Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing
ABSTRACT Medication prescribing is imperfect, and unintended side effects complicate patient care. Pharmacogenomics (PGx) is an emerging solution that associates genotypes with personalized drug‐related outcomes, but it has not been widely adopted. We hypothesize that patient and provider attributes...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Clinical and Translational Science |
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| Online Access: | https://doi.org/10.1111/cts.70193 |
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| author | Zhong Huang Matthew Jack Kevin J. O'Leary Edith A. Nutescu Thomas Chen Gregory W. Ruhnke David George Larry K. House Randall Knoebel Seth Hartman Anish Choksi Kiang‐Teck J. Yeo Minoli A. Perera Mark J. Ratain David O. Meltzer Peter H. O'Donnell |
| author_facet | Zhong Huang Matthew Jack Kevin J. O'Leary Edith A. Nutescu Thomas Chen Gregory W. Ruhnke David George Larry K. House Randall Knoebel Seth Hartman Anish Choksi Kiang‐Teck J. Yeo Minoli A. Perera Mark J. Ratain David O. Meltzer Peter H. O'Donnell |
| author_sort | Zhong Huang |
| collection | DOAJ |
| description | ABSTRACT Medication prescribing is imperfect, and unintended side effects complicate patient care. Pharmacogenomics (PGx) is an emerging solution that associates genotypes with personalized drug‐related outcomes, but it has not been widely adopted. We hypothesize that patient and provider attributes may predict and promote PGx utilization. We studied PGx using data from the ACCOuNT study, a multi‐institutional prospective trial that implemented broad preemptive PGx result delivery for African American inpatients [Clinicaltrials.gov NCT03225820]. Patients were genotyped, and their PGx information was made available within an integrated informatics portal. Utilization of PGx data (defined as the active choice to review PGx information) was left to the enrolled provider's discretion. Our primary endpoint was to identify patient and care team attributes associated with PGx use. We identified statistically significant univariate predictors and utilized logistic regression to compare relative predictiveness. This study included 187 patients (60.4% female, median age 55, 75.4% treated at the University of Chicago, 17.6% at Northwestern University, and 7.0% at the University of Illinois Chicago) and 188 providers (63.8% MD, 22.3% PharmD, 6.4% PA, and 7.4% APN). In multivariate analysis, we found that the use of PGx information in a prior admission significantly predicted the use in subsequent admissions (OR 7.62, p < 0.05). Similarly, pharmacist participation on care teams significantly predicted PGx use (OR 4.52, p < 0.05). In the first systematic analysis of the impact of patient and care team factors on inpatient PGx clinical decision support (CDS) adoption, we found that actionable care team attributes, such as pharmacist participation or successful initial adoption measures, predict PGx CDS use. |
| format | Article |
| id | doaj-art-ae07852ea63a451584f858cb8c9f5bc0 |
| institution | OA Journals |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-ae07852ea63a451584f858cb8c9f5bc02025-08-20T02:18:43ZengWileyClinical and Translational Science1752-80541752-80622025-04-01184n/an/a10.1111/cts.70193Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient PrescribingZhong Huang0Matthew Jack1Kevin J. O'Leary2Edith A. Nutescu3Thomas Chen4Gregory W. Ruhnke5David George6Larry K. House7Randall Knoebel8Seth Hartman9Anish Choksi10Kiang‐Teck J. Yeo11Minoli A. Perera12Mark J. Ratain13David O. Meltzer14Peter H. O'Donnell15Pritzker School of Medicine The University of Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USADepartment of Medicine, Division of Hospital Medicine Northwestern University Chicago Illinois USADepartment of Pharmacy Practice University of Illinois Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USADepartment of Medicine, Section of Hospital Medicine The University of Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USADepartment of Pharmacy The University of Chicago Chicago Illinois USADepartment of Pharmacy The University of Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USADepartment of Pharmacology Northwestern University Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USADepartment of Medicine, Section of Hospital Medicine The University of Chicago Chicago Illinois USACenter for Personalized Therapeutics The University of Chicago Chicago Illinois USAABSTRACT Medication prescribing is imperfect, and unintended side effects complicate patient care. Pharmacogenomics (PGx) is an emerging solution that associates genotypes with personalized drug‐related outcomes, but it has not been widely adopted. We hypothesize that patient and provider attributes may predict and promote PGx utilization. We studied PGx using data from the ACCOuNT study, a multi‐institutional prospective trial that implemented broad preemptive PGx result delivery for African American inpatients [Clinicaltrials.gov NCT03225820]. Patients were genotyped, and their PGx information was made available within an integrated informatics portal. Utilization of PGx data (defined as the active choice to review PGx information) was left to the enrolled provider's discretion. Our primary endpoint was to identify patient and care team attributes associated with PGx use. We identified statistically significant univariate predictors and utilized logistic regression to compare relative predictiveness. This study included 187 patients (60.4% female, median age 55, 75.4% treated at the University of Chicago, 17.6% at Northwestern University, and 7.0% at the University of Illinois Chicago) and 188 providers (63.8% MD, 22.3% PharmD, 6.4% PA, and 7.4% APN). In multivariate analysis, we found that the use of PGx information in a prior admission significantly predicted the use in subsequent admissions (OR 7.62, p < 0.05). Similarly, pharmacist participation on care teams significantly predicted PGx use (OR 4.52, p < 0.05). In the first systematic analysis of the impact of patient and care team factors on inpatient PGx clinical decision support (CDS) adoption, we found that actionable care team attributes, such as pharmacist participation or successful initial adoption measures, predict PGx CDS use.https://doi.org/10.1111/cts.70193personalized medicinepharmacogenomicsprecision medicine |
| spellingShingle | Zhong Huang Matthew Jack Kevin J. O'Leary Edith A. Nutescu Thomas Chen Gregory W. Ruhnke David George Larry K. House Randall Knoebel Seth Hartman Anish Choksi Kiang‐Teck J. Yeo Minoli A. Perera Mark J. Ratain David O. Meltzer Peter H. O'Donnell Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing Clinical and Translational Science personalized medicine pharmacogenomics precision medicine |
| title | Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing |
| title_full | Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing |
| title_fullStr | Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing |
| title_full_unstemmed | Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing |
| title_short | Care Team Attributes Predict Likelihood of Utilizing Pharmacogenomic Information During Inpatient Prescribing |
| title_sort | care team attributes predict likelihood of utilizing pharmacogenomic information during inpatient prescribing |
| topic | personalized medicine pharmacogenomics precision medicine |
| url | https://doi.org/10.1111/cts.70193 |
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