Since the publication of the Institute of Medicine’s report titled “To Err is Human: Building a safer health system,” the important role of information technology in detecting outbreaks, providing superior quality of care at lower costs and in the prevention of adverse events has been widely recognized.
Assistant Professor of Information Systems, Heinz College
Professor of Management Science & Healthcare Informatics, Heinz College
This center is organized around three thrusts:
The main goal of this thrust is to assist hospitals, government, and public health organizations by facilitating the early and accurate detection of major health events such as: a bioterrorist attack (e.g., anthrax release), an outbreak of emerging infectious disease (e.g., avian influenza or SARS), and contamination of the food or water supply. Accurate identification of outbreaks and other public health threats requires us to integrate information from a variety of sources, including both traditional health data sources (e.g. hospital records and medication sales) and new web-scale data sources (e.g. health-related blogs and Internet search queries). New statistical, computational, and machine learning methods are being developed to improve the timeliness and quality of event detection in massive and high-dimensional health data, and to provide public health with new tools for outbreak detection and investigation.
Healthcare IT adoption and evaluation:
Working in close partnership with clinicians, CIOs and health systems in the region, this thrust will focus on the development of models, methods, tools and generalizeable insights for use by both clinicians and patients with the objective of conducting field studies to understand the drivers of successful adoption and use of information and decision technologies in healthcare. Operational objectives such as analyzing patient throughput and process visibility for enhancing and standardizing care delivery processes, evaluating adoption and use of mobile clinical applications in patient care, and examining impact of web-based personalized and interactive health applications on the beliefs and behavior of users are some examples of ongoing research in this area.
IT-enabled healthcare decision making:
The main goal of this thrust is to improve the quality of patient care and lower healthcare costs by developing and evaluating decision support approaches that combine visualization, machine learning, and optimization techniques to point-of-care decision making. Some examples of current research include a) providing medical professionals with understandable, easy-to-use, and up-to-date information about their patient population (e.g., identifying risk factors and patient groups at-risk for diabetes and other chronic diseases), b) preventing medical errors (e.g., adverse drug events) by improving the quality of error-correcting procedures such as medication reconciliation, c) identifying patterns and standards of patient care that lead to improved health outcomes and lower costs, and d) using the “wisdom of crowds” combined with automatic pattern detection systems to assist physicians in common medical tasks such as identifying anomalies in medical images.