Pharmacogenomics within the EHR Session #258, March 8, 2018 2:30 PM 3:30 PM Jon Walter McKeeby, DSc NIH CC CIO Jharana Tina Patel, PharmD, MBA, Pharmacy Information Officer 1
Conflict of Interest Jon Walter McKeeby, DSc NIH CC CIO Jharana Tina Patel, PharmD, MBA Has no real or apparent conflicts of interest to report. 2
Agenda Introduction of Pharmacogenomics Approaches to Implementation of a Pharmacogenomics Program Options for Incorporating Pharmacogenomics within an EHR Lessons Learned 3
Learning Objectives 1. Describe the requirements to support a Pharmacogenomics (PG) Monitoring Program 2. Discuss the Clinical Decision Support Algorithm used for PG 3. Evaluate the Technical Infrastructure for HLA Testing within EHR 4
Is it in your Genes? Pharmacogenomics is the study of how an individual genetic inheritance affects the individual s response to medications Pharmacogenomics may one day allow medications to be tailor-made for individuals and adapted to each person's own genetic makeup It is really the key to creating personalized drugs with greater efficacy and safety 5
How to Start? Decide on the model to implement Specialized clinics or pharmacogenomics consult services NIH CC model is one in which testing is intended to be available to all clinicians Review Financial implications to implement and support Review EHR options Gain Executive Leadership Support 6
Establish Guidelines Pharmacy and Therapeutics Committee Review Multi-disciplinary Review Members of the committee included Physicians, Pharmacists, Laboratory Medicine, Nursing, and IT representatives Establish a clinical review process for coming up with the clinical logic What medications are we most concerned with? What are the criteria for inclusion? What is the expected behavior of the Clinician? Other restrictions/requirements 7
Review the Evidence PharmGKB Centralized source for PG Data Assists in evaluating the quality of data Clinical Pharmacogenetics Implementation Consortium (CPIC) Royal Dutch Association for the Advancement of Pharmacy FDA Provides guidance on how to use existing genetic test results to optimize pharmacotherapy Pharmacogenomic information in drug labeling 8
Genomics Testing Establish Procedures for testing CLIA certification required to include in EHR Review turn around times for results Establish process for notification 9
EHR Implementation Decisions Corresponding test was not ordered and resulted Do we stop ordering process? Do we allow the medication order to be placed on Hold? Result found with matching result What will the alert contain? Do we stop ordering process? Result found with result that did not match What will the alert contain? Waiting on Genomics/HLA result How is the prescriber notified? Who else needs to be notified? What happens with the existing medication order? Do we automatically start the medication order without prescriber intervention? Answers are conditional on medication Alerts/actions will be determined and approved by Program and P&T. 10
Levels of CDS Guidance based on Medication only Guidance based on Results 1. Healthcare provider to review results outside of order entry 2. Display results at the point of medication order entry as guidance to the provider 3. Results used at the point of medication order entry by CDS to guide the provider through the ordering process 11
Types of Results PDF of Genetic report Genetic results in a separate system manually entered in EHR as a non-discrete interpretation of the results Genetic results interfaced to the EHR as discrete values 12
Where are Results Stored Data stored in Laboratory Information System accessed via API or Link from the EHR All genomic results stored in separate database All genomic results stored within EHR Only actionable results within EHR All results stored in a separate database As new medication-result pairs are determined added to EHR 13
NIH Decisions PDF of Genetic report stored in EHR Genetic results interfaced to the EHR as discrete values Only actionable results within EHR All results stored in a separate database As new medication-result pairs are determined added to EHR Guidance based on Results Results used at the point of medication order entry by CDS to guide the provider through the ordering process 14
Discrete Results Interfaced to EHR Step 1: Retrieve the result HL7 message Step 2: Store the order identifier, patient identifier, gene, medication, phenotype call Step 3: Analyze the phenotype call against a result control table Step 4: Notify the prescribers identified on the order as well as the pharmacy point of contact of the existence of test results. 15
HLA Control Table 16
HLA Result 17
DMET PDF Report 18
DMET Result Identification numbers for the patient, laboratory test, and medication order. Table logic parameters (medication name, laboratory test name, allele information). Dates (laboratory test requested date, results received date, and date added to table). The result for the variant. A textual description of the results value. Fields Record 1 Record 2 Primary Key 46345 48276 Added When 2016-03-10 15:48:26 2016-03-10 15:49:27 Added By ppcuser ppcuser Db SNP Version 132 132 Source File 002DJTL_20160310.z 002DJTL_20160310.zip Name ip Client ID 92100200 92100200 Order ID 80500680 80500680 Result When 2016-03-10 2016-03-10 CHP File Name DMET Prof.dmet.chp gdna#5.dmet.chp Probe Set ID AM_10001 AM_10001 Call C/C C/C Confidence 2.442491E-15 6.661338E-16 Forced Call C/C C/C Allele Count 2 2 Signal A 5630.507 5000.783 Signal B 595.5302 536.3198 19
NIH Pharmacogenomics Program Program was implemented in two phases. 1 st HLA variations for prediction of potential dermatologic reactions 2 nd Phase included genetic variants in ADME genes that are associated with the dose or toxicity of a medication HLA 1 st because the lab already had assays for high resolution HLA sequencing and we use these particular medications frequently in our patient population 20
EHR Configuration Medications in the PG program are orderable only through an order set form Not available for Agent for Orders Remove ability to re-order the medication Address all medication dosage forms Required Lab tests are orderable only through an order set form HLA and Genetic Test results are stored in the EHR for both LIP review and for use within CDS 21
Define the CDS Logic 22
Carbamazepine A = Message box for clinical information B = Message box for override reason description The Override Reason Number field is required based on the patient case C = Message box where PG test result information is displayed D = Grid for ordering PG tests These are automatically preselected depending on the case E = Grid where medications can be ordered 23
Education Clinicians, pharmacy, nursing, and laboratory staff were provided education about the program including the pharmacogenomics guidelines, the availability of the lab test, and the order entry process. Clinical Alert flyers, in-service training programs, and email communication Warning messages, instructions, and information for the medications and the genomic tests provided during order entry Nursing developed a Genetics and Genomics in Healthcare course and competency One day introduction course, required of all nursing staff Optional two day intermediate course Educational materials were created for the patient. Available in the medical order 24
Monitoring Email notification is also sent to two members of the PG Subcommittee to monitor the process Upon results being received in our EHR, the pharmacy PG Subcommittee member and the provider entering the medication order receive an email An identification number within the email message allows the user to login to EHR and retrieve the results for the patient The prescriber can then proceed in ordering the medication if appropriate 25
Additional Considerations Maintenance of tables, guidelines, order sets Addition of new drug gene pairs Reprocessing of previous results for new indications Genomics viewer 26
Lessons Learned Restrictions were too restrictive for users familiar with reviewing this information The second phase was more challenging The problem we found post implementation was that the phenotype report from the Affymetrix software produced multiple phenotype results when a single genotype could not be determined This is a result of the software making all possible calls when a single call can t be made. We revised our methodology to have the lab staff interpret the calls rather than the software. 27
Questions Jon Walter McKeeby, DSc NIH CC CIO jmckeeby@nih.gov Jharana Tina Patel, PharmD, MBA pateljh@mail.nih.gov Please complete online session evaluation 28