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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 21st year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2019: 5.03), ranking Q1 in the medical informatics category, and is also the largest journal in the field. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth and informatics applications for patient education, prevention, population health and clinical care. As a leading high-impact journal in its disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope, and which together receive over 6.000 submissions a year. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journal but can simply transfer it between journals. 

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

Be a widely cited leader in the digitial health revolution and submit your paper today!

 

Recent Articles:

  • Source: FlickR; Copyright: Trinity Care Foundation; URL: https://www.flickr.com/photos/43024725@N05/49706777733; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

    Abstract:

    Background: With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective: The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods: Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results: Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

  • Source: freepik; Copyright: pressfoto; URL: https://www.freepik.com/free-photo/doctor-looking-information-database_863249.htm#page=1&query=personal%20information&position=8; License: Licensed by JMIR.

    Proposed Implementation of Blockchain in British Columbia’s Health Care Data Management

    Abstract:

    Background: There are several challenges such as information silos and lack of interoperability with the current electronic medical record (EMR) infrastructure in the Canadian health care system. These challenges can be alleviated by implementing a blockchain-based health care data management solution. Objective: This study aims to provide a detailed overview of the current health data management infrastructure in British Columbia for identifying some of the gaps and inefficiencies in the Canadian health care data management system. We explored whether blockchain is a viable option for bridging the existing gaps in EMR solutions in British Columbia’s health care system. Methods: We constructed the British Columbia health care data infrastructure and health information flow based on publicly available information and in partnership with an industry expert familiar with the health systems information technology network of British Columbia’s Provincial Health Services Authorities. Information flow gaps, inconsistencies, and inefficiencies were the target of our analyses. Results: We found that hospitals and clinics have several choices for managing electronic records of health care information, such as different EMR software or cloud-based data management, and that the system development, implementation, and operations for EMRs are carried out by the private sector. As of 2013, EMR adoption in British Columbia was at 80% across all hospitals and the process of entering medical information into EMR systems in British Columbia could have a lag of up to 1 month. During this lag period, disease progression updates are continually written on physical paper charts and not immediately updated in the system, creating a continuous lag period and increasing the probability of errors and disjointed notes. The current major stumbling block for health care data management is interoperability resulting from the use of a wide range of unique information systems by different health care facilities. Conclusions: Our analysis of British Columbia’s health care data management revealed several challenges, including information silos, the potential for medical errors, the general unwillingness of parties within the health care system to trust and share data, and the potential for security breaches and operational issues in the current EMR infrastructure. A blockchain-based solution has the highest potential in solving most of the challenges in managing health care data in British Columbia and other Canadian provinces.

  • Source: Unsplash; Copyright: Christin Hume; URL: https://unsplash.com/photos/mfB1B1s4sMc; License: Licensed by JMIR.

    Evaluation of the Perceived Persuasiveness Questionnaire: User-Centered Card-Sort Study

    Abstract:

    Background: eHealth technologies aim to change users’ health-related behavior. Persuasive design and system features can make an eHealth technology more motivating, engaging, or supportive to its users. The Persuasive Systems Design (PSD) model incorporates software features that have the possibility to increase the persuasiveness of technologies. However, the effects of specific PSD software features on the effectiveness of an intervention are still largely unknown. The Perceived Persuasiveness Questionnaire (PPQ) was developed to gain insight into the working mechanisms of persuasive technologies. Although the PPQ seems to be a suitable method for measuring subjective persuasiveness, it needs to be further evaluated to determine how suitable it is for measuring perceived persuasiveness among the public. Objective: This study aims to evaluate the face and construct validity of the PPQ, identify points of improvement, and provide suggestions for further development of the PPQ. Methods: A web-based closed-ended card-sort study was performed wherein participants grouped existing PPQ items under existing PPQ constructs. Participants were invited via a Massive Open Online Course on eHealth. A total of 398 people (average age 44.15 years, SD 15.17; 251/398, 63.1% women) completed the card sort. Face validity was evaluated by determining the item-level agreement of the original PPQ constructs. Construct validity was evaluated by determining the construct in which each item was placed most often, regardless of the original placement and how often 2 items were (regardless of the constructs) paired together and what interitem correlations were according to a cluster analysis. Results: Four PPQ constructs obtained relatively high face validity scores: perceived social support, use continuance, perceived credibility, and perceived effort. Item-level agreement on the other constructs was relatively low. Item-level agreement for almost all constructs, except perceived effort and perceived effectiveness, would increase if items would be grouped differently. Finally, a cluster analysis of the PPQ indicated that the strengths of the newly identified 9 clusters varied strongly. Unchanged strong clusters were only found for perceived credibility support, perceived social support, and use continuance. The placement of the other items was much more spread out over the other constructs, suggesting an overlap between them. Conclusions: The findings of this study provide a solid starting point toward a redesigned PPQ that is a true asset to the field of persuasiveness research. To achieve this, we advocate that the redesigned PPQ should adhere more closely to what persuasiveness is according to the PSD model and to the mental models of potential end users of technology. The revised PPQ should, for example, enquire if the user thinks anything is done to provide task support but not how this is done exactly.

