Center for Pharmacy Innovation and Outcomes
Despite advances in treatments and growing evidence supporting best use, gaps in care related to medication use remain, resulting in substantial patient burden (cost, morbidity and mortality) and economic waste in an already stretched healthcare ecosystem.
The Center for Pharmacy Innovation and Outcomes (CPIO) aims to improve medication use for our patients and the health system by investigating medication-related gaps, innovating to develop improved solutions and implementing, evaluating and disseminating scientific insights from these investigations.
Established in 2015, the center was created in partnership with Geisinger’s Enterprise Pharmacy, which oversees system-wide medication use platforms in acute, ambulatory, community and managed care settings, and Geisinger’s Research enterprise.
The CPIO uses:
- Quantitative and qualitative designs: Interrogating data and gathering relevant information on patients, providers and payors
- Health services research: Evaluating the effectiveness of embedded pharmacists in teams
- Informatics: Leveraging a highly evolved health information technology infrastructure
- Pharmacogenomics: Integrating a growing reservoir of genomic information
- Implementation science: Scaling and sustaining high-fidelity effective medication use approaches
Contact us
Center for Pharmacy Innovation and Outcomes
Geisinger Commonwealth School of Medicine Medical Sciences Building
525 Pine St.
Scranton, PA 18509
570-714-6635
Geisinger
100 N. Academy Ave.
Danville, PA 17822-4400
Eric Wright, PharmD, MPH, System Director and Professor
ewright2@geisinger.edu
Melissa Kern, MPH, Program Manager
mskern1@geisinger.edu
Follow our conversations on X using #GeisingerCPIO
Leadership
Faculty
Benjamin Andrick, PharmD
Assistant Professor and Director of Pharmacy Services, Hematology/Oncology
Sarah Krahe Dombrowski, PharmD
Assistant Professor and Clinical MTDM Pharmacist
Staff
Melissa Kern, MPH
Program Manager
Shannon Getchey
Senior Administrative Assistant
Apoorva Pradhan, MPH
Staff Scientist Senior and Instructor Senior
Michael Gionfriddo, PharmD, PhD
Staff Scientist Senior and Instructor Senior
Lorraine Tusing
Project Manager II
Vanessa Hayduk
Project Manager II
Christina Gregor
Project Coordinator
Payton Whary
Data Analyst
Investigational drug pharmacists
Featured projects
Machine Learning Approach to Venous Thromboembolism Prediction in Newly Diagnosed Patients with Cancer Receiving Chemotherapy
Venous Thromboembolism (VTE) is a major source of morbidity and mortality in cancer patients. Currently, the Khorana score is the standard clinical predictive model to stratify cancer patients’ risk for VTE. However, evidence has shown limited predictability of Khorana score to predict VTE. Thus, there is unmet clinical predictive need for this high-risk cancer patient population. Supported by a grant from the Hematology/Oncology Pharmacy Association (HOPA), this study aims to test new machine learning models against established clinical tools like the Khorana score to predict which cancer patients with chemotherapy may develop venous thromboembolism.
Implementation and Evaluation of Preemptive Pharmacogenomics Testing in an Aging Population
It is estimated that >90% of patients above the age of 65 years will presently or within a 5-year period be prescribed a medication influenced by variable genomic expression in patients, which is not predictable without preemptive pharmacogenomic testing. Despite evidence, preemptive pharmacogenomic testing is not standard practice, often confined to select medications in defined populations (e.g. testing for CYP 2C19 genomic variations in PCI patients) and regularly limited to only single gene testing despite similar costs of pharmacogenomic panel tests.
Supported by a Pennsylvania Department of Health grant, the goal of this study is to conduct pharmacogenomics testing (a type of DNA test) within an aging population and measure the impact of this test on medication selection, dosing, healthcare utilization, and costs of care. Learn more here.
Evaluating Strategies to Improve Guideline Directed Medical Therapy: The GDMT Research, Education & Assist Trial for Heart Failure Care (GREAT-HF Care)
Appropriate Guideline-Directed Medical Therapy (GDMT) can reduce all-cause mortality in patients with heart failure with reduced ejection fraction (HFrEF) but uptake of GDMT is suboptimal, driven by clinical inertia, cost concerns and knowledge gaps. GREAT-HF Care aims to optimize medication use and reduce heart failure complications. This pragmatic cluster randomized controlled trial will compare a composite of multiple low-cost electronically administered nudge assists to patients and clinicians, to clinical pharmacist involvement on improvement in GDMT and HFrEF outcomes. Educational sessions will also be studied across groups. This study is supported by an internal quality grant from Geisinger Health Plan. Learn more here.
Clinic Randomized Trial of Clinical Decision Support for Opioid Use Disorders in Medical Settings
Supported by a grant from the National Institute on Drug Abuse (NIDA) from the National Institutes of Health (NIH), this multi-site pragmatic cluster randomized study evaluates the impact of a web-based electronic health record (EHR)-integrated Opioid Use Disorder (OUD) Shared-Decision Making Tool to help identify and manage OUD among patients seen by Primary Care.
Geisinger is one of three large diverse care systems (HealthPartners as prime and Essentia Health) in the study and has randomized 25 Geisinger primary care clinics. A protocol paper of the project is available here along with registration on clinicaltrials.gov. Results are expected in 2024.
Geisinger is one of three large diverse care systems (HealthPartners as prime and Essentia Health) in the study and has randomized 25 Geisinger primary care clinics. A protocol paper of the project is available here along with registration on clinicaltrials.gov. Results are expected in 2024.
Marijuana Reporting Among Geisinger Patients
Supported by a grant from the newly formed Geisinger Academic Clinical Research Center and Ascend Holdings, this study aims to characterize the marijuana use and its documentation within the electronic health record (EHR) among patients seen over a 10 year period at Geisinger. The purpose of this project is to establish the current state of data capture of marijuana within various settings at Geisinger and elucidate barriers and facilitators influencing data capture. This research will help build a foundational base for additional studies enabling associations between marijuana use and outcomes.
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