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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 2017: 4.671), 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: Defining clinical phenotypes from EHR-derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw fr...
Background: Defining clinical phenotypes from EHR-derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely-grained clinical terminology—either native SNOMED CT, or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does vetting that their contents accurately represent the clinically-intended condition. Objective: To compare an intensional (concept hierarchy-based) vs. extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT encoded data from EHRs, by evaluating value set conciseness, time to create, and completeness. Methods: Starting from published CMS 2018 high-priority eCQMs, we selected ten clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (a) (VSAC) downloaded list-based (extensional) value sets, (b) corresponding hierarchy-based intensional value sets for the same conditions, and (c) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional vs. intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts, and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians. Results: The 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 vs. 78 concepts to define, and 5 vs. 37 min to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms. Conclusions: In the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets, rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to include and exclude, and streamline broad EHR implementation of condition-specific decision support promoting guideline adherence for patient benefit.
Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases, and its prevalence is increasing in the U.S...
Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases, and its prevalence is increasing in the U.S. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations. The objective of this review is to evaluate the effectiveness of telemonitoring to manage the chronic disease of COPD. Objective: Researchers want to look at how telemonitoring has been used to observe COPD, and we’re hoping this will lead to more research in telemonitoring of this disease. Methods: The review was conducted and reported in accordance with Assessment for Multiple Systematic Reviews (AMSTAR) and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), respectively. Authors performed a systematic review of Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PubMed databases to obtain relevant articles. Then, articles were accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD. Results: The review’s results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 of 29 articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 indicated no improvement. For facilitators, authors recognized reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost. Conclusions: The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations.
Background: Health data collected during routine care has important potential for reuse for other purposes, especially as part of a learning health system to advance quality of care. Many sources of b...
Background: Health data collected during routine care has important potential for reuse for other purposes, especially as part of a learning health system to advance quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures to ensure privacy. Objective: In this paper we address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring accuracy, validity, safety and privacy. We also demonstrate how these processes can be used in various research designs relevant for learning health systems. Methods: The Healthdata.be platform urges uniformity of data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer is organized through end-to-end encrypted eHealth channels and data is encoded using token keys. Patient identifiers are pseudonymised so that health data from the same patient collected across various sources can still be linked without compromising the deidentification. Results: The Healthdata.be platform currently collects data for more than 150 clinical registries in Belgium. We demonstrate how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial. Conclusions: Collecting health data in various sources and linking this data to a single patient is a promising feature that can potentially address important concerns on validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to the research facility. More research is required to demonstrate that these methods improve the quality of data collection allowing researchers to rely on electronic health records as a valid source for scientific data. Clinical Trial: N/A
Objective: Todays, the smart systems and developed tools such as wearable systems have significantly increased for monitoring the patients and controlling their conditions consistently. The present st...
Objective: Todays, the smart systems and developed tools such as wearable systems have significantly increased for monitoring the patients and controlling their conditions consistently. The present study aimed at determining the requirements for designing a wearable smart blanket system that is able to monitoring the condition of patients in ambulance. Method: After identifying the features of wearable systems based on comparative study, the description of the requirements for creating the proposed system in ambulance was considered. Firstly, some studies were conducted for identifying the wearable system development. Secondly, the questionnaire elicited from the studies was distributed among the physicians and specialists. Results: Wearable smart blanket system has some specific functional features such as monitoring the vital signs, communicating with the surroundings, processing the vital signals instantly, warning when the vital signs exceed the threshold, and storing all vital signs. In addition, they should have the non-functional features such as easy installment and function, interactivity, error fault tolerance, low energy consumption, the accuracy of signs stability, and data analysis. Conclusion: Wearable smart blanket system records all the required vital signs for controlling the individuals in an integrative way and provides the interpreted data for the treatment team in ambulance. Thus, all medical, diagnostic, and monitoring data related to the individuals are stored in the physician assistant system enabling the ambulance physician to take the early diagnosis without delay. The benefits of wearable smart blankets can be converted as an alternative to the current equipment in ambulance.