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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 20th 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 2017: 4.671, ranked #1 out of 22 journals) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As 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. 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 journals. 

As open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as 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: Pixabay; Copyright: picjumbo_com; URL:; License: Public Domain (CC0).

    Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms


    Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor’s skills, and bedside manners. Most previous work on automatic analysis of Web-based customer reviews assumes that (1) product features are described unambiguously by a small number of keywords, for example, battery for phones and (2) the opinion for each feature has a positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as visit duration for doctor reviews may be expressed in many ways and does not necessarily have a positive or negative sentiment. Objective: This study aimed to adapt existing and propose novel text classification methods on the domain of doctor reviews. These methods are evaluated on their accuracy to classify a diverse set of doctor review features. Methods: We first manually examined a large number of reviews to extract a set of features that are frequently mentioned in the reviews. Then we proposed a new algorithm that goes beyond bag-of-words or deep learning classification techniques by leveraging natural language processing (NLP) tools. Specifically, our algorithm automatically extracts dependency tree patterns and uses them to classify review sentences. Results: We evaluated several state-of-the-art text classification algorithms as well as our dependency tree–based classifier algorithm on a real-world doctor review dataset. We showed that methods using deep learning or NLP techniques tend to outperform traditional bag-of-words methods. In our experiments, the 2 best methods used NLP techniques; on average, our proposed classifier performed 2.19% better than an existing NLP-based method, but many of its predictions of specific opinions were incorrect. Conclusions: We conclude that it is feasible to classify doctor reviews. Automatically classifying these reviews would allow patients to easily search for doctors based on their personal preference criteria.

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

    The Generalizability of Randomized Controlled Trials of Self-Guided Internet-Based Cognitive Behavioral Therapy for Depressive Symptoms: Systematic Review...


    Background: Self-guided internet-based cognitive behavioral therapies (iCBTs) for depressive symptoms may substantially increase accessibility to mental health treatment. Despite this, questions remain as to the generalizability of the research on self-guided iCBT. Objective: We sought to describe the clinical entry criteria used in studies of self-guided iCBT, explore the criteria’s effects on study outcomes, and compare the frequency of use of these criteria with their use in studies of face-to-face psychotherapy and antidepressant medications. We hypothesized that self-guided iCBT studies would use more stringent criteria that would bias the sample toward those with a less complex clinical profile, thus inflating treatment outcomes. Methods: We updated a recently published meta-analysis by conducting a systematic literature search in PubMed, MEDLINE, PsycINFO, and EMBASE. We conducted a meta-regression analysis to test the effect of the different commonly used psychiatric entry criteria on the treatment-control differences. We also compared the frequency with which exclusion criteria were used in the self-guided iCBT studies versus studies of face-to-face psychotherapy and antidepressants from a recently published review. Results: Our search yielded 5 additional studies, which we added to the 16 studies identified by Karyotaki and colleagues in 2017. Few self-guided iCBT studies excluded patients with severe depressive symptoms (6/21, 29%), but self-guided iCBT studies were more likely than antidepressant (14/170, 8.2%) studies to use this criterion. However, self-guided iCBT studies did not use this criterion more frequently than face-to-face psychotherapy studies (6/16, 38%). Beyond this, we found no evidence that self-guided iCBTs used more stringent entry criteria. Strong evidence suggested that they were actually less likely to use most entry criteria, especially exclusions on the basis of substance use or personality pathology. None of the entry criteria used had an effect on outcomes. Conclusions: A conservative interpretation of our findings is that the patient population sampled in the literature on self-guided iCBT is relatively comparable with that of studies of antidepressants or face-to-face psychotherapy. Alternatively, studies of unguided cognitive behavioral therapy may sample from a more heterogeneous and representative patient population. Until evidence emerges to suggest otherwise, the patient population sampled in self-guided iCBT studies cannot be considered as less complex than the patient population from face-to-face psychotherapy or antidepressant studies.

