple-to-use classification and may be a useful starting point in a setting devoid of any primary care classification system for morbidity and mortality registration at such a critical level of public health importance.

Introduction: Provision of palliative care is inequitable with wide variations across conditions and settings in the UK. Lack of a standard way to classify by case complexity is one of the principle obstacles to addressing this. We aim to develop and validate a casemix classification to support the prediction of costs of specialist palliative care provision., Methods and Analysis: Phase I: A cohort study to determine the variables and potential classes to be included in a casemix classification. Data are collected from clinicians in palliative care services across inpatient hospice, hospital and community settings on: patient demographics, potential complexity/casemix criteria and patient-level resource use. Cost predictors are derived using multivariate regression and then incorporated into a classification using classification and regression trees. Internal validation will be conducted by bootstrapping to quantify any optimism in the predictive performance (calibration and discrimination) of the developed classification. Phase II: A mixed-methods cohort study across settings for external validation of the classification developed in phase I. Patient and family caregiver data will be collected longitudinally on demographics, potential complexity/casemix criteria and patient-level resource use. This will be triangulated with data collected from clinicians on potential complexity/casemix criteria and patient-level resource use, and with qualitative interviews with patients and caregivers about care provision across difference settings. The classification will be refined on the basis of its performance in the validation data set., Ethics and Dissemination: The study has been approved by the National Health Service Health Research Authority Research Ethics Committee. The results are expected to be disseminated in 2018 through papers for publication in major palliative care journals; policy briefs for clinicians, commissioning leads and policy makers; and lay summaries for patients and public., Trial Registration Number: ISRCTN90752212., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

The new treatment procedures of the German Statutory Accident Insurance (DGUV) have ramifications for the injury type procedure clinics (VAV) from medical, economic and structural aspects. Whereas the latter can be assessed as positive, the medical and economical aspects are perceived as being negative. Problems arise from the partially unclear formulation of the injury type catalogue, which results in unpleasant negotiations with the occupational insurance associations with respect to financial remuneration for services rendered. Furthermore, the medical competence of the VAV clinics will be reduced by the preset specifications of the VAV catalogue, which opens up an additional field of tension between medical treatment, fulfillment of the obligatory training and acquisition of personnel as well as the continually increasing economic pressure. From the perspective of the author, the relinquence of medical competence imposed by the regulations of the new VAV catalogue is "throwing the baby out with the bathwater" because many VAV clinics nationwide also partially have competence in the severe injury type procedure (SAV). A concrete "competence-based approval" for the individual areas of the VAV procedure would be sensible and would maintain the comprehensive care of insured persons and also increase or strengthen the willingness of participating VAV hospitals for unconditional implementation of the new VAV procedure.

Introduction: Diverticulosis is a relevant disease in Germany with a prevalence of over 60 % in patients aged ≥70 years. The S2k guidelines for the treatment of diverticulosis were recently published. Systematic epidemiological data on treatment modalities do not exist., Methods: Analysis of in-hospital treatment modalities for diverticulosis based on data from the Federal Office of Statistics., Results: Approximately 130,000 inpatient cases of diverticulosis are treated in Germany per year. Approximately 25 % undergo surgery and of these slightly under 50 % (12,000 procedures) are carried out by laparoscopy. The complication rates are 18 % in a best case scenario and up to 85 % in a worst case scenario. A stage-adjusted classification of treatment modalities based on data from the Federal Office of Statistics is currently practically impossible., Conclusion: To enable stage-adjusted epidemiological analysis of diverticulosis, a standardized and transparent documentation system enabling systematic analysis is necessary, which does not currently exist (e. g. ICD 10 coding); moreover, information on conservative and interventional treatment options are not included in the operations and procedures key (OPS) coding system.

Background: We present data on quality of care (QC) improvement in 35 of 45 National Quality Forum metrics reported annually by 52 primary care practices recognized as patient-centered medical homes (PCMHs) that participated in the Maryland Multi-Payor Program from 2011 to 2013., Methods: We assigned QC metrics to (1) chronic, (2) preventive, and (3) mental health care domains. The study used a panel data design with no control group. Using longitudinal fixed-effects regressions, we modeled QC and case mix severity in a PCMH., Results: Overall, 35 of 45 quality metrics reported by 52 PCMHs demonstrated improvement over 3 years, and case mix severity did not affect the achievement of quality improvement. From 2011 to 2012, QC increased by 0.14 (P < .01) for chronic, 0.15 (P < .01) for preventive, and 0.34 (P < .01) for mental health care domains; from 2012 to 2013 these domains increased by 0.03 (P = .06), 0.04 (P = .05), and 0.07 (P = .12), respectively. In univariate analyses, lower National Commission on Quality Assurance PCMH level was associated with higher QC for the mental health care domain, whereas case mix severity did not correlate with QC. In multivariate analyses, higher QC correlated with larger practices, greater proportion of older patients, and readmission visits. Rural practices had higher proportions of Medicaid patients, lower QC, and higher QC improvement in interaction analyses with time., Conclusions: The gains in QC in the chronic disease domain, the preventive care domain, and, most significantly, the mental health care domain were observed over time regardless of patient case mix severity. QC improvement was generally not modified by practice characteristics, except for rurality., (© Copyright 2016 by the American Board of Family Medicine.)

