Methods for Development of Optimized Complex Expert Systems

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Expert systems (ie, computer systems that emulate the decision-making ability of a human expert) can support decision-making in numerous fields including medicine, finance, and aviation. In our digital age, more data is being collected than ever before. This is particularly true in healthcare for both clinicians and patients who try to manage complex (multiple) chronic conditions. New sources of health data, including continuous physiological measurements taken with home medical devices can now be factored in for decision-making. In addition, computerized access to lab results and medication lists can now be leveraged for use in expert systems for both patients and clinicians. While all these data may be relevant, people have the ability to assimilate only a limited amount of information for decision-making. Reported studies have indicated that expert systems supporting clinical decision-making can improve the performance of healthcare providers especially for purposes of diagnosing, but research on their use with patients are few. In addition, the processes to develop expert systems that incorporate varied and continuous sources of patient data, such as physiological data from the patient’s home, have not been well studied. This research is timely not only because of the proliferation of novel and large data sources, but also because of the recent awareness of the significant impact of poor self-care and suboptimal clinical management of people with multiple chronic conditions (5% of the population consumes > 50% of all dollars devoted to healthcare). However, expert systems used in healthcare must meet an especially high level of rigor in terms of ensuring safety, due to the potential serious negative consequences of inappropriate recommendations from the expert systems. This Discovery Grant would support research investigating and applying expert systems for complex decision-making that incorporates emerging sources of data. This will include investigation into: 1) methods to program and visualize complex expert systems 2) the applicability of different types of logic to be used in expert systems, 3) the safety and utility of embedding user preferences into expert systems, and 4) the role and methods to integrate novel data sources into expert systems for chronic disease management. In particular, this Discovery Grant will fund graduate students to research three specific projects:1) investigate the processes to develop optimized patient expert systems, 2) investigate the processes to develop optimized clinician expert systems, and 3) determine how to incorporate patient health record (PHR) data into expert systems. Many of the processes, techniques, and insights could be applied to other fields. For example, expert systems could be developed that incorporate the growing available data on an individual’s spending, savings, and investments to support their financial decision-making. Future planned research includes investigation into increasingly complex healthcare expert systems (ie, incorporating additional sources of data such as genomics) and other areas of artificial intelligence (eg, predictions of health outcomes or acute events). A process to create optimized expert systems can help manage the "big data challenge" in many sectors.