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Research and applications: Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening
Authors:Kavishwar Balwant Wagholikar  Kathy L MacLaughlin  Thomas M Kastner  Petra M Casey  Michael Henry  Robert A Greenes  Hongfang Liu  Rajeev Chaudhry
Abstract:

Objectives

We previously developed and reported on a prototype clinical decision support system (CDSS) for cervical cancer screening. However, the system is complex as it is based on multiple guidelines and free-text processing. Therefore, the system is susceptible to failures. This report describes a formative evaluation of the system, which is a necessary step to ensure deployment readiness of the system.

Materials and methods

Care providers who are potential end-users of the CDSS were invited to provide their recommendations for a random set of patients that represented diverse decision scenarios. The recommendations of the care providers and those generated by the CDSS were compared. Mismatched recommendations were reviewed by two independent experts.

Results

A total of 25 users participated in this study and provided recommendations for 175 cases. The CDSS had an accuracy of 87% and 12 types of CDSS errors were identified, which were mainly due to deficiencies in the system''s guideline rules. When the deficiencies were rectified, the CDSS generated optimal recommendations for all failure cases, except one with incomplete documentation.

Discussion and conclusions

The crowd-sourcing approach for construction of the reference set, coupled with the expert review of mismatched recommendations, facilitated an effective evaluation and enhancement of the system, by identifying decision scenarios that were missed by the system''s developers. The described methodology will be useful for other researchers who seek rapidly to evaluate and enhance the deployment readiness of complex decision support systems.
Keywords:Uterine Cervical Neoplasms  Decision Support Systems  Clinical  Guideline Adherence  Validation Studies as Topic  Vaginal Smears  Crowdsourcing
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