Bone marrow testing by the hematopathology, flow cytometry and cytogenetics laboratories provides valuable information utilized in the diagnosis, prognosis and treatment of leukemias. Not much is known about unexpected informatics issues which arise during the analysis of bone marrow, which impact information about the patient's hematological status. This status needs to be clearly communicated to the clinician since it impacts clinical decision making and patient care. This research addresses whether bone marrow diagnostic discordance can be utilized as an indicator of issues in the bone marrow information process, providing the foundation for clinical decision support tool development.
The study first measures disagreement in the diagnoses reported by the three laboratories, on bone marrow specimens collected at the same time, to determine lexical diagnostic discordance. Semantic diagnostic discordance is determined utilizing the 2001 World Health Organization leukemia classifications. Statistical significance of diagnostic discordance is measured with Cohen's Kappa statistic.
The second research phase categorizes factors contributing to the discordances found in the first phase to further understand the etiology of the discordances. It is important to distinguish discordances due to expected testing process limitations from unexpected discordances due to other etiologies. It is also vital to denote which are clinically significant and likely to impact patient care. These factors are critical in designing an effective decision support tool which alerts the clinician appropriately.
Results of the first research phase show lexical and semantic discordance can be measured successfully from three laboratories reporting on bone marrows. Cohen's Kappa statistic also provides an automatic means of detection and measurement of semantic discordance. Categorization of discordances distinguishes which discordances are due to limitations in laboratory testing. Categorization also indicates where in the testing process interventions such as a decision support tool are optimally placed in alerting pathologists of problems in the information process needing further assessment.
University of Minnesota Ph.D. dissertation. May 2010. Major: Health Informatics. Advisor:Dr. Donald P. Connelly. 1 computer file (PDF); vii, 121, appendices A-C.
Pitkus, Andrea Renee’.
Bone marrow diagnostic discordance determination: a foundation for clinical decision support..
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