Medical Policy Determinations for Pharmacogenetic Tests Among US Health Plans

ABSTRACT

Objectives: To evaluate medical policy determinations for pharmacogenetic (PGx) testing for 65 clinically relevant drug-gene pairs and evidence cited to support determinations across major US health plans and laboratory benefit managers (LBMs).

Study Design: Landscape analysis of available PGx medical policies to determine coverage status of certain drug-gene pairs.

Methods: PGx medical policies as of February 1, 2024, were ascertained through Policy Reporter for top national insurers, LBMs, and the Palmetto GBA Molecular Diagnostic Services (MolDX) Program, which determines whether a molecular diagnostic test is covered by Medicare. Data elements included date of last policy update, coverage status for each drug-gene pair, and evidence cited for or against coverage. A drug-gene pair was considered covered if the policy indicated that a PGx test was deemed medically necessary and/or meets coverage criteria.

Results: Policies from 8 insurers, 3 LBMs, and MolDX were available and reviewed. MolDX covered all 65 individual drug-gene pairs, followed by Avalon Healthcare Solutions (n = 50) and UnitedHealthcare (n = 45); these 3 also covered multigene panels. Eight policies covered 10 or fewer drug-gene pairs. HLA-B*57:01 testing prior to abacavir initiation and HLA-B*15:02 testing prior to carbamazepine initiation were covered across all policies. Drug-gene pairs with Clinical Pharmacogenetics Implementation Consortium guidelines and/or included in the FDA’s Table of Pharmacogenetic Associations Section 1 were more commonly covered. Society guidelines were the most frequently cited evidence (413 times), and cost-effectiveness studies were infrequently cited (43 times).

Conclusions: We found significant variability in medical policy determinations and evidence cited for clinically relevant PGx tests among major US health insurers and LBMs. A collaborative effort between payers and the PGx community to standardize evidence evaluation may lead to more consistent coverage and improve patient access to PGx tests meeting evidence requirements.

Am J Manag Care. 2025;31(2):e47-e55. https://doi.org/10.37765/ajmc.2025.89683

Takeaway Points

We found significant variability in medical policy determinations and evidence cited for clinically relevant pharmacogenetic tests among major US health insurers and laboratory benefit managers.

• Molecular Diagnostic Services, which determines whether a molecular diagnostic test is covered by Medicare, covered pharmacogenetic testing for all 65 clinically relevant drug-gene pairs evaluated; Avalon Healthcare Solutions, UnitedHealthcare, and Centene Corporation covered more than half; and 8 of the 12 policies reviewed covered 10 or fewer drug-gene pairs.
• Drug-gene pairs with Clinical Pharmacogenetics Implementation Consortium guidelines and on the FDA’s Table of Pharmacogenetic Associations Section 1 were more likely to be covered, but coverage was still variable.
• Society guidelines were the most frequently cited evidence across policies, whereas cost-effectiveness studies were not commonly cited.

The estimated annual cost of prescription drug–related morbidity and mortality resulting from nonoptimized medication therapy was $528.4 billion in 2016.¹ Molecular diagnostic tests may aid in optimizing medication use, potentially resulting in significant cost savings.²

A pharmacogenetic (PGx) test is a type of molecular diagnostic test that can be used to identify individuals at increased risk of altered drug response.³ PGx-guided treatment can optimize systemic drug exposure, reduce drug adverse events, and/or improve clinical response to certain drugs.³ A recent randomized trial showed that multigene testing reduced adverse drug reactions by 30%.⁴ The FDA has identified more than 60 drug-gene interactions for which the data support therapeutic management recommendations (listed in the FDA Table of Pharmacogenetic Associations Section 1, hereafter referred to as the FDA Table),⁵ including 8 drugs with boxed warnings mandated by the FDA to appear in the drug’s labeling regarding PGx interactions. The Clinical Pharmacogenetics Implementation Consortium (CPIC)^6,7 is an international consortium of PGx experts with the goal of creating peer-reviewed, evidence-based clinical practice guidelines on how to translate PGx results into actionable prescribing decisions. Thus far, CPIC has developed and published 26 drug-gene clinical practice guidelines for more than 100 drugs, which are purposefully designed to help clinicians understand how PGx results can be used to optimize therapy. Prescribing guidelines from medical societies may also reference CPIC guidelines where appropriate.⁸

