[Regional and gender variations in appendicectomy : Nationwide small-area development of operation rates in the time series]. / Regionale und geschlechtsspezifische Unterschiede bei Appendektomien : Bundesweite kleinräumige Entwicklung der Operationsraten in der Zeitreihe.

BACKGROUND: Previous analyses of small-area appendicectomy rates showed significantly higher regional differences in the frequency of operations in women than in men OBJECTIVE: This work proposes valid measures to represent regional variations and analyzes gender-specific changes of appendicectomy rates at the county level in the time series. MATERIAL AND METHODS: Appendicectomy frequencies for 2014, 2016 and 2018 by gender and at the county level were taken from the DRG statistics. Regional variations were calculated and assessed using the systematic component of variation (SCV). In comparison to the extreme ratio and coefficient of variation, the SCV is more robust with respect to strongly fluctuating denominator populations. The SCV values greater than 5 indicate high variation and greater than 10 indicate very high variation. RESULTS: In the male population only minor regional variations in operation rates could be observed, remaining at similar levels over time (SCV2014 = 2.1, SCV2016 = 1.8, and SCV2018 = 2.0). For women the SCV was above 5 in 2014 as well as in 2016 (SCV2014 = 6.1, SCV2016 = 5.3) and dropped to 4.5 in 2018. Plots as a funnel plot account for higher scatter in surgery rates in counties with low populations. DISCUSSION: Regarding women, a decreasing trend in regional variation of appendicectomy could be observed. It remains unclear whether this trend reflects a change in the indications or a modified clinical management when appendicitis is suspected. Using robust variation measures and the graphic preparation as funnel plots it is possible to distinguish systematically caused regional differences in care from random effects.

ICD-11 extension codes support detailed clinical abstraction and comprehensive classification.

BACKGROUND: The new International Classification of Diseases-11th revision (ICD-11) succeeds ICD-10. In the three decades since ICD-10 was released, demands for detailed information on the clinical history of a morbid patient have increased. METHODS: ICD-11 has now implemented an addendum chapter X called "Extension Codes". This chapter contains numerous codes containing information on concepts including disease stage, severity, histopathology, medicaments, and anatomical details. When linked to a stem code representing a clinical state, the extension codes add significant detail and allow for multidimensional coding. RESULTS: This paper discusses the purposes and uses of extension codes and presents three examples of how extension codes can be used in coding clinical detail. CONCLUSION: ICD-11 with its extension codes implemented has the potential to improve precision and evidence based health care worldwide.

Evaluation of outcome relevance of quality indicators in the emergency department (ENQuIRE): study protocol for a prospective multicentre cohort study.

INTRODUCTION: Quality of emergency department (ED) care affects patient outcomes substantially. Quality indicators (QIs) for ED care are a major challenge due to the heterogeneity of patient populations, health care structures and processes in Germany. Although a number of quality measures are already in use, there is a paucity of data on the importance of these QIs on medium-term and long-term outcomes. The evaluation of outcome relevance of quality indicators in the emergency department study (ENQuIRE) aims to identify and investigate the relevance of QIs in the ED on patient outcomes in a 12-month follow-up. METHODS AND ANALYSIS: The study is a prospective non-interventional multicentre cohort study conducted in 15 EDs throughout Germany. Included are all patients in 2019, who were ≥18 years of age, insured at the Techniker Krankenkasse (statutory health insurance (SHI)) and gave their written informed consent to the study.The primary objective of the study is to assess the effect of selected quality measures on patient outcome. The data collected for this purpose comprise medical records from the ED treatment, discharge (claims) data from hospitalised patients, a patient questionnaire to be answered 6-8 weeks after emergency admission, and outcome measures in a 12-month follow-up obtained as claims data from the SHI.Descriptive and analytical statistics will be applied to provide summaries about the characteristics of QIs and associations between quality measures and patient outcomes. ETHICS AND DISSEMINATION: Approval of the leading ethics committee at the Medical Faculty of the University of Magdeburg (reference number 163/18 from 19 November 2018) has been obtained and adapted by responsible local ethics committees.The findings of this work will be disseminated by publication of peer-reviewed manuscripts and presentations as conference contributions (abstracts, poster or oral presentations).Moreover, results will be discussed with clinical experts and medical associations before being proposed for implementation into the quality management of EDs. TRIAL REGISTRATION NUMBER: German Clinical Trials Registry (DRKS00015203); Pre-results.