Background: In undertaking a major revision to the Australian Refined Diagnosis Related Group (ARDRG) classification, we set out to contrast Australia's approach to using data on additional (not principal) diagnoses with major international approaches in splitting base or Adjacent Diagnosis Related Groups (ADRGs)., Methods: Comparative policy analysis/narrative review of peer-reviewed and grey literature on international approaches to use of additional (secondary) diagnoses in the development of Australian and international DRG systems., Analysis: European and US approaches to characterise complexity of inpatient care are well-documented, providing useful points of comparison with Australia's. Australia, with good data sources, has continued to refine its national DRG classification using increasingly sophisticated approaches. Hospital funders in Australia and in other systems are often under pressure from provider groups to expand classifications to reflect clinical complexity. DRG development in most healthcare systems reviewed here reflects four critical factors: these socio-political factors, the quality and depth of the coded data available to characterise the mix of cases in a healthcare system, the size of the underlying population, and the intended scope and use of the classification. Australia's relatively small national population has constrained the size of its DRG classifications, and development has been concentrated on inpatient care in public hospitals., Discussion and Conclusions: Development of casemix classifications in health care is driven by both technical and socio-political factors. Use of additional diagnoses to adjust for patient complexity and cost needs to respond to these in each casemix application., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

We use the introduction of diagnosis related groups (DRGs) in German neonatology to study the determinants of upcoding. Since 2003, reimbursement is based inter alia on birth weight, with substantial discontinuities at eight thresholds. These discontinuities create incentives to upcode preterm infants into classes of lower birth weight. Using data from the German birth statistics 1996-2010 and German hospital data from 2006 to 2011, we show that (1) since the introduction of DRGs, hospitals have upcoded at least 12,000 preterm infants and gained additional reimbursement in excess of 100 million Euro; (2) upcoding rates are systematically higher at thresholds with larger reimbursement hikes and in hospitals that subsequently treat preterm infants, i.e. where the gains accrue; (3) upcoding is systematically linked with newborn health conditional on birth weight. Doctors and midwives respond to financial incentives by not upcoding newborns with low survival probabilities, and by upcoding infants with higher expected treatment costs., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The objective of this paper is to evaluate the extent to which early determination of diagnosis-related groups (DRGs) can be used for better allocation of scarce hospital resources. When elective patients seek admission, the true DRG, currently determined only at discharge, is unknown. We approach the problem of early DRG determination in three stages: (1) test how much a Naïve Bayes classifier can improve classification accuracy as compared to a hospital's current approach; (2) develop a statistical program that makes admission and scheduling decisions based on the patients' clincial pathways and scarce hospital resources; and (3) feed the DRG as classified by the Naïve Bayes classifier and the hospitals' baseline approach into the model (which we evaluate in simulation). Our results reveal that the DRG grouper performs poorly in classifying the DRG correctly before admission while the Naïve Bayes approach substantially improves the classification task. The results from the connection of the classification method with the mathematical program also reveal that resource allocation decisions can be more effective and efficient with the hybrid approach.

Objectives: A difference in the medical treatment situation between the first group and the second group of the hospital group in the DPC system was clarified using Diagnosis Procedure Combination (DPC) survey data according to Major Diagnostic Category (MDC). Furthermore, the division between the first group and the second group was examined., Methods: DPC survey data collected in 2012 was used. According to MDC, significant differences in the patient ratio of hospitalization, the number of planned hospitalizations, the number of emergency hospitalizations, the number of ambulance conveyances, and the number of treatments were considered. Then, by the Mahalanobis-Taguchi method, distributions of the Mahalanobis distance and item choice according to MDC were considered., Results: Many items according to MDC showed significant differences between the first group and the second group. The Mahalanobis distance was increased by MDC 16 disease when divided by the Mahalanobis distance of 1.0 between the first group and the second group. The item, which contributed to the calculation of the Mahalanobis distance by item choice, varied and showed a difference between the first group and the second group., Conclusions: The second group was authorized by the hospital followed by the first group. However, the results showed significant differences in the number of DPC survey data and the Mahalanobis distance of many items.

