End-End Systems


Multi-source Integrated Platform for Answering Clinical Questions

What obstacles deter physicians from pursuing answers to their clinical questions? In a series of studies, Ely and colleagues (Ely et al., 2005) investigate the obstacles to answering questions posed by healthcare professionals. They conclude that physician-related obstacles include "the failure to recognize an information need, the decision to pursue questions only when answers are thought to exist, the preference for the most convenient resource rather than the most appropriate one and the tendency to formulate questions that are difficult to answer with general resources." Resource-related obstacles include "the excessive time and effort required to find answers in existing resources, the lack of access to information resources, the difficulty navigating the overwhelming body of literature to find the specific information that is needed, the inability of literature search technology to directly answer clinical questions, and the lack of evidence that addresses questions arising in practice."

The Center for Computational Language and Education Research (CLEAR) has teamed up with Mayo Clinic to pursue the shared goal of creating a Multi-source Integrated Platform for Answering Clinical Questions (MIPACQ) intended to remove or mitigate many of the preceding obstacles impeding physicians' pursuit of answers to clinical questions.

MIPACQ aims to provide concise summary responses to clinical questions by accessing multiple data sources - the integrated patient's electronic medical record, a physician-vetted knowledge of the most frequently asked questions, the World Wide Web and the scientific literature in MedLine - and by focusing on the semantic processing of the language through a unified semantic representation thus "understanding" the question and the meaning of related narratives. This focus on semantic representation and understanding of the clinical domain and the integration of multiple complementary data sources sets our effort apart from prior work. Prior research focused almost exclusively on using the keyword searches (for example, Demner-Fushman and Lin, 2006). Semantically-based question processing and information extraction from multiple data sources will fuse complementary information for more comprehensive and relevant fact and event retrieval.

MIPACQ establishes another use for the patient electronic medical record (EMR) as an information-rich source for a variety of applications. This further emphasizes the inherent importance and significance of EMR systems, which are currently viewed by most hospital administrators as serving only the important but limited function of billing.

This work has the potential to facilitate significant improvement in and impact on healthcare delivery, as it addresses well-documented and studied health care professional needs (Ely et al., 2005). We aim to provide a unified multi-source solution for semantic retrieval, analysis and summarization of relevant information at the point of care or the lab. As such, the proposed clinical QA system has the potential to play a vital and important decision-support role for the physician or the clinical/biomedical investigator.