Data Analytics of Codified Patient Data: Identifying Factors Influencing Coding Trends, Productivity, and Quality

Cost containment and quality of care have always been major challenges to the health care delivery system in the United States. Health care organizations utilize coded clinical data for health care monitoring, and reporting that includes a wide range of diseases and clinical conditions along with adverse events that could occur to patients during hospitalization. Furthermore, coded clinical data is utilized for patient safety and quality of care assessment in addition to research, education, resource allocation, and health service planning. Thus, it is critical to maintain high quality standards of clinical data and promote funding of health care research that addresses clinical data quality due to its direct impact on individual health outcomes as well as population health. This dissertation research is aimed at identifying current coding trends and other factors that could influence coding quality and productivity through two major emphases: (1) quality of coded clinical data; and (2) productivity of clinical coding. It has adopted a mix-method approach utilizing varied quantitative and qualitative data analysis techniques. Data analysis includes a wide range of univariate, bivariate, and multivariate analyses. Results of this study have shown that length of stay (LOS), case mix index (CMI) and DRG relative weight were not found to be significant predictors of coding quality. Based on the qualitative analysis, history and physical (H&P), discharge summary, and progress notes were identified as the three most common resources cited by Ciox auditors for coding changes. Also, results have shown that coding productivity in ICD-10 is improving over time. Length of stay, case mix index, DRG weight, and bed size were found to have a significant impact on coding productivity. Data related to coder’s demographics could not be secured for this analysis. However, factors related to coders such as education, credentials, and years of experience are believed to have a significant impact on coding quality as well as productivity. Linking coder’s demographics to coding quality and productivity data represents a promising area for future research