  • US News and World Report Best Hospital Rankings and hospital Twitter feed (montage). Source: JMIR Publications/Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Correlations Between Hospitals’ Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis


    Background: The US News and World Report reputation score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the reputation score for adult and children’s hospitals relates to social media presence. Objective: The objective of our study was to analyze the association between a hospital’s social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children’s hospitals. Methods: We conducted a cross-sectional analysis of the reputation score, total score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital’s total points (ie, rank) and reputation score for both adult and children’s hospitals while controlling for the bed size and time on Twitter. Results: We observed significant correlations for children’s hospitals’ reputation score and total points with the number of Twitter followers (total points: r=.465, P<.001; reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; reputation: r=.518, P<.001). Significant correlations for the adult hospitals reputation score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). Conclusions: A statistically significant correlation exists between multiple social media metrics and both a hospital’s reputation score and total points (ie, overall rank). This association may indicate that a hospital’s reputation may be influenced by its social media presence or that the reputation or rank of a hospital drives social media followers.

  • Sample Facebook ad from the UCare study (montage). Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Using Facebook for Large-Scale Online Randomized Clinical Trial Recruitment: Effective Advertising Strategies


    Targeted Facebook advertising can be an effective strategy to recruit participants for a large-scale online study. Facebook advertising is useful for reaching people in a wide geographic area, matching a specific demographic profile. It can also target people who would be unlikely to search for the information and would thus not be accessible via Google AdWords. It is especially useful when it is desirable not to raise awareness of the study in a demographic group that would be ineligible for the study. This paper describes the use of Facebook advertising to recruit and enroll 1145 women over a 15-month period for a randomized clinical trial to teach support skills to female partners of male smokeless tobacco users. This tutorial shares our study team’s experiences, lessons learned, and recommendations to help researchers design Facebook advertising campaigns. Topics covered include designing the study infrastructure to optimize recruitment and enrollment tracking, creating a Facebook presence via a fan page, designing ads that attract potential participants while meeting Facebook’s strict requirements, and planning and managing an advertising campaign that accommodates the rapid rate of diminishing returns for each ad.

  • Source: Edwards Air Force Base (Kate Blais); Copyright: US Air Force; URL:; License: Public Domain (CC0).

    Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application


    Background: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. Objective: Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. Methods: We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. Results: We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. Conclusions: Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets.

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

    Health Care Professionals’ Social Media Behavior and the Underlying Factors of Social Media Adoption and Use: Quantitative Study


    Background: In the last decade, social media has emerged as a newer platform for knowledge dissemination, information exchange, and interpersonal communication for health care professionals (HCPs). However, the underlying behaviors of HCPs and the ethical use of social media for productivity enhancement and a sustainable health care system remain ambiguous. Objective: This study seeks to understand the factors that relate to the frequency use of social media in the health care discipline. It also aims to explore the underlying online behaviors of HCPs, which include the exchange of medical information with peers, interpersonal communication, and productivity enhancement in their daily practice. Methods: This study adopted the quantitative method in collecting and analyzing data. A survey instrument based on the behavioral and technology acceptance theories was developed for this purpose. The survey was distributed via social media platforms to 973 participants that included physicians, pharmacists, and allied HCPs working in the United Arab Emirates. The responses from 203 completed questionnaires (response rate 20.3%) were analyzed. Results: Of 203 respondents, 133 HCPs used WhatsApp (65.5%); therefore, WhatsApp had the highest number of users compared to Facebook and YouTube, with 101 users out of 203 (49.7%). Overall, 109 of 203 (53.6%) HCPs used social media platforms for the exchange of peer medical information and 108 of 203 (53.2%) used social media several times during the day to improve their interpersonal communication with colleagues. However, only 71 of 203 (34.9%) utilized social media to enhance their productivity in general. The structural model equation showed that behavioral intention (beta=.47; P<.001), habit (beta=.26; P=.001), attitude (beta=.20; P=.002), and perceived usefulness (beta=.12; P=.09) were positively and significantly related to frequency of use. The model explained a rate of 45% variance in the frequency of use and a rate of 17% variance in the social media intention of use. Conclusions: The research highlights the significant factors that relate to the adoption of social media platforms in health care practice. Based on the findings of this study, the use of online platforms facilitates the exchange of medical information among peers and enhances the share of experiences that support HCP’s learning and development. Moreover, social media platforms foster a higher level of communication among practitioners and might improve daily productivity. Future researchers might explore other variables such as training and external factors. For instance, they may draw on areas related to guidelines and policies. From this standpoint, the health care discipline can benefit from highly interactive platforms and adopt them for development, collaboration, and better health outcomes.