  • Man playing Virtual Reality with Beat Saber background. Source: Author merged images from FlickR and Unsplash; Copyright: Minh Pham (https://unsplash.com/photos/HI6gy-p-WBI) and PlayStation Europe (FlickR); URL: https://www.flickr.com/photos/playstationblogeurope/27771433617; License: Licensed by JMIR.

    Exergaming With Beat Saber: An Investigation of Virtual Reality Aftereffects

    Abstract:

    Background: Virtual reality (VR) exergaming has the potential to target sedentary behavior. Immersive environments can distract users from the physical exertion of exercise and can motivate them to continue exergaming. Despite the recent surge in VR popularity, numerous users still experience VR sickness from using head-mounted displays (HMDs). Apart from the commonly assessed self-reported symptoms, depth perception and cognition may also be affected. Considering the potential benefits of VR exergaming, it is crucial to identify the adverse effects limiting its potential and continued uptake. Objective: This study aims to investigate the consequences of playing one of the most popular VR exergames for 10 and 50 min on aspects of vision, cognition, and self-reported VR sickness. Methods: A total of 36 participants played an exergame, called Beat Saber, using an HMD. A repeated measures within-subject design was conducted to assess changes in vision, cognition, and well-being after short (10 min) and long (50 min) durations of VR exposure. We measured accommodation, convergence, decision speed, movement speed, and self-reported sickness at 3 test periods—before VR, immediately after VR, and 40 min after VR (late). Results: Beat Saber was well tolerated, as there were no dropouts due to sickness. For most participants, any immediate aftereffects were short-lived and returned to baseline levels after 40 min of exiting VR. For both short and long exposures, there were changes in accommodation (F1,35=8.424; P=.006) and convergence (F1,35=7.826; P=.008); however, in the late test period, participants returned to baseline levels. Measures on cognition revealed no concern. The total simulator sickness questionnaire (SSQ) scores increased immediately after VR (F1,35=26.515; P<.001) and were significantly higher for long compared with short exposures (t35=2.807; P=.03), but there were no differences in exposure duration in the late test period, with scores returning to baseline levels. Although at a group level, participants’ sickness levels returned to baseline 40 min after VR exposure, approximately 14% of the participants still reported high levels of sickness in the late test period after playing 50 min of Beat Saber. We also showed that the participants who experienced a high level of sickness after a short exposure were almost certain to experience a high level of symptoms after a longer exposure. Conclusions: Irrespective of the duration of exposure, this study found no strong evidence for adverse symptoms 40 min after exiting VR; however, some individuals still reported high levels of VR sickness at this stage. We recommend that users commit to a waiting period after exiting VR to ensure that any aftereffects have deteriorated. Exergames in HMDs have the potential to encourage people to exercise but are understudied, and the aftereffects of exergaming need to be closely monitored to ensure that VR exergames can reach their full potential. Trial Registration:

  • Biomedical Text Summarization. Source: freepik; Copyright: Designed by pressfoto; URL: https://www.freepik.com/free-photo/doctor-working-table_5766602.htm#query=doctor%20computer%20writing%20medical%20&position=6; License: Licensed by JMIR.

    Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

    Abstract:

    Background: Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. Objective: Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. Methods: In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. Results: Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. Conclusions: By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

  • Source: AIDSVu.org / Placeit; Copyright: The Authors / Placeit; URL: http://www.jmir.org/2020/10/e23173/; License: Licensed by JMIR.

    A Data Visualization and Dissemination Resource to Support HIV Prevention and Care at the Local Level: Analysis and Uses of the AIDSVu Public Data Resource

    Abstract:

    Background: AIDSVu is a public resource for visualizing HIV surveillance data and other population-based information relevant to HIV prevention, care, policy, and impact assessment. Objective: The site, AIDSVu.org, aims to make data about the US HIV epidemic widely available, easily accessible, and locally relevant to inform public health decision making. Methods: AIDSVu develops visualizations, maps, and downloadable datasets using results from HIV surveillance systems, other population-based sources of information (eg, US Census and national probability surveys), and other data developed specifically for display and dissemination through the website (eg, pre-exposure prophylaxis [PrEP] prescriptions). Other types of content are developed to translate surveillance data into summarized content for diverse audiences using infographic panels, interactive maps, local and state fact sheets, and narrative blog posts. Results: Over 10 years, AIDSVu.org has used an expanded number of data sources and has progressively provided HIV surveillance and related data at finer geographic levels, with current data resources providing HIV prevalence data down to the census tract level in many of the largest US cities. Data are available at the county level in 48 US states and at the ZIP Code level in more than 50 US cities. In 2019, over 500,000 unique users consumed AIDSVu data and resources, and HIV-related data and insights were disseminated through nearly 4,000,000 social media posts. Since AIDSVu’s inception, at least 249 peer-reviewed publications have used AIDSVu data for analyses or referenced AIDSVu resources. Data uses have included targeting of HIV testing programs, identifying areas with inequitable PrEP uptake, including maps and data in academic and community grant applications, and strategically selecting locations for new HIV treatment and care facilities to serve high-need areas. Conclusions: Surveillance data should be actively used to guide and evaluate public health programs; AIDSVu translates high-quality, population-based data about the US HIV epidemic and makes that information available in formats that are not consistently available in surveillance reports. Bringing public health surveillance data to an online resource is a democratization of data, and presenting information about the HIV epidemic in more visual formats allows diverse stakeholders to engage with, understand, and use these important public health data to inform public health decision making.

  • Source: Unsplash; Copyright: Mimi Thian; URL: https://unsplash.com/s/photos/trackpad-hand; License: Licensed by JMIR.

    Stress Tracker—Detecting Acute Stress From a Trackpad: Controlled Study

    Abstract:

    Background: Stress is a risk factor associated with physiological and mental health problems. Unobtrusive, continuous stress sensing would enable precision health monitoring and proactive interventions, but current sensing methods are often inconvenient, expensive, or suffer from limited adherence. Prior work has shown the possibility to detect acute stress using biomechanical models derived from passive logging of computer input devices. Objective: Our objective is to detect acute stress from passive movement measurements of everyday interactions on a laptop trackpad: (1) click, (2) steer, and (3) drag and drop. Methods: We built upon previous work, detecting acute stress through the biomechanical analyses of canonical computer mouse interactions and extended it to study similar interactions with the trackpad. A total of 18 participants carried out 40 trials each of three different types of movement—(1) click, (2) steer, and (3) drag and drop—under both relaxed and stressed conditions. Results: The mean and SD of the contact area under the finger were higher when clicking trials were performed under stressed versus relaxed conditions (mean area: P=.009, effect size=0.76; SD area: P=.01, effect size=0.69). Further, our results show that as little as 4 clicks on a trackpad can be used to detect binary levels of acute stress (ie, whether it is present or not). Conclusions: We present evidence that scalable, inexpensive, and unobtrusive stress sensing can be done via repurposing passive monitoring of computer trackpad usage.

  • Source: Unsplash.com; Copyright: Nathana Rebouças; URL: https://unsplash.com/photos/XzHYjPuE9y4; License: Licensed by JMIR.

