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

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

Capture and Reuse of Knowledge in ICT-based Decisional Environments

Health care practitioners continually confront with a wide range of challenges, seeking to making difficult diagnoses, avoiding errors, ensuring highest quality, maximizing efficacy and reducing costs. Information technology has the potential to reduce clinical errors and to im-prove the decision making in the clinical milieu. This paper presents a pilot development of a clinical decision support systems (CDSS) entitled MEDIS that was designed to incorporate knowledge from heterogeneous environments with the purpose of increasing the efficiency and the quality of the decision making process, and reducing costs based on advances of in-formation technologies, especially under the impact of the transition towards the mobile space. The system aims to capture and reuse knowledge in order to provide real-time access to clinical knowledge for a variety of users, including medical personnel, patients, teachers and students.Clinical Decision Support Systems, Knowledge Management, Knowledge Interoperability, Mobile Interface, Object-relational Mapping

Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA maturesincluding the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missionsproject focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided

A review on clinical decision support systems in healthcare

Expert systems are widely used in healthcare for predicting and diagnosing diseases. Several efforts have been made to help diagnose diseases, and to identify their codes, signs and symptoms. However, abnormal findings, social circumstances and external causes of diseases in the psychological arena are a huge burden of illness in the community and still a complicated task. Several intelligent techniques based on different rules are used in developing CDSS. Therefore, further investigation on the current state of the field is required in order to identify the related issues and the future directions. This study intends to analyze the current state of the expert systems’ development for psychodiagnostics. That provides a comprehensive background of the available methods, techniques and issues related to CDSS

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

Clinical decision support system (CDSS) – effects on care quality

Purpose – Despite their efficacy, some recommended therapies are underused. The purpose of this paper is to describe clinical decision support system (CDSS) development and its impact on clinical guideline adherence. Design/methodology/approach - A new CDSS was developed and introduced in a cardiac intensive care unit (CICU) in 2003, which provided physicians with patient-tailored reminders and permitted data export from electronic patient records into a national quality registry. To evaluate CDSS effects in the CICU, process indicators were compared to a control group using registry data. All CICUs were in the same region and only patients with acute coronary syndrome were included. Findings – CDSS introduction was associated with increases in guideline adherence, which ranged from 16 to 35 per cent, depending on the therapy. Statistically significant associations between guideline adherence and CDSS use remained over the five-year period after its introduction. During the same period, no relapses occurred in the intervention CICU. Practical implications – Guideline adherence and healthcare quality can be enhanced using CDSS. This study suggests that practitioners should turn to CDSS to improve healthcare quality. Originality/value – This paper describes and evaluates an intervention that successfully increased guideline adherence, which improved healthcare quality when the intervention CICU was compared to the control group

Nurses' views of using computerized decision support software in NHS Direct

Background. Nurses working in NHS Direct, the 24-hour telephone advice line in England, use computerized decision support software to recommend to callers the most appropriate service to contact, or to advise on self-care. Aims. To explore nurses' views of their roles and the computerized decision support software in NHS Direct. Methods. Qualitative analysis of semi-structured interviews with 24 NHS Direct nurses in 12 sites. Findings. Nurses described both the software and themselves as essential to the clinical decision-making process. The software acted as safety net, provider of consistency, and provider of script, and was relied upon more when nurses did not have clinical knowledge relevant to the call. The nurse handled problems not covered by the software, probed patients for the appropriate information to enter into the software, and interpreted software recommendations in the light of contextual information which the software was unable to use. Nurses described a dual process of decision-making, with the nurse as active decision maker looking for consensus with the software recommendation and ready to override recommendations made by the software if necessary. However, nurses' accounts of the software as a guide, prompt or support did not fully acknowledge the power of the software, which they are required to use, and the recommendation of which they are required to follow under some management policies. Over time, the influence of nurse and software merges as nurses internalize the software script as their own knowledge, and navigate the software to produce recommendations that they feel are most appropriate. Conclusions. The nurse and the software have distinct roles in NHS Direct, although the effect of each on the clinical decision-making process may be difficult to determine in practice

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Background: The clinical decision-making process in pressure ulcer management is complex, and its quality depends on both the nurse's experience and the availability of scientific knowledge. This process should follow evidence-based practices incorporating health information technologies to assist health care professionals, such as the use of clinical decision support systems. These systems, in addition to increasing the quality of care provided, can reduce errors and costs in health care. However, the widespread use of clinical decision support systems still has limited evidence, indicating the need to identify and evaluate its effects on nursing clinical practice. Objective: The goal of the review was to identify the effects of nurses using clinical decision support systems on clinical decision making for pressure ulcer management. Methods: The systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. The search was conducted in April 2019 on 5 electronic databases: MEDLINE, SCOPUS, Web of Science, Cochrane, and CINAHL, without publication date or study design restrictions. Articles that addressed the use of computerized clinical decision support systems in pressure ulcer care applied in clinical practice were included. The reference lists of eligible articles were searched manually. The Mixed Methods Appraisal Tool was used to assess the methodological quality of the studies. Results: The search strategy resulted in 998 articles, 16 of which were included. The year of publication ranged from 1995 to 2017, with 45% of studies conducted in the United States. Most addressed the use of clinical decision support systems by nurses in pressure ulcers prevention in inpatient units. All studies described knowledge-based systems that assessed the effects on clinical decision making, clinical effects secondary to clinical decision support system use, or factors that influenced the use or intention to use clinical decision support systems by health professionals and the success of their implementation in nursing practice. Conclusions: The evidence in the available literature about the effects of clinical decision support systems (used by nurses) on decision making for pressure ulcer prevention and treatment is still insufficient. No significant effects were found on nurses' knowledge following the integration of clinical decision support systems into the workflow, with assessments made for a brief period of up to 6 months. Clinical effects, such as outcomes in the incidence and prevalence of pressure ulcers, remain limited in the studies, and most found clinically but nonstatistically significant results in decreasing pressure ulcers. It is necessary to carry out studies that prioritize better adoption and interaction of nurses with clinical decision support systems, as well as studies with a representative sample of health care professionals, randomized study designs, and application of assessment instruments appropriate to the professional and institutional profile. In addition, long-term follow-up is necessary to assess the effects of clinical decision support systems that can demonstrate a more real, measurable, and significant effect on clinical decision making.info:eu-repo/semantics/publishedVersio

Using Clinical Decision Support to Maintain Medication and Problem Lists: A Pilot Study to Yield Higher Patient Safety

To Investigate Whether Clinical Decision Support that Automates the Matching of Ordered Drugs to Problems (Clinical Diagnoses) on the Problem List Can Enhance the Maintenance of Both Medication and Problem Lists in the Electronic Medical Record, We Designed a Clinical Decision Support System to Match Ordered Drugs on the Medication List and Ongoing Problems on the Problem List. We Evaluated the Capability and Performance of This Clinical Decision Support System in Medication-Problem Matching using Physician Expert Chart Audits to Match Ordered Drugs to Ongoing Clinical Problems. a Clinical Decision Support System Was Shown to Be Useful in Improving Medication-Problem Matches in 140 Randomly Selected Audited Patient Encounters in Three Inpatient Units. Enhanced Maintenance of Both the Medication and Problem Lists Can Permit the Exploitation of Advanced Decision Support Strategies that Yield Higher Patient Safety. © 2008 IEEE