Despite the potential for PGx testing to improve therapeutic management, several barriers exist to widespread adoption, including cost, lack of clinician awareness or education, and limited access to testing, a problem exacerbated by the variability in coverage or reimbursement for certain diagnostic tests including PGx tests.⁹ PGx tests can be cost prohibitive without insurance coverage, with a cash price ranging anywhere from $200 to more than $500 per test. The actual cost billed to insurers may be considerably higher depending on the vendor or hospital laboratory policies.¹⁰ Health plan medical policies that align with CPIC and medical society clinical practice guidelines, FDA recommendations, and/or published evidence are a key factor to enabling patient access to testing. However, previous studies have reported inconsistent insurance coverage of PGx tests,¹¹ despite data suggesting that testing is cost-effective for many drug-gene examples.¹² An evaluation of 108 cost studies comparing PGx-guided therapy vs standard of care for drugs with CPIC guidelines demonstrated that roughly three-quarters of all studies determined PGx testing was cost-effective or cost saving.¹²

The Standardizing Laboratory Practices in Pharmacogenomics (STRIPE) initiative, convened by the American Society of Pharmacovigilance in 2020, is a collaborative effort aimed at developing best practices for PGx testing. The STRIPE Study Designs Taskforce (SDTF) was created to provide recommendations on study design methods to demonstrate clinical utility of PGx testing by (1) collating recommendations from clinical, payer, and regulatory organizations to understand the levels of evidence required to make recommendations for pretreatment testing; (2) developing consensus on the type and strength of evidence required to demonstrate clinical utility; and (3) making recommendations for study design considerations to demonstrate clinical utility. In this analysis, we collated information from publicly available health insurance policies and/or guidance documents from laboratory benefit managers (LBMs) to describe the medical policy determinations for 65 clinically relevant drug-gene pairs. Secondarily, we evaluated the evidence cited to support policy determinations to assess evidence thresholds for clinical utility.

METHODS

Insurance Policy Selection

Policy Reporter was used to establish total covered lives data across all lines of business for the top 10 health insurers plus CMS, which are calculated using a proprietary methodology based on a plan’s self-reported data via annual/quarterly reports, press releases, US Securities and Exchange Commission filings, National Association of Insurance Commissioners reported data, third-party sources, and CMS data with permissions/licenses. Any conclusions or analyses are not endorsed by entities of original or commingled data sources. Policy Reporter is a source of payer policy data and intelligence with an online subscription commercial and government coverage policy database of more than 1000 payers covering 325 million individuals, including 220 individual private and 411 public payers.

The top 10 national insurers by market share were identified based on total health plan enrollment in 2022: UnitedHealthcare, Anthem Inc (now Elevance Health Inc), Aetna (CVS Health), Centene Corporation, Health Care Service Corporation (HCSC), Cigna, Humana, Kaiser Permanente, US Department of Veterans Affairs Health Plan, and Molina Healthcare, Inc. The Molecular Diagnostic Services (MolDX) Program administered by Palmetto GBA represents a Medicare local coverage determination (LCD) (ID LC38294) for CMS. MolDX was created in 2011 to identify and establish coverage and reimbursement for molecular diagnostic tests on behalf of the Medicare program. Other Medicare administrative contractors (MACs) that participate in MolDX include Noridian Healthcare Solutions (MAC Jurisdiction [J] E and F); CGS Administrators, LLC (J15); and WPS Health Solutions (J5 and J8), covering more than half of all states. Independence Health Group was added to represent one of the largest regional Blue Cross Blue Shield (BCBS)–affiliated insurers other than Anthem and HCSC. The BCBS Federal Employee Program (FEP) was added to represent a large BCBS plan with molecular diagnostics medical policies not delegated to an LBM. Health plans use LBMs to inform coverage decisions by evaluating the clinical validity and utility of laboratory tests, developing comprehensive coverage policies, and managing utilization through prior authorization and step therapy protocols. Given the increasing volume and complexity of diagnostic tests, it is expected that more health plans will utilize LBMs.^13,14 At the time of our analysis, at least 5 LBMs were known to exist and were included in our evaluation: AIM Specialty Health (now Carelon Insights), Avalon Healthcare Solutions, Beacon Lab Benefit Solutions (LBS), eviCore healthcare, and Kentmere Healthcare Consulting Corporation. PGx test medical policies for the 12 listed insurers, 5 LBMs, and MolDX in place as of August 1, 2022, were searched through Policy Reporter.¹⁵ Updates to available policies were reviewed as of February 1, 2024.

