Challenges and Adverse Outcomes of Implementing Reimbursement Mechanisms Based on the Diagnosis-Related Group Classification System: A systematic review.

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.

The Australian National Aged Care Classification (AN-ACC): a new casemix classification for residential aged care.

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.

Incorporation of expert knowledge in the statistical detection of diagnosis related group misclassification.

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.

Problems and Barriers during the Process of Clinical Coding: a Focus Group Study of Coders' Perceptions.

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.

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases.

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.

Health records as the basis of clinical coding: Is the quality adequate? A qualitative study of medical coders' perceptions.

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).

A review of the complexity adjustment in the Korean Diagnosis-Related Group (KDRG).

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.

Experiencia en el desarrollo e implementación de la metodología de grupos relacionados por diagnóstico en un hospital universitario chileno. Evaluación a diez años de funcionamiento / Implementation of diagnosis related groups methodology in a university hospital

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.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

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.

[Validity of adjusted morbidity groups with respect to clinical risk groups in the field of primary care]. / Validez de los grupos de morbilidad ajustados respecto a los clinical risk groups en el ámbito de la atención primaria.

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.

[Implementation of diagnosis related groups methodology in a university hospital]. / Experiencia en el desarrollo e implementación de la metodología de grupos relacionados por diagnóstico en un hospital universitario chileno. Evaluación a diez años de funcionamiento.

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.

Reliability measurement and ICD-10 validation of ICPC-2 for coding/classification of diagnoses/health problems in an African primary care setting.

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.

Development and validation of a casemix classification to predict costs of specialist palliative care provision across inpatient hospice, hospital and community settings in the UK: a study protocol.

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.

[The new treatment procedures of the DGUV from the perspective of an injury type procedure (VAV) clinic]. / Die neuen Heilverfahren der DGUV aus der Sicht einer VAV-Klinik.

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.

[Sigmoid colon diverticulitis : Treatment modalities 2011-2013]. / Sigmadivertikulitis : Behandlungsmodalitäten 2011 bis 2013.

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.

Impact of Case Mix Severity on Quality Improvement in a Patient-centered Medical Home (PCMH) in the Maryland Multi-Payor Program.

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.