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JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.


Journal Description

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 (


Recent Articles:

  • User engaged in at-home telemonitoring. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Effect of Seasonal Variation on Clinical Outcome in Patients with Chronic Conditions: Analysis of the Commonwealth Scientific and Industrial Research...


    Background: Seasonal variation has an impact on the hospitalization rate of patients with a range of cardiovascular diseases, including myocardial infarction and angina. This paper presents findings on the influence of seasonal variation on the results of a recently completed national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia. Objective: The aim is to evaluate the effect of the seasonal timing of hospital admission and length of stay on clinical outcome of a home telemonitoring trial involving patients (age: mean 72.2, SD 9.4 years) with chronic conditions (chronic obstructive pulmonary disease coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) and to explore methods of minimizing the influence of seasonal variations in the analysis of the effect of at-home telemonitoring on the number of hospital admissions and length of stay (LOS). Methods: Patients were selected from a hospital list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered patients who had one or more admissions in the years from 2010 to 2012. Three different groups were analyzed separately because of substantially different climates: (1) Queensland, (2) Australian Capital Territory and Victoria, and (3) Tasmania. Time series data were analyzed using linear regression for a period of 3 years before the intervention to obtain an average seasonal variation pattern. A novel method that can reduce the impact of seasonal variation on the rate of hospitalization and LOS was used in the analysis of the outcome variables of the at-home telemonitoring trial. Results: Test patients were monitored for a mean 481 (SD 77) days with 87% (53/61) of patients monitored for more than 12 months. Trends in seasonal variations were obtained from 3 years’ of hospitalization data before intervention for the Queensland, Tasmania, and Australian Capital Territory and Victoria subgroups, respectively. The maximum deviation from baseline trends for LOS was 101.7% (SD 42.2%), 60.6% (SD 36.4%), and 158.3% (SD 68.1%). However, by synchronizing outcomes to the start date of intervention, the impact of seasonal variations was minimized to a maximum of 9.5% (SD 7.7%), thus improving the accuracy of the clinical outcomes reported. Conclusions: Seasonal variations have a significant effect on the rate of hospital admission and LOS in patients with chronic conditions. However, the impact of seasonal variation on clinical outcomes (rate of admissions, number of hospital admissions, and LOS) of at-home telemonitoring can be attenuated by synchronizing the analysis of outcomes to the commencement dates for the telemonitoring of vital signs. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12613000635763; (Archived by WebCite at 6xLPv9QDb)

  • Source: Freepik; Copyright: jannoon028; URL:; License: Licensed by JMIR.

    Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation


    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. Objective: Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods: Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results: Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions: To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time.

  • Improving aseptic practices with newborns in India. Source: Pixabay; Copyright: Engin Akyurt; URL:; License: Public Domain (CC0).

    Experiences of Indian Health Workers Using WhatsApp for Improving Aseptic Practices With Newborns: Exploratory Qualitative Study


    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. Mobile technologies can support communication within a team when face-to-face meetings are not possible. WhatsApp, a mobile messaging platform, was implemented as a communication tool by a neonatal intensive care unit (NICU) team in an Indian tertiary hospital seeking to reduce nosocomial infections in newborns. Objective: This exploratory qualitative study aimed to examine experiences with WhatsApp as a communication tool among improvement team members and an external coach to improve adherence to aseptic protocols. Methods: Ten QI team members and the external coach were interviewed on communication processes and approaches and thematically analyzed. The WhatsApp transcript for the implementation period was also included in the analysis. Results: WhatsApp was effective for disseminating information, including guidance on QI and clinical practice, and data on performance indicators. It was not effective as a platform for group discussion to generate change ideas or analyze the performance indicator data. The decision of who to include in the WhatsApp group and how members engaged in the group may have reinforced existing hierarchies. Using WhatsApp created a work environment in which members were accessible all the time, breaking down barriers between personal and professional time. The continual influx of messages was distracting to some respondents, and how respondents managed these messages (eg, using the silent function) may have influenced their perceptions of WhatsApp. The coach used WhatsApp to share information, schedule site visits, and prompt action on behalf of the team. Conclusions: WhatsApp is a productive communication tool that can be used by teams and coaches to disseminate information and prompt action to improve the quality of care, but cannot replace in-person meetings.

