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

Alakrawi, Zahraa (2017) Data Analytics of Codified Patient Data: Identifying Factors Influencing Coding Trends, Productivity, and Quality. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Alakrawi, Zahraazaa9@pitt.eduzaa9
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWatzlaf,
Committee MemberAbdelhak,
Committee MemberHughes,
Committee MemberAnania-Firouzan,
Committee MemberSheridan,
Date: 5 June 2017
Date Type: Publication
Defense Date: 31 March 2017
Approval Date: 5 June 2017
Submission Date: 21 March 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 221
Institution: University of Pittsburgh
Schools and Programs: School of Health and Rehabilitation Sciences > Health Information Management
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Coding productivity, coding quality, ICD-10
Date Deposited: 05 Jun 2017 17:02
Last Modified: 05 Jun 2017 17:02


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