Drug-Gene Pair Selection

Drug-gene pairs with potential clinical utility were identified by the STRIPE SDTF clinical subcommittee, which recently published a landscape analysis of recommendations for PGx testing in clinical practice guidelines in the US.⁸ All drug-gene pairs with CPIC guidelines and/or listed on the FDA Table were considered for inclusion. The STRIPE SDTF clinical subcommittee narrowed this list to only those drug-gene pairs that would, in their opinion, have sufficient clinical benefit to justify recommendations for pretreatment testing and for drugs commonly used in clinical practice.⁸ A total of 65 individual drug-gene pairs were included, of which 27 had CPIC guidelines and were mentioned in the FDA Table, 33 had CPIC guidelines only, and 3 were only in the FDA Table (Table). Two drugs (isoniazid and risperidone) did not have CPIC guidelines and were not in the FDA Table but were included based on expert opinion of the STRIPE SDTF and the FDA label.^16,17 Multigene panels were also evaluated within each policy regardless of which genes were included in the panel and any other requirements for eligible population or testing characteristics.

Data Collection

Although medical policies were obtained from Policy Reporter, data elements within the policy were collected manually, including company name, date of last policy update, coverage status for each drug-gene pair, and category of evidence cited for or against coverage (evidence categories are shown in the eAppendix Figure [available at ajmc.com]). A drug-gene pair or multigene panel was considered covered if the policy explicitly indicated that a PGx test was considered medically necessary. For purposes of this analysis, medical necessity criteria refers to the clinical criteria that need to be met for covered testing, the medical policy is the broader policy or document that contains the medical necessity criteria, and coverage is used as a broad term that depends on multiple factors in addition to the medical necessity criteria (eg, benefit design for the member’s health plan, prior authorization, in network vs out of network). Drug-gene pairs that were not mentioned in existing policies were classified as not medically necessary (and therefore not covered). Data from each policy were collected by one coauthor into a spreadsheet and verified independently in entirety by another coauthor. Discrepancies were discussed with the entire coauthor group until consensus was made. The corresponding author (J.N.P.) performed a random quality check of data once finalized. Representatives (eg, medical directors) from each insurer or LBM were invited by email to review coverage data for their policy in this study.

Analysis and Outcomes

The primary objective was to determine whether a PGx test was deemed medically necessary and thus covered for the 65 individual drug-gene pairs and for multigene panels. The secondary objective was to evaluate the evidence cited within each policy that was used to support coverage or lack of coverage for each drug-gene pair. Descriptive statistics were used to summarize all study-related data.

RESULTS

Policies from 12 insurers, 5 LBMs, and MolDX were searched for in Policy Reporter. Of these, policies from 8 insurers (Aetna, Anthem, BCBS FEP, Centene, Cigna, Humana, Independence Health Group, and UnitedHealthcare), 3 LBMs (AIM Specialty Health, Avalon Healthcare Solutions, and eviCore), and MolDX (totaling 12 policies) were available and manually reviewed for the primary and secondary objectives. Policies were unavailable for HCSC, Molina, BeaconLBS, and Kentmere Healthcare Consulting Corporation, and information available in Kaiser Permanente’s policy was insufficient to accurately estimate coverage. Representatives from 3 insurers (Medicare/MolDX, UnitedHealthcare, and Anthem) reviewed our analysis of their medical policy and provided feedback.

Figure 1 illustrates the number of drug-gene pairs covered per policy. MolDX had the highest number of single drug-gene pairs covered (n = 65), followed by Avalon (n = 50), UnitedHealthcare (n = 45), and Centene (n = 33). The remaining 8 policies covered 10 or fewer drug-gene pairs (range, 2-10). Policies from MolDX, Avalon, and UnitedHealthcare indicated coverage or medical necessity for multigene panels, although each had unique criteria to define medical necessity (eg, UnitedHealthcare covers panel testing only for individuals with a diagnosis of major depressive disorder or generalized anxiety disorder).

