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JMIR Medical Informatics

Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed
BACKGROUND: Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data...
March 5, 2018: JMIR Medical Informatics
Parika Pahwa, Sarah Lunsford, Nigel Livesley
BACKGROUND: Quality improvement (QI) involves the following 4 steps: (1) forming a team to work on a specific aim, (2) analyzing the reasons for current underperformance, (3) developing changes that could improve care and testing these changes using plan-do-study-act cycles (PDSA), and (4) implementing successful interventions to sustain improvements. Teamwork and group discussion are key for effective QI, but convening in-person meetings with all staff can be challenging due to workload and shift changes...
March 1, 2018: JMIR Medical Informatics
Kamela Charmaine Ng, Conor Joseph Meehan, Gabriela Torrea, Léonie Goeminne, Maren Diels, Leen Rigouts, Bouke Catherine de Jong, Emmanuel André
BACKGROUND: Tuberculosis (TB) is the highest-mortality infectious disease in the world and the main cause of death related to antimicrobial resistance, yet its surveillance is still paper-based. Rifampicin-resistant TB (RR-TB) is an urgent public health crisis. The World Health Organization has, since 2010, endorsed a series of rapid diagnostic tests (RDTs) that enable rapid detection of drug-resistant strains and produce 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...
February 27, 2018: JMIR Medical Informatics
Brett K Beaulieu-Jones, Daniel R Lavage, John W Snyder, Jason H Moore, Sarah A Pendergrass, Christopher R Bauer
BACKGROUND: Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results...
February 23, 2018: JMIR Medical Informatics
Henry W Chen, Jingcheng Du, Hsing-Yi Song, Xiangyu Liu, Guoqian Jiang, Cui Tao
BACKGROUND: Today, there is an increasing need to centralize and standardize electronic health data within clinical research as the volume of data continues to balloon. Domain-specific common data elements (CDEs) are emerging as a standard approach to clinical research data capturing and reporting. Recent efforts to standardize clinical study CDEs have been of great benefit in facilitating data integration and data sharing. The importance of the temporal dimension of clinical research studies has been well recognized; however, very few studies have focused on the formal representation of temporal constraints and temporal relationships within clinical research data in the biomedical research community...
February 22, 2018: JMIR Medical Informatics
Rhea E Powell, Danica Stone, Judd E Hollander
BACKGROUND: Real-time video visits are increasingly used to provide care in a number of settings because they increase access and convenience of care, yet there are few reports of health system experiences. OBJECTIVE: The objective of this study is to report health system and patient experiences with implementation of a telehealth scheduled video visit program across a health system. METHODS: This is a mixed methods study including (1) a retrospective descriptive report of implementation of a telehealth scheduled visit program at one large urban academic-affiliated health system and (2) a survey of patients who participated in scheduled telehealth visits...
February 13, 2018: JMIR Medical Informatics
Amanda Nikolic, Nilmini Wickramasinghe, Damian Claydon-Platt, Vikram Balakrishnan, Philip Smart
BACKGROUND: The use of communication apps on mobile phones offers an efficient, unobtrusive, and portable mode of communication for medical staff. The potential enhancements in patient care and education appear significant, with clinical details able to be shared quickly within multidisciplinary teams, supporting rapid integration of disparate information, and more efficient patient care. However, sharing patient data in this way also raises legal and ethical issues. No data is currently available demonstrating how widespread the use of these apps are, doctor's attitudes towards them, or what guides clinician choice of app...
February 9, 2018: JMIR Medical Informatics
Shuai Zheng, Salma K Jabbour, Shannon E O'Reilly, James J Lu, Lihua Dong, Lijuan Ding, Ying Xiao, Ning Yue, Fusheng Wang, Wei Zou
BACKGROUND: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies...
February 1, 2018: JMIR Medical Informatics
Delphine Carli, Guillaume Fahrni, Pascal Bonnabry, Christian Lovis
BACKGROUND: Computerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts. OBJECTIVE: The objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE)...
January 24, 2018: JMIR Medical Informatics
Dino Trtovac, Joon Lee
BACKGROUND: Malnutrition is a condition most commonly arising from the inadequate consumption of nutrients necessary to maintain physiological health and is associated with the development of cardiovascular disease, osteoporosis, and sarcopenia. Malnutrition occurring in the hospital setting is caused by insufficient monitoring, identification, and assessment efforts. Furthermore, the ability of health care workers to identify and recognize malnourished patients is suboptimal. Therefore, interventions focusing on the identification and treatment of malnutrition are valuable, as they reduce the risks and rates of malnutrition within hospitals...
