Abstract
Thoughtful integration of interruptive clinical decision support (CDS) alerts within the electronic health record is essential to guide clinicians on the application of pharmacogenomic results at point of care. St. Jude Children’s Research Hospital implemented a preemptive pharmacogenomic testing program in 2011 in a multidisciplinary effort involving extensive education to clinicians about pharmacogenomic implications. We conducted a retrospective analysis of clinicians’ adherence to 4783 pharmacogenomically guided CDS alerts that triggered for 12 genes and 60 drugs. Clinicians adhered to the therapeutic recommendations provided in 4392 alerts (92%). In our population of pediatric patients with catastrophic illnesses, the most frequently presented gene/drug CDS alerts were TPMT/NUDT15 and thiopurines (n = 3850), CYP2D6 and ondansetron (n = 667), CYP2D6 and oxycodone (n = 99), G6PD and G6PD high-risk medications (n = 51), and CYP2C19 and proton pump inhibitors (omeprazole and pantoprazole; n = 50). The high adherence rate was facilitated by our team approach to prescribing and our collaborative CDS design and delivery.
Keywords: pharmacogenomics, clinical decision support, precision medicine, medication alert systems, pharmacogenetics
INTRODUCTION
Clinical decision support (CDS) alerts are intended to provide pertinent and concise evidence-based recommendations to aid clinicians in their clinical decision making processes.1 Implementing pharmacogenomics often relies on interruptive CDS alerts to provide patient-specific recommendations at the point of care as relevant medications are prescribed.2–5 However, implementing CDS alerts without thoughtful attention to design and delivery can lead to excessive, low-value alerts that contribute to alert fatigue. Clinicians might then ignore clinically important messages and thus increase the risk of patient harm.6,7 Therefore, it is important to assess clinician adherence to the alerts to ensure that the patient safety benefits intended by the alerts are not compromised by alert fatigue.
Alert adherence can be measured as (1) proximal outcomes which capture the clinician’s response to available alert actions (eg, “continue with order” or “cancel order”) or (2) distal outcomes which capture whether the chosen alert action was performed accordingly and/or if it was clinically appropriate.8,9 Best practices have suggested incorporating both proximal and distal outcome assessments to refine alert adherence evaluations.8,9
The available literature has focused on institutions sharing their best practices in developing and incorporating pharmacogenomic CDS alerts.2,4,5,10–13 There are few published evaluations of clinician adherence to pharmacogenomically guided prescribing recommendations with most being limited to single gene/drug pairs such as TPMT/thiopurines, CYP2D6/codeine, and CYP2C19/clopidogrel.5,10,11,14,15 Furthermore, different adherence definitions have been used among published reports.5,10,11,14,15 There are currently no published reports incorporating a comprehensive analysis of CDS alert adherence on multiple implemented gene/drug pairs at a single institution.
OBJECTIVE
The objective of this study was to evaluate the rate of clinician adherence to pharmacogenomically guided CDS prescribing recommendations over a 10-year period.
METHODS
Setting
This study was performed at St. Jude Children’s Research Hospital (St. Jude), a specialty hospital providing care for children with catastrophic illnesses, focusing on childhood cancers, blood disorders, and infectious diseases. As of November 2021, 14 genes and 66 drugs (Figure 1) have been implemented as part of the institutional review board (IRB)-approved PG4KDS protocol (www.stjude.org/pg4kds), a preemptive pharmacogenomic testing model.16,17 Informed consent was obtained from either the parents of patients (if younger than 18 years of age) or the patients themselves if 18 years of age or older. Consent discussions occurred approximately 4 weeks after patients were accepted to receive care at St. Jude. The PG4KDS protocol incorporated a rational and stepwise process to implementing gene/drug pairs in a sequential manner over time.17 Each implementation was prioritized based on the severity of the clinical consequences (ie, adverse effects or lack of response) if pharmacogenomics were not used to inform pharmacotherapy, frequency of medication use, availability of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines or primary literature, and with approval from the institutional Pharmacogenomics Oversight Committee (POC; reporting to the Pharmacy and Therapeutics and then to the Medical Executive Committees).18 Gene test results were only added as discrete diagnoses in the patient’s problem list if they conferred a high-risk actionable phenotype and CDS was available for at least one affected medication. In addition, each gene test result was coupled with an interpretive consult written by a pharmacist and placed in the electronic health record (EHR) to guide clinical care.4,17,19
Figure 1.