  • GeneXpert® testing of patient sample to confirm the presence of Mycobacterium tuberculosis and susceptibility/resistance to rifampicin. Source: Flickr; Copyright: USAID Asia; URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Potential Application of Digitally Linked Tuberculosis Diagnostics for Real-Time Surveillance of Drug-Resistant Tuberculosis Transmission: Validation and...


    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. However, these connected diagnostics and readily available test results are not used 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 to real-time surveillance. Objective: We aimed to validate and analyze 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: We selected 107 RR-TB strains harboring 34 unique rpoB mutations, including 30 within the rifampicin resistance–determining region (RRDR), from the Belgian Coordinated Collections of Microorganisms, Antwerp, Belgium. We subjected these strains to Xpert MTB/RIF, GenoType MTBDRplus v2.0, and Genoscholar NTM + MDRTB II, the results of which were validated against the strains’ available rpoB gene sequences. We determined the reproducibility of the results, analyzed and visualized the probe reactions, and proposed these for potential use in evaluating transmission. Results: The RDT probe reactions detected most RRDR mutations tested, although we found a few critical discrepancies between observed results and manufacturers’ claims. Based on published frequencies of probe reactions and RRDR mutations, we found specific probe reactions with high potential use in transmission studies: Xpert MTB/RIF probes A, Bdelayed, C, and Edelayed; Genotype MTBDRplus v2.0 WT2, WT5, and WT6; and Genoscholar NTM + MDRTB II S1 and S3. 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 using digitally linked 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 to accelerate efforts to eliminate TB. This is evidenced by the ability of our proposed approach to facilitate comparison of probe reactions between 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 epidemiologic linking.

  • Source: Pixabay; Copyright: Free-Photos; URL:; License: Public Domain (CC0).

    Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis


    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. Objective: The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. Methods: We analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling). Results: Our results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation. Conclusions: The analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available.

  • Source: Flickr; Copyright: US Food and Drug Administration; URL:; License: Public Domain (CC0).

    Representation of Time-Relevant Common Data Elements in the Cancer Data Standards Repository: Statistical Evaluation of an Ontological Approach


    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. In particular, temporal information can be extremely powerful to enable high-quality cancer research. Objective: The objective of the study was to develop and evaluate an ontological approach to represent the temporal aspects of cancer study CDEs. Methods: We used CDEs recorded in the National Cancer Institute (NCI) Cancer Data Standards Repository (caDSR) and created a CDE parser to extract time-relevant CDEs from the caDSR. Using the Web Ontology Language (OWL)–based Time Event Ontology (TEO), we manually derived representative patterns to semantically model the temporal components of the CDEs using an observing set of randomly selected time-related CDEs (n=600) to create a set of TEO ontological representation patterns. In evaluating TEO’s ability to represent the temporal components of the CDEs, this set of representation patterns was tested against two test sets of randomly selected time-related CDEs (n=425). Results: It was found that 94.2% (801/850) of the CDEs in the test sets could be represented by the TEO representation patterns. Conclusions: In conclusion, TEO is a good ontological model for representing the temporal components of the CDEs recorded in caDSR. Our representative model can harness the Semantic Web reasoning and inferencing functionalities and present a means for temporal CDEs to be machine-readable, streamlining meaningful searches.