January 19, 2018: JMIR Medical Informatics
Cheng-Yi Yang, Yu-Sheng Lo, Ray-Jade Chen, Chien-Tsai Liu
BACKGROUND: A computerized physician order entry (CPOE) system combined with a clinical decision support system can reduce duplication of medications and thus adverse drug reactions. However, without infrastructure that supports patients' integrated medication history across health care facilities nationwide, duplication of medication can still occur. In Taiwan, the National Health Insurance Administration has implemented a national medication repository and Web-based query system known as the PharmaCloud, which allows physicians to access their patients' medication records prescribed by different health care facilities across Taiwan...
January 19, 2018: JMIR Medical Informatics
Jennifer Hornung Garvin, Youngjun Kim, Glenn Temple Gobbel, Michael E Matheny, Andrew Redd, Bruce E Bray, Paul Heidenreich, Dan Bolton, Julia Heavirland, Natalie Kelly, Ruth Reeves, Megha Kalsy, Mary Kane Goldstein, Stephane M Meystre
BACKGROUND: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. OBJECTIVE: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. METHODS: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications...
January 15, 2018: JMIR Medical Informatics
Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang
BACKGROUND: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain...
January 5, 2018: JMIR Medical Informatics
Deborah Levesque, Cindy Umanzor, Emma de Aguiar
BACKGROUND: In 2016, 21 million Americans aged 12 years and older needed treatment for a substance use disorder (SUD). However, only 10% to 11% of individuals requiring SUD treatment received it. Given their access to patients, primary care providers are in a unique position to perform universal Screening, Brief Intervention, and Referral to Treatment (SBIRT) to identify individuals at risk, fill gaps in services, and make referrals to specialty treatment when indicated. Major barriers to SBIRT include limited time among providers and low motivation to change among many patients...
January 2, 2018: JMIR Medical Informatics
Kolsoum Deldar, Fatemeh Tara, Kambiz Bahaadinbeigy, Mohammad Khajedaluee, Mahmood Tara
BACKGROUND: Teleconsultation is a guarantor for virtual supervision of clinical professors on clinical decisions made by medical residents in teaching hospitals. Type, format, volume, and quality of exchanged information have a great influence on the quality of remote clinical decisions or tele-decisions. Thus, it is necessary to develop a reliable and standard model for these clinical relationships. OBJECTIVE: The goal of this study was to design and evaluate a data model for teleconsultation in the management of high-risk pregnancies...
December 14, 2017: JMIR Medical Informatics
Anton Oscar Beitia, Tina Lowry, Daniel J Vreeman, George T Loo, Bradley N Delman, Frederick L Thum, Benjamin H Slovis, Jason S Shapiro
BACKGROUND: A health information exchange (HIE)-based prior computed tomography (CT) alerting system may reduce avoidable CT imaging by notifying ordering clinicians of prior relevant studies when a study is ordered. For maximal effectiveness, a system would alert not only for prior same CTs (exams mapped to the same code from an exam name terminology) but also for similar CTs (exams mapped to different exam name terminology codes but in the same anatomic region) and anatomically proximate CTs (exams in adjacent anatomic regions)...
December 14, 2017: JMIR Medical Informatics
Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig
BACKGROUND: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs...
December 8, 2017: JMIR Medical Informatics
Laura Desveaux, James Shaw, Ross Wallace, Onil Bhattacharyya, R Sacha Bhatia, Trevor Jamieson
Virtual technologies have the potential to mitigate a range of challenges for health care systems. Despite the widespread use of mobile devices in everyday life, they currently have a limited role in health service delivery and clinical care. Efforts to integrate the fast-paced consumer technology market with health care delivery exposes tensions among patients, providers, vendors, evaluators, and system decision makers. This paper explores the key tensions between the high bar for evidence prior to market approval that guides health care regulatory decisions and the "fail fast" reality of the technology industry...
December 8, 2017: JMIR Medical Informatics
Isabel Segura Bedmar, Paloma Martínez, Adrián Carruana Martín
BACKGROUND: Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. OBJECTIVE: The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. METHODS: Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity...
December 1, 2017: JMIR Medical Informatics
Ben Wellner, Joan Grand, Elizabeth Canzone, Matt Coarr, Patrick W Brady, Jeffrey Simmons, Eric Kirkendall, Nathan Dean, Monica Kleinman, Peter Sylvester
BACKGROUND: Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation. OBJECTIVE: Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children's hospitals...
November 22, 2017: JMIR Medical Informatics
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