Timeline of implemented gene/drug pairs at St. Jude Children’s Research Hospital. *Other G6PD high-risk medications included pegloticase, tafenoquine, aspirin, chloramphenicol, chlorpropamide, dabrafenib, dimercaprol, glimepiride, glipizide, glyburide, mafenide, mesalamine, nalidixic acid, norfloxacin, probenecid, quinine, sulfacetamide, sulfadiazine, sulfamethoxazole, sulfanilamide, sulfasalazine, and sulfisoxazole.
To increase baseline knowledge about pharmacogenomic associations, clinicians were educated via in-services and informational e-mails about each gene/drug pair prior to implementation. Pharmacists were extensively educated and had to complete competencies related to each gene (eg, https://www.stjude.org/mtrnr1/competency).
During the study period, St. Jude had a fully implemented EHR system (Millennium; Cerner Corporation, North Kansas City, MO) and provided comprehensive care for both inpatient stays and outpatient visits.20 Since St. Jude dispenses all medications including take home prescriptions, a comprehensive record of medication use for each patient is maintained.
A pharmacist is embedded in the care teams for each primary service (eg, Leukemia and Lymphoma, Solid Tumor, Neuro-Oncology, Hematology, and Bone Marrow Transplantation) at St. Jude. Eligible clinical pharmacists may apply and be appointed as credentialed members of the medical staff and practice through an institutional collaborative agreement. This allows them to provide comprehensive medication management including medication selection and dosing based on a patient’s pharmacogenomic profile.21
Design of CDS alerts
A total of 340 unique interruptive post-test CDS alerts were custom built for the gene/drug pairs implemented. Due to logistical reasons, post-test CDS alerts were not created in 2 cases: (1) CYP2C19 intermediate and poor metabolizers for proton pump inhibitors and (2) CACNA1S or RYR1 and halogenated volatile anesthetics (eg, sevoflurane). Alternative methods of clinician notification about the gene/drug associations were created (Supplementary Materials). Therefore, excluding these special cases, post-test CDS alerts that triggered for 12 genes and 60 drugs were included in this analysis.
St. Jude built CDS alerts in collaboration with the Information Services Department and the most relevant groups of prescribing clinicians, with approval by the POC. The involvement of clinicians provided valuable input on the CDS recommendation language. This allowed the design of short, concise CDS alert wording to maximize alert usefulness for busy clinicians. Supplementary Table S1 provides a comprehensive list of the recommendation wording in each of the implemented post-test CDS alert that presented to clinicians.
Study design
A retrospective analysis of implemented pharmacogenomically guided CDS alerts from May 2011 through November 2021 was undertaken. Two types of pharmacogenomically guided CDS alerts have been implemented5: (1) pretest CDS alerts present to clinicians when an order is placed for a high-risk medication, but the patient does not have the corresponding pharmacogenomic test result in the EHR; (2) post-test CDS alerts interrupt clinicians with a pharmacogenomically guided recommendation when attempting to prescribe a high-risk medication for a patient with an associated high-risk problem list entry. High-risk pharmacogenomic problem list entries, using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codes, serve as discrete triggers for the post-test CDS alerts. These codes reflect previously established standardized pharmacogenomic terminology.22
This analysis focused on clinician adherence to post-test CDS alerts. For this analysis, clinicians were defined as those with prescribing authority (eg, attending physicians, medical fellows and residents, nurse practitioners, physician assistants, and pharmacists). Included alert data pertained to the first post-test alert from each encounter for a medication order. An encounter was defined as a documented in-person contact between the patient and a provider for either an inpatient or outpatient visit. Reasons for alert exclusions are provided in Figure 2. The CDS event log for each alert occurrence was obtained from St. Jude’s enterprise data warehouse. The PG4KDS protocol is an ongoing study that was approved by the St. Jude IRB in May 2011. This analysis corresponds to one of the PG4KDS protocol objectives: To incorporate CDS tools linking pharmacogenomic testing to medication use and assess their level of use in the EHR.