[Routine Data from Emergency Departments: Varying Documentation Standards, Billing Modalities and Data Custodians at an Identical Unit of Care]. / Routinedaten aus Notaufnahmen: Unterschiedliche Dokumentationsanforderungen, Abrechnungsmodalitäten und Datenhalter bei identischem Ort der Leistungserbringung.

BACKGROUND: Currently, there is a big need for data on emergency department (ED) utilization in Germany. One reason is the ongoing reorganisation of emergency care. Possible sources are routine data that are being collected based on legal regulations. Different payers and compensation systems have their own requirements for data collection. METHODOLOGICAL CHALLENGES: Due to the sectoral separation of health care services, there is no dataset or data holder to provide information on all ED treatments in Germany. From an administrative point of view, emergency care in Germany is considered ambulatory outpatient or inpatient care from the time point of admission to the ED. In contrast, clinical decision about inpatient admission can sometimes only be made towards the end of emergency care. EDs themselves cannot be identified in claims data; only the medical discipline (e. g. surgery) is classified. In the case of outpatient treatment, reimbursed by the Association of Statutory Health Insurance Physicians, at least one coded diagnosis (ICD) has to be recorded, accompanied by an additional code for the likelihood of this diagnosis. In case of multiple ICDs, a primary diagnosis cannot be specified. In the case of in-hospital treatment, an admission diagnosis must be recorded. After completion of hospital treatment, the main diagnosis and possibly secondary diagnoses are transferred to the respective health insurance fund. The statutory occupational accident insurance has its own requirements. SOLUTIONS: Depending on the research question and study design, different approaches are required. If data are queried directly in emergency departments or hospitals, additional information on the designated data holder and billing mode is crucial. When using health insurance data from inpatient care, the identification of emergency departments can be estimated on the basis of the reason for hospital admission and defined "unique" emergency ICDs. The case-related hospital statistics has its own limitations, but includes inpatients of all payers. DISCUSSION: Differing requirements for the administrative documentation cause a high workload in emergency departments. A standardised data collection system for all payers for inpatient and outpatient emergency care is recommended. This would contribute to the creation of valid and comparable datasets. The introduction of a particular identifier for EDs in claims data would enhance health services research.

Secondary data in diabetes surveillance - co-operation projects and definition of references on the documented prevalence of diabetes.

In addition to the Robert Koch Institute's health surveys, analyses of secondary data are essential to successfully developing a regular and comprehensive description of the progression of diabetes as part of the Robert Koch Institute's diabetes surveillance. Mainly, this is due to the large sample size and the fact that secondary data are routinely collected, which allows for highly stratified analyses in short time intervals. The fragmented availability of data means that various sources of secondary data are required in order to provide data for the indicators in the four fields of action for diabetes surveillance. Thus, a milestone in the project was to check the suitability of different data sources for their usability and to carry out analyses. Against this backdrop, co-operation projects were specifically funded in the context of diabetes surveillance. This article presents the results that were achieved in co-operation projects between 2016 and 2018 that focused on a range of topics: from evaluating the usability of secondary data to statistically modelling the development of epidemiological indices. Moreover, based on the data of the around 70 million people covered by statutory health insurance, an initial estimate was calculated for the documented prevalence of type 2 diabetes for the years 2010 and 2011. To comparably integrate these prevalences over the years in diabetes surveillance, a reference definition was established with external expertise.

[Hospitalizations and amputations for diabetes mellitus-trends and small-area variation in Germany]. / Krankenhausfälle und Amputationen bei Diabetes mellitus ­ Zeitreihen und Unterschiede auf kleinräumiger Ebene in Deutschland.

