A Relation Extraction Approach for Clinical Decision Support

In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be leveraged to improve the effectiveness of retrieval functionalities of clinical decision support systems. We present preliminary results and show how relations are able to provide a sizable increase of the precision for several topics, albeit having no impact on others. We then discuss some future directions to minimize the impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th Conference on Information and Knowledge Management (CIKM), October 22-26 2018, Lingotto, Turin, Ital

Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of "Unremarkable Computing", that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience

A clinical informaticist to support primary care decision making

Objectives—To develop and evaluate an information service in which a "clinical informaticist" (a GP with training in evidence-based medicine) provided evidence-based answers to questions posed by GPs and nurse practitioners. Design—Descriptive pilot study with systematic recording of the process involved in searching for and critically appraising literature. Evaluation by questionnaire and semi-structured interview. Setting—General practice. Participants—34 clinicians from two London primary care groups (Fulham and Hammersmith). Main outcome measures—Number and origin of questions; process and time involved in producing summaries; satisfaction with the service. Results—All 100 clinicians in two primary care groups were approached. Thirty four agreed to participate, of whom 22 asked 60 questions over 10 months. Participants were highly satisfied with the summaries they received. For one third of questions the clinicians stated they would change practice in the index patient, and for 55% the participants stated they would change practice in other patients. Answering questions thoroughly was time consuming (median 130 minutes). The median turnaround time was 9 days; 82% of questions were answered within the timeframe specified by the questioner. Without the informaticist, one third of questions would not have been pursued. Conclusion—The clinical informaticist service increased access to evidence for busy clinicians. Satisfaction was high among users and clinicians stated that changes in practice would occur. However, uptake of the service was lower than expected (22% of those offered the service). Further research is needed into how this method of increasing access to evidence compares with other strategies, and whether it results in improved health outcomes for patients

Decision time for clinical decision support systems

Clinical decision support systems are interactive software systems designed to assist clinicians with decision making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the Computer Science community but their inner workings are less well understood by and known to clinicians. In this article we provide a brief explanation of clinical decision support systems and provide some examples of real world systems. We also describe some of the challenges to implementing these systems in clinical environments and posit some of the reasons for limited adoption of decision support systems in practice. We aim to engage clinicians in the development of decision support system that can meaningfully help with their decision making tasks and open up a discussion about the future of automated clinical decision support as a part of healthcare delivery

Extending remote patient monitoring with mobile real time clinical decision support

Large scale implementation of telemedicine services such as telemonitoring and teletreatment will generate huge amounts of clinical data. Even small amounts of data from continuous patient monitoring cannot be scrutinised in real time and round the clock by health professionals. In future huge volumes of such data will have to be routinely screened by intelligent software systems. We investigate how to make m-health systems for ambulatory care more intelligent by applying a Decision Support approach in the analysis and interpretation of biosignal data and to support adherence to evidence-based best practice such as is expressed in treatment protocols and clinical practice guidelines. The resulting Clinical Decision Support Systems must be able to accept and interpret real time streaming biosignals and context data as well as the patient’s (relatively less dynamic) clinical and administrative data. In this position paper we describe the telemonitoring/teletreatment system developed at the University of Twente, based on Body Area Network (BAN) technology, and present our vision of how BAN-based telemedicine services can be enhanced by incorporating mobile real time Clinical Decision Support. We believe that the main innovative aspects of the vision relate to the implementation of decision support on a mobile platform; incorporation of real time input and analysis of streaming\ud biosignals into the inferencing process; implementation of decision support in a distributed system; and the consequent challenges such as maintenance of consistency of knowledge, state and beliefs across a distributed environment

Clinical Decision Support System Sonares

A decision support system SonaRes destined to guide and help the ultrasound operators is proposed and compared with the existing ones. The system is based on rules and images and can be used as a second opinion in the process of ultrasound examination