Figure 2.
Flowchart for inclusion of alert data.
CDS alert evaluation
The primary outcome was clinician adherence to pharmacogenomically guided CDS alerts. Adherence was defined as documentation that the recommendation provided in the CDS alert was followed (eg, dose adjustment or selecting an alternative medication) or by documentation of a clinically appropriate change in therapy (eg, elevated white blood cell counts necessitating higher chemotherapy dosing). Each gene/drug pair has unique pharmacogenomically guided recommendations; therefore, the study team developed a procedure for assessing adherence prior to chart review (Supplementary Table S1). We evaluated differences in clinician adherence rates for alerts that recommended a change in pharmacotherapy versus alerts in which a consideration for therapy alteration was suggested. Pharmacogenomically guided recommendations for high-risk phenotypes were divided into 2 major categories: (1) an alternative therapy for the attempted high-risk medication order or (2) a dose adjustment (Supplementary Figure S2). For the first recommendation category, a proximal outcome is when the clinician selected to cancel the high-risk medication order while a distal outcome is when the clinician prescribed an alternative medication. For the second recommendation category, a proximal outcome is when the clinician selected that they would modify the dose while a distal outcome is when the clinician prescribed the appropriate dose.
RESULTS
We identified 5665 post-test CDS alerts that presented to clinicians during the 10-year period. A total of 882 alerts were excluded due to duplicate alerts (n = 448), pharmacogenomic recommendations not applicable at time of alert (n = 222), technical errors (n = 211), and for one patient who had received a liver transplant after genotyping (Figure 2). Reasons for alert exclusion are detailed in the Supplementary Materials. The remaining post-test CDS alerts (n = 4783) were evaluated for adherence by clinicians.
During the 10-year period, no post-test CDS alerts presented to clinicians for tramadol, clomipramine, trimipramine, doxepin, paroxetine, clopidogrel, lansoprazole, methadone, simvastatin, fluorouracil, capecitabine, celecoxib, succinylcholine, amikacin, gentamicin, and tobramycin. This could be explained by the infrequent use of those medications at St. Jude, the low frequency of certain high-risk phenotypes, or recent implementation of the gene/drug pair (eg, CYP2B6 and methadone was not implemented until June 2021).
Clinicians adhered to pharmacogenomically guided recommendations provided in 4392 out of the 4783 alerts (92%) (Figure 3). The most common gene/drug pairs for which CDS alerts presented were TPMT/NUDT15 and thiopurines (n = 3850), CYP2D6 and ondansetron (n = 667), CYP2D6 and oxycodone (n = 99), G6PD and G6PD high-risk medications as defined in the published CPIC guidelines for rasburicase therapy in the context of G6PD deficiency23 (n = 51), and CYP2C19 and omeprazole or pantoprazole (n = 50). The number of unique patients for whom CDS alerts presented is described in Supplementary Table S2. Our CYP2D6 and codeine post-test CDS alert volume declined as codeine use decreased in response to the U.S. Food and Drug Administration (FDA) warnings in 2013 and 2017 (Supplementary Figure S3). Table 1 provides adherence rate results for pharmacogenomically guided CDS prescribing recommendations by individual gene/drug pairs. Clinicians with the highest adherence rates were attending physicians (94%) followed by medical fellows (90%) and nurse practitioners or physician assistants (90%) (Table 2 and Supplementary Table S3). There were 3780 alerts in which a consideration for therapy alteration was suggested, with an observed 99% adherence rate (n = 3753). There were 1003 alerts that recommended a change in pharmacotherapy, with an observed 64% adherence rate (n = 639; P < .001).