BACKGROUND: Hospitalizations and lower limb amputations related to diabetes mellitus (DM) are considered to be potentially avoidable. Appropriate outpatient care of diabetes prevents complications. Rates on potentially avoidable hospitalizations for diabetes are core indicators of the German diabetes surveillance program. International comparisons showed high hospitalization rates in Germany for both indicators. OBJECTIVES: The objective of this analysis is to describe time trends on hospitalizations and inpatient lower limb amputations (major amputations) for DM. Furthermore, we analyze small area variations. MATERIALS AND METHODS: Based on the German diagnosis related groups (DRGs) dataset we calculated age-standardized rates covering 2005-2015. Calculations rely on the Organisation for Economic Co-operation and Development (OECD) indicator definitions. Time trends are obtained by linear regression modelling. We also stratified into age groups and analyzed 2015 small-area variations using age-adjusted rates. RESULTS: Crude hospitalization rates were 310 admissions per 100,000 inhabitants in men (amputation rate: 15.6) and 216 admissions per 100,000 inhabitants in women (amputation rate: 7.1) in 2015. Age-adjusted hospitalizations and amputations rates in women decreased over time (10.3 and 1.2 cases per 100,000 inhabitants and year, respectively). In men, the amputation rate decreased significantly (1.5 cases per 100,000 inhabitants and year). We found higher rates for men than for women in almost all age categories. In eastern Germany and parts of Bavaria and North Rhine-Westphalia rates are particularly high. CONCLUSIONS: A decrease in hospitalization rates may indicate improvements in ambulatory diabetes care over time. Future studies should consider age-specific differences and small-area variations.

[Quality of care analyses using ICD 11 : Detailed capture of treatment events]. / Analyse der Behandlungsqualität mittels ICD­11 : Differenzierte Kodierung von Behandlungsfehlern.

The identification of treatment errors, the so-called "undesirable" or "critical incidents", is crucial for improving and developing the quality of care. The new International Statistical Classification of Diseases and Related Health Problems-ICD-11-supports a structured data collection in the context of the quality of care and patient safety. Documentation conceptually relies on the multiple coding of the three dimensions of a critical incident: harm, cause, and mode. In this way, it is possible to capture the event in great detail, including the reasons for it and the effects it has. An evaluation of this concept in a field trial using 45 clinical case studies showed good concordance in coding across the documented participants.As the ICD-11 permits the detailed capture of near misses and their context, it could be used for structured documentation in incident reporting systems (databanks for the anonymous reporting of treatment errors). In this way, the error reports can be gathered in a more systematic way, so that they can be used for better quality improvement.In quality assessment, it is important to consider the time of diagnosis. Thus, the feature present on admission (POA) is a diagnosis qualifier that is of substantial importance for quality assessment and is widely used internationally. Up to now, it has not been permanently available in Germany. ICD-11 includes the relevant code.

Impact of Disease Prevalence Adjustment on Hospitalization Rates for Chronic Ambulatory Care-Sensitive Conditions in Germany.

OBJECTIVES: To explore effects of disease prevalence adjustment on ambulatory care-sensitive hospitalization (ACSH) rates used for quality comparisons. DATA SOURCES/STUDY SETTING: County-level hospital administrative data on adults discharged from German hospitals in 2011 and prevalence estimates based on administrative ambulatory diagnosis data were used. STUDY DESIGN: A retrospective cross-sectional study using in- and outpatient secondary data was performed. DATA COLLECTION: Hospitalization data for hypertension, diabetes, heart failure, chronic obstructive pulmonary disease, and asthma were obtained from the German Diagnosis Related Groups (DRG) database. Prevalence estimates were obtained from the German Central Research Institute of Ambulatory Health Care. PRINCIPAL FINDINGS: Crude hospitalization rates varied substantially across counties (coefficients of variation [CV] 28-37 percent across conditions); this variation was reduced by prevalence adjustment (CV 21-28 percent). Prevalence explained 40-50 percent of the observed variation (r = 0.65-0.70) in ACSH rates for all conditions except asthma (r = 0.07). Between 30 percent and 38 percent of areas moved into or outside condition-specific control limits with prevalence adjustment. CONCLUSIONS: Unadjusted ACSH rates should be used with caution for high-stakes public reporting as differences in prevalence may have a marked impact. Prevalence adjustment should be considered in models analyzing ACSH.

A World Health Organization field trial assessing a proposed ICD-11 framework for classifying patient safety events.

