Documenting Pharmacogenomic Test Results in Electronic Health Records: Practical Considerations for Primary Care Teams

With increasing patient interest in and access to pharmacogenomic testing, clinicians practicing in primary care are more likely than ever to encounter a patient seeking or presenting with pharmacogenomic test results. Gene-based prescribing recommendations are available to healthcare providers through Food and Drug Administration-approved drug labeling and Clinical Pharmacogenetics Implementation Consortium guidelines. Given the lifelong utility of pharmacogenomic test results to optimize pharmacotherapy for commonly prescribed medications, appropriate documentation of these results in a patient’s electronic health record (EHR) is essential. The current “gold standard” for pharmacogenomics implementation includes entering pharmacogenomic test results into EHRs as discrete results with associated clinical decision support (CDS) alerts that will fire at the point of prescribing, similar to drug allergy alerts. However, such infrastructure is limited to the few institutions that have invested in the resources and personnel to develop and maintain it. For the majority of clinicians who do not practice at an institution with a dedicated clinical pharmacogenomics team and integrated pharmacogenomics CDS in the EHR, this report provides practical tips for documenting pharmacogenomic test results in the problem list and allergy field to maximize the visibility and utility of results over time, especially when such results could prevent the occurrence of serious adverse drug reactions or predict therapeutic failure

Correction: Alternating Hemiplegia of Childhood: Retrospective Genetic Study and Genotype-Phenotype Correlations in 187 Subjects from the US AHCF Registry

<p>Correction: Alternating Hemiplegia of Childhood: Retrospective Genetic Study and Genotype-Phenotype Correlations in 187 Subjects from the US AHCF Registry</p

Ages at unsupported sitting acquisition in each group of patients defined by their genotype.

<p>Cumulative probability of acquiring unsupported sitting by patients presenting the E815K mutation, compared to patientsmutation (3b). Patients with the E815K mutation are likely to gain unsupported sitting at a later age than patients in each of the other groups (respectively P = 0.0002 and P = 0.0020).</p

Schematic representation of <i>ATP1A3</i> mutations.

<p>Mutations identified in our cohort are indicated above the gene; all the mutations previously published are indicated in black; novel mutations are indicated in light blue; mutations identified in multiplex cases are underlined; mutations reported in DYT12 are indicated in green; the mutation reported in CAPOS syndrome is indicated in red. The mutation associated with a phenotype combining features of both AHC and RDP is in orange. The 2 most common mutations are in bold. Asterisks mean that 2 different nucleotide changes have been identified for these protein variants.</p

Ages at onset of AHC in each group of patients defined by their genotype.

<p>The horizontal lines in the boxes indicate the 25th percentile (bottom), the median (middle) and the 75 percentile (top) values. Crosses indicate the mean values. Numbers of patients analyzed in each group are indicated above the boxes.</p

Odds ratio for occurrence of status epilepticus in AHC patients with different <i>ATP1A3</i> mutations.

<p>Odds ratio for occurrence of status epilepticus in AHC patients with different <i>ATP1A3</i> mutations.</p

Summary of 164 AHC patients included in the genotype-phenotype correlation study.

<p>Summary of 164 AHC patients included in the genotype-phenotype correlation study.</p

Summary of the 187 patients included in the genetic study.

<p>Summary of the 187 patients included in the genetic study.</p