Figure 2 illustrates the number of policies covering each drug-gene pair. Abacavir/HLA-B and carbamazepine/HLA-B were covered across all policies. Drugs related to HLA-based hypersensitivity reactions and/or those with FDA boxed warnings (except for codeine and tramadol) tended to have more coverage. Only 3 of the drugs are known to have specific requirements or recommendations for testing in the drug label—abacavir/HLA-B, carbamazepine/HLA-B, and rasburicase/G6PD—and oxcarbazepine/HLA-B, allopurinol/HLA-B, mercaptopurine/TPMT/NUDT15, azathioprine/TPMT/NUDT15, thioguanine/TPMT/NUDT15, clopidogrel/CYP2C19, capecitabine/DPYD, and 5-fluorouracil/DPYD had statements to consider testing. Figure 3 categorizes the drug-gene pairs by therapeutic area. The mean number of policies covering drug-gene pairs in rheumatology was 7.67 (range, 7-9), in oncology was 7.0 (range, 3-11), in neurology was 5.71 (range, 3-12), in infectious diseases was 5.0 (range, 2-12), in pain management was 3.25 (range, 2-4), in cardiology was 2.75 (range, 1-9), in gastroenterology was 2.67 (range, 2-4), and in psychology was 2.59 (range, 1-4).

Society guidelines were the most frequently cited evidence across all policies (413 total citations, of which CPIC guidelines were cited 271 times). Other commonly cited clinical practice guidelines included those from the National Comprehensive Cancer Network (NCCN)¹⁸ and American Heart Association (AHA).¹⁹ Randomized controlled trials (RCTs) and prospective studies, retrospective association studies, systematic reviews, and meta-analyses were also cited, listed here in order of frequency. Cost-effectiveness studies were infrequently cited (43 total citations across all medical policies), as were editorials, prescribing information, and case reports. Society guidelines and government agencies were more commonly cited for covered drug-gene pairs, whereas RCTs and prospective studies, association studies, and systematic reviews were more commonly cited for noncovered drug-gene pairs (Figure 4). In some situations, the same references were cited in different medical policies with different coverage determinations (eg, RCTs and systematic reviews for antidepressants were cited to support coverage in one policy but lack of coverage in another policy).

DISCUSSION

Health plan coverage of molecular diagnostics is a key factor to ensure equitable access to personalized medicine for all patients. Variability in coverage between health insurers influences clinicians’ attitudes toward diagnostic testing and their ability to recommend or provide testing to their patients.²⁰ Without health plan coverage, patients who are not offered testing due to provider concerns about coverage or who are unable to pay out of pocket may miss out on the benefits of molecular diagnostics, potentially resulting in suboptimal treatment. This is especially true when the benefits of testing outweigh the cost.

Our analysis confirms the significant variability and, in some cases, limited coverage of common PGx tests across major US health insurers. Encouragingly, CMS (specifically MolDX administered by Palmetto GBA) covered all 65 drug-gene pairs evaluated in this landscape analysis, and UnitedHealthcare, Avalon, and Centene covered at least half. There were significant differences in the evidence cited to support medical policy determinations, suggesting that health insurers have variable evidence thresholds to determine clinical utility. Although a recent systematic review showed that most single-gene PGx tests demonstrated cost-effectiveness or cost savings,¹² advancements in technology and more drug-gene pairs with evidence-based interactions suggest that multigene testing is likely to be more cost-effective. However, only 3 policies covered multigene panels.