    Accuracy of Mobile Device–Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

    Abstract:

    Background: The accurate assessment and acquisition of facial anatomical information significantly contributes to enhancing the reliability of treatments in dental and medical fields, and has applications in fields such as craniomaxillofacial surgery, orthodontics, prosthodontics, orthopedics, and forensic medicine. Mobile device–compatible 3D facial scanners have been reported to be an effective tool for clinical use, but the accuracy of digital facial impressions obtained with the scanners has not been explored. Objective: We aimed to review comparisons of the accuracy of mobile device–compatible face scanners for facial digitization with that of systems for professional 3D facial scanning. Methods: Individual search strategies were employed in PubMed (MEDLINE), Scopus, Science Direct, and Cochrane Library databases to search for articles published up to May 27, 2020. Peer-reviewed journal articles evaluating the accuracy of 3D facial models generated by mobile device–compatible face scanners were included. Cohen d effect size estimates and confidence intervals of standardized mean difference (SMD) data sets were used for meta-analysis. Results: By automatic database searching, 3942 articles were identified, of which 11 articles were considered eligible for narrative review, with 6 studies included in the meta-analysis. Overall, the accuracy of face models obtained using mobile device–compatible face scanners was significantly lower than that of face models obtained using professional 3D facial scanners (SMD 3.96 mm, 95% CI 2.81-5.10 mm; z=6.78; P<.001). The difference between face scanning when performed on inanimate facial models was significantly higher (SMD 10.53 mm, 95% CI 6.29-14.77 mm) than that when performed on living participants (SMD 2.58 mm, 95% CI 1.70-3.47 mm, P<.001, df=12.94). Conclusions: Overall, mobile device–compatible face scanners did not perform as well as professional scanning systems in 3D facial acquisition, but the deviations were within the clinically acceptable range of <1.5 mm. Significant differences between results when 3D facial scans were performed on inanimate facial objects and when performed on the faces of living participants were found; thus, caution should be exercised when interpreting results from studies conducted on inanimate objects.

  • DREX (Durham Reading and Exploration) visual impairment training tool talk. Source: Image taken at recent talk; Copyright: The Authors; License: Creative Commons Attribution (CC-BY).

    Maximizing Telerehabilitation for Patients With Visual Loss After Stroke: Interview and Focus Group Study With Stroke Survivors, Carers, and Occupational...

    Abstract:

    Background: Visual field defects are a common consequence of stroke, and compensatory eye movement strategies have been identified as the most promising rehabilitation option. There has been a move toward compensatory telerehabilitation options, such as the Durham Reading and Exploration (DREX) training app, which significantly improves visual exploration, reading, and self-reported quality of life. Objective: This study details an iterative process of liaising with stroke survivors, carers, and health care professionals to identify barriers and facilitators to using rehabilitation tools, as well as elements of good practice in telerehabilitation, with a focus on how the DREX package can be maximized. Methods: Survey data from 75 stroke survivors informed 12 semistructured engagement activities (7 focus groups and 5 interviews) with 32 stroke survivors, 10 carers, and 24 occupational therapists. Results: Thematic analysis identified key themes within the data. Themes identified problems associated with poststroke health care from both patients’ and occupational therapists’ perspectives that need to be addressed to improve uptake of this rehabilitation tool and telerehabilitation options generally. This included identifying additional materials or assistance that were required to boost the impact of training packages. The acute rehabilitation setting was an identified barrier, and perceptions of technology were considered a barrier by some but a facilitator by others. In addition, 4 key features of telerehabilitation were identified: additional materials, the importance of goal setting, repetition, and feedback. Conclusions: The data were used to try to overcome some barriers to the DREX training and are further discussed as considerations for telerehabilitation in general moving forward.

  • Source: Burst; Copyright: Thought Catalog; URL: https://burst.shopify.com/photos/person-using-laptop?q=laptop; License: Licensed by JMIR.

    Portals of Change: How Patient Portals Will Ultimately Work for Safety Net Populations

    Abstract:

    Despite the implementation of internet patient portals into the safety net after the introduction of the Affordable Care Act in the United States, little attention has been paid to the process of engaging vulnerable patients into these portals. The portal is a health technology tool that was developed with a mainstream, English-speaking audience in mind. Thus, there are valid concerns that such technologies will actually exacerbate health care disparities, conferring further advantages to the already advantaged. In this paper, we describe a framework for portal engagement (awareness, registration, and use) among safety net patients. We incorporate the experiences in the Los Angeles County Department of Health Services to illustrate important contextual factors for portal outreach in our safety net. Finally, we discuss considerations for moving forward with health technology in the safety net as the next version of patient portals are being developed.

