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
BE-EPIC
Palliative care can provide relief from the symptoms and stress of serious illnesses such as cancer. In our study, BE-EPIC (Behavioral economic interventions to embed palliative care in community oncology), we implemented an intervention consisting of default palliative care (PC) referral orders with an opt-out option to oncologists at Lancaster General Health on rates of PC referrals and utilization among patients with advanced solid cancers. The primary study hypothesis is that default PC referrals directed to clinicians, with an opt-out option, will increase rates of PC utilization in a community oncology setting. BE-EPIC is ongoing – see here for the study protocol recently published with BMJ Open.
HAC Blog
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.
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)