  • A Jefferson Health doctor in your medicine cabinet. Source: Image created by Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Patient and Health System Experience With Implementation of an Enterprise-Wide Telehealth Scheduled Video Visit Program: Mixed-Methods Study


    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. Health system and patient-reported survey measures were aligned with the National Quality Forum telehealth measure reporting domains of access, experience, and effectiveness of care. Results: This study describes implementation of a scheduled synchronous video visit program over an 18-month period. A total of 3018 scheduled video visits were completed across multiple clinical departments. Patient experiences were captured in surveys of 764 patients who participated in telehealth visits. Among survey respondents, 91.6% (728/795) reported satisfaction with the scheduled visits and 82.7% (628/759) reported perceived quality similar to an in-person visit. A total of 86.0% (652/758) responded that use of the scheduled video visit made it easier to get care. Nearly half (46.7%, 346/740) of patients estimated saving 1 to 3 hours and 40.8% (302/740) reported saving more than 3 hours of time. The net promoter score, a measure of patient satisfaction, was very high at 52. Conclusions: A large urban multihospital health system implemented an enterprise-wide scheduled telehealth video visit program across a range of clinical specialties with a positive patient experience. Patients found use of scheduled video visits made it easier to get care and the majority perceived time saved, suggesting that use of telehealth for scheduled visits can improve potential access to care across a range of clinical scenarios with favorable patient experiences.

  • Source:; Copyright: David Castillo Dominici; URL:; License: Licensed by the authors.

    The Use of Communication Apps by Medical Staff in the Australian Health Care System: Survey Study on Prevalence and Use


    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. Objective: The objective of this study was to quantify and qualify the use of communication apps among medical staff in clinical situations, their role in patient care, and knowledge and attitudes towards safety, key benefits, potential disadvantages, and policy implications. Methods: Medical staff in hospitals across Victoria (Australia) were invited to participate in an anonymous 33-question survey. The survey collected data on respondent’s demographics, their use of communication apps in clinical settings, attitudes towards communication apps, perceptions of data “safety,” and why one communication app was chosen over others. Results: Communication apps in Victorian hospitals are in widespread use from students to consultants, with WhatsApp being the primary app used. The median number of messages shared per day was 12, encompassing a range of patient information. All respondents viewed these apps positively in quickly communicating patient information in a clinical setting; however, all had concerns about the privacy implications arising from sharing patient information in this way. In total, 67% (60/90) considered patient data “moderately safe” on these apps, and 50% (46/90) were concerned the use of these apps was inconsistent with current legislation and policy. Apps were more likely to be used if they were fast, easy to use, had an easy login process, and were already in widespread use. Conclusions: Communication app use by medical personnel in Victorian hospitals is pervasive. These apps contribute to enhanced communication between medical staff, but their use raises compliance issues, most notably with Australian privacy legislation. Development of privacy-compliant apps such as MedX needs to prioritize a user-friendly interface and market the product as a privacy-compliant comparator to apps previously adapted to health care settings.

  • Source: Pixnio; Copyright: Pixnio; URL:; License: Public Domain (CC0).

    Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study


    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. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. Objective: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non–small cell lung cancer (NSCLC). Methods: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. Results: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. Conclusions: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies.

  • Source:; Copyright: Marcy Sanchez; URL:; License: Public Domain (CC0).

    Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review


    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). Methods: A systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated. Results: Additive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients’ characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%. Conclusions: There is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low.

  • Source:; Copyright: hamiltonpaviana; URL:; License: Public Domain (CC0).

