Pharmacogenomics Implementation at the National Institutes of Health Clinical Center.

The National Institutes of Health Clinical Center (NIH CC) is the largest hospital in the United States devoted entirely to clinical research, with a highly diverse spectrum of patients. Patient safety and clinical quality are major goals of the hospital, and therapy is often complicated by multiple cotherapies and comorbidities. To this end, we implemented a pharmacogenomics program in 2 phases. In the first phase, we implemented genotyping for HLA-A and HLA-B gene variations with clinical decision support (CDS) for abacavir, carbamazepine, and allopurinol. In the second phase, we implemented genotyping for drug-metabolizing enzymes and transporters: SLCO1B1 for CDS of simvastatin and TPMT for CDS of mercaptopurine, azathioprine, and thioguanine. The purpose of this review is to describe the implementation process, which involves clinical, laboratory, informatics, and policy decisions pertinent to the NIH CC.

Integrating pharmacogenetic information and clinical decision support into the electronic health record.

Pharmacogenetics (PG) examines gene variations for drug disposition, response, or toxicity. At the National Institutes of Health Clinical Center (NIH CC), a multidepartment Pharmacogenetics Testing Implementation Committee (PGTIC) was established to develop clinical decision support (CDS) algorithms for abacavir, carbamazepine, and allopurinol, medications for which human leukocyte antigen (HLA) variants predict severe hypersensitivity reactions. Providing PG CDS in the electronic health record (EHR) during order entry could prevent adverse drug events. Medical Logic Module (MLM) programming was used to implement PG CDS in our EHR. The MLM checks to see if an HLA sequence-based gene test is ordered. A message regarding test status (result present, absent, pending, or test not ordered) is displayed on the order form, and the MLM determines if the prescriber can place the order, place it but require an over-ride reason, or be blocked from placing the order. Since implementation, more than 725 medication orders have been placed for over 230 patients by 154 different prescribers for the three drugs included in our PG program. Prescribers commonly used an over-ride reason when placing the order mainly because patients had been receiving the drug without reaction before implementation of the CDS program. Successful incorporation of PG CDS into the NIH CC EHR required a coordinated, interdisciplinary effort to ensure smooth activation and a positive effect on patient care. Prescribers have adapted to using the CDS and have ordered PG testing as a direct result of the implementation.

Improving adherence to research protocol drug exclusions using a clinical alerting system.

OBJECTIVE: To develop a general method for using the alerting function of an electronic health record (EHR) system to warn prescribers when a drug order may be in conflict with the restrictions of a patient's research protocol. METHODS: We examined a sample of clinical research protocols at the National Institutes of Health (NIH) to identify the frequency with which drugs were excluded by protocols. We analyzed two protocols and modeled the exclusions they contained. We then developed a data model to represent the exclusions, expanded the terminology in the NIH's Biomedical Translational Research Information System (BTRIS) to include relevant drug concepts, and wrote a medical logic module (MLM) for the EHR to match terms for ordered drugs with the drug concepts in the protocol. RESULTS: We found that 50% of protocols in our sample included drug exclusions. Our model represented exclusion concepts and also concepts related to exemptions from the exclusions. The MLM was deployed in a test environment where it successfully detected orders for excluded drugs and delivered messages to users explaining the exclusion, providing information about the clinical setting and timing where the exclusion applies. BTRIS reports using the same terminology information were able to identify instances where protocol exceptions occurred. CONCLUSIONS: Drug exclusions are frequent components of research protocols; nonadherenece to these exclusions could result in harm to subjects, erroneous study results or inefficiencies due to disqualification of research subjects. Our approach uses an MLM and a simple knowledge base, together with a controlled terminology, to provide a solution to the detection and prevention of possible protocol violations. Further work is needed to model additional aspects of the exclusions, such as timing and co-occurring conditions, to improve MLM accuracy.

Use of clinical alerting to improve the collection of clinical research data.

Data errors in electronic health records have been shown to have the potential to adversely impact the conclusions drawn from clinical research. We prospectively studied the efficacy of a new alert to infer errors in previously stored data and to decrease the frequency of data entry errors, in an attempt to improve the quality of data for clinical trials. For the purpose of this study, we monitored data entry errors in height or weight measurements. We predetermined the criteria for probable error as a ten percent variance from a patient's reference value. The care provider entering a value satisfying our error criteria received a disruptive pop-up alert message. The study revealed a significant decrease in the frequency of data errors stored in the EHR, from 2.4% before the alert to 0.9% after the alert. These findings have implications for the development of clinical research trial data collection support tools.