Center for Artificial Intelligence in Clinical Informatics (CAICI)

Clinical care is complex, data-driven and increasingly outstripping the cognitive abilities of clinicians. Artificial intelligence (AI) has the potential to support, enable and improve clinical decision-making to make it faster, accurate, and economical. In particular, AI-enabled realtime and near realtime monitoring, alerting and guiding at the point of clinical care will power the next generation of clinical decision support. The Center for Artificial Intelligence in Clinical Informatics (CAICI) focuses on developing, implementing and evaluating high performance clinical decision support tools that aid in very specific clinical tasks and are powered by AI and machine learning. CAICI is a core informatics center in the Department of Biomedical Informatics at the University of Pittsburgh and is directed by Shyam Visweswaran, MD, PhD.

Goal

The goal of CAICI is to 1) develop AI tools for unmet clinical needs; 2) demonstrate internal and external validity of tools; 3) ensure that the tools are fair (algorithmic fairness), 4) obtain FDA approval, and 5) monitor for degradation in performance (algorithmic robustness). We will advance both the science and the engineering of AI-powered clinical decision support to improve health.

  • 1) Develop. We will identify unmet clinical needs that can be addressed by using AI for pattern recognition or for prediction. The AI solution will generate insightful predictions that are actionable and have the potential to impact patient care. Specifically, we will focus on AI-augmented CDS that is used at the point of care and involves in-person patient interactions. Moreover, we will focus on using AI with a human in the loop rather than on autonomous AI with the human out of the loop.
  • 2) Demonstrate validity. Validation of an AI tool will typically involve multiple iterations. The initial iteration will use retrospective data to evaluate statistical validity, clinical utility and economic utility. Statistical validity addresses the question: does the AI model perform well on metrics of discrimination and calibration? Clinical utility addresses the question: can the AI model improve clinical care and patient outcomes? Economic utility addresses the question: can the AI model produce cost savings and increase efficiency?
  • 3) Ensure fairness. The AI tools should be non-discriminatory (algorithmic fairness) for sensitive attributes such as age, sex, race, and socioeconomic status. Ensuring fairness is vital as AI tools increasingly play an important role in decisions related to health and the potential for harm increases.
  • 4) Obtain FDA approval. The Software as a Medical Device (SaMD) regulatory framework that is evolving at the FDA will enable rapid approval of AI that is designed to aid clinical decision-making. It is critical to obtain FDA certification for real-word deployment of AI tools.
  • 5) Monitor for degradation in performance. AI tools that are clinically deployed need to be evaluated and monitored for degradation in performance (algorithmic robustness) including degradation over time, across different geographical locations, and across populations that differ in disease severity or prevalence of the outcome.

Current projects at the CAICI include:

  • utilizing machine learning to enable EMR systems to deliver the right data of the right patient at the right time in the intensive care unit (ICU)
  • applying machine learning to identify adverse brain events in real time during surgical procedures
  • building machine learning models to predict outcomes in central line-associated bloodstream infections

News

2015 September NLM R01 funded for Development and evaluation of a learning electronic medical record system. Electronic medical records (EMRs) are capturing increasing amounts of patient data that can be leveraged by machine learning methods for computerized clinical decision support. This project focuses on developing a learning EMR system that uses machine learning to provide decision support using the right data, at the right time.