  • Veteran and provider meet using Clinical Video Telehealth. Source: US Department of Veterans Affairs; Copyright: US Department of Veterans Affairs; URL:; License: Public Domain (CC0).

    Dual Use of a Patient Portal and Clinical Video Telehealth by Veterans with Mental Health Diagnoses: Retrospective, Cross-Sectional Analysis


    Background: Access to mental health care is challenging. The Veterans Health Administration (VHA) has been addressing these challenges through technological innovations including the implementation of Clinical Video Telehealth, two-way interactive and synchronous videoconferencing between a provider and a patient, and an electronic patient portal and personal health record, My HealtheVet. Objective: This study aimed to describe early adoption and use of My HealtheVet and Clinical Video Telehealth among VHA users with mental health diagnoses. Methods: We conducted a retrospective, cross-sectional analysis of early My HealtheVet adoption and Clinical Video Telehealth engagement among veterans with one or more mental health diagnoses who were VHA users from 2007 to 2012. We categorized veterans into four electronic health (eHealth) technology use groups: My HealtheVet only, Clinical Video Telehealth only, dual users who used both, and nonusers of either. We examined demographic characteristics and mental health diagnoses by group. We explored My HealtheVet feature use among My HealtheVet adopters. We then explored predictors of My HealtheVet adoption, Clinical Video Telehealth engagement, and dual use using multivariate logistic regression. Results: Among 2.17 million veterans with one or more mental health diagnoses, 1.51% (32,723/2,171,325) were dual users, and 71.72% (1,557,218/2,171,325) were nonusers of both My HealtheVet and Clinical Video Telehealth. African American and Latino patients were significantly less likely to engage in Clinical Video Telehealth or use My HealtheVet compared with white patients. Low-income patients who met the criteria for free care were significantly less likely to be My HealtheVet or dual users than those who did not. The odds of Clinical Video Telehealth engagement and dual use decreased with increasing age. Women were more likely than men to be My HealtheVet or dual users but less likely than men to be Clinical Video Telehealth users. Patients with schizophrenia or schizoaffective disorder were significantly less likely to be My HealtheVet or dual users than those with other mental health diagnoses (odds ratio, OR 0.50, CI 0.47-0.53 and OR 0.75, CI 0.69-0.80, respectively). Dual users were younger (53.08 years, SD 13.7, vs 60.11 years, SD 15.83), more likely to be white, and less likely to be low-income than the overall cohort. Although rural patients had 17% lower odds of My HealtheVet adoption compared with urban patients (OR 0.83, 95% CI 0.80-0.87), they were substantially more likely than their urban counterparts to engage in Clinical Video Telehealth and dual use (OR 2.45, 95% CI 1.95-3.09 for Clinical Video Telehealth and OR 2.11, 95% CI 1.81-2.47 for dual use). Conclusions: During this study (2007-2012), use of these technologies was low, leaving much potential for growth. There were sociodemographic disparities in access to My HealtheVet and Clinical Video Telehealth and in dual use of these technologies. There was also variation based on types of mental health diagnosis. More research is needed to ensure that these and other patient-facing eHealth technologies are accessible and effectively used by all vulnerable patients.

  • Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Relations Between the Use of Electronic Health and the Use of General Practitioner and Somatic Specialist Visits in Patients With Type 1 Diabetes:...