Figure 3.
Clinician adherence rates to pharmacogenomically guided recommendations presented in CDS alerts from May 2011 until November 2021. Adherence was defined as documentation that the recommendations provided in the CDS alerts were followed (eg, dose adjustment or selecting an alternative medication) or documentation of a clinically appropriate change in therapy for a patient. CDS: clinical decision support.
Table 1.
Adherence to post-test pharmacogenomically guided CDS prescribing recommendations by individual gene/drug pair
| Implemented gene/drug pairs with post-test CDS alerts | Number of pharmacogenomic CDS alerts | Number of unique patients | Alerts with adherence | Alerts with nonadherence |
|---|---|---|---|---|
| (N = 4783) | (N = 361) | (N = 4392) | (N = 391) | |
| n (%) | n | n (%) | n (%) | |
| TPMT/NUDT15 and thiopurinesa | 3850 (80.5%) | 116 | 3842 (99.8%) | 8 (0.2%) |
| CYP2D6 and ondansetron | 667 (14%) | 110 | 319 (48%) | 348 (52%) |
| CYP2D6 and oxycodone | 99 (2.1%) | 46 | 92 (93%) | 7 (7%) |
| G6PD and G6PD high-risk medicationsb | 51 (1.1%) | 22 | 51 (100%) | 0 (0%) |
| CYP2C19 and proton pump inhibitorsc | 50 (1%) | 11 | 34 (68%) | 16 (32%) |
| CYP2D6 and codeine | 31 (0.6%) | 27 | 27 (87%) | 4 (13%) |
| CYP2C19 and voriconazole | 19 (0.4%) | 16 | 15 (79%) | 4 (21%) |
| Other gene–drug pairsd | 16 (0.3%) | 13 | 12 (75%) | 4 (25%) |
CDS: clinical decision support.
Thiopurines included mercaptopurine, thioguanine, and azathioprine. Prior to March 2017, this alert only provided dosing recommendations based on TPMT phenotype.
G6PD high-risk medications included aspirin, phytonadione, rasburicase, and sulfamethoxazole-trimethoprim.
Proton pump inhibitors included omeprazole and pantoprazole.
Other medications included amitriptyline, imipramine, atazanavir, ibuprofen, meloxicam, fluoxetine, and tacrolimus.
Table 2.
Adherence to post-test pharmacogenomically guided CDS prescribing recommendations by alert characteristics
| Alert characteristics | Number of pharmacogenomic CDS alerts | Alerts with adherence | Alerts with nonadherence |
|---|---|---|---|
| (N = 4783) | (N = 4392) | (N = 391) | |
| n (%) | n (%) | n (%) | |
| Alerts by location | |||
| Outpatient (ambulatory clinics)a | 4065 (85%) | 3945 (97%) | 120 (3%) |
| Inpatient | 718 (15%) | 447 (62%) | 271 (38%) |
| Alerts by medical service | |||
| Oncologic disordersb | 4509 (95%) | 4177 (92%) | 332 (8%) |
| Stem cell transplant and cell therapy | 160 (3%) | 133 (83%) | 27 (17%) |
| Nonmalignant hematology and infectious diseases | 114 (2%) | 82 (72%) | 32 (28%) |
| Alerts presented to type of provider | |||
| Attending physician | 2422 (50.6%) | 2283 (94%) | 139 (6%) |
| Nurse practitioner or physician assistant | 1984 (41.5%) | 1780 (90%) | 204 (10%) |
| Medical fellow | 352 (7.4%) | 317 (90%) | 35 (10%) |
| Medical resident | 23 (0.5%) | 10 (43%) | 13 (57%) |
| Pharmacist | 2 (0.04%) | 2 (100%) | 0 (0%) |
CDS: clinical decision support.
Alerts for the outpatient service consisted of take-home prescriptions.