We also found that most health insurers covered 10 or fewer drug-gene pairs and that relatively few (n = 4) covered at least half of the 65 evaluated. A 2012 analysis of 5 major US insurers demonstrated that 30% of the 27 PGx tests reviewed were covered,²¹ and a 2017 analysis found that 25 of 55 evaluated policies (across the 5 largest US private payers at the time) mentioned PGx but that none covered testing.²² A 2020 analysis revealed that coverage of PGx tests varied by insurer and that many medical policies related to PGx testing were not readily accessible online.¹¹ However, the availability of the FDA Table and existing CPIC guidelines has led to increased coverage of certain drug-gene pairs by some insurers. In August 2020, the MolDX LCD established expanded coverage of certain PGx tests, with a heavy reliance on CPIC guidelines and the FDA Table.²³ This LCD includes both single-gene and multigene panel tests, the latter of which have additional qualifying criteria. Notably, more than half of all states are covered under MACs that have adopted MolDX.²³ Another MAC, Novitas Solutions,²⁴ has a nearly identical LCD and covers the same drug-gene pairs (eg, those with CPIC level A/B and/or on the FDA Table Section 1 or 2) as MolDX. The remaining MAC, National Government Services, covers 10 states in the Northeast and Midwest and offers limited coverage (fewer than 10 of the 65 drug-gene pairs from this study).²⁵ Despite recent evidence showing the benefits of multigene panels for reducing adverse drug reactions⁴ and the minimal increase in cost compared with single-gene testing, coverage for these tests remains limited. Nonetheless, a recent retrospective analysis of 1039 outpatient claims for PGx testing showed an overall reimbursement rate of 46%, and PGx panels were reimbursed at a higher rate than single-gene tests (74% vs 43%).²⁶ Reimbursement of claims was variable and depended on test type, indication, year of claim, and number of diagnosis codes submitted.

PGx test coverage may depend on the drug’s FDA label. For instance, the FDA requires (eg, HLA-B*57:01 testing for abacavir) or recommends (eg, HLA-B*15:02 testing for carbamazepine) testing for specific patient populations. These examples had the highest and most consistent coverage across policies in this analysis. However, only a small number of drugs have PGx-based recommendations in the FDA label, which could be partly due to infrequent label updates. The FDA’s Project Renewal aims to update the prescribing information for certain older oncology drugs—such as capecitabine, which now includes a recommendation in the FDA label to consider DPYD testing.²⁷ Although this effort is positive, it is unlikely to influence coverage decisions because it falls short of recommending or requiring the test prior to chemotherapy.²⁷

It is important to examine the evidence used to support coverage determinations for PGx testing to better understand the drivers of coverage decisions. These data can help inform the research community on what types of studies, study design parameters, and evidence are needed to improve coverage of PGx tests that show clinical utility. Medical policies frequently cited society guidelines (including CPIC and other clinical practice guidelines), prospective studies, retrospective association studies, systematic reviews and meta-analyses, and government agencies as supporting evidence for coverage determinations. There were few citations for health technology assessments (HTAs) and cost-effectiveness studies. HTAs and cost-effectiveness analyses have historically been uncommon in the US, and decision makers are not routinely using or conducting such analyses, which aligns with our findings.²⁸ The Quality of Health Economic Studies instrument is a resource that can help decision makers assess the quality of health economic studies and can be used as a guide for conducting cost-effectiveness studies.²⁹ There is a critical need to develop a uniform set of HTA guidelines and transparency standards for PGx testing and other molecular diagnostics in the US to help support effective, safe, and cost-effective tests. Ensuring that health policies are cost-effective is especially important in the US given that it spends twice as much on medical care as other high-income countries but does not achieve population health outcomes that are proportional with that level of spending.³⁰

Coverage variability and evidence cited in policies raise questions about what factors are driving coverage and the threshold for coverage. In addition to clinical utility (which appears to be subject to different interpretations among payers), comparative effectiveness outcomes, economic analysis, patient safety, regulatory status, and quality of evidence may also influence coverage decisions. Nonetheless, the evidence review process for each health insurer has resulted in varied medical policy decisions for PGx tests. For example, there is variability in coverage of PGx tests among the various regional BCBS plans across the country, in part because some plans base their medical policies on proprietary evidence reviews from the BCBS Association and others leverage a policy that was developed by an LBM. BCBS North Carolina adopted Avalon’s policy, which according to our analysis covered 50 of the 65 drug-gene pairs, but BCBS FEP covered only 9 drug-gene pairs. Similarly, wide variability was noted between LBMs, with AIM Specialty Health and eviCore covering significantly fewer drug-gene pairs than Avalon.

