Search

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. [ABSTRACT FROM AUTHOR]

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]

In Portugal, trained physicians undertake the clinical coding process, which serves as the basis for hospital reimbursement systems. In 2017, the classification version used for coding of diagnoses and procedures for hospital morbidity changed from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS).To assess the perceptions of medical coders on the transition of the clinical coding process from ICD-9-CM to ICD-10-CM/PCS in terms of its impact on data quality, as well as the major differences, advantages, and problems they faced.We conducted an observational study using a web-based survey submitted to medical coders in Portugal. Survey questions were based on a literature review and from previous focus group studies.A total of 103 responses were obtained from medical coders with experience in the two versions of the classification system (i.e. ICD-9-CM and ICD-10-CM/PCS). Of these, 82 (79.6%) medical coders preferred the latest version and 76 (73.8%) considered that ICD-10-CM/PCS guaranteed higher quality of the coded data. However, more than half of the respondents (N = 61; 59.2%) believed that more time for the coding process for each episode was needed.Quality of clinical coded data is one of the major priorities that must be ensured. According to the medical coders, the use of ICD-10-CM/PCS appeared to achieve higher quality coded data, but also increased the effort.According to medical coders, the change off classification systems should improve the quality of coded data. Nevertheless, the extra time invested in this process might also pose a problem in the future. [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]