Medical oncology services included: Leukemia and Lymphoma, Solid Tumor, Neuro-Oncology, Radiation Oncology and After Completion of Therapy (survivorship clinic).
DISCUSSION
This is the first comprehensive review of clinician adherence to pharmacogenomically guided CDS alerts. Other groups have reported pharmacogenomically guided CDS alert adherence for single/gene drug pairs and one pilot analysis of multi gene/drug pairs with rates ranging from 20% to 42%.10,11,15 Our clinician adherence rate (92%) appears higher than that observed in a pilot multi gene/drug pair analysis by the Electronic Medical Records and Genomics (eMERGE) Network (42%).10
The individual gene/drug pair adherence rates in this 10-year analysis are consistent with our previously reported single gene/drug pair adherence rates.5,14 Gammal et al evaluated CYP2D6 and codeine CDS alerts that presented for patients with sickle cell disease and reported an adherence rate of 98%14; with additional data, we observed an 87% adherence rate for this gene/drug pair (Table 1). Bell et al evaluated TPMT and thiopurines and reported a 93% alert adherence5 over a 12-month period which is comparable to this 10-year analysis which included NUDT15 for more recent alerts, with an observed 99.8% adherence rate (Table 1).
Interruptive CDS alerts are most frequently used for drug-allergy interactions, drug–drug interactions, dose warning, geriatric medication-related, and renal medication-related alerts which have reported adherence rates ranging from 4% to 54%.24 We hypothesize that our higher adherence rate can be attributed to several factors. First, with our high-risk pediatric population and our long-standing integration of clinical pharmacists into patient care, our clinicians are accustomed to receiving multidisciplinary input into prescribing decisions and to following evidence-based recommendations. Also, we worked diligently to include all relevant clinicians in our pharmacogenomic CDS alert recommendations and designs. This can be framed in the context of our local institutional efforts to rationally design interruptive alerts in the EHR. To reduce the number of low-value interruptive CDS alerts at our institution, we implemented a multidimensional quality improvement effort. This effort resulted in a significant reduction in the number of CDS alerts for drug–drug interactions, and through which we established a model for sustained alert refinements (eg, filtering alerts to only present when applicable patient-specific data is found in the EHR).25 We obtained frequent feedback from our clinical services as to their preferences for alert firing and wording. This considerable effort influenced the thoughtful development of pharmacogenomically guided CDS alerts.
The high adherence rate could also be attributed in part to our CDS design of using short, concise wording to help busy clinicians make a therapeutic change. This design is similar to CDS approaches by other sites that have implemented pharmacogenomics.11,26,27 In addition, St. Jude took the initial steps of introducing pharmacogenomics to the direct patient care workflow in 2005 which was met with support by clinicians.28 The long-standing emphasis on clinical pharmacogenomics at our institution has led to clinicians viewing pharmacogenomic testing as part of routine patient care.28 Pharmacists at St. Jude practice as part of multidisciplinary inpatient and outpatient care teams. This model encourages physicians, nurse practitioners, and physician assistants to consult their pharmacist team member after encountering a pharmacogenomics CDS alert. Furthermore, our implementation model incorporates extensive education for attending physicians, medical fellows, nurse practitioners, and physician assistants on the pharmacogenomically guided recommendations prior to the go-live of each CDS alert. Due to the transient rotation of medical residents at St. Jude, they are not exposed to pharmacogenomic educational topics.