    A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation


    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. Objective: This study aimed to develop a scalable, flexible, and thematic design-based clinical decision support (CDS) engine, which integrates a national medication repository to support CPOE systems in the detection of potential duplication of medication across health care facilities, as well as to analyze its impact on clinical encounters. Methods: A CDS engine was developed that can download patients’ up-to-date medication history from the PharmaCloud and support a CPOE system in the detection of potential duplicate medications. When prescribing a medication order using the CPOE system, a physician receives an alert if there is a potential duplicate medication. To investigate the impact of the CDS engine on clinical encounters in outpatient services, a clinical encounter log was created to collect information about time, prescribed drugs, and physicians’ responses to handling the alerts for each encounter. Results: The CDS engine was installed in a teaching affiliate hospital, and the clinical encounter log collected information for 3 months, during which a total of 178,300 prescriptions were prescribed in the outpatient departments. In all, 43,844/178,300 (24.59%) patients signed the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. The rate of duplicate medication was 5.83% (1843/31,614) of prescriptions. When prescribing using the CDS engine, the median encounter time was 4.3 (IQR 2.3-7.3) min, longer than that without using the CDS engine (median 3.6, IQR 2.0-6.3 min). From the physicians’ responses, we found that 42.06% (1908/4536) of the potential duplicate medications were recognized by the physicians and the medication orders were canceled. Conclusions: The CDS engine could easily extend functions for detection of adverse drug reactions when more and more electronic health record systems are adopted. Moreover, the CDS engine can retrieve more updated and completed medication histories in the PharmaCloud, so it can have better performance for detection of duplicate medications. Although our CDS engine approach could enhance medication safety, it would make for a longer encounter time. This problem can be mitigated by careful evaluation of adopted solutions for implementation of the CDS engine. The successful key component of a CDS engine is the completeness of the patient’s medication history, thus further research to assess the factors in increasing the PharmaCloud consent rate is required.

  • Source: Pexels; Copyright:; URL:; License: Public Domain (CC0).

    The Use of Technology in Identifying Hospital Malnutrition: Scoping Review


    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. Technology may be a particularly useful ally in identifying malnutrition due to scalability, timeliness, and effectiveness. In an effort to explore the issue, this scoping review synthesized the availability of technological tools to detect and identify hospital malnutrition. Objective: Our objective was to conduct a scoping review of the different forms of technology used in addressing malnutrition among adults admitted to hospital to (1) identify the extent of the published literature on this topic, (2) describe key findings, and (3) identify outcomes. Methods: We designed and implemented a search strategy in 3 databases (PubMed, Scopus, and CINAHL). We completed a descriptive numerical summary and analyzed study characteristics. One reviewer independently extracted data from the databases. Results: We retrieved and reviewed a total of 21 articles. We categorized articles by the computerized tool or app type: malnutrition assessment (n=15), food intake monitoring (n=5), or both (n=1). Within those categories, we subcategorized the different technologies as either hardware (n=4), software (n=13), or both (n=4). An additional subcategory under software was cloud-based apps (n=1). Malnutrition in the acute hospital setting was largely an unrecognized problem, owing to insufficient monitoring, identification, and initial assessments of identifying both patients who are already malnourished and those who are at risk of malnourishment. Studies went on to examine the effectiveness of health care workers (nurses and doctors) with a knowledge base focused on clinical care and their ability to accurately and consistently identify malnourished geriatric patients within that setting. Conclusions: Most articles reported effectiveness in accurately increasing malnutrition detection and awareness. Computerized tools and apps may also help reduce health care workers’ workload and time spent assessing patients for malnutrition. Hospitals may also benefit from implementing malnutrition technology through observing decreased length of stay, along with decreased foregone costs related to missing malnutrition diagnoses. It is beneficial to study the impact of these technologies to examine possible areas of improvement. A future systematic review would further contribute to the evidence and effectiveness of the use of technologies in assessing and monitoring hospital malnutrition.

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  • The Wearable Smart Blanket System Model for Monitoring the Vital Signs of Patients in Ambulance

    Date Submitted: Mar 17, 2018

    Open Peer Review Period: Mar 18, 2018 - May 13, 2018

    Objective: The timely and managed intervention reduces the consequences due to disease and sudden death of patients in emergency conditions. Monitoring and caring for the patients in emergency conditi...