  • Source: Pexels; Copyright: bongkarn thanyakij; URL: https://www.pexels.com/photo/photo-of-person-in-black-sweater-and-blue-denim-jeans-using-a-laptop-while-sitting-on-wooden-floor-3759117/; License: Licensed by JMIR.

    COVID-19 Self-Reported Symptom Tracking Programs in the United States: Framework Synthesis

    Abstract:

    Background: With the continued spread of COVID-19 in the United States, identifying potential outbreaks before infected individuals cross the clinical threshold is key to allowing public health officials time to ensure local health care institutions are adequately prepared. In response to this need, researchers have developed participatory surveillance technologies that allow individuals to report emerging symptoms daily so that their data can be extrapolated and disseminated to local health care authorities. Objective: This study uses a framework synthesis to evaluate existing self-reported symptom tracking programs in the United States for COVID-19 as an early-warning tool for probable clusters of infection. This in turn will inform decision makers and health care planners about these technologies and the usefulness of their information to aid in federal, state, and local efforts to mobilize effective current and future pandemic responses. Methods: Programs were identified through keyword searches and snowball sampling, then screened for inclusion. A best fit framework was constructed for all programs that met the inclusion criteria by collating information collected from each into a table for easy comparison. Results: We screened 8 programs; 6 were included in our final framework synthesis. We identified multiple common data elements, including demographic information like race, age, gender, and affiliation (all were associated with universities, medical schools, or schools of public health). Dissimilarities included collection of data regarding smoking status, mental well-being, and suspected exposure to COVID-19. Conclusions: Several programs currently exist that track COVID-19 symptoms from participants on a semiregular basis. Coordination between symptom tracking program research teams and local and state authorities is currently lacking, presenting an opportunity for collaboration to avoid duplication of efforts and more comprehensive knowledge dissemination.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2020/10/e20748/; License: Creative Commons Attribution + Noncommercial + ShareAlike (CC-BY-NC-SA).

    Covidom, a Telesurveillance Solution for Home Monitoring Patients With COVID-19

    Abstract:

    In a matter of months, COVID-19 has escalated from a cluster of cases in Wuhan, China, to a global pandemic. As the number of patients with COVID-19 grew, solutions for the home monitoring of infected patients became critical. This viewpoint presents a telesurveillance solution—Covidom—deployed in the greater Paris area to monitor patients with COVID-19 in their homes. The system was rapidly developed and is being used on a large scale with more than 65,000 registered patients to date. The Covidom solution combines an easy-to-use and free web application for patients (through which patients fill out short questionnaires on their health status) with a regional control center that monitors and manages alerts (triggered by questionnaire responses) from patients whose health may be deteriorating. This innovative solution could alleviate the burden of health care professionals and systems while allowing for rapid response when patients trigger an alert.

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  • Interoperable HL7 FHIR platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government

    Date Submitted: Oct 22, 2020

    Open Peer Review Period: Oct 19, 2020 - Oct 29, 2020

    Background: Testing, traceability, and the isolation (TTI strategy) actions are a central strategy defined by WHO to contain the COVID-19 pandemic. In this sense, countries have had difficulties in co...