For most CDS alerts evaluated, the volume of medication use was relatively constant over this 10-year period, except for codeine. Codeine is a common opioid that at the beginning of our evaluation period was frequently prescribed for management of vaso-occlusive pain crises in patients with sickle cell disease29 and for mild pain management in patients with oncologic disorders because of its inability of masking a fever. However, after the FDA added a boxed warning cautioning against codeine use postoperatively in children in 2013, our institution implemented a pharmacogenomic-based strategy for safe codeine prescribing in our patients.14,30,31 Subsequently, the FDA found that the majority of serious codeine side effects occurred in children less than 12 years of age and the codeine warning was changed to a contraindication for use in children younger than 18 years of age in 2017.31 At that time, our clinicians started prescribing alternative opioids and our codeine use declined (Supplementary Figure S3). The lower adherence rate observed with CYP2D6 and ondansetron (48%) can be attributed to the fact that initial antiemetic regimen selection was not guided by the patients’ CYP2D6 genotype test results because patients were typically approached for genotyping 4 weeks after starting therapy. By the time the genotype result was returned, the antiemetic regimen selection was tailored to patient’s tolerance. Future advances in medicine such as preemptively genotyping patients at birth would allow to better tailor therapy of all high-risk pharmacogenomic medications at the time the medication is initially prescribed.32
This study was limited to a retrospective review and the available documentation from chart review. This limitation was mitigated by the extensive information available in the charts based on the fully implemented EHR system used in all the services provided to our patients. Furthermore, a standard procedure and well-defined adherence metrics were established prior to performing the chart review. While aspects of our experience are likely difficult to replicate, many specific lessons can apply to any setting implementing pharmacogenomic CDS, such as providing concise and actionable messages, engaging clinicians in alert design, and consistent refinements of alerts.
Alert adherence data incorporating distal outcomes from pharmacogenomically guided CDS alerts are scarce in the published literature. More available data are expected as additional institutions implement pharmacogenomics and share their CDS adherence metrics. Because the implementation of pharmacogenomics depends on interruptive CDS alerts to make recommendations at the point of prescribing, it is crucial to adhere to interruptive alert stewardship8 and the 5 rights of CDS (namely right information, right person, right intervention format, right channel, and right time in workflow)33 for thoughtful and effective integration of alerts. Furthermore, the use of a consistent alert adherence definition across institutions implementing pharmacogenomics would enable benchmarking and development of translational dashboards for CDS strategies.8 An alert adherence definition that includes proximal and distal outcomes through chart review is recommended to properly assess actions taken by clinicians.8,9
CONCLUSION
This study reports our experience with a preemptive pharmacogenomic institutional model that has implemented multiple gene/drug pairs with interruptive post-test CDS alerts. A comprehensive analysis of clinician adherence to our pharmacogenomically guided CDS alerts revealed that therapeutic regimens were optimized in 92% of high alert encounters. The high adherence rate is made possible by our collaborative and intentional institutional effort in evidence-based prescribing and CDS design and delivery. This analysis illustrates the institution’s continued effort to ensure that pharmacogenomic information can be effectively incorporated into CDS to improve safe medication prescribing.
FUNDING
This work was funded in part by the American Lebanese Syrian Associated Charities (ALSAC) and by the National Institutes of Health grant U24HG010135.
AUTHOR CONTRIBUTIONS
All authors contributed to the conception and design. DKB, BM, MH, and NMK retrieved the data. JQN, CEH, and KRC were involved in analysis and interpretation of data. JQN, KRC, CEH, MVR, and JMH contributed to drafting the article or revising it critically for important intellectual content. All authors gave final approval of the version to be published.
SUPPLEMENTARY MATERIAL
Supplementary material is available at Journal of the American Medical Informatics Association online.
CONFLICT OF INTEREST STATEMENT
None declared.
Supplementary Material
Contributor Information
Jenny Q Nguyen, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Kristine R Crews, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Ben T Moore, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Nancy M Kornegay, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Donald K Baker, Department of Information Services, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Murad Hasan, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Patrick K Campbell, Department of Information Services, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA; Department of Oncology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Shannon M Dean, Department of Information Services, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA; Department of Pediatrics, St. Jude Children’s Research Hospital, Memphis, Tennesse, USA.
Mary V Relling, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
James M Hoffman, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA; Department of the Office of Quality and Patient Safety, St. Jude Children’s Research Hospital, Memphis, Tennesse, USA.
Cyrine E Haidar, Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.
Data Availability
The data underlying this article will be shared on reasonable request to the corresponding author.
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Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.