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College of
Health Sciences

Center for Pharmacy Innovation and Outcomes

Medications are a primary therapeutic option for patients. 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.
Within the modern healthcare environment, real-world evaluation studies provide evidence of effectiveness of therapy and gaps in care, new and evolving models of healthcare delivery offer hope of improved outcomes, and implementation of optimal and nimble models of care afford insight into improving medication use.
The Center for Pharmacy Innovation and Outcomes (CPIO) aims to improve medication use within our patients and health system by investigating medication-related gaps, innovating for improved solutions, and implementing, evaluating, and disseminating scientific insights gained from investigations.
Established in 2015, the Center for Pharmacy Innovation and Outcomes 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.
Specifically, the CPIO uses:
  • Quantitative and qualitative designs: Interrogating data and gathering relevant information on patients, providers and payors
  • Health services research: Evaluating effectiveness of embedded and trained pharmacists within a team-based setting
  • Informatics: Leveraging a highly evolved health information technology infrastructure
  • Pharmacogenomics: Integrating a growing genomic reservoir of information
  • Implementation science: Scaling and sustaining high-fidelity effective medication use approaches

Contact us

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Center for Pharmacy Innovation and Outcomes
Geisinger Precision Health Center
190 Welles Street, Suite 128
Forty Fort, PA 18704

100 North Academy Avenue
Danville, PA 17822

Eric Wright, PharmD, MPH, System Director and Professor

Melissa Kern, MPH, Program Manager

Follow our conversations on Twitter/X using #GeisingerCPIO


Eric Wright, PharmD, MPH, Research lead

Eric Wright



Benjamin Andrick, PharmD, BCOP

Ben Andrick

Karina Phang, MD

Karina Phang

Ryley Uber, PharmD

Ryley Uber

Jove Graham, PhD

Jove Graham

Daniel Longyhore, PharmD, EdD, BCACP

	Daniel Longyhore

Brian Piper, PhD, MS

Brian Piper

Katrina Romagnoli, PhD

Photo not available.

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 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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