Background: Since the introduction of Diagnosis-Re-lat-ed Groups (DRGs) in Germany, the variables of remuneration have continuously changed. Subjectively, reimbursement by DRG has a negative connotation among all specialities. We analysed the development of reimbursement and length of stay in different surgical specialties., Material and Methods: By grouping the top-10-diagnoses and therapies in hand surgery, trauma surgery, general surgery as well as cardiothoracic and vascular surgery between 2004 and 2010, DRGs were obtained, compared and the data deduced., Results: While the lower threshold of length of stay remained almost the same, mean value and upper threshold became shorter in most of the top-10-diagnoses. During the observation period, total reimbursement increased by 30 % in hand surgery, 20 % in general surgery and 17 % in cardiothoracic and vascular surgery, while in trauma surgery it decreased by 1 %. This corresponds to mean annual growth rates of 4.47 %, 3.08 %, 2.68 % and - 0.15 %, respectively. No correlation was found between the 4 disciplines and macro-economic parameters., Conclusion: Reductions of mean and upper thresholds of length of stay are present in all surgical disciplines. Total reimbursements developed partially in a contradictory manner. Negative growth involves the danger that hospital investments cannot be realised, especially in the presence of high personnel costs., (Georg Thieme Verlag KG Stuttgart · New York.)

Models of the determinants of individuals' primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Nursing care inputs represent one of the major cost components in the Swiss Diagnosis Related Group (DRG) structure. High and low nursing workloads in individual cases are supposed to balance out via the DRG group. Research results indicating possible problems in this area cannot be reliably extrapolated to SwissDRG. An analysis of nursing workload figures with DRG indicators was carried out in order to decide whether there is a need to develop SwissDRG classification criteria that are specific to nursing care. The case groups were determined with SwissDRG 0.1, and nursing workload with LEP Nursing 2. Robust statistical methods were used. The evaluation of classification accuracy was carried out with R2 as the measurement of variance reduction and the coefficient of homogeneity (CH). To ensure reliable conclusions, statistical tests with bootstrapping methods were performed. The sample included 213 groups with a total of 73930 cases from ten hospitals. The DRG classification was seen to have limited explanatory power for variability in nursing workload inputs, both for all cases (R2 = 0.16) and for inliers (R2 = 0.32). Nursing workload homogeneity was statistically significant unsatisfactory (CH < 0.67) in 123 groups, including 24 groups in which it was significant defective (CH < 0.60). Therefore, there is a high risk of high and low nursing workloads not balancing out in these groups, and, as a result, of financial resources being wrongly allocated. The development of nursing-care-specific SwissDRG classification criteria for improved homogeneity and variance reduction is therefore indicated.

Introduction: In the emergency departments (ED), a heterogeneous mix of patients is seen. The aim of this study was to establish a limited number of categories of complaints and symptoms covering the majority of admissions in a Danish ED and to quantify the volume of cases in each category., Material and Methods: This was a cross-sectional study of all acute patients admitted to a Danish ED in 2010. Information was collected from electronic screens where the ED nurses registered the presenting symptoms or complaints according to the referring doctor or patient. A list of complaint categories covering all patient complaints was produced. Presumptive diagnoses and categories with frequencies less than 1% were pooled with other groups, unless keeping them was clinically relevant., Results: Among the 9,863 patients, 49% were medical, 31% surgical, 15% orthopaedic and 5% vascular surgical patients. In 35% of cases, the patients were referred with a presumptive diagnosis, in 65% with a complaint or a symptom; and 11,031 complaints were placed in 13 main categories, 77 subcategories and 44 presumptive diagnoses. This aggregation resulted in 99 groups holding less than 1% of the patients' complaints. Further aggregation resulted in 31 categories covering 93% of the complaints. Of the complaints not covered, the largest groups were patients with post-operative complications and special examination for various diseases (5%)., Conclusion: We have presented a first suggestion for complaint categories and distribution among admitted patients in a Danish ED setting. Further studies from other EDs are required., Funding: not relevant., Trial Registration: NCT01747434.

