Difference between revisions of "Journal:Process variation detection using missing data in a multihospital community practice anatomic pathology laboratory"
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|Full article title||Process variation detection using missing data in a multihospital community practice anatomic pathology laboratory|
|Journal||Journal of Pathology Informatics|
|Author(s)||Galliano, Gretchen E.|
|Author affiliation(s)||Ochsner Health System|
|Primary contact||Email: login at original journal required|
|Volume and issue||10|
|Distribution license||Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International|
|This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.|
Objectives: Barcode-driven workflows reduce patient identification errors. Missing process timestamp data frequently confound our health system's pending lists and appear as actions left undone. Anecdotally, it was noted that missing data could be found when there is procedure noncompliance. This project was developed to determine if missing timestamp data in the histology barcode-driven workflow correlated with other process variations, procedure noncompliance, or is an indicator of workflows needing focus for improvement projects.
Materials and methods: Data extracts of timestamp data from January 1, 2018 to December 15, 2018 for the major histology process steps were analyzed for missing data. Case-level analysis to determine the presence or absence of expected barcoding events was performed on 1031 surgical pathology cases to determine the cause of the missing data and determine if additional data variations or procedure noncompliance events were present. The data variations were classified according to a scheme defined in the study.
Results: Of 70,085 cases, there were 7218 (10.3%) with missing process timestamp data. Missing histology process step data was associated with other additional data variations in case-level deep dives (P < 0.0001). Of the cases missing timestamp data in the initial review, 18.4% of the cases had no identifiable cause for the missing data (all expected events took place in the case-level deep dive).
Conclusions: Operationally, valuable information can be obtained by reviewing the types and causes of missing data in the anatomic pathology laboratory information system, but only in conjunction with user input and feedback.
Keywords: anatomic pathology laboratory information system, anatomic pathology, laboratory information systems, laboratory management
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added.