OBJECTIVE: To assess the utility of the proposed World Health Organization (WHO)'s International Classification of Disease (ICD) framework for classifying patient safety events. SETTING: Independent classification of 45 clinical vignettes using a web-based platform. STUDY PARTICIPANTS: The WHO's multi-disciplinary Quality and Safety Topic Advisory Group. MAIN OUTCOME MEASURE(S): The framework consists of three concepts: harm, cause and mode. We defined a concept as 'classifiable' if more than half of the raters could assign an ICD-11 code for the case. We evaluated reasons why cases were nonclassifiable using a qualitative approach. RESULTS: Harm was classifiable in 31 of 45 cases (69%). Of these, only 20 could be classified according to cause and mode. Classifiable cases were those in which a clear cause and effect relationship existed (e.g. medication administration error). Nonclassifiable cases were those without clear causal attribution (e.g. pressure ulcer). Of the 14 cases in which harm was not evident (31%), only 5 could be classified according to cause and mode and represented potential adverse events. Overall, nine cases (20%) were nonclassifiable using the three-part patient safety framework and contained significant ambiguity in the relationship between healthcare outcome and putative cause. CONCLUSIONS: The proposed framework enabled classification of the majority of patient safety events. Cases in which potentially harmful events did not cause harm were not classifiable; additional code categories within the ICD-11 are one proposal to address this concern. Cases with ambiguity in cause and effect relationship between healthcare processes and outcomes remain difficult to classify.

[Memorandum III, Part 3: Quality of Care and Patient Safety Research Methods]. / DNVF-Memorandum III "Methoden für die Versorgungsforschung", Teil 3: Methoden der Qualitäts- und Patientensicherheitsforschung.

The German Network for Health Services Research [Deutsches Netzwerk Versorgungsforschung e.V. (DNVF)] fosters the methodological quality of health services research studies by memoranda and other initiatives. Quality of care and patient safety research (QCPSR) form core areas of health services research. The present memorandum explicates principal QCPSR questions and methods. Based on the issues' particular relevance for health policy, the memorandum exemplifies methods for developing and testing indicators, risk adjustment techniques, methods for collecting patient safety data, tools to analyse patient safety incidents and methods for evaluating often complex and multicomponent QCPS interventions. Furthermore, we point out urgent research topics.

How many diagnosis fields are needed to capture safety events in administrative data? Findings and recommendations from the WHO ICD-11 Topic Advisory Group on Quality and Safety.

OBJECTIVE: As part of the WHO ICD-11 development initiative, the Topic Advisory Group on Quality and Safety explores meta-features of morbidity data sets, such as the optimal number of secondary diagnosis fields. DESIGN: The Health Care Quality Indicators Project of the Organization for Economic Co-Operation and Development collected Patient Safety Indicator (PSI) information from administrative hospital data of 19-20 countries in 2009 and 2011. We investigated whether three countries that expanded their data systems to include more secondary diagnosis fields showed increased PSI rates compared with six countries that did not. Furthermore, administrative hospital data from six of these countries and two American states, California (2011) and Florida (2010), were analysed for distributions of coded patient safety events across diagnosis fields. RESULTS: Among the participating countries, increasing the number of diagnosis fields was not associated with any overall increase in PSI rates. However, high proportions of PSI-related diagnoses appeared beyond the sixth secondary diagnosis field. The distribution of three PSI-related ICD codes was similar in California and Florida: 89-90% of central venous catheter infections and 97-99% of retained foreign bodies and accidental punctures or lacerations were captured within 15 secondary diagnosis fields. CONCLUSIONS: Six to nine secondary diagnosis fields are inadequate for comparing complication rates using hospital administrative data; at least 15 (and perhaps more with ICD-11) are recommended to fully characterize clinical outcomes. Increasing the number of fields should improve the international and intra-national comparability of data for epidemiologic and health services research, utilization analyses and quality of care assessment.

ICD-11 for quality and safety: overview of the WHO Quality and Safety Topic Advisory Group.

This paper outlines the approach that the WHO's Family of International Classifications (WHO-FIC) network is undertaking to create ICD-11. We also outline the more focused work of the Quality and Safety Topic Advisory Group, whose activities include the following: (i) cataloguing existing ICD-9 and ICD-10 quality and safety indicators; (ii) reviewing ICD morbidity coding rules for main condition, diagnosis timing, numbers of diagnosis fields and diagnosis clustering; (iii) substantial restructuring of the health-care related injury concepts coded in the ICD-10 chapters 19/20, (iv) mapping of ICD-11 quality and safety concepts to the information model of the WHO's International Classification for Patient Safety and the AHRQ Common Formats; (v) the review of vertical chapter content in all chapters of the ICD-11 beta version and (vi) downstream field testing of ICD-11 prior to its official 2015 release. The transition from ICD-10 to ICD-11 promises to produce an enhanced classification that will have better potential to capture important concepts relevant to measuring health system safety and quality-an important use case for the classification.