Although not specifically stated by any insurer, it is possible that factors such as the frequency of the genetic variant and/or severity of the drug-gene interaction (eg, severe hypersensitivity reaction vs diarrhea vs reduced efficacy) are important to medical necessity determinations. Importantly, society guidelines, mainly those from CPIC, were the top-cited resource, but in many cases, they are cited in health plan policies that nevertheless do not recommend PGx testing. Clinical practice guidelines by the NCCN, AHA, and other medical societies remain key influencers for third-party payers to cover or not cover tests, and these guidelines often do not align with CPIC guidelines or the FDA Table, contributing to the variability in coverage and adoption. Although cost-effectiveness studies are infrequently cited in policies, the impact of testing on patient health outcomes, health care utilization, and costs is important to understand for adoption of tests. It is also important to consider data beyond RCTs, including real-world data from retrospective and observational studies, to better understand the impact of testing in clinical practice.

Limitations

There are limitations to this analysis, including the lack of historical information on coverage policies to evaluate trends over time and the fact that policies are frequently updated. Although Policy Reporter is an established source of payer policy data and has been used in multiple prior publications,^31,32 it may not include all policies from all US private payers. Third-party payers may also leverage recommendations from LBMs without specifically referencing them. Some policies had limited publicly available information for which drug-gene pairs were covered and what evidence was used to support their determination. If a medical policy did not mention a specific PGx test, it generally means that the test was not considered medically necessary and thus was assumed to have no coverage; however, other pathways may still allow reimbursement such as prior authorizations or exceptions by demonstrating medical necessity. The exact reimbursement of PGx tests depends upon individuals’ particular benefit type and cost-sharing structure. Although we relied on publicly available information, we also took steps to ensure the accuracy of data collection. For example, each policy was independently reviewed by 2 separate reviewers, and we attempted to confer with medical directors from each insurer to verify findings; however, only 3 responded. Lastly, this analysis focused on PGx tests that were identified as most clinically relevant by PGx experts to better understand the policy landscape and evidence thresholds for tests with the greatest potential impact on clinical care and not all known drug-gene interactions.

CONCLUSIONS

This analysis demonstrated significant variability in policy determinations for clinically relevant PGx tests among major US insurers and LBMs. It is imperative that third-party payers take steps to increase transparency regarding their evidence thresholds for determining clinical utility and coverage of PGx tests. Improved transparency will allow test manufacturers to understand the criteria their tests must meet to be covered, thereby incentivizing innovation and ensuring that newly developed tests meet these standards. Clear and standardized evidence thresholds can facilitate consistent and fair coverage decisions, reducing variability and uncertainty in the market, which can enhance patient access to evidence-based PGx tests. Further, transparency helps build trust among stakeholders, including health care providers, patients, and policy makers, by demonstrating that coverage decisions are based on rigorous and objective criteria. This will in turn benefit the PGx research community and ensure that optimal study designs are developed to inform coverage decisions. We urge stakeholders to take action toward standardizing evidence evaluation processes for PGx testing, thus ensuring equitable access to evidence-based PGx tests.

Acknowledgments

The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. This article was developed by members of the STRIPE collaborative community and reviewed by the STRIPE Steering Committee. The authors would like to acknowledge Dawn Cardeiro, MS, for her contributions and thorough review of the manuscript, and Mori Crocker, Minnie Ng, and Monica Deleon for their assistance with data collection.

Author Affiliations: Division of Cancer Pharmacology & Pharmacogenomics, Atrium Health Levine Cancer Institute (JNP), Charlotte, NC; Atrium Health Wake Forest Baptist Comprehensive Cancer Center (JNP), Winston-Salem, NC; Wake Forest University School of Medicine (JNP), Charlotte, NC; Alva10 (LC), Cambridge, MA; RTI Health Solutions (OMD), Research Triangle Park, NC; Harvard Medical School (CYL), Boston, MA; Harvard Pilgrim Health Care Institute (CYL), Boston, MA; Invitae (CM), San Francisco, CA; EMD Serono, Inc (ER), Billerica, MA; Arbit Consulting LLC (WT), Saint Paul, MN; American Society of Pharmacovigilance (BB, SR), Houston, TX; Department of Pharmacy Practice, Irma Lerma Rangel College of Pharmacy, Texas A&M University (SR), Kingsville, TX; Department of Translational Medical Sciences, College of Medicine, Texas A&M University (SR), Bryan, TX.

