What We Do
Our Approach
- Develop
- Train and validate predictive and diagnostic algorithms using real-world data
- Plan
- Quantitative and mixed-methods exploration to identify optimal use cases and implementation strategies
- Test
- Implementation and prospective evaluation of algorithm-enabled interventions
- Scale
- Scaling algorithms for clinical impact
Latest Updates
Featured Project
Precision Bone Screening in Prostate Cancer
In this study, we validate biomechanical computed tomography (BCT) as an independent predictor of fracture among men with advanced prostate cancer. BCT-assessed bone density was highly concordant with DXA measurements. Given the low use of guideline-directed DXA screening in the real world, BCT of routinely collected CT scans may offer an accurate and convenient means to screen for fracture risk among patients with mHSPC.
HAC Blog
HACBlog: Staff Spotlight #2
Updates from the HACLab for October 2023
Author: Will Ferrell
Introduction to Algorithmic Bias
Algorithms leverage existing data to predict an outcome, using inputs that are associated with the outcome. One problem with the forthcoming tide of machine learning algorithms is that such algorithms can be biased. In this blog, we start exploring the concept of "Algorithmic Bias"
Author: Caleb Hearn
Introduction to Performance Drift
Algorithms are routinely used in the clinic to make decisions on patient care. Over time these algorithms may deteriorate in performance. Here, we start the explore the concept of “performance drift”.
Author: Likhitha Kolla
HACBlog: Staff Spotlight #1
Updates from the HACLab for August 2023
Author: Will Ferrell
How do we regulate AI in healthcare?
AI's role in our daily lives continues to become more omnipresent and healthcare is no exception. As AI becomes more routinely incorporated in healthcare, a central question becomes – how do we regulate it?
Author: Ravi Parikh
HACLab at the 2023 AcademyHealth Annual Research Meeting!
Caleb M. Hearn, MPH, CAPM will have a podium talk “Hospice Provider Perspectives on Providing Earlier Palliative Care for Patients with Serious Illness”
Jenna Steckel, MSW will have a poster presentation “Clinician Perspectives on Virtual Palliative Care for Patients with Advanced Illness”
HACLab at the 2023 ASCO Annual Meeting!
Welcome to the Human-Algorithm Collaboration Lab (HACLab)!
Founded at the Perelman School of Medicine and the Abramson Cancer Center at the University of Pennsylvania, we are a laboratory focusing on the development, validation, implementation, and scaling of advanced algorithms in clinical care and health policy.
Author: Ravi Parikh
What is..
Machine learning?
Cancer survivorship?
Algorithm unfairness?
Real World Data?
Patient Generated Health Data?
A branch of AI and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy (source:IBM)
The health and well-being of a person with cancer from the time of diagnosis until the end of life. This includes issues related to follow-up care, late effects of treatment, cancer recurrence, second cancers, and quality of life (source:cancer.gov)
Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. It also, occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process (source:FSU)
The data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources- electronic health records (EHRs), claims and billing activities, pghd, product and disease registries (source:FDA)
Health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern (source:healthit)