Case definitions for acute myocardial infarction in administrative databases and their impact on in-hospital mortality rates.

OBJECTIVE: To identify validated ICD-9-CM/ICD-10 coded case definitions for acute myocardial infarction (AMI). DATA SOURCES: Ovid Medline (1950-2010) was searched to identify studies that validated acute myocardial infarction (AMI) case definitions. Hospital discharge abstract data and chart data were linked to validate identified AMI definitions. STUDY DESIGN: Systematic literature review, chart review, and administrative data analysis. DATA COLLECTION/EXTRACTION METHODS: Data on sensitivity/specificity/positive and negative predictive values (PPV and NPV) were extracted from previous studies to identify validated case definitions for AMI. These case definitions were validated in administrative data through chart review and applied to hospital discharge data to assess in-hospital mortality. PRINCIPAL FINDINGS: Of the eight ICD-9-CM definitions validated in the literature, use of ICD-9-CM code 410 to define AMI had the highest sensitivity (94 percent) and specificity (99 percent). In our data, ICD-9-CM/ICD-10 codes 410/I21-I22 in all available coding fields had high sensitivity (83.3 percent/82.8 percent) and PPV (82.8 percent/82.2 percent). The in-hospital mortality among AMI patients identified using this case definition was 7.6 percent in ICD-9-CM data and 6.6 percent in ICD-10 data. CONCLUSIONS: We recommend that ICD-9-CM 410 or ICD-10 I21-I22 in the primary diagnosis coding field should be used to define AMI. The use of a consistent validated case definition would improve comparability across studies.

International comparability of patient safety indicators in 15 OECD member countries: a methodological approach of adjustment by secondary diagnoses.

OBJECTIVE: To improve the international comparability of patient safety indicators based on administrative hospital data, adjustment of country-specific rates by a proxy measure of diagnostic coding intensity was tested. DATA SOURCES: Secondary data (numerator and denominator counts of patient safety indicators) based on adults discharged from acute care hospitals between 2006 and 2008 was used. STUDY DESIGN: A retrospective cross-sectional study using hospital administrative data was performed. DATA COLLECTION: Belgium, Canada, Denmark, Germany, Italy, Ireland, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States provided data according to detailed instructions. PRINCIPAL FINDINGS: Age- and sex-standardized rates varied across countries. An ordinary least squares regression model was estimated for each Patient Safety Indicator (PSI) using the mean number of secondary diagnoses among denominator cases as the predictor (R(2) =23 percent to 56 percent). Estimated country-specific residuals were linearly transformed into adjusted PSI rates. Variation among age-sex standardized PSI rates decreased substantially after this adjustment. CONCLUSIONS: International comparisons of health system performance based on unadjusted patient safety indicators are problematic due to suspected coding or ascertainment bias. The model could be an interim approach to provide comparable information on hospital quality, with a long-term goal of improving international consistency in diagnostic reporting in administrative data.

The development, evolution, and modifications of ICD-10: challenges to the international comparability of morbidity data.

BACKGROUND: The United States is about to make a major nationwide transition from ICD-9-CM coding of hospital discharges to ICD-10-CM, a country-specific modification of the World Health Organization's ICD-10. As this transition occurs, the WHO is already in the midst of developing ICD-11. Given this context, we undertook this review to discuss: (1) the history of the International Classification of Diseases (a core information "building block" for health systems everywhere) from its introduction to the current era of ICD-11 development; (2) differences across country-specific ICD-10 clinical modifications and the challenges that these differences pose to the international comparability of morbidity data; (3) potential strategic approaches to achieving better international ICD-11 comparability. LITERATURE REVIEW AND DISCUSSION: A literature review and stakeholder consultation was carried out. The various ICD-10 clinical modifications (ICD-10-AM [Australia], ICD-10-CA [Canada], ICD-10-GM [Germany], ICD-10-TM [Thailand], ICD-10-CM [United States]) were compared. These ICD-10 modifications differ in their number of codes, chapters, and subcategories. Specific conditions are present in some but not all of the modifications. ICD-11, with a similar structure to ICD-10, will function in an electronic health records environment and also provide disease descriptive characteristics (eg, causal properties, functional impact, and treatment). CONCLUSION: The threat to the comparability of international clinical morbidity is growing with the development of many country-specific ICD-10 versions. One solution to this threat is to develop a meta-database including all country-specific modifications to ensure more efficient use of people and resources, decrease omissions and errors but most importantly provide a platform for future ICD updates.

