Schnittbilddiagnostik im InEK‑Benchmark

Zusammenfassung Hintergrund Anhand der vom Institut für das Entgeltsystem im Krankenhaus (InEK) kalkulierten Fallpauschalen ist ein Vergleich der eigenen Leistungsdaten gegenüber allen nach der DRG („diagnosis-related groups“, diagnosebezogene Gruppen) abrechnenden Kliniken in Deutschland möglich. Ziel der vorliegenden Arbeit ist es, Über- oder Unterschreitungen von CT- oder MRT-Untersuchungen im Vergleich mit den InEK-Daten zu ermitteln und mögliche Verbesserungspotenziale zu erschließen. Methodik Die InEK-Kalkulationsdaten für 2021 wurden zur Bildung von Vergleichskennzahlen der CT- und MRT-Diagnostik auf DRG-Ebene herangezogen. Auf Fallebene wurden Daten eines universitären Maximalversorgers auf Gesamthaus‑, Klinik‑, DRG- und Hauptdiagnosen-Ebene gegenübergestellt. Ergebnis Auf Gesamthausebene zeigt sich eine Überschreitung der MRTs um 1025 und der CTs um 371 gegenüber InEK. Die Analyse nach Fachabteilungen ergab am Beispiel der Neurologie eine Überschreibung der MRTs gegenüber InEK um 489 sowie eine Unterschreitung der CTs um $$-$$ - 620. Der Benchmark der DRGs zeigte in beiden Untersuchungsmodalitäten insbesondere die DRG B70B als Treiber der Abweichungen (MRT + 42,7; CT − 273). Die identifizierten Abweichungen lassen sich auf Hauptdiagnosen-Ebene weiter herunterbrechen. Diskussion Das Bewusstsein über eine überdurchschnittliche Schnittbilddiagnostik kann einen wichtigen Anstoß zur Weiterentwicklung der Behandlungspfade einer Klinik bilden. Die Methodik des InEK-Benchmarks ist für jedes Krankenhaus anwendbar und identifiziert valide bereits erbrachte Leistungen und Prozesse mit einem Verbesserungspotenzial. Die Prüfung beeinflussender Faktoren sowie die Bewertung durch Mediziner und Kaufleute bildet die Voraussetzung für Akzeptanz und Erfolg der daraus generierten Maßnahmen.

A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

Background: To develop a set of R scripts that could efficiently and accurately identify the home page information of medical records and perform China Healthcare Security Diagnosis Related Groups (CHS-DRG) simulating grouping.Methods: Based on the CHS-DRG grouping rules, we abstracted the DRG grouping process into a standard algorithm and compiled the R script Z-DRG. The DRG simulating groupings by Z-DRG were compared with the DRG results from the regional CHS-DRG integrated service platform to evaluate the accuracy.Results: The Z-DRG includes one function module (zdrgfun. Rc), one operation module (zdrgpro. R) and one database form (zdrgcodes.RData). The function module set 7 algorithm steps and 8 custom functions. The functions were set for multiple diagnoses, multiple operations, joint diagnosis and operation. Only (17.85±0.11) milliseconds were taken for CHS-DRG simulating grouping of one case. Compared with the regional CHS-DRG results, the accuracy rate was 99.10%. The difference in the number of other diagnoses is the main reason that affected the accuracy.Conclusions: Z-DRG is easy to operate. The CHS-DRG simulating groupings were efficient and accurate. The simulation results could be effectively applied for medical institutions to carry out CHS-DRG grouping prediction and improve the implementation effect of CHS-DRG payment work.

Changes in Public Hospital Employees’ Perceptions Following the Introduction of the New Diagnosis-Related Groups (DRG)-Based Payment System in the Republic of Korea

Purpose: The aim of this study was to investigate the changes in perception of the New Diagnosis-Related Group (DRG)- based payment system, make overall evaluation after participation, and examine opinions on further policy improvement among employees of a public hospital participating in the pilot project in Korea.Methods: We investigated changes in perception of the New DRG-based payment system before and after participation in the pilot project using a qualitative research method. We conducted individual in-depth interviews with the management and healthcare professionals and Focus Group Interviews (FGIs) with the staff in the nursing and administrative departments.Results: Before implementing the pilot project of the New DRG-based payment system, the management was in favor of participating in the pilot project, whereas the healthcare professionals were strongly opposed to participation in the pilot project, and the staff in the nursing and administrative departments were slightly opposed to participation. After implementing the pilot project, there were remarkable changes in the perception of the New DRG-based payment system among healthcare professionals and the administrative staff. Healthcare professionals’ perception was altered in a positive way, while the administrative staff’s perception of the system became negative.Conclusion: There were no restrictions on clinical practice or deterioration of quality of care observed in association with the participation in the New DRG-based payment system. However, certain unintended consequences of the New DRG-based payment system may arise as well. Therefore, the government needs to examine the problems identified in this study to reflect on and improve the New DRG-based payment system for stable expansion.

