A Fast Interactive Search System for Healthcare Services

Abstract

In this paper we describe the design, development, and evaluation of a general human-machine interaction search system, and its potential and use in the context of a collaboration project with SAP and Saffron. The objective of a specialized version of the system is to provide medical and healthcare information services to users via interactive search for personalized patient needs. Patients usually have questions regarding healthcare, including those which concern illness symptoms, duration and types of treatment, possible drug effects, and more. Authorized personnel would often be ideal in responding to such needs; however they could potentially be very expensive, and not easy to support and maintain. If patients could have access to information at their home, by means of i-phone or online access, this could save time, doctor office visit expenses, as well as valuable and restricted medical time. What is more, information concerning other anonymized and similar patient cases provides knowledge and perspective on a wide range of patient issues. From the doctors' perspective, they typically need to spend time on differential analysis about new patient cases: study symptoms, research possible causes, rank results by emergency priority and treat them accordingly. A search system that would direct a doctor (or patient/user) to similar patient cases would save significant amount of manual search time. The powerful new feature of this system is the storage and mining of past patient cases knowledge, to create metadata to be used in the subsequent retrieval of relevant documents. Finally, the interactive search system would speed up identification of rare cases; for instance, symptoms that do not appear commonly in past cases may require special treatment or expert referral. We build a model which dynamically learns medical needs of interacting MDs and patients. The model works on free or unstructured text, allowing disambiguation of vague words and flexibility in describing medical needs. In addition, both experts with an advanced knowledge of medical terminology, and beginning users using basic medical terms, can achieve high search relevance. Furthermore, our approach obviates the need for the assignment of tags or labels, such as treatment, symptoms, causes, to documents, to respond effectively to user queries. In particular, we build a temporal difference algorithm to predict user's information needs by incorporating both current and predicted knowledge into learning the user profile. Our source of information about the user consists of submitted queries and feedback on the returned results. We tested our system on publicly available medical data (OhsuMed TREC dataset 2002) and we achieved a significant improvement in retrieval accuracy, compared to the literature. We provide quantitative results as well as demonstration screenshots which illustrate a) the value of interaction (user time spent with system versus results accuracy), b) the value of using medical terminology understanding, when compared with simple general words, and c) the value of allowing the maximum number of feedback submissions to vary.