    Objective: The timely and managed intervention reduces the consequences due to disease and sudden death of patients in emergency conditions. Monitoring and caring for the patients in emergency conditions requires the rapid and correct decisions to maintain their lives. The present study aimed at modeling the wearable smart blanket system for monitoring the patients in the emergency conditions of ambulance. Method: The present study was applied and descriptive-developmental. Firstly, the requirements and features of wearable smart blanket system were elicited and secondly a smart blanket system was modeled by using the UML charts and elicited requirements. Finally, the designed architecture was evaluated by using ARID scenario-based method. Results: The functional requirements of wearable smart blanket system with its data elements and physical-structural features of this system as well as non-functional requirements were elicited. Based on the requirements and data elements elicited from the questionnaire, class diagram, activity, use case diagram, sequence, deployment, and component were drawn. Then, the ARID scenario-based evaluation method was used to show that the designed architecture can provide the expected scenarios from the proposed system by using the UML and the relationships between components, systems, and users from the structural and behavioral perspectives. Conclusion: wearable smart blanket system collects the data related to medical signals by the sensors installed on the blanket and such data are processed by the smart system. The obtained data about the conditions of patient help the physician in ambulance to intervene timely and rapidly without any delay.

  • Impact of electronic versus paper vital sign observations on length-of-stay in trauma patients: a stepped-wedge cluster randomised study

    Date Submitted: Feb 26, 2018

    Open Peer Review Period: Feb 27, 2018 - Apr 24, 2018

    Background: Electronic recording of vital sign observations (e-Obs) has become increasingly prevalent in hospital care. The evidence of clinical impact for these systems is mixed. Objective: To assess...

    Background: Electronic recording of vital sign observations (e-Obs) has become increasingly prevalent in hospital care. The evidence of clinical impact for these systems is mixed. Objective: To assess the effect of e-Obs versus paper documentation (paper) on length of stay (time between trauma unit admission and ‘fit to discharge’) for trauma patients. Methods: A single centre randomised stepped-wedge study of e-Obs against paper in two 26-bed trauma wards at a medium-sized UK teaching hospital. Randomisation of the phased intervention order to the 12 study areas was computer-generated. The primary outcome was length of stay. Results: 1232 patient episodes were randomised (paper: 628, e-Obs: 604). There were 37 deaths in hospital, 21 in the Paper arm and 16 in the e-Obs arm. For discharged patients the median length of stay was 5.4 days (range: 0.2 to 79.0) on paper and 5.6 days (range: 0.1 to 236.7) on e-Obs arm. Competing risks regression analysis for time to discharge showed no difference between the treatment arms, subhazard ratio: 1.05 (0.82, 1.35) P=.68. More patient episodes contained an EWS≥3 using the e-Obs system than paper, subhazard ratio 1.63 (95% CI 1.28, 2.09 P<0.001). However, there was no difference in the time to the subsequent observation (‘escalation time’) hazard ratio 1.05 (95% CI 0.80, 1.38 P=.70). Conclusions: The phased introduction of an e-Obs documentation system was not associated with a change in length of stay. More patient episodes contained an EWS≥3 using the e-Obs system, but this was not associated with a change in ‘escalation time’. Clinical Trial: ISRCTN91040762

  • Emergency physician use and perceptions of the Alberta Netcare Portal, a province-wide interoperable electronic health record

    Date Submitted: Feb 20, 2018

    Open Peer Review Period: Feb 22, 2018 - Apr 19, 2018

    Background: The adoption and use of an Electronic Health Record can facilitate real-time access to key health information and support improved outcomes. Many Canadian provinces use interoperable Elect...

