Refining Information Technology Support for Genetics in Medicine

Trial Information

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.