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The quality of clinical data held in administrative databases is crucial for appropriate funding of health care services. As Diagnosis-Related Groups (DRGs) continue to play an important role in hospital payment mechanisms, proper coding of diagnoses and procedures is of most concern. This study used an administrative, nationwide Portuguese inpatient database to characterize and assess coding patterns in burn-related hospitalization data, with a special focus on identifying suspected miscoding practices that could be affecting APR-DRG (All-Patient Refined Diagnosis-Related Groups) classification. Using coded clinical data of 4,182 burn-related admissions occurred between 2011 and 2015, we compared APR-DRG and Severity of Illness (SOI) frequencies between hospitals with a burn unit in Portugal. The frequencies of individual diagnosis and procedure codes among episodes grouped within the same APR-DRG were also compared. Hospitals with a burn unit in Portugal differed significantly in the frequencies of APR-DRGs 842 and 844. Proper coding of extensive third-degree burns might be related with the observed discrepant frequencies of APR-DRGs across the evaluated hospitals. Facilities also differed significantly concerning the proportions of SOI levels in certain APR-DRGs. Significant differences in reporting certain comorbidities and common hospital procedures, especially non-operating room procedures, might have influenced the observed discrepancies in SOI levels. Moreover, there seems to be a lack of standard in coding debridement procedures among the evaluated hospitals. Overall, we found some suspected coding patterns that could potentially be associated with miscoding practices impacting APR-DRG classification. Those findings could not only be relevant for planning future audit processes and improving medical coding practices, but also for discussing quality and desirable features of burn-related clinical data, keeping in mind their use for other purposes beyond DRG grouping, namely clinical and health care services research, as well as health care management. [ABSTRACT FROM AUTHOR]

Introduction: Administrative hospital databases represent an important tool for hospital financing in many national health systems and are also an important data source for clinical, epidemiological and health services research. Therefore, the data quality of such databases is of utmost importance. This paper aims to present a systematic review of root causes of data quality problems affecting administrative hospital data, creating a catalogue of potential issues for data quality analysts to explore.Methods: The MEDLINE and Scopus databases were searched using inclusion criteria based on two following concept blocks: (1) administrative hospital databases and (2) data quality. Studies' titles and abstracts were screened by two reviewers independently. Three researchers independently selected the screened studies based on their full texts and then extracted the potential root causes inferred from them. These were subsequently classified according to the Ishikawa model based on 6 categories: "Personnel", "Material", "Method", "Machine", "Mission" and "Management".Results: The result of our investigation and the contribution of this paper is a classification of the potential (105) root causes found through a systematic review of the 77 relevant studies we have identified and analyzed. The result was represented by an Ishikawa diagram. Most of the root causes (25.7%) were associated with the category "Personnel" - people's knowledge, preferences, education and culture, mostly related to clinical coders and health care providers activities. The quality of hospital documentation, within category "Material", and aspects related to financial incentives or disincentives, within category "Mission", were also frequently cited in the literature as relevant root causes for data quality issues.Conclusions: The resultant catalogue of root causes, systematized using the Ishikawa framework, provides a compilation of potential root causes of data quality issues to be considered prior to reusing these data and that can point to actions aimed at improving data quality. [ABSTRACT FROM AUTHOR]