    Background: Testing, traceability, and the isolation (TTI strategy) actions are a central strategy defined by WHO to contain the COVID-19 pandemic. In this sense, countries have had difficulties in counting the number of people infected with SARS-CoV-2. Errors in reporting results are a common factor as well as the lack of interoperability between laboratories and governments. Approaches aimed at sending spreadsheets via email expose patients' privacy and have increased the probability of errors due to re-typing and generate a delay in the notification of results. Objective: Design and develop an interoperable platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government. Methods: The methodology to design and develop the interoperable platform was comprised of six well-structured stages: 1) Creation of a minimum dataset to PCR SARS-CoV-2 tests, 2) Modeling process and endpoints where institutions interchange information, 3) Standards and interoperability design, 4) Software development, 5) Quality assurance and 6) Software implementation. Results: The main result was the interoperable FHIR platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government. The platform was designed, developed, tested, and implemented following a structured methodology. The platform's performance to 1,000 requests resulted in a response time of 240 milliseconds, throughput was 28.3 requests per second, and the process management time was 131 milliseconds. The platform has availability of 99.9 %. The security was implemented with JSON Web Token (JWT) to ensure confidentiality, authorization, and authentication. All the PCR SARS-CoV-2 tests were accessible through an Application Programming Interface (API) gateway with valid credentials and the right access control list. Conclusions: The platform was implemented and is currently being used by UC Christus Laboratory. The platform is secure. It was tested adequately for confidentiality, secure authorization, authentication, and message integrity. This platform simplifies the reporting of PCR SARS-CoV-2 tests and reduces the time and probability of mistakes in counting positive cases. The interoperable solution with FHIR is working successfully and is open for the community, laboratories, and any institution that needs to report PCR SARS-CoV-2 tests.

  • Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility

    Date Submitted: Oct 19, 2020

    Open Peer Review Period: Oct 19, 2020 - Dec 14, 2020

    Background: Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure t...

    Background: Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. Objective: The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. Methods: I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. Results: Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. Conclusions: With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

  • Enhancing obstructive sleep apnea diagnosis with screening through disease phenotypes: a diagnostic research design

    Date Submitted: Oct 20, 2020

    Open Peer Review Period: Oct 19, 2020 - Dec 14, 2020

    Background: American Academy of Sleep Medicine guidelines suggests that clinical prediction algorithms can be used in obstructive sleep apnea (OSA) patients without replacing polysomnography (PSG) –...

    Background: American Academy of Sleep Medicine guidelines suggests that clinical prediction algorithms can be used in obstructive sleep apnea (OSA) patients without replacing polysomnography (PSG) – the gold standard. Objective: We aim to develop a clinical decision support system for OSA diagnosis, according to its standard definition (AHI plus symptoms), identifying high pre-test probability individuals based on risk and diagnostic factors. Methods: Forty-seven predictive variables were extracted from a cohort of patients who performed PSG. Fourteen variables found univariately significant were then used to compute the distance between OSA patients, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk to OSA phenotypes was later computed and cluster membership used as an additional predictor in a Bayesian network classifier. Results: A total of 318 patients at risk were included, from which 207 individuals were diagnosed with OSA (mild=54%, moderate=24%, severe=22%). Based on predictive variables, three phenotypes were defined (Low=36%, Medium=50%, High=14%), with an increasing prevalence of symptoms and co-morbidities, the latter describing older and obese patients, and a substantive increase in some co-morbidities, suggesting their beneficial use as combined predictors (median AHI of 10, 14 and 31, respectively). Crossed-validation results demonstrate that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26% [24%-29%] to 38% [35%-40%]) while keeping sensitivity high (93% [91%-95%]), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool, enhancing our understanding of the disease, and allowing the derivation of a predictive algorithm which can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

  • Pain recognition with ECG features in postoperative patients: A method validation study

    Date Submitted: Oct 19, 2020

    Open Peer Review Period: Oct 16, 2020 - Dec 11, 2020

    Background: There is a strong demand for an accurate and objective means for assessing acute pain among hospitalized patients to help clinicians provide a proper dosage of pain medications and in a ti...

    Background: There is a strong demand for an accurate and objective means for assessing acute pain among hospitalized patients to help clinicians provide a proper dosage of pain medications and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiogram (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time- and frequency-domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain on healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. Objective: To develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a Transcutaneous Electrical Nerve Stimulation unit was employed to obtain baseline discomfort threshold for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale (NRS). A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine-learning methods. Mean prediction accuracy was calculated using Leave-One-Subject-Out cross-validation. We compared the performance of these models with the results from a previously published research study. Results: Five different machine-learning algorithms were applied to perform binary classification of no pain (NP) vs. 4 distinct pain levels (PL1 through PL4). Highest validation accuracy using 3 time-domain HRV features of BioVid research paper for no pain vs. any other pain level was achieved by SVM 62.72% (NP vs. PL4) to 84.14% (NP vs. PL2). Similar results were achieved for the top 8 features based on the Gini Index using the SVM method; with an accuracy ranging from 63.86% (NP vs. PL4) to 84.79% (NP vs. PL2). Conclusions: We propose a novel pain assessment method for postoperative patients using the ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine-learning algorithm to accurately and objectively assess acute pain among hospitalized patients.