Source of Funding: None.

Author Disclosures: Dr Patel serves as a paid consultant to VieCure Inc and Clarified Precision Medicine. Ms Chaihorsky is a shareholder of Alva10 and an independent board member at Cellens, Inc. Dr Lu received institutional funding to Harvard Pilgrim Health Care Institute from Illumina Inc for an unrelated research study. Dr Moretz is employed by and a stockholder of Invitae. Dr Reese is employed by EMD Serono. All authors serve as unpaid members of the Study Design Taskforce within the Standardizing Laboratory Practices in Pharmacogenomics initiative.

Authorship Information: Concept and design (JNP, LC, CYL, CM, ER, WT, BB, SR); acquisition of data (JNP, OMD, CM, ER, BB, SR); analysis and interpretation of data (JNP, LC, OMD, CYL, CM, ER, WT); drafting of the manuscript (JNP, CM, BB, SR); critical revision of the manuscript for important intellectual content (JNP, LC, OMD, CYL, CM, ER, WT, BB, SR); provision of patients or study materials (ER); administrative, technical, or logistic support (JNP); and supervision (JNP, BB).

Address Correspondence to: Jai N. Patel, PharmD, Division of Cancer Pharmacology & Pharmacogenomics, Atrium Health Levine Cancer Institute, 1021 Morehead Medical Dr, Charlotte, NC 28204. Email: jai.patel@atriumhealth.org.

REFERENCES

1. Watanabe JH, McInnis T, Hirsch JD. Cost of prescription drug-related morbidity and mortality. Ann Pharmacother. 2018;52(9):829-837. doi:10.1177/1060028018765159

2. Siddiqui MK, Luzum J, Coenen M, Mahmoudpour SH. Editorial: pharmacogenomics of adverse drug reactions. Front Genet. 2022;13:859909. doi:10.3389/fgene.2022.859909

3. Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011;364(12):1144-1153. doi:10.1056/NEJMra1010600

4. Swen JJ, van der Wouden CH, Manson LE, et al; Ubiquitous Pharmacogenomics Consortium. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet. 2023;401(10374):347-356. doi:10.1016/S0140-6736(22)01841-4

5. Table of Pharmacogenetic Associations. FDA. Updated October 26, 2022. Accessed December 1, 2020. https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations

6. Relling MV, Klein TE, Gammal RS, Whirl-Carrillo M, Hoffman JM, Caudle KE. The Clinical Pharmacogenetics Implementation Consortium: 10 years later. Clin Pharmacol Ther. 2020;107(1):171-175. doi:10.1002/cpt.1651

7. Relling MV, Klein TE. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin Pharmacol Ther. 2011;89(3):464-467. doi:10.1038/clpt.2010.279

8. Hertz DL, Bousman CA, McLeod HL, et al. Recommendations for pharmacogenetic testing in clinical practice guidelines in the US. Am J Health Syst Pharm. 2024;81(16):672-683. doi:10.1093/ajhp/zxae110

9. Qureshi S, Latif A, Condon L, Akyea RK, Kai J, Qureshi N. Understanding the barriers and enablers of pharmacogenomic testing in primary care: a qualitative systematic review with meta-aggregation synthesis. Pharmacogenomics. 2022;23(2):135-154. doi:10.2217/pgs-2021-0131

10. Rogers SL, Keeling NJ, Giri J, et al. PARC report: a health-systems focus on reimbursement and patient access to pharmacogenomics testing. Pharmacogenomics. 2020;21(11):785-796. doi:10.2217/pgs-2019-0192

11. Park SK, Thigpen J, Lee IJ. Coverage of pharmacogenetic tests by private health insurance companies. J Am Pharm Assoc (2003). 2020;60(2):352-356.e3. doi:10.1016/j.japh.2019.10.003

12. Morris SA, Alsaidi AT, Verbyla A, et al. Cost effectiveness of pharmacogenetic testing for drugs with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines: a systematic review. Clin Pharmacol Ther. 2022;112(6):1318-1328. doi:10.1002/cpt.2754