    Background: The prevalence of diabetes and the use of electronic health (eHealth) are increasing. People with diabetes need frequent monitoring and follow-up of health parameters, and eHealth services can be of great value. However, little is known about the association between the use of eHealth and provider-based health care services among people with diabetes. Objective: The objective of this study was to investigate the use of 4 different eHealth platforms (apps, search engines, video services, and social media sites) and associations with the use of provider-based health care visits among people diagnosed with type 1 diabetes mellitus (T1DM). Methods: We used email survey data collected from 1250 members of the Norwegian Diabetes Association (aged 18 to 89 years) in 2018. Eligible for analyses were the 523 respondents with T1DM. Using descriptive statistics, we estimated the use of eHealth and the use of general practitioners (GPs) and somatic specialist outpatient services. By logistic regressions, we studied the associations between the use of these provider-based health services and the use of eHealth, adjusted for gender, age, education, and self-rated health. Results: Of the sample of 523 people with T1DM, 90.7% (441/486) had visited a GP once or more, and 61.0% (289/474) had visited specialist services during the previous year. Internet search engines (such as Google) were used for health purposes sometimes or often by 84.0% (431/513), apps by 55.4% (285/514), social media (such as Facebook) by 45.2% (232/513), and video services (such as YouTube) by 23.3% (118/506). Participants aged from 18 to 39 years used all forms of eHealth more than people aged 40 years and older, with the exception of social media. The use of search engines was positively associated with the use of somatic specialist services (odds ratio 2.43, 95% CI 1.33-4.45). GP visits were not associated with any kind of eHealth use. Conclusions: eHealth services are now widely used for health support and health information by people with T1DM, primarily in the form of search engines but often in the form of apps and social media as well. We found a positive association between the use of search engines and specialist visits and that people with T1DM are frequent users of eHealth, GPs, and specialist services. We found no evidence that eHealth reduces the use of provider-based health care; these services seem to be additional rather than alternative. Future research should focus on how health care services can meet and adapt to the high prevalence of eHealth use. Our results also indicate that many patients with T1DM do not visit specialist clinics once a year as recommended. This raises questions about collaboration in health care services and needs to be followed up in future research.

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

    Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review


    Background: In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective: This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results: All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions: The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

  • Source: Imaged created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Clinicians’ Selection Criteria for Video Visits in Outpatient Care: Qualitative Study


    Background: Video visits with patients were introduced into outpatient care at a hospital in Sweden. New behaviors and tasks emerged due to changes in roles, work processes, and responsibilities. This study investigates the effects of the digital transformation—in this case, how video visits in outpatient care change work processes and introduce new tasks—to further improve the concept of video visits. The overarching goal was to increase the value of these visits, with a focus on the value of conducting the treatment for the patient. Objective: Through the real-time, social interactional features of preparing for and conducting video visits with patients with obesity, this study examines which patients the clinicians considered suitable for video visits and why. The aim was to identify the criteria used by clinicians when selecting patients for video visits to understand what criteria the clinicians used as the grounds for their selection. Methods: Qualitative methods were used, including 13 observations of video visits at 2 different clinics and 14 follow-up interviews with clinicians. Transcripts of interviews and field notes were thematically analyzed, discussed, and synthesized into themes. Results: From the interviews, 20 different arguments for selecting a specific patient for video visits were identified. Analyzing interviews and field notes also revealed unexpressed arguments that played a part in the selection process. The unexpressed arguments, as well as the implicit reasons, for why a patient was given the option of video visits can be understood as the selection criteria for helping clinicians in their decision about whether to offer video visits or not. The criteria identified in the collected data were divided into 3 themes: practicalities, patient ability, and meeting content. Conclusions: Not all patients with obesity undergoing treatment programs should be offered video visits. Patients’ new responsibilities could influence the content of the meeting and the progress of the treatment program. The selection criteria developed and used by the clinicians could be a tool for finding a balance between what the patient wants and what the clinician thinks the patient can manage and achieving good results in the treatment program. The criteria could also reduce the number and severity of disturbances and limitations during the meeting and could be used to communicate the requirements they represent to the patient. Some of the criteria are based on facts, whereas others are subjective. A method for how and when to involve the patient in the selection process is recommended as it may strengthen the patient’s sense of responsibility and the relationship with the clinician.