    Background: The adoption and use of an Electronic Health Record can facilitate real-time access to key health information and support improved outcomes. Many Canadian provinces use interoperable Electronic Health Records (iEHRs) to facilitate Health Information Exchange (HIE), but, to date, the clinical use and utility of iEHRs has not been well-described. Objective: Our study's primary objective was to describe the use and reported utility of a provincial iEHR known as the Alberta Netcare Portal (ANP) in four urban Alberta emergency departments (EDs). The secondary objectives were to characterize the time spent using the respective electronic tools, and which aspects were perceived as most useful by ED physicians. Methods: Four EDs were included in the study, two using paper-based ordering (University of Alberta Hospital [UAH] and Grey Nuns Community Hospital [GNCH]) and two using a commercial vendor Clinical Information System (Peter Lougheed Centre [PLC] and Foothills Medical Centre [FMC]). Structured clinical observations of ANP use, semi-structured interviews, and system audit logs analysis were compared at the four sites from October 2014 to March 2016. Results: Observers followed 142 physicians for a total of 566 hours over 376 occasions. The median percentage of observed time spent using ANP was 8.5% at UAH (interquartile range IQR: 3.7% - 13.3%), 4.4% at GNCH (2.4%-4.4%), 4.6% at FMC (2.4%-7.6%), and 5.1% at PLC (3.0%-7.7%). By combining administrative and access audit data, the median number of ANP screens (i.e., results and reports displayed on a screen) accessed per patient visit were 20 at UAH (IQR: 6-67), 9 at GNCH (4-29), 7 at FMC (2-18) and 5 at PLC (2-14) indicating that clinicians found significant value in using ANP while providing ED care. To explore this hypothesis, semi-structured interviews were analyzed using an inductive approach. The themes that emerged from the interviews were that the ANP improved the quality and continuity of care and patient safety. Further enhancements related to medication management would support better outcomes for patients. Conclusions: This study shows that the iEHR is well utilized at the four sites studied and physicians participating in the study perceived ANP had a positive impact on knowledge of their patients, patient safety, and quality and continuity of care. Physicians described high utility and usability of ANP. More study about the clinical impacts of using iEHRs in the Canadian context, including longer term impacts on quality of practice and safety are required.

  • Utilizing Electronic Health Records for Clinical Research: A Pilot to Build and Test Silent Best Practice Alert (BPA) Notifications for Patient Recruitment in Clinical Research

    Date Submitted: Feb 5, 2018

    Open Peer Review Period: Feb 5, 2018 - Apr 2, 2018

    Background: Participant recruitment, especially for frail elderly hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart...

    Background: Participant recruitment, especially for frail elderly hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart reviews or word of mouth notifications for patients in the inpatient setting are often inefficient, low-yielding, time consuming and expensive. Silent Best Practice Alert (BPA) systems have previously been used to improve clinical care but not in clinical research. Objective: This pilot project examined a new EPIC BPA system developed to identify potentially eligible participants in real time to help research teams maximize recruitment accuracy and efficiency of resources. We hypothesized that this tool would reduce the daily screening time, the number of missed potential participants as well as the overall cost needed to recruit the targeted number of patients. Methods: The BPA system was jointly developed by a clinical research and electronic medical records implementation/management team at Partners Healthcare. The was developed and pilot tested in an observational clinical trial to enroll patients admitted for acute exacerbation of chronic pulmonary disease (COPD). We compared the BPA system with our usual method of patient identification (chart reviews and word of mouth referrals) and evaluated for daily screening time, number of missed potential participants as well as the overall cost needed to recruit the targeted number of patients. Results: 559 potentially eligible patients were identified through the two screening methods compared. Of those, 460 patients were identified by both methods, with 99 found by just the Epic Workbench Method and 42 identified by just the silent BPA method. Of the 99 identified by the Epic Workbench, only 12 (12.12%) were considered eligible. Of the 42 identified by the silent BPA method, 30 (71.43%) were considered eligible. A total of 319 “Eligible” patients were identified, and of those 60 participants enrolled in the Emerald-COPD Study. Since implementation, the silent BPA system has found an equivalent of 3 additional patients per week. From the comparison, the silent BPA screening method was shown to be approximately 4 times (23.58%) faster than our previous screening method, projected to save 442.5 hours over the duration of the study. Conclusions: Automation of the recruitment process has allowed us to identify potential participants in real time and avoid missing patients. Silent BPA screening is a considerably faster method which allows for more efficient use of resources. This innovative and instrumental functionality can be specified to the needs of other research studies hoping to utilize the electronic medical records system for participant recruitment.

