Proven by Science
Backed by Data
Peer-reviewed studies validate the safety impact, cost savings, and real-world results of Proove Drug Metabolism.
30%
Reduction in Adverse Drug Reactions
39%
of Inpatient AEs Are Drug-Related
$7,000+
Savings Per Patient (32 Months)
Outpatient Impact of PGx + CDS + PharmD in Four Months
Brixner D, et al. J Med Econ. 2016;19(3):213-228.5
p=0.0002
Fewer ED Visits
p=0.027
Fewer Hospitalizations
prior to test cost
Lower Costs
Post-Hospitalization Impact of PGx + CDS + PharmD in 60 Days
Elliott LS, et al. PLoS One. 2017;12(2):e01709056
p=0.007
Fewer Readmissions
p=0.045
Fewer ED Visits
p=0.054
Lower Mortality
prior to test cost
Lower Costs
Reduction in Care Utilization & Mortality
Brixner et al. (outpatient)5 & Elliott et al. (post-hospitalization)6, combined
Relative Reduction by Outcome
Share of Patients PGx Tested7
36,000
- PGx tested (~13%)
- Not PGx tested (~87%)
Cumulative Savings Over 32 Months
PGx produced cumulative savings of more than $7,000 per patient over a 32-month period, saving $218.34 per patient per month.7
72
Average Patient Age
13
Average Meds Per Patient
4,716 of 36,000
Patients PGx Tested
$7,000+
Savings Per Patient (32 Mo.)
$218.34
PMPM Savings
References
- [1] Li B, et al. Frequencies of pharmacogenomic alleles across biogeographic groups in a large-scale biobank. Am J Hum Genet. 2023 Oct 5;110(10):1628-1647.
- [2] Watanabe JH, McInnis T, Hirsch JD. Cost of Prescription Drug-Related Morbidity and Mortality. Ann Pharmacother. 2018;52(9):829-837.
- [3] Bates DW, Levine DM, Salmasian H, Syrowatka A, Shahian DM, Lipsitz S, Zebrowski JP, Myers LC, Logan MS, Roy CG, Iannaccone C, Frits ML, Volk LA, Dulgarian S, Amato MG, Edrees HH, Sato L, Folcarelli P, Einbinder JS, Reynolds ME, Mort E. The Safety of Inpatient Health Care. N Engl J Med. 2023 Jan 12;388(2):142-153. doi: 10.1056/NEJMsa2206117. PMID: 36630622.
- [4] Swen JJ, van der Wouden CH, Manson LE, et al. 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.
- [5] Brixner D, Biltaji E, Bress A, Unni S, Ye X, Mamiya T, Ashcraft K, Biskupiak J. The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy. J Med Econ. 2016;19(3):213-228. doi: 10.3111/13696998.2015.1110160.
- [6] Elliott LS, Henderson JC, Neradilek MB, Moyer NA, Ashcraft KC, Thirumaran RK. Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: A prospective pilot randomized controlled trial. PLoS One. 2017 Feb 2;12(2):e0170905. doi: 10.1371/journal.pone.0170905. PMID: 28151991.
- [7] Jarvis JP, Peter AP, Keogh M, Baldasare V, Beanland GM, Wilkerson ZT, Kradel S, Shaman JA. Real-World Impact of a Pharmacogenomics-Enriched Comprehensive Medication Management Program. J Pers Med. 2022;12(3):421. doi: 10.3390/jpm12030421. PMID: 35330421.
Peer-Reviewed Studies & Publications
Clinical Utility & Validation Studies
A Prospective, Longitudinal Study to Evaluate the Clinical Utility of a Predictive Algorithm That Detects Risk of Opioid Use Disorder
Brenton A, et al. J Pain Res. 2018;11:119-131.
In 5,397 patients across 100 clinics, physicians used the algorithm to guide treatment for over half of patients, and patients whose treatment was guided by results saw an average pain reduction of 3.4 points on a 1-10 scale.
Read Full StudyObservational Study to Calculate Addictive Risk to Opioids: A Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder
Brenton A, et al. Pharmacogenomics Pers Med. 2017;10:187-195.
Validated the algorithm in 258 patients with diagnosed opioid use disorder and 650 controls, achieving 95.7% sensitivity in identifying high- and moderate-risk patients — sensitivity stayed above 95% even as OUD prevalence varied.
Read Full StudyAdding Genetic Testing to Evidence-Based Guidelines to Determine the Safest and Most Effective Chronic Pain Treatment for Injured Workers
Meshkin B, et al. Int J Biomed Sci. 2015;11(4):157-165.
Makes the case for incorporating genetic testing into evidence-based chronic pain treatment guidelines for injured workers, given the documented genetic contribution to both pain experience and addiction susceptibility.
Read Full StudyA Precision Medicine Approach to a Patient With Unresolved Pain Following Orthopedic Surgery: A Case Report
Gazzaniga D, Brenton A, Meshkin B. J Med Case Rep. 2017;11:50.
A patient previously labeled a “drug seeker” had a Proove Opioid Risk profile showing low addiction risk, correcting the mislabeling and leading clinicians to resolve her treatment complications by narrowing to a single opioid.
Read Full StudyEvaluation of a Predictive Algorithm that Detects Aberrant Use of Opioids in an Addiction Treatment Centre
Farah J, et al. J Addict Res Ther. 2017;8:2.
The average POR score of patients with diagnosed OUD was significantly greater than controls. The algorithm's ROC curve had an AUC of 0.967 — correctly categorizing patients with OUD nearly 97% of the time, with 98% sensitivity and 100% specificity.
Read Full StudyAn Observational Study of the Impact of Genetic Testing for Pain Perception in the Clinical Management of Chronic Non-Cancer Pain
Sharma M, Kantorovich S, Lee C, et al. J Psychiatr Res. 2017;89:65-72.
Based on genetic testing results, physicians adjusted treatment plans for 40% of patients. When medication changes were made, 72% of patients showed clinical improvement, and physicians believed the genetic test results were consistent with patient pain levels in 85% of cases.
Read Full StudyA Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting?
Lee C, Sharma M, Kantorovich S, Brenton A. Health Serv Res Manag Epidemiol. 2018;5.
The profile gave primary care physicians a useful tool to stratify risk of opioid use disorder, rated as beneficial for decision-making and patient improvement by most physicians surveyed — including greater clinical improvement reported for minority patients.
Read Full StudyValidation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting
Sharma M, Lee C, Kantorovich S, et al. Health Serv Res Manag Epidemiol. 2017;4.
In a validation study of 452 patients diagnosed with OUD and 1,237 controls, the algorithm categorized high- and moderate-risk patients with 91.8% sensitivity — staying above 90% regardless of changes in OUD prevalence.
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