Background: The significant risk of adverse events following medical procedures supports a clinical epidemiological approach based on the analyses of collections of electronic medical records. Data analytical tools might help clinical epidemiologists develop more appropriate case-crossover designs for monitoring patient safety.Objective: To develop and assess the methodological quality of an interactive tool for use by clinical epidemiologists to systematically design case-crossover analyses of large electronic medical records databases.Material and Methods: We developed IT-CARES, an analytical tool implementing case-crossover design, to explore the association between exposures and outcomes. The exposures and outcomes are defined by clinical epidemiologists via lists of codes entered via a user interface screen. We tested IT-CARES on data from the French national inpatient stay database, which documents diagnoses and medical procedures for 170 million inpatient stays between 2007 and 2013. We compared the results of our analysis with reference data from the literature on thromboembolic risk after delivery and bleeding risk after total hip replacement.Results: IT-CARES provides a user interface with 3 columns: (i) the outcome criteria in the left-hand column, (ii) the exposure criteria in the right-hand column, and (iii) the estimated risk (odds ratios, presented in both graphical and tabular formats) in the middle column. The estimated odds ratios were consistent with the reference literature data.Discussion: IT-CARES may enhance patient safety by facilitating clinical epidemiological studies of adverse events following medical procedures. The tool’s usability must be evaluated and improved in further research.
Introduction: Healthcare information systems can generate and/or record huge volumes of data, some of which may be reused for research, clinical trials, or teaching. However, these databases can be affected by data quality problems; hence, an important step in the data reuse process consists in detecting and rectifying these issues. With a view to facilitating the assessment of data quality, we developed a taxonomy of data quality problems in operational databases.
Material: We searched the literature for publications that mentioned "data quality problems", "data quality taxonomy", "data quality assessment", or "dirty data". The publications were then reviewed, compared, summarized, and structured using a bottom-up approach, in order to provide an operational taxonomy of data quality problems. The latter were illustrated with fictional examples (though based on reality) from clinical databases.
Results: Twelve publications were selected, and 286 instances of data quality problems were identified and were classified according to six distinct levels of granularity. We used the classification defined by Oliveira et al to structure our taxonomy. The extracted items were grouped into 53 data quality problems.
Discussion: This taxonomy facilitated the systematic assessment of data quality in databases by presenting the data’s quality according to their granularity. The definition of this taxonomy is the first step in the data cleaning process. The subsequent steps include the definition of associated quality assessment methods and data cleaning methods.
Conclusion: Our new taxonomy enabled the classification and illustration of 53 data quality problems found in hospital databases.
Drug-induced hyperkalemia is a frequent and severe complication in the hospital setting. Other risk factors may also induce hyperkalemia but the combination of drugs and precipitating factors has not been extensively studied. The aim was to identify drug-induced hyperkalemia events in hospitalized older patients and to describe their combinations with precipitating factors. Two experts independently analyzed retrospective data of patients aged 75 years or more. Experts identified 471 hyperkalemia events and concluded that 379 (80.5%) were induced by drugs. The cause was multifactorial (i.e., at least one drug with a precipitating factor) in 300 (79.2%) of the 379 drug-induced hyperkalemia. Most of the drug-induced hyperkalemia events were avoidable (79.9%)-mainly because of the multifactorial cause (e.g., dosage adaptation during acute kidney injury). Drug-induced hyperkalemia events are frequently combined with precipitating factors in hospitalized older patients and their prevention should focus on these combinations.
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