  • The construction principles, approaches, design considerations, and representation challenges of an ontology-based knowledge base prototype: OntoKBCF

    Date Submitted: Jan 29, 2018

    Open Peer Review Period: Jan 30, 2018 - Mar 27, 2018

    Ontology is a key enabling technology for the Semantic Web. Web Ontology Language (OWL) is the semantic markup language for publishing and sharing data via ontologies on the Web. OntoKBCF is an ontolo...

    Ontology is a key enabling technology for the Semantic Web. Web Ontology Language (OWL) is the semantic markup language for publishing and sharing data via ontologies on the Web. OntoKBCF is an ontology-based knowledge base prototype built in OWL to supply customizable molecular genetics information and health information about cystic fibrosis via EHR interfaces. This paper introduces the construction principles, approaches, design considerations, and representation challenges we faced in the construction of OntoKBCF. More specifically, we examine: (1) what is included in OntoKBCF; (2) how we organized and represented complicated knowledge facts by utilizing basic atomic concepts in a formal and machine-processable manner; (3) how the knowledge facts (i.e., known facts with straightforward or complicated statements) can be made automatically usable via an electronic health record system prototype; and 4) why we constructed OntoKBCF in this way. The main challenges include representing: (1) patient groups comprehensively; (2) uncertain knowledge, ambiguous concepts, and negative statements; and (3) more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis. Although cystic fibrosis is utilized as an example, OntoKBCF should be able to be expanded in a straightforward manner based on its current structure. The construction principles can be referenced for building other human monogenetic diseases knowledge bases.

  • Validation of a Computerized Method for Measuring CTPA Yield in the Emergency Department

    Date Submitted: Jan 29, 2018

    Open Peer Review Period: Jan 30, 2018 - Mar 27, 2018

    Background: Use of computed tomography pulmonary angiography (CTPA) in the assessment for pulmonary embolism (PE) has significantly increased over the past two decades. While this technology has impro...

    Background: Use of computed tomography pulmonary angiography (CTPA) in the assessment for pulmonary embolism (PE) has significantly increased over the past two decades. While this technology has improved the accuracy of radiologic testing for PE, CTPA also carries the risk of significant iatrogenic harm. Each CTPA carries a 14% risk of contrast induced nephropathy and a lifetime malignancy risk that can be as high as 2.76%. Appropriate use of CTPA can be estimated by monitoring CTPA yield, the percentage of tests positive for PE. This is the first study to propose and validate a computerized method for measuring CTPA yield in the ED. Objective: To assess the validity of a novel computerized method of calculating CTPA yield in the ED. Methods: The electronic health record (EHR) databases at two tertiary care academic hospitals were queried for CTPA orders completed in the ED over one month periods. These visits were linked with an inpatient admission with a discharge diagnosis of PE based on International Classification of Diseases (ICD) codes. The computerized CTPA yield was calculated as the number of CTPA orders with an associated inpatient discharge diagnosis of PE divided by the total number of orders for completed CTPA. This computerized method was then validated by two independent reviewers performing a manual chart review, which included reading the free text radiology reports for each CTPA. Results: A total of 349 CTPA orders were completed during the one month periods at the two institutions. Acute PE was diagnosed on CTPA in 28 of these studies, with a CTPA yield of 7.7%. The computerized method correctly identified 27 of 28 scans positive for PE. The one discordant scan was tied to a patient who was discharged directly from the ED and as a result never received an inpatient discharge diagnosis. Conclusions: This is the first successful validation study of a computerized method for calculating CTPA yield in the ED. This method for data extraction allows for an accurate determination of CTPA yield and is more efficient than manual chart review. With this ability, healthcare systems can monitor for appropriate use of CTPA and the effect of interventions to reduce overuse and decrease preventable iatrogenic harm.