Refining Information Technology Support for Genetics in Medicine

A. Specific Aims

Aim 1: To assess the usability of successive versions of our EHR genetic display screens and variant-based patient search functionality.

Formal usability studies will be conducted with each new release of the GeneInsight Clinic (GIC) application in order to maximize its effectiveness and efficiency, and user satisfaction. Results from these studies will be used along with functional and technical requirements in designing enhancements to each successive version of the software.

Hypothesis: The usability of GeneInsight Clinic and the application's effectiveness, efficiency, and user satisfaction will improve with each successive version.

Aim 2. To assess the decision-making process associated with issuing alerts relating to new knowledge on germline variants.

Changes to cardiomyopathy and hearing loss variant level information will be placed in a queue for evaluation. A board-certified clinical laboratory geneticist will determine which changes should be released as an "alert" resulting in an update to the GIC and a notification to the clinician. This decision-making process will be evaluated.

Hypothesis: Evaluation of decision-making regarding release of genetic variant update alerts will identify patient and physician characteristics, and levels of significance of genetic variant updates that influence alerting decisions.

Aim 3. To measure the impact on efficiency of new genetic knowledge being incorporated into clinical care as a result of improved genetic IT infrastructure support.

Currently, clinicians learn of germline genetic variant updates when they choose to call the genetic laboratories to check for any possible new information on genetic tests of interest. With the GIC alerting system, treating clinicians will proactively receive genetic variant updates relevant to their patients. For cancer genotyping tests, once an associated variant is determined to have clinical significance, treating oncologists are interested in identifying all their patients with this variant to evaluate whether the patient's care plan should be modified. With the GIC patient search functionality, treating clinicians will be able to identify all their patients with the genetic variant of interest.

Hypothesis: The availability of the GIC tool will greatly reduce the time delay associated with distributing updated variant information to treating clinicians and will reduce the number of calls the Laboratory of Molecular Medicine (LMM) receives requesting variant updates. The efficiency of identifying all patients with clinically significant variants will be improved through use of the PGE tool.

Aim 4: To evaluate the satisfaction of treating clinicians, perceived impact on clinical care, and net effect on clinician workload associated with deploying genetic infrastructure.

Hypothesis: The introduction and subsequent revisions of the PGE tool will result in improved satisfaction, a perceived reduction in clinician workload, and a perceived improvement in clinical care.