Detection of postoperative respiratory failure: how predictive is the Agency for Healthcare Research and Quality's Patient Safety Indicator?

BACKGROUND: Patient Safety Indicator (PSI) 11, or postoperative respiratory failure, was developed by the US Agency for Healthcare Research and Quality to detect incident cases of respiratory failure after elective operations through use of ICD-9-CM diagnosis and procedure codes. We sought to determine the positive predictive value (PPV) of this indicator. STUDY DESIGN: We conducted a retrospective cross-sectional study, sampling consecutive cases that met PSI 11 criteria from 18 geographically diverse academic medical centers on or before June 30, 2007. Trained abstractors from each center reviewed medical records using a standard instrument. We assessed the PPV of the indicator (with 95% CI adjusted for clustering within centers) and conducted descriptive analyses of the cases. RESULTS: Of 609 cases that met PSI 11 criteria, 551 (90.5%; 95% CI, 86.5-94.4%) satisfied the technical criteria of the indicator and 507 (83.2%; 95% CI, 77.2-89.3%) represented true cases of postoperative respiratory failure from a clinical standpoint. The most frequent reasons for being falsely positive were nonelective hospitalization, prolonged intubation for airway protection, and insufficient evidence to support a diagnosis of acute respiratory failure. Fifty percent of true-positive cases involved substantial baseline comorbidities, and 23% resulted in death. CONCLUSIONS: Although PSI 11 predicts true postoperative respiratory failure with relatively high frequency, the indicator does not limit detection to preventable cases. The PPV of PSI 11 might be increased by excluding cases with a principal diagnosis suggestive of a nonelective hospitalization and those with head or neck procedures. Removing the diagnosis code criterion from the indicator might also increase PPV, but would decrease the number of true positive cases detected by 20%.

Application of patient safety indicators internationally: a pilot study among seven countries.

OBJECTIVE: To explore the potential for international comparison of patient safety as part of the Health Care Quality Indicators project of the Organization for Economic Co-operation and Development (OECD) by evaluating patient safety indicators originally published by the US Agency for Healthcare Research and Quality (AHRQ). DESIGN: A retrospective cross-sectional study. SETTING: Acute care hospitals in the USA, UK, Sweden, Spain, Germany, Canada and Australia in 2004 and 2005/2006. DATA SOURCES: Routine hospitalization-related administrative data from seven countries were analyzed. Using algorithms adapted to the diagnosis and procedure coding systems in place in each country, authorities in each of the participating countries reported summaries of the distribution of hospital-level and overall (national) rates for each AHRQ Patient Safety Indicator to the OECD project secretariat. RESULTS: Each country's vector of national indicator rates and the vector of American patient safety indicators rates published by AHRQ (and re-estimated as part of this study) were highly correlated (0.821-0.966). However, there was substantial systematic variation in rates across countries. CONCLUSIONS: This pilot study reveals that AHRQ Patient Safety Indicators can be applied to international hospital data. However, the analyses suggest that certain indicators (e.g. 'birth trauma', 'complications of anesthesia') may be too unreliable for international comparisons. Data quality varies across countries; undercoding may be a systematic problem in some countries. Efforts at international harmonization of hospital discharge data sets as well as improved accuracy of documentation should facilitate future comparative analyses of routine databases.

[Are quality indicators derived from routine data suitable for evaluating hospital performance? First results using the AHRQ patient safety indicators in Germany]. / Eignen sich Qualitäitsindikatoren aus Routine- daten zur Qualitätsmessung im Krankenhaus? Erste Ergebnisse mit den amerikanischen Indi- katoren zur Patientensicherheit in Deutschland.

Patients and health insurances are increasingly interested in the quality of care provided by hospitals. Quality indicators are often used to evaluate the quality of inpatient treatment. Most of these evaluations require the collection of additional data. The patient safety indicators (PSI) introduced by the Agency for Healthcare Research and Quality (AHRQ) are precisely validated and exclusively depend on routine data. The original PSI definitions were transferable to the classifications of diagnosis, procedures and DRG used in Germany, and applied to routine data of 2.3 million cases from more than 200 hospitals. The comparison of the results to the US references reveals high concordance between the rates and demonstrates that PSI can be applied to detect critical incidents of patient care. For PSI-based hospital benchmarking further development of appropriate methods of risk adjustment is necessary.