Synoptic operative reports for spinal cord injury patients as a tool for data quality

2016 ◽  
Vol 22 (4) ◽  
pp. 984-991 ◽  
Author(s):  
Grace I Paterson ◽  
Sean Christie ◽  
Wilfred Bonney ◽  
Ginette Thibault-Halman

The advent of synoptic operative reports has revolutionized how clinical data are captured at the time of care. In this article, an electronic synoptic operative report for spinal cord injury was implemented using interoperable standards, HL7 and Systematized Nomenclature of Medicine–Clinical Terms. Subjects ( N = 10) recruited for a pilot study completed recruitment and feedback questionnaires, and produced both an electronic synoptic operative report for spinal cord injury report and a dictated narrative operative report for an actual patient case. Results indicated heterogeneity by subjects in access and use of electronic sources of patient data. Feedback questionnaire results confirmed that subjects were comfortable using both methods for data entry of operative reports, and that some were unable to find the diagnosis terms they needed in electronic synoptic operative report for spinal cord injury. Data quality improved. Electronic synoptic operative report for spinal cord injury reports were more complete (95.26%) than dictated (80%) for all subjects. An accuracy assessment, which considered usability for secondary data use, was conducted and the electronic synoptic operative report for spinal cord injury was demonstrated to improve accuracy.

2018 ◽  
Vol 24 (2) ◽  
pp. 110-120
Author(s):  
Yuying Chen ◽  
Hui-Yi Lin ◽  
Tung-Sung Tseng ◽  
Huacong Wen ◽  
Michael J. DeVivo

ACI Open ◽  
2021 ◽  
Vol 05 (02) ◽  
pp. e94-e103
Author(s):  
Nandini Anantharama ◽  
Wray Buntine ◽  
Andrew Nunn

Abstract Background Secondary use of electronic health record's (EHR) data requires evaluation of data quality (DQ) for fitness of use. While multiple frameworks exist for quantifying DQ, there are no guidelines for the evaluation of DQ failures identified through such frameworks. Objectives This study proposes a systematic approach to evaluate DQ failures through the understanding of data provenance to support exploratory modeling in machine learning. Methods Our study is based on the EHR of spinal cord injury inpatients in a state spinal care center in Australia, admitted between 2011 and 2018 (inclusive), and aged over 17 years. DQ was measured in our prerequisite step of applying a DQ framework on the EHR data through rules that quantified DQ dimensions. DQ was measured as the percentage of values per field that meet the criteria or Krippendorff's α for agreement between variables. These failures were then assessed using semistructured interviews with purposively sampled domain experts. Results The DQ of the fields in our dataset was measured to be from 0% adherent up to 100%. Understanding the data provenance of fields with DQ failures enabled us to ascertain if each DQ failure was fatal, recoverable, or not relevant to the field's inclusion in our study. We also identify the themes of data provenance from a DQ perspective as systems, processes, and actors. Conclusion A systematic approach to understanding data provenance through the context of data generation helps in the reconciliation or repair of DQ failures and is a necessary step in the preparation of data for secondary use.


Sign in / Sign up

Export Citation Format

Share Document