  • Source: iStock by Getty Images; Copyright: franckreporter; URL:; License: Licensed by the authors.

    Insights Into Older Adult Patient Concerns Around the Caregiver Proxy Portal Use: Qualitative Interview Study


    Background: Electronic patient portals have become common and offer many potential benefits for patients’ self-management of health care. These benefits could be especially important for older adult patients dealing with significant chronic illness, many of whom have caregivers, such as a spouse, adult child, or other family member or friend, who help with health care management. Patient portals commonly contain large amounts of personal information, including diagnoses, health histories, medications, specialist appointments, lab results, and billing and insurance information. Some health care systems provide proxy accounts for caregivers to access a portal on behalf of a patient. It is not well known how much and in what way caregivers are using patient portals on behalf of patients and whether patients see any information disclosure risks associated with such access. Objective: The objective of this study was to examine how older adult patients perceive the benefits and risks of proxy patient portal access by their caregivers. Methods: We conducted semistructured interviews with 10 older adult patients with chronic illness. We asked them about their relationship with their caregivers, their use of their patient portal, their caregiver’s use of the portal, and their perceptions about the benefits and risks of their caregiver’s use of the portals. We also asked them about their comfort level with caregivers having access to information about a hypothetical diagnosis of a stigmatized condition. Two investigators conducted a thematic analysis of the qualitative data. Results: All patients identified caregivers. Some had given caregivers access to their portals, in all cases by sharing log-in credentials, rather than by setting up an official proxy account. Patients generally saw benefits in their caregivers having access to the information and functions provided by the portal. Patients generally reported that they would be uncomfortable with caregivers learning of stigmatized conditions and also with caregivers (except spouses) accessing financial billing information. Conclusions: Patients share their electronic patient portal credentials with caregivers to receive the benefits of those caregivers having access to important medical information but are unaware of all the information those caregivers can access. Better portal design could alleviate these unwanted information disclosures.

  • Mobile device and laptop displaying study recruitment and registry informational web pages (montage). Source: The Authors; Copyright: The Authors; URL:; License: Licensed by JMIR.

    A Practical Do-It-Yourself Recruitment Framework for Concurrent eHealth Clinical Trials: Simple Architecture (Part 1)


    Background: The ability to identify, screen, and enroll potential research participants in an efficient and timely manner is crucial to the success of clinical trials. In the age of the internet, researchers can be confronted with large numbers of people contacting the program, overwhelming study staff and frustrating potential participants. Objective: This paper describes a “do-it-yourself” recruitment support framework (DIY-RSF) that uses tools readily available in many academic research settings to support remote participant recruitment, prescreening, enrollment, and management across multiple concurrent eHealth clinical trials. Methods: This work was conducted in an academic research center focused on developing and evaluating behavioral intervention technologies. A needs assessment consisting of unstructured individual and group interviews was conducted to identify barriers to recruitment and important features for the new system. Results: We describe a practical and adaptable recruitment management architecture that used readily available software, such as REDCap (Research Electronic Data Capture) and standard statistical software (eg, SAS, R), to create an automated recruitment framework that supported prescreening potential participants, consent to join a research registry, triaging for management of multiple trials, capture of eligibility information for each phase of a recruitment pipeline, and staff management tools including monitoring of participant flow and task assignment/reassignment features. The DIY-RSF was launched in July 2015. As of July 2017, the DIY-RSF has supported the successful recruitment efforts for eight trials, producing 14,557 participant records in the referral tracking database and 5337 participants in the center research registry. The DIY-RSF has allowed for more efficient use of staff time and more rapid processing of potential applicants. Conclusions: Using tools already supported at many academic institutions, we describe the architecture and utilization of an adaptable referral management framework to support recruitment for multiple concurrent clinical trials. The DIY-RSF can serve as a guide for leveraging common technologies to improve clinical trial recruitment procedures.

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  • Artificial Intelligence and the Future of Primary Care: An Exploratory Qualitative