A Simple Diagnosis-Related Groups-Based Reimbursement System Is Cost Ineffective for Elderly Patients With Displaced Femoral Neck Fracture Undergoing Hemiarthroplasty in Beijing

Displaced femoral neck fractures (FNF) in the elderly are a major public health concern that necessitates hemiarthroplasty (HA) as the mainstay treatment option. Diagnosis-Related Groups (DRG) are a patient classification system that categorizes patients based on the resources expended on them. The first objective of this study was to evaluate if a simplified DRG-based reimbursement system in Beijing would lower total HA treatment costs for elderly patients with displaced FNF. In addition, we aimed to determine how age, gender, year of admission, length of in-hospital stay, and the Charlson index affected total treatment costs. This retrospective study included 513 patients from the Peking Union Medical College Hospital. The patients were diagnosed with unilateral displaced femoral neck fractures and had HA. Medical information was gathered, including baseline demographic and clinical data, as well as treatment costs. Patients were classified into two groups: those who spent more than the predetermined cut-off cost and those who did not. The cost did not include the use of a bipolar prosthesis. Data from the two groups were compared, and multiple regression analysis models were constructed. The median total cost of treatment was ¥49,626 ($7,316). The majority of the patients (89.7%; 460/513) were categorized as exceeding the cost cut-off. Multiple linear regression analysis revealed that total treatment cost was positively correlated with age (p < 0.01) and the duration of in-hospital stay (p < 0.01) but not with gender (p = 0.160) or the Charlson index (p = 0.548). On implementing the DRG-based reimbursement system, the overall treatment costs increased by ¥21,028 ($3,099) (p < 0.01). The implementation of simplified DRG-prospective payment systems did not result in a significant reduction in total treatment costs for elderly patients with FNF who underwent HA in Beijing. The overall cost of treatment was associated with several factors, including age, length of hospitalization, and year of admission.

ECMO use in Germany: An analysis of 29,929 ECMO runs

Background Extracorporeal Membrane Oxygenation (ECMO) use is increasing despite limited evidence. The aim of this study was to demonstrate heterogeneity of ECMO use and its association with hospital size and annual frequency in Germany. Methods This is a database analysis of all ECMO cases in Germany from 2010 to 2016 using the German Diagnosis Related Groups (DRG) coding system for ECMO. Results During the study period, 510 hospitals performed 29,929 ECMO runs (12,572 vvECMO, 11,504 vaECMO, 1993 pECLA) with an increase over time. Mortality ranged between 58% and 66% for vaECMO cases and 66% and 53% for vvECMO cases. 304 (61%) hospitals performed only one ECMO per year. 78%% of all ECMO runs were performed in centres with more than 20 cases per year and more than half of all ECMO runs were performed in hospitals with >1.000 beds. Mortality for vv and vaECMO was highest in very small hospitals (< 200 beds; 70%; 74%) and very large hospitals (>1000 beds; 60%; 62%). Conclusions Use of ECMO is still increasing and a substantial proportion of hospitals performs very few ECMO runs. Small hospitals had a significantly higher mortality, but dependence on hospital size and ECMO mortality was irregular.

Trends in Treatment of Head and Neck Cancer in Germany: A Diagnosis-Related-Groups-Based Nationwide Analysis, 2005–2018

Advances in head and neck cancer (HNC) treatment might have changed treatment strategies. This study determined, with focus on gender disparity, whether treatment rates have changed for inpatients in Germany between 2005 and 2018. Nation-wide population-based diagnosis-related groups (DRG) data of virtually all HNC cases (1,226,856 procedures; 78% men) were evaluated. Poisson regression analyses were used to study changes of annual treatment rates per German population. For surgery, the highest increase was seen for women with cancer of the oral cavity (relative risk (RR) 1.14, 95% confidence interval (CI) 1.11–1.18, p < 0.0001) and the highest decrease for men with laryngeal cancer (RR 0.90, CI 0.87–0.93). In women with oropharyngeal cancer, the highest increase of radiotherapy rates was seen (RR 1.18, CI 1.10–1.27, p < 0.0001). A decrease was seen in men for hypopharyngeal cancer (RR 0.93, CI 0.87–0.98, p = 0.0093). The highest increase for chemotherapy/immunotherapy was seen for women with oropharyngeal cancer (RR 1.16, CI 1.08–1.24, p < 0.0001), and a decrease in men with hypopharyngeal cancer (RR 0.93, CI 0.88–0.97, p = 0.0014). Treatment patterns had changed for nearly all subsites and therapy types. There were relevant gender disparities, which cannot be explained by the DRG data.

