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Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.
JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (http://www.jmir.org/issue/current).
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Background: Effective diabetes self-management that rests on the behavior of an individual can improve outcomes, decrease the risk of complications, reduce diabetes related hospitalizations and costs....
Background: Effective diabetes self-management that rests on the behavior of an individual can improve outcomes, decrease the risk of complications, reduce diabetes related hospitalizations and costs. Objective: To develop and evaluate a computerized decision support platform called “Diabetes Web-Centric Information and Support Environment (DWISE)” that assist primary care practitioners (PCP) in applying standardized behavior change strategies and clinical practice guidelines (CPG)-based recommendations to an individual patient and, empower the patient with the skills and knowledge to self-manage their diabetes through planned, personalized and pervasive behavior change strategies. Methods: Healthcare Knowledge Management approach is used to implement DWISE that features the following functionalities: (i) Assessment of PCP’s readiness to administer validated behavior change interventions to patients with diabetes patients; (ii) Educational support to PCP to help them offer behavior change interventions to patients; (iii) Access to evidence-based material, such as the CDA CPG, to the PCP; (iv) Development of personalized patient self-management programs to help patients with diabetes achieve healthy behaviors to meet CDA targets for managing type 2 diabetes; (v) Educational support for patients to help them achieve behavior change; (vi) Monitoring the patients’ progress in adhering to their behavior change program and motivating them to be in compliance with their program. DWISE offers these functionalities through an interactive web-based interface to PCP, whereas the patient’s self-management program and associated behavior interventions are delivered through mobile patient diary on the smart phones and tablets Results: DWISE has been tested for its usability, functionality usefulness and acceptance through a series of qualitative studies. For the PCP tool, most usability problems were associated with the navigation of the tool, and the presentation, formatting, understandability and suitability of the content. For the patient tool, most issues are related to the tool’s screen layout and design features, understandability of the content, clarity of the labels used and navigation across the tool. Facilitators and barriers to DWISE use in shared decision-making environment have also been identified. Conclusions: This work has provided a unique e-health solution to translate complex healthcare knowledge in terms of easy-to use, evidence-informed, point-of-care decision aids for PCP and patients The results are been used to guide the necessary modification of DWISE. Clinical Trial: N/A
Background: “COMPASS” (“Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers) is an eHealth platform designed to improve cancer care delivery through passive monito...
Background: “COMPASS” (“Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers) is an eHealth platform designed to improve cancer care delivery through passive monitoring of patients’ health status and delivering customizable reports to clinicians. Based on data from sensors and context-driven administration of patient-reported outcome (PRO) measures, key indices of patients’ functional status can be collected between regular clinic visits, supporting clinicians in the delivery of patient care. Objective: The aim of the first phase of this project was to systematically collect input from oncology providers and patients on potential clinical applications for COMPASS in order to refine the system. Methods: Ten clinicians representing various oncology specialties and disciplines completed semi-structured interviews designed to solicit clinician input on how COMPASS can best support clinical care delivery. Three cancer patients tested a prototype of COMPASS for 7 days and provided feedback. Interview data was tabulated using thematic content analysis (TCA) to identify the most clinically relevant objective and PRO domains. Results: TCA revealed that clinicians were most interested in monitoring vital statistics, symptoms and functional status, including physical activity level (n=9), weight (n=5), fatigue (n=9), sleep quality (n=8) and anxiety. Patients (2 in active treatment, 1 in remission) reported that they would use such a device, were enthusiastic about their clinicians monitoring their health status, especially the tracking of symptoms, and felt knowing their clinicians were monitoring and reviewing their health status provided valuable reassurance. Patients would however like to provide some context to their data. Conclusions: Clinicians and patients both articulated potential benefits of the COMPASS system in improving cancer care. From a clinician standpoint, data needs to be easily interpretable and actionable. The fact that patients and clinicians both see potential value in eHealth systems suggests wider adoption and utilization could prove to be a useful tool for improving care delivery.
Background: Tuberculosis (TB) is the top killer infectious disease in the world, and yet the surveillance of this disease is still paper-based. Rifampicin resistant TB (RR-TB) is an urgent public heal...
