Unexpected deaths of hospitalized patients continue to be common despite evidence that patients who are at risk often show signs of clinical deterioration hours in advance. Existing early warning systems have significant shortcomings because of their poor reliability and the need for monitoring by overburdened clinical staff. Almost 1 out of 5 patients are readmitted within 30 days of hospital discharge with an annual cost to tax payers of $15-17 Billion. Hence, there is an urgent need for automated early warning systems that can provide timely and accurate information.
The project seeks to integrate and mine patient data from multiple sources, including routine clinical processes, bedside monitoring, at-home sensing, and existing electronic data sources to facilitate optimized patient-centered decision making. Specifically, the project aims to develop techniques and systems to provide early warning of clinical deterioration and hospital readmission of discharged patients using a novel two-tier system. Tier 1 uses data mining algorithms on existing hospital data records to identify patients who are most at risk of clinical deterioration and readmission. Tier 2 combines clinical data with sensor data to improve the accuracy of predictions on patients who are identified as being at risk by Tier 1.
Key innovative aspects of the project include: (1) new data mining algorithms for predicting clinical deterioration and readmission from heterogeneous, multi-scale, and high-dimensional data streams; (2) an alert explanation system to identify the most relevant prognostic factors and suggests possible intervention based on novel feature ranking algorithms; (3) a novel scheme based on cost-sensitive learning to dynamically reconfigure the sensors for achieving good tradeoff between monitoring cost and effectiveness. The resulting advances in healthcare practices that are currently employed in general wards offer several key benefits including (1) reduced workload on clinical staff; (2) capability for continuous monitoring of ward patients that can be used to triage nursing efforts in order to optimize the desired clinical outcomes; (3) capability to extend hospital monitoring to patients at high-risk for hospital readmission with the attendant benefits of reducing readmissions by targeting early preemptive therapeutic interventions.
Plans for transitioning the technology to clinical practice include rigorous evaluation of the technology in real-world settings and broad dissemination of the algorithms and their open-source implementations. Some potential broader impacts of the project include improved clinical outcomes, reduced patient mortality rates and healthcare costs, and enhanced opportunities for research-based interdisciplinary training of graduate students in health informatics. Additional information about the project can be found at: www.cse.wustl.edu/~wenlinchen/project/clinical/