The French tarification à l'activité (T2A) prospective payment system is a financial system in which a health-care institution's resources are based on performed activity. Activity is described via the PMSI medical information system (programme de médicalisation du système d'information). The PMSI classifies hospital cases by clinical and economic categories known as diagnosis-related groups (DRG), each with an associated price tag. Coding a hospital case involves giving as realistic a description as possible so as to categorize it in the right DRG and thus ensure appropriate payment. For this, it is essential to understand what determines the pricing of inpatient stay: namely, the code for the surgical procedure, the patient's principal diagnosis (reason for admission), codes for comorbidities (everything that adds to management burden), and the management of the length of inpatient stay. The PMSI is used to analyze the institution's activity and dynamism: change on previous year, relation to target, and comparison with competing institutions based on indicators such as the mean length of stay performance indicator (MLS PI). The T2A system improves overall care efficiency. Quality of care, however, is not presently taken account of in the payment made to the institution, as there are no indicators for this; work needs to be done on this topic., (Copyright © 2014. Published by Elsevier Masson SAS.)

Recruitment of large samples of patients is crucial for evidence level and efficacy of clinical trials (CT). Clinical Trial Recruitment Support Systems (CTRSS) used to estimate patient recruitment are generally specific to Hospital Information Systems and few were evaluated on a large number of trials. Our aim was to assess, on a large number of CT, the usefulness of commonly available data as Diagnosis Related Groups (DRG) databases in order to estimate potential recruitment. We used the DRG database of a large French multicenter medical institution (1.2 million inpatient stays and 400 new trials each year). Eligibility criteria of protocols were broken down into in atomic entities (diagnosis, procedures, treatments...) then translated into codes and operators recorded in a standardized form. A program parsed the forms and generated requests on the DRG database. A large majority of selection criteria could be coded and final estimations of number of eligible patients were close to observed ones (median difference = 25). Such a system could be part of the feasability evaluation and center selection process before the start of the clinical trial.

Objective: A clinically sensible diagnosis grouping system (DGS) is needed for describing pediatric emergency diagnoses for research, medical resource preparedness, and making national policy for pediatric emergency medical care. The Pediatric Emergency Care Applied Research Network (PECARN) developed the DGS successfully. We developed the modified PECARN DGS based on the different pediatric population of South Korea and validated the system to obtain the accurate and comparable epidemiologic data of pediatric emergent conditions of the selected population., Methods: The data source used to develop and validate the modified PECARN DGS was the National Emergency Department Information System of South Korea, which was coded by the International Classification of Diseases, 10th Revision (ICD-10) code system. To develop the modified DGS based on ICD-10 code, we matched the selected ICD-10 codes with those of the PECARN DGS by the General Equivalence Mappings (GEMs). After converting ICD-10 codes to ICD-9 codes by GEMs, we matched ICD-9 codes into PECARN DGS categories using the matrix developed by PECARN group. Lastly, we conducted the expert panel survey using Delphi method for the remaining diagnosis codes that were not matched., Results: A total of 1879 ICD-10 codes were used in development of the modified DGS. After 1078 (57.4%) of 1879 ICD-10 codes were assigned to the modified DGS by GEM and PECARN conversion tools, investigators assigned each of the remaining 801 codes (42.6%) to DGS subgroups by 2 rounds of electronic Delphi surveys. And we assigned the remaining 29 codes (4%) into the modified DGS at the second expert consensus meeting. The modified DGS accounts for 98.7% and 95.2% of diagnoses of the 2008 and 2009 National Emergency Department Information System data set. This modified DGS also exhibited strong construct validity using the concepts of age, sex, site of care, and seasons. This also reflected the 2009 outbreak of H1N1 influenza in Korea., Conclusions: We developed and validated clinically feasible and sensible DGS system for describing pediatric emergent conditions in Korea. The modified PECARN DGS showed good comprehensiveness and demonstrated reliable construct validity. This modified DGS based on PECARN DGS framework may be effectively implemented for research, reporting, and resource planning in pediatric emergency system of South Korea.