Background: Tuberculosis (TB) is the top killer infectious disease in the world, and yet the surveillance of this disease is still paper-based. Rifampicin resistant TB (RR-TB) is an urgent public health crisis, and the World Health Organization has endorsed since 2010 a series of rapid diagnostic tests (RDTs) that allowed rapid detection of drug resistant strains and produced large volumes of data. In parallel, most high burden countries have adopted connectivity solutions that allow linking of diagnostics, real-time capture and shared repository of these test results. However, these connected diagnostics and readily available test results are not utilised to their full capacity as we have yet to capitalize on fully understanding the relationship between test results and specific rpoB mutations to elucidate its potential application on real-time surveillance. Objective: We aimed to validate and analyse RDT data in detail, and propose the potential use of connected diagnostics and associated test results for real-time evaluation of RR-TB transmission. Methods: From the Belgian Coordinated Collections of Microorganisms at the Institute of Tropical Medicine, 107 RR-TB strains harbouring 34 unique rpoB mutations, including 30 within the Rifampicin Resistance Determining Region (RRDR), were selected. These strains were subjected to XpertMTB/RIF (Cepheid), GenoTypeMTBDRplusv2.0 (Hain LifeScience GmbH), and GenoscholarNTM+MDRTBII (Nipro), the results of which were validated against the strains’ available rpoB gene sequences. The reproducibility of the results was determined, and the probe reactions were analysed and visualised, and proposed for potential use in evaluating transmission. Results: TB diagnostic test results, particularly the RDT probe reactions detected the majority of RRDR mutations tested, although a few critical discrepancies between observed probe reactions and manufacturer claims were found. Based on published frequencies of probe reactions and RRDR mutations, we found specific probe reactions with high potential use in transmission studies namely XpertMTB/RIF probes A, Bdelayed, C, Edelayed; GenotypeMTBDRplusv2.0 WT2, WT5, WT6; and GenoscholarNTM+MDRTBII S1, S3. Additionally, inspection of probe reactions of disputed mutations may potentially resolve discordance between genotypic and phenotypic test results. Conclusions: We propose a novel approach for potential real-time detection of RR-TB transmission through fully utilizing connected TB diagnostics and shared repository of test results. To our knowledge, this is the first pragmatic and scalable work in response to the consensus of world-renowned TB experts in 2016 on the potential of diagnostic connectivity for accelerated efforts toward TB elimination. This is evidenced by the ability of our proposed approach to facilitate comparison of probe reactions between and among different RDTs used in the same setting. Integrating this proposed approach as a plug-in module to a connectivity platform will increase usefulness of connected TB diagnostics for RR-TB outbreak detection through real-time investigation of suspected RR-TB transmission cases based on epidemiological linking.
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Background: Successful models of information and communication technology (ICT) applied to cost-effective delivery of quality care in low- and middle-income countries (LMIC) are an increasing necessit...
Background: Successful models of information and communication technology (ICT) applied to cost-effective delivery of quality care in low- and middle-income countries (LMIC) are an increasing necessity. Severe thalassemia (ST) is one of the most common life-threatening non-communicable diseases of children globally. Objective: To study the impact of ICT on quality of care for ST patients in LMIC. Methods: A total of 1110 patients with ST from 5 centers in India were followed over a one-year period. The impact of consistent use of a web-based application platform designed to assist comprehensive management of ST (ThalcareTM) on key indicators of quality of care such as minimum (pre-transfusion) hemoglobin, serum ferritin, liver size and spleen size was assessed. Results: For four centers, the improvement in mean pre-transfusion hemoglobin level was statistically very significant (P<0.001). Four out of five centers achieved reduction in mean ferritin levels with two displaying a highly significant drop in ferritin (P=0.003 and P=0.0002). One of the five centers did not record liver and spleen size on palpation, but out of the remaining 4 centers, 2 witnessed a strongly significant drop in liver and spleen size (P <0.01), 1 witnessed moderate drop (P= 0.05 for liver P =0.03 for spleen size) while the fourth witnessed a moderately increase in liver size (P =0.08) and insignificant change in spleen size (P=0.12). Conclusions: Implementation of Computer-Assisted Treatment Planning and Performance Assessment positively impacted on indices reflecting effective delivery of care to patients suffering from ST in LMIC consistently.
Background: Advances in magnetic resonance imaging (MRI) and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyse relevant informat...
Background: Advances in magnetic resonance imaging (MRI) and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyse relevant information from MRI data to aid decision-making, prevent errors and enhance health care. Objective: The aim of this study was to design and develop a modular Medical Image Region of interest analysis tool and Repository (MIROR) for automatic processing, classification, evaluation and representation of advanced MRI data. Methods: The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumours in children (cohort of 48 children, with 37 malignant and 11 benign tumours). Mevislab software and Python have been used for development of MIROR. Regions of interests were drawn around benign and malignant body tumours on different diffusion parametric maps and extracted information was used to discriminate the malignant from benign tumours. Results: Using MIROR, the various histogram parameters derived for each tumour case when compared with the information in the repository, provided additional information for tumour characterization and facilitated the discrimination between benign and malignant tumours. Clinical decision support system cross validation showed high sensitivity and specificity in discriminating between these tumour groups for various histogram parameters, 100 % for kurtosis and entropy 85% and 78% respectively over all parameters. Conclusions: MIROR as a diagnostic tool and repository allowed the interpretation and analysis of MRI images to be more accessible and comprehensive for clinicians. It aims to increase clinicians’ skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision-making. The modular-based format of the tool allowed integration of analyses which are not readily available clinically and streamlines future developments. Clinical Trial: N/A