Assessment of the reproducibility of clinical coding in routinely collected hospital activity data: a study in two hospitals (original) (raw)
Related papers
Clinical code usage in UK general practice: a cohort study exploring 18 conditions over 14 years
BMJ Open, 2022
Objective To assess the diagnostic Read code usage for 18 conditions by examining their frequency and diversity in UK primary care between 2000 and 2013. Design Population-based cohort study Setting 684 UK general practices contributing data to the Clinical Practice Research Datalink (CPRD) GOLD. Participants Patients with clinical codes for at least one of asthma, chronic obstructive pulmonary disease, diabetes, hypertension (HT), coronary heart disease, atrial fibrillation (AF), heart failure, stroke, hypothyroidism, chronic kidney disease, learning disability (LD), depression, dementia, epilepsy, severe mental illness (SMI), osteoarthritis, osteoporosis and cancer. Primary and secondary outcome measures For the frequency ranking of clinical codes, canonical correlation analysis was applied to correlations of clinical code usage of 1, 3 and 5 years. Three measures of diversity (Shannon entropy index of diversity, richness and evenness) were used to quantify changes in incident and total clinical codes. Results Overall, all examined conditions, except LD, showed positive monotonic correlation. HT, hypothyroidism, osteoarthritis and SMI codes' usage had high 5-year correlation. The codes' usage diversity remained stable overall throughout the study period. Cancer, diabetes and SMI had the highest richness (code lists need time to define) unlike AF, hypothyroidism and LD. SMI (high richness) and hypothyroidism (low richness) can last for 5 years, whereas cancer and diabetes (high richness) and LD (low richness) only last for 2 years. Conclusions This is an under-reported research area and the findings suggest the codes' usage diversity for most conditions remained overall stable throughout the study period. Generated mental health code lists can last for a long time unlike cardiometabolic conditions and cancer. Adopting more consistent and less diverse coding would help improve data quality in primary care. Future research is needed following the transfer to the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) coding.
Reliability of diagnoses coding with ICD-10
International Journal of Medical Informatics, 2008
Objective: Reliability of diagnoses coding is essential for the use of routine data in a national health care system. The present investigation compares reliability of diagnoses coding with ICD-10 between three groups of coding subjects.
Measuring Diagnoses: ICD Code Accuracy
Health Services Research, 2005
Objective. To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process.Data Sources/Study Setting. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications.Study Design/Methods. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy.Principle Findings. Main error sources along the “patient trajectory” include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the “paper trail” include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding.Conclusions. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.
Health Services Research, 2008
Objective. The goal of this study was to assess the validity of the International Classification of Disease, 10th Version (ICD-10) administrative hospital discharge data and to determine whether there were improvements in the validity of coding for clinical conditions compared with ICD-9 Clinical Modification (ICD-9-CM) data. Methods. We reviewed 4,008 randomly selected charts for patients admitted from January 1 to June 30, 2003 at four teaching hospitals in Alberta, Canada to determine the presence or absence of 32 clinical conditions and to assess the agreement between ICD-10 data and chart data. We then recoded the same charts using ICD-9-CM and determined the agreement between the ICD-9-CM data and chart data for recording those same conditions. The accuracy of ICD-10 data relative to chart data was compared with the accuracy of ICD-9-CM data relative to chart data. Results. Sensitivity values ranged from 9.3 to 83.1 percent for ICD-9-CM and from 12.7 to 80.8 percent for ICD-10 data. Positive predictive values ranged from 23.1 to 100 percent for ICD-9-CM and from 32.0 to 100 percent for ICD-10 data. Specificity and negative predictive values were consistently high for both ICD-9-CM and ICD-10 databases. Of the 32 conditions assessed, ICD-10 data had significantly higher sensitivity for one condition and lower sensitivity for seven conditions relative to ICD-9-CM data. The two databases had similar sensitivity values for the remaining 24 conditions. Conclusions. The validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions was generally similar though validity differed between coding versions for some conditions. The implementation of ICD-10 coding has not significantly improved the quality of administrative data relative to ICD-9-CM. Future assessments like this one are needed because the validity of ICD-10 data may get better as coders gain experience with the new coding system.
Background: Co-morbidity information derived from administrative data needs to be validated to allow its regular use. We assessed evolution in the accuracy of coding for Charlson and Elixhauser co-morbidities at three time points over a 5-year period, following the introduction of the International Classification of Diseases, 10th Revision (ICD-10), coding of hospital discharges. Methods: Cross-sectional time trend evaluation study of coding accuracy using hospital chart data of 3'499 randomly selected patients who were discharged in 1999, 2001 and 2003, from two teaching and one non-teaching hospital in Switzerland. We measured sensitivity, positive predictive and Kappa values for agreement between administrative data coded with ICD-10 and chart data as the 'reference standard' for recording 36 co-morbidities.
Measurement of adverse events using 'incidence flagged' diagnosis codes
Journal of Health Services Research & Policy, 2006
n ¼ 1, 645,992) to estimate the rates of adverse events using International Classi¢cation of Diseases 10th Revision Australian Modi¢cation codes alone and in combination with an 'incidence' data £ag indicating complicating diagnoses which arise after hospitalization; rates of incidence and pre-existing adverse events, and rates for same-day and multi-day admissions.
Variation in clinical coding lists in UK general practice: a barrier to consistent data entry?
Informatics in primary care, 2007
Routinely collected general practice computer data are used for quality improvement; poor data quality including inconsistent coding can reduce their usefulness. To document the diversity of data entry systems currently in use in UK general practice and highlight possible implications for data quality. General practice volunteers provided screen shots of the clinical coding screen they would use to code a diagnosis or problem title in the clinical consultation. The six clinical conditions examined were: depression, cystitis, type 2 diabetes mellitus, sore throat, tired all the time, and myocardial infarction. We looked at the picking lists generated for these problem titles in EMIS, IPS, GPASS and iSOFT general practice clinical computer systems, using the Triset browser as a gold standard for comparison. A mean of 19.3 codes is offered in the picking list after entering a diagnosis or problem title. EMIS produced the longest picking lists and GPASS the shortest, with a mean number ...