Background: Due to the heterogeneity of severely injured patients (multiple trauma) it is difficult to assign them to homogeneic diagnosis-related groups (DRG). In recent years this has led to a systematic underfunding in the German reimbursement system (G-DRG) for cases of multiply injured patients. This project aimed to improve the reimbursement by modifying the case allocation algorithms of multiply injured patients within the G-DRG system., Methods: A retrospective analysis of standardized G-DRG data according to §21 of the Hospital Reimbursement Act (§ 21 KHEntgG) including case-related cost data from 3,362 critically injured patients from 2007 and 2008 from 10 university hospitals and 7 large municipal hospitals was carried out. For 1,241 cases complementary detailed information was available from the trauma registry of the German Trauma Society to monitor the case allocation of multiply injured patients within the G-DRG system. Analysis of coding and grouping, performance of case allocation and the homogeneity of costs in the G-DRG versions 2008-2012 was carried out., Results: The results showed systematic underfunding of trauma patients in the G-DRG version 2008 but adequate cost covering in the majority of cases with the G-DRG versions 2011 and 2012. Cost coverage was foundfor multiply injured patients from the clinical viewpoint who were identified as multiple trauma by the G-DRG system. Some of the overfunded trauma patients had high intensive care costs. Also there was underfunding for multiple injured patients not identified as such in the G-DRG system., Conclusions: Specific modifications of the G-DRG allocation structures could increase the appropriateness of reimbursement of multiply injured patients. Data-based analysis is an essential prerequisite for a constructive development of the G-DRG system and a necessary tool for the active participation of medical specialist societies.

Objective: In the TNM seventh edition, a prognostic grouping for prostate cancer incorporating prostate-specific antigen and Gleason score was advocated. The present study was carried out to evaluate and validate prognostic grouping in prostate cancer patients., Methods: The 15 259 study patients treated with primary androgen deprivation therapy were enrolled in the Japan Study Group of Prostate Cancer. Overall survival was stratified by tumor-nodes-metastasis, Gleason score and prostate-specific antigen, and extensively analyzed. The accuracy of grouping systems was evaluated by the concordance index., Results: The 5-year overall survival in prognostic grouping-I, IIA, IIB, III and IV was 90.0%, 88.3%, 84.8%, 80.6% and 57.1%, respectively. When considering subgroup stratification, the 5-year overall survival of subgroups prognostic grouping-IIA, IIB, III and IV was 80.9∼90.5%, 75.4∼91.8%, 75.7∼89.0% and 46.9∼86.2%, respectively. When prognostic grouping-IIB was subclassified into IIB1 (except IIB2) and IIB2 (T1-2b, prostate-specific antigen >20, Gleason score ≥8, and T2c, Gleason score ≥8), the 5-year overall survival of IIB2 was significantly lower than that of IIB1 (79.4% and 87.3%, P < 0.0001). Also, when prognostic grouping-IV was subclassified into IV1 (except IV2) and IV2 (M1, prostate-specific antigen >100 or Gleason score ≥8), the 5-year overall survival of prognostic grouping-IV1 was superior to that of IV2 (72.9% and 49.5%, P < 0.0001). Prognostic groupings were reclassified into modified prognostic groupings, divided into modified prognostic grouping-A (prognostic grouping-I, IIA, and IIB1), modified prognostic grouping-B (prognostic grouping-IIB2 and III), modified prognostic grouping-C (prognostic grouping-IV1) and modified prognostic grouping-D (prognostic grouping-IV2). The concordance index of prognostic grouping and modified prognostic grouping for overall survival was 0.670 and 0.685, respectively., Conclusion: Prognostic grouping could stratify the prognosis of prostate cancer patients. However, there is considerable variation among the prognostic grouping subgroups. Thus, the use of a modified prognostic grouping for patients treated with primary androgen deprivation therapy is advisable., (© 2013 The Japanese Urological Association.)

Aims: As part of the diagnosis related groups in Europe (EuroDRG) project, researchers from 11 countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their DRG systems deal with patients admitted to hospital for acute myocardial infarction (AMI). The study aims to assist cardiologists and national authorities to optimize their DRG systems., Methods and Results: National or regional databases were used to identify hospital cases with a primary diagnosis of AMI. Diagnosis-related group classification algorithms and indicators of resource consumption were compared for those DRGs that individually contained at least 1% of cases. Six standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems vary widely: they classify AMI patients according to different sets of variables into diverging numbers of DRGs (between 4 DRGs in Estonia and 16 DRGs in France). The most complex DRG is valued 11 times more resource intensive than an index case in Estonia but only 1.38 times more resource intensive than an index case in England. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the index case amount to only €420 in Poland but to €7930 in Ireland., Conclusions: Large variation exists in the classification of AMI patients across Europe. Cardiologists and national DRG authorities should consider how other countries' DRG systems classify AMI patients in order to identify potential scope for improvement and to ensure fair and appropriate reimbursement.