A Game Model for Medical Service Pricing Based on the Diagnosis Related Groups

Background: Currently there are various issues that exist in the medical institutions in China as a result of the price-setting in DRGs, which include the fact that medical institutions tend to choose patients and that the payment standard for complex cases cannot reasonably compensate the cost.Objective: The main objective is to prevent adverse selection problems in the operations of a diagnosis-related groups (DRGs) system with the game pricing model for scientific and reasonable pricing.Methods: The study proposes an improved bargaining game model over three stages, with the government and patients forming an alliance. The first stage assumes the alliance is the price maker in the Stackelberg game to maximize social welfare. Medical institutions are a price taker and decide the level of quality of medical service to maximize their revenue. A Stackelberg equilibrium solution is obtained. The second stage assumes medical institutions dominate the Stackelberg game and set an optimal service quality for maximizing their revenues. The alliance as the price taker decides the price to maximize the social welfare. Another Stackelberg equilibrium solution is achieved. The final stage establishes a Rubinstein bargaining game model to combine the Stackelberg equilibrium solutions in the first and second stage. A new equilibrium between the alliance and medical institutions is established.Results: The results show that if the price elasticity of demand increases, the ratio of cost compensation on medical institutions will increase, and the equilibrium price will increase. The equilibrium price is associated with the coefficient of patients' quality preference. The absolute risk aversion coefficient of patients affects government compensation and total social welfare.Conclusion: In a DRGs system, considering the demand elasticity and the quality preference of patients, medical service pricing can prevent an adverse selection problem. In the future, we plan to generalize these models to DRGs pricing systems with the effects of competition of medical institutions. In addition, we suggest considering the differential compensation for general hospitals and community hospitals in a DRGs system, in order to promote the goal of hierarchical diagnosis and treatment.

Impact of the SARS-CoV-2 (COVID19) pandemic on the morbidity and mortality of high risk patients undergoing surgery: a non-inferiority retrospective observational study

Background During the COVID-19 crisis it was necessary to generate a specific care network and reconvert operating rooms to attend emergency and high-acuity patients undergoing complex surgery. The aim of this study is to classify postoperative complications and mortality and to assess the impact that the COVID-19 pandemic may have had on the results. Methods this is a non-inferiority retrospective observational study. Two different groups of surgical patients were created: Pre-pandemic COVID and Pandemic COVID. Severity of illness was rated according to the Diagnosis-related Groups (DRG) score. Comparisons were made between groups and between DRG severity score-matched samples. Non-inferiority was set at up to 10 % difference for grade III to V complications according to the Clavien-Dindo classification, and up to 2 % difference in mortality. Results A total of 1649 patients in the PreCOVID group and 763 patients in the COVID group were analysed; 371 patients were matched for DRG severity score 3-4 (236 preCOVID and 135 COVID). No differences were found in relation to re-operation (22.5 % vs. 21.5 %) or late admission to critical care unit (5.1 % vs. 4.5 %). Clavien grade III to V complications occurred in 107 patients (45.3 %) in the PreCOVID group and in 56 patients (41.5 %) in the COVID group, and mortality was 12.7 % and 12.6 %, respectively. During the pandemic, 3 % of patients tested positive for Covid-19 on PCR: 12 patients undergoing elective surgery and 11 emergency surgery; there were 5 deaths, 3 of which were due to respiratory failure following Covid-19-induced pneumonia. Conclusions Although this study has some limitations, it has shown the non-inferiority of surgical outcomes during the COVID pandemic, and indicates that resuming elective surgery is safe. Trial registration Clinicaltrials.gov identifier: NCT04780594.

DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance.

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China

Background. Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region. Of these variables, the question of whether the length of stay (LOS) should be used as a grouping variable is controversial. Aim. To identify the factors influencing inpatient medical expenditure in cerebrovascular disease patients. The performance of two sets of classification rules, and the effects of the extent of control of unreasonable medical treatment, were compared, to investigate whether the classification variables should include LOS. Methods. Data from 45,575 inpatients from a Healthcare Security Administration of a city in western China were used. Kruskal–Wallis H tests were used for single-factor analysis, and multiple linear stepwise regression was used to determine the main factors. A chi-squared automatic interaction detector (CHAID) algorithm was built as a decision tree model for grouping related data. The intensity of oversupply of service was controlled step by step from 10% to 100%, and the performance was calculated for each group. Results. The average hospitalization cost was 1,284 US dollars, and the total was 51.17 million US dollars. Of this, 43.42 million were paid by the government, and 7.75 million were paid by individuals. Factors including gender, age, type of insurance, level of hospital, LOS, surgery, therapeutic outcomes, main concomitant disease, and hypertension significantly influenced inpatient expenditure ( P < 0.05 ). Incorporating LOS, the patients were divided into seven DRG groups, while without LOS, the patients were divided into eight DRG groups. More clinical variables were needed to achieve good results without LOS. Of the two rule sets, smaller coefficient of variation (CV) and a lower upper limit for patient costs were found in the group including LOS. Using this type of economic control, 3.35 million US dollars could be saved in one year.