13. Cohen P. The time is now for laboratory benefit management. Managed Healthcare Executive^®. April 18, 2022. Accessed June 6, 2024. https://www.managedhealthcareexecutive.com/view/the-time-is-now-for-
laboratory-benefit-management

14. Tepper N. Insurers invest in lab benefit managers amid growing scrutiny of PBMs. Modern Healthcare. May 15, 2023. Accessed June 6, 2024. https://www.modernhealthcare.com/insurance/lab-benefit-managers-pbm-crackdown

15. Policy Reporter. Accessed March 01, 2023. https://www.policyreporter.com

16. Risperdal. Prescribing information. Janssen; 2009. Accessed June 22, 2023. https://www.accessdata.fda.gov/drugsatfda_docs/label/2009/020272s056,020588s044,021346s033,021444s03lbl.pdf

17. Isoniazid. Prescribing information. Sandoz Inc; 2016. Accessed June 22, 2023. https://www.accessdata.fda.gov/drugsatfda_docs/label/2016/008678s028lbl.pdf

18. NCCN guidelines. National Comprehensive Cancer Network. Accessed June 27, 2023. https://www.nccn.org/guidelines/category_1

19. Guidelines & statements. American Heart Association. Accessed June 27, 2023. https://professional.heart.org/en/guidelines-and-statements

20. Lin GA, Trosman JR, Douglas MP, et al. Influence of payer coverage and out-of-pocket costs on ordering of NGS panel tests for hereditary cancer in diverse settings. J Genet Couns. 2022;31(1):130-139. doi:10.1002/jgc4.1459

21. Hresko A, Haga SB. Insurance coverage policies for personalized medicine. J Pers Med. 2012;2(4):201-216. doi:10.3390/jpm2040201

22. Phillips KA, Deverka PA, Trosman JR, et al. Payer coverage policies for multigene tests. Nat Biotechnol. 2017;35(7):614-617. doi:10.1038/nbt.3912

23. MolDX: pharmacogenomics testing. CMS. Updated June 5, 2020. Accessed June 22, 2023. https://localcoverage.cms.gov/mcd_archive/view/lcd.aspx?lcdInfo=38294:16

24. Pharmacogenomics testing L39063 and A58801. Novitas Solutions. Updated November 18, 2021. Accessed August 1, 2023. https://www.novitas-solutions.com/webcenter/portal/MedicareJL/pagebyid?contentId=00255505

25. Molecular pathology procedures. CMS. Updated June 3, 2024. Accessed June 1, 2024. https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=35000

26. Lemke LK, Alam B, Williams R, et al. Reimbursement of pharmacogenetic tests at a tertiary academic medical center in the United States. Front Pharmacol. 2023;14:1179364. doi:10.3389/fphar.2023.1179364

27. Project Renewal. FDA. Updated December 5, 2024. Accessed June 22, 2023. https://www.fda.gov/about-fda/oncology-center-excellence/project-renewal

28. Drummond MF, Augustovski F, Bhattacharyya D, et al; ISPOR HTA Council Working Group on HTA in Pluralistic Healthcare Systems collaborators. Challenges of health technology assessment in pluralistic healthcare systems: an ISPOR council report. Value Health. 2022;25(8):1257-1267. doi:10.1016/j.jval.2022.02.006

29. Ofman JJ, Sullivan SD, Neumann PJ, et al. Examining the value and quality of health economic analyses: implications of utilizing the QHES. J Manag Care Pharm. 2003;9(1):53-61. doi:10.18553/jmcp.2003.9.1.53

30. Papanicolas I, Woskie LR, Jha AK. Health care spending in the United States and other high-income countries. JAMA. 2018;319(10):1024-1039. doi:10.1001/jama.2018.1150

31. Douglas MP, Ragavan MV, Chen C, et al. Private payer and Medicare coverage policies for use of circulating tumor DNA tests in cancer diagnostics and treatment. J Natl Compr Canc Netw. 2023;21(6):609-616.e4. doi:10.6004/jnccn.2023.7011

32. Brant A, Weinstein IC, Lewicki P, et al. Insurer coverage of prostate cancer biomarkers. Urol Oncol. 2023;41(7):324.e9-324.e12. doi:10.1016/j.urolonc.2023.04.020