Purpose: Casemix models for funding activity in health care and assessing performance depend on data based on uniformity of resource utilisation. It has long been an ideal to relate the measure of value more to patient outcome than output. A problem frequently expressed by clinicians is that measures of activity such as Functional Independence Measure (FIM) and Barthel Index scores may not sufficiently represent the aspirations of patients in many care programs., Method: Firstly, the key features of the International Classification of Functioning, Disability and Health are outlined. Secondly, the use of ICF dimensions in Australia and other countries is reviewed. Thirdly, a broader set of domains with potential for casemix funding models and performance reporting is considered., Results: In recent years, the ICF has provided a more developed set of domains against which outcome goals can be expressed. Additional dimensions could be used to supplement existing data. Instances of developments in this area are identified and their potential discussed., Conclusions: A well-selected set of data items representing the broader dimensions of outcome goals may provide the ability to more meaningfully and systematically measure the goals of both curative and rehabilitation care against which outcome should be measured. More information about patient goals may be needed.

Background: The new 2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for rheumatoid arthritis (RA) aim at earlier diagnosis of RA compared to the 1987 ACR criteria., Objective: To evaluate the ability of the 2010 ACR/EULAR and the 1987 ACR classification criteria to predict radiographic progression after 10 years of follow-up., Methods: All early arthritis patients referred to Central Hospital in Jyväskylä from 1997 to 1999 (cases with peripheral joint synovitis, other specific diseases excluded) were included in this 10-year follow-up study. Radiographs of hands and feet were analysed according to Larsen on a scale of 0-100., Results: At 10 years, 58% of the patients had an erosive disease (defined as Larsen ≥2 in at least one joint). The discriminative power of the 2010 ACR/EULAR and the 1987 ACR criteria (erosive disease at 10 years) were comparable, with area under the curve 0.72 (95% CI 0.65 to 0.79) (2010 ACR/EULAR criteria) and 0.65 (95% CI 0.58 to 0.72) (1987 ACR criteria). The respective sensitivities and specificities were 0.87 and 0.70, and 0.44 and 0.47. At 10 years, median (IQR) Larsen score was 6 (0, 15) among patients who had fulfilled both sets of criteria, 2 (0, 8) in those who met the 2010 ACR/EULAR and did not meet the ACR 1987 criteria, 0 (0, 5) in those who met ACR 1987 criteria but did not meet 2010 ACR/EULAR criteria, and 0 (0, 2) among patients who did not fulfil either of the criteria. The percentage of patients with erosions was 69%, 64%, 32% and 26%, respectively., Conclusions: The ability of the 2010 ACR/EULAR and 1987 ACR classification criteria to identify erosive disease in early arthritis is low. The discriminative power of the 2010 ACR/EULAR criteria of erosiveness in 10 years is slightly better than that of the 1987 ACR criteria.

In Japan, medical staff must select a diagnosis procedure combination (DPC) code for each inpatient upon admission. We report on the development and evaluation of a supporting system for DPC code selection. This system, based on a machine learning method developed by Okamoto et al., makes DPC code suggestions that are derived from medical practice information pertaining to inpatients. The use of the suggestions helps medical staff select an appropriate DPC code for each inpatient. We asked health information management professionals to evaluate the system and to compare the suggested DPC codes with those selected by doctors. They reported that the system was generally useful and that using this system they could find some cases of hospitalized patients whose DPC codes needed correction. However, they also determined the precision of the system needs improvement.

Based on the adjudicative process, the author classifies claimant-level data over an 8-year period (1997-2004) into four mutually exclusive categories: (1) initial allowances, (2) initial denials not appealed, (3) final allowances, and (4) final denials. The ability to predict those outcomes is explored within a multilevel modeling framework, with applicants clustered by state and primary diagnosis code. Variance decomposition suggests that medical diagnoses play a substantial role in explaining individual-level variation in initial allowances. Moreover, there is statistically significant high positive correlation between the predictions of an initial allowance and a final allowance across the diagnoses. This finding suggests that the ordinal ranking of impairments between these two adjudicative outcomes is widely preserved. In other words, impairments with a higher expectation of an initial allowance also tend to have a higher expectation of a final allowance.

In many applications, especially in cancer treatment and diagnosis, investigators are interested in classifying patients into various diagnosis groups on the basis of molecular data such as gene expression or proteomic data. Often, some of the diagnosis groups are known to be related to higher or lower values of some of the predictors. The standard methods of classifying patients into various groups do not take into account the underlying order. This could potentially result in high misclassification rates, especially when the number of groups is larger than two. In this article, we develop classification procedures that exploit the underlying order among the mean values of the predictor variables and the diagnostic groups by using ideas from order-restricted inference. We generalize the existing methodology on discrimination under restrictions and provide empirical evidence to demonstrate that the proposed methodology improves over the existing unrestricted methodology. The proposed methodology is applied to a bladder cancer data set where the researchers are interested in classifying patients into various groups., (Copyright © 2012 John Wiley & Sons, Ltd.)

Background: Controversy exists regarding the use of concurrent cholecystectomy during Roux-en-Y gastric bypass performed for morbid obesity., Methods: A decision model was developed to evaluate the cost-effectiveness of current strategies: routine concurrent cholecystectomy, Roux-en-Y gastric bypass alone with or without postoperative ursodiol therapy, and selective cholecystectomy based on preoperative findings on ultrasonography. Probabilities were obtained from a comprehensive literature review. Costs and hospital days were obtained from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample. One-way sensitivity analyses were performed., Results: The least expensive strategy was to perform RYGB alone without preoperative ultrasonography, with an average cost (over RYGB costs) of $537 per patient. RYGB with concurrent cholecystectomy had a cost of $631. Selective cholecystectomy based on preoperative ultrasonography was dominated by the other 2 strategies. Our model was most sensitive to the probability of developing gallbladder-related symptoms after RYGB alone. When the incidence of gallbladder-related symptoms was <4.6%, the dominant strategy was to perform a RYGB alone without preoperative ultrasonography. For values >6.9%, performing concurrent cholecystectomy at the time of the RYGB was superior to other strategies. When ursodiol was used, the least expensive strategy was to perform a concurrent cholecystectomy during RYGB., Conclusion: The main factor determining the most cost-effective strategy is the incidence of gallbladder-related symptoms after RYGB. The use of ursodiol was associated with an increase in cost that does not justify its use after RYGB. Finally, selective cholecystectomy based on preoperative ultrasonography was dominated by the other strategies in the scenarios evaluated., (Published by Mosby, Inc.)

Background: Temporomandibular disorder (TMD) patients might present a number of concurrent clinical diagnoses that may be clustered according to their similarity. Profiling patients' clinical presentations can be useful for better understanding the behavior of TMD and for providing appropriate treatment planning. The aim of this study was to simultaneously classify symptomatic patients diagnosed with a variety of subtypes of TMD into homogenous groups based on their clinical presentation and occurrence of comorbidities., Methods: Clinical records of 357 consecutive TMD patients seeking treatment in a private specialized clinic were included in the study sample. Patients presenting multiple subtypes of TMD diagnosed simultaneously were categorized according to the AAOP criteria. Descriptive statistics and two-step cluster analysis were used to characterize the clinical presentation of these patients based on the primary and secondary clinical diagnoses., Results: The most common diagnoses were localized masticatory muscle pain (n = 125) and disc displacement without reduction (n = 104). Comorbidity was identified in 288 patients. The automatic selection of an optimal number of clusters included 100% of cases, generating an initial 6-cluster solution and a final 4-cluster solution. The interpretation of within-group ranking of the importance of variables in the clustering solutions resulted in the following characterization of clusters: chronic facial pain (n = 36), acute muscle pain (n = 125), acute articular pain (n = 75) and chronic articular impairment (n = 121)., Conclusion: Subgroups of acute and chronic TMD patients seeking treatment can be identified using clustering methods to provide a better understanding of the clinical presentation of TMD when multiple diagnosis are present. Classifying patients into identifiable symptomatic profiles would help clinicians to estimate how common a disorder is within a population of TMD patients and understand the probability of certain pattern of clinical complaints.

Background: In Norway, admission teams at Community Mental Health Centres (CMHCs) assess referrals from General Practitioners (GPs), and classify the referrals into priority groups according to treatment needs, as defined in the Act of Patient Rights. In this study, we analyzed classification of similar referrals to determine the reliability of classification into priority groups (i.e., horizontal equity)., Methods: Twenty anonymous case vignettes based on representative referrals were classified by 42 admission team members at 16 CMHCs in the South-East Health Region of Norway. All clinicians were experienced, and were responsible for priority setting at their centres. The classifications were first performed independently by the 42 clinicians (i.e., individual rating), and then evaluated utilizing team consensus within each CMHC (i.e., team rating). Interrater reliability was estimated using intraclass correlation coefficients (ICCs) while the reliability of rating across raters and units (generalizability) were estimated using generalizability analysis., Results: The ICCs (2.1 single measure, absolute agreement) varied between 0.40 and 0.51 using individual ratings and between 0.39 and 0.58 using team ratings. Our findings suggest a fair (low) degree of interrater reliability, and no improvement of team ratings was observed when compared to individual ratings. The generalizability analysis, for one rater within each unit, yields a generalizability coefficient of 0.50 and a dependability coefficient of 0.53 (D study). These findings confirm that the reliability of ratings across raters and across units is low. Finally, the degree of inconsistency, for an average measurement, appears to be higher within units than between units (G study)., Conclusion: The low interrater reliability and generalizability found in our study suggests that horizontal equity to mental health services is not ensured with respect to priority. Priority -setting in teams provides no significant improvement compared to individual rating, and the additional use of these resources may be questionable. Improved guidelines, tutorials, training and calibration of clinicians may be utilized to improve the reliability of priority-setting.

Background: Acute myocardial infarction (AMI; ICD9-CM 410*) is a leading cause of morbidity and mortality all over the world, and its community surveillance is essential to monitor variation in the occurrence of the disease. Between the late 1990s and the early 2000s more sensitive and specific biomarkers of myocardial necrosis (ie, troponins) were introduced and new diagnostic criteria, emphasising the role of biomarkers, have been developed for clinical and epidemiological purposes., Methods: Tosc-AMI is a population-based registry based on the record linkage between hospital and mortality databases; it provides trends of coronary events in Tuscany, Italy. Two random samples of patients admitted to hospital in 2003 were validated according to the American Heart Association (AHA; 2003) and the Multinational MONItoring of trends and determinants in CArdiovascular disease (MONICA) (1983) criteria. Sample 1 (380 cases) was represented by patients admitted to hospital for AMI and sample 2 (380 cases) for other coronary diagnosis., Results: Tosc-AMI attack rates increased from the period 1997 to 2005 (men: +17%; women: +30%) and then they decreased in the following 2 years (men: -8%; women: -13%). The rise of AMI hospital admissions was due to cases with ICD9-CM code 410.7 (largely representing non-ST elevation MI). According to the AHA criteria, 94.6% events of sample 1 and 29.8% events of sample 2 fulfilled the most extensive criteria for definite, probable or possible AMI. As expected, the more updated AHA definition identified as definite AMI an additional 33.3% when compared to the MONICA criteria (86.0% vs 52.7%)., Conclusions: The study suggests an influence of the new diagnostic criteria on the rising AMI trend observed in the early 2000s, an increase of less severe cases and a decreasing trend of forms with a more extended myocardial damage.

Pulmonary hypertension (PH) is a heterogeneous condition. To date, no registry data exists reflecting the spectrum of disease across the five diagnostic groups encountered in a specialist referral centre. Data was retrieved for consecutive, treatment-naïve cases diagnosed between 2001 and 2010 using a catheter-based approach. 1,344 patients were enrolled, with a mean follow-up of 2.9 yrs. The 3-yr survival was 68% for pulmonary arterial hypertension (PAH), 73% for PH associated with left heart disease, 44% for PH associated with lung disease (PH-lung), 71% for chronic thromboembolic PH (CTEPH) and 59% for miscellaneous PH. Compared with PAH, survival was inferior in PH-lung and superior in CTEPH (p<0.05). Multivariate analysis demonstrated that diagnostic group independently predicted survival. Within PAH, Eisenmenger's survival was superior to idiopathic PAH, which was superior to PAH associated with systemic sclerosis (p<0.005). Within PH-lung, 3-yr survival in sleep disorders/alveolar hypoventilation (90%) was superior to PH-lung with chronic obstructive pulmonary disease (41%) and interstitial lung disease (16%) (p<0.05). In CTEPH, long-term survival was best in patients with surgically accessible disease undergoing pulmonary endarterectomy. In this large registry of consecutive, treatment-naïve patients identified at a specialist PH centre, outcomes and characteristics differed between and within PH groups. The current system of classification of PH has prognostic value even when adjusted for age and disease severity, emphasising the importance of systematic evaluation and precise classification.

The purpose of this study was to build a healthcare quality assessment system with disease category as the basic unit of assessment based on the principles of case classification, and to assess the quality of care in a large hospital in Shanghai. Using the Delphi method, four quality indicators were selected. The data of 124,125 patients discharged from a large general hospital in Shanghai, from October 1, 2004 to September 30, 2007, were used to establish quality indicators estimates for each disease. The data of 51,760 discharged patients from October 1, 2007 to September 30, 2008 were used as the testing sample, and the standard scores of each quality indicator for each clinical department were calculated. Then the total score of various clinical departments in the hospital was calculated based on the differences between the practical scores and the standard. Based on quality assessment scores, we found that the quality of healthcare in departments of thyroid and mammary gland surgery, obstetrics and gynaecology, stomatology, dermatology, and paediatrics was better than in other departments. Implementation of the case classification for healthcare quality assessment permitted the comparison of quality among different healthcare departments.