  • Which electronic health record system should we use? - a systematic review

    Date Submitted: Oct 13, 2020

    Open Peer Review Period: Oct 13, 2020 - Dec 8, 2020

    Background: Electronic health records are digital records of a patient’s health and care. At present in the UK, patients may have several paper and electronic records stored in various settings. The...

    Background: Electronic health records are digital records of a patient’s health and care. At present in the UK, patients may have several paper and electronic records stored in various settings. The UK government, via NHS England, intends to introduce a comprehensive system of electronic health records in England by 2020. These electronic records will run across primary, secondary and social care linking all data in a single digital platform. Objective: This is the first systematic review to look at all published data on EHRs to determine which systems are advantageous. Methods: Design: A systematic review was performed by searching EMBASE and Ovid MEDLINE between 1974 and November 2019. Participants: All original studies that appraised EHR systems were included. Main outcome measures: EHR system comparison, implementation, user satisfaction, efficiency and performance, documentation, and research and development. Results: The search strategy identified 701 studies, which were filtered down to 46 relevant studies. Level of evidence ranged from 1 to 4 according to the Oxford Centre for Evidence-based Medicine. The majority of the studies were performed in the USA (n = 44). N=6 studies compared more than one EHR, and Epic followed by Cerner were the most favourable through direct comparison. N=17 studies evaluated implementation which highlighted that it was challenging, and productivity dipped in the early phase. N=5 studies reflected on user satisfaction, with women demonstrating higher satisfaction than men. Efficiency and performance issues were the driving force behind user dissatisfaction. N=26 studies addressed efficiency and performance, which improved with long-term use and familiarity. N=18 studies considered documentation and showed that EHRs had a positive impact with basic and speciality tasks. N=29 studies assessed research and development which revealed vast capabilities and positive implications. Conclusions: Epic is the most studied EHR system and the most commonly used vendor on the market. There is limited comparative data between EHR vendors, so it is difficult to assess which is the most advantageous system.

  • Exploratory Feasibility Study of Quokka: A Local Community-Based Social Network for Wellbeing

    Date Submitted: Oct 12, 2020

    Open Peer Review Period: Oct 12, 2020 - Dec 7, 2020

    Background: Developing healthy habits and maintaining prolonged behavior change is often a difficult task. Mental health is one of the largest health concerns globally, including for people in college...

    Background: Developing healthy habits and maintaining prolonged behavior change is often a difficult task. Mental health is one of the largest health concerns globally, including for people in college. Objective: We conduct an exploratory feasibility study of local community-based interventions, like Quokka, and evaluate the intervention’s potential for promotion of local, social, and unfamiliar activities as they pertain to healthy habits. Methods: To evaluate this framework’s potential for increased participation in healthy habits, we conducted a 6 to 8 week feasibility study via a ‘challenge’ across 4 university campuses with a total of 277 participants. A different wellbeing theme was chosen for each week. We conducted weekly surveys to gauge factors that motivated users to complete or not complete the weekly challenge, identified participation trends, and evaluated the effectiveness of the intervention. We tested the hypotheses that Quokka participants will self-report participation in more local activities over remote activities for all challenges, more social activities than individual activities, and new over familiar activities. Results: After Bonferroni correction using a Clopper-Pearson Binomial proportion confidence interval for one test, we reject the hypothesis that similar proportion of users would participate in local and remote activities during the challenges (p < 0.001 for all challenge themes). Instead, there was a strong preference for local activities for all challenge themes. Similarly, users significantly preferred group activities over individual activities (p < 0.001 for most challenge themes). For most challenge themes, there were not enough data to significantly distinguish preference towards familiar or new activities (p < 0.001 for a subset of challenge themes in some schools). Conclusions: We find that local community-based wellbeing interventions like Quokka can facilitate positive behavior change. We discuss these findings and their implications for the research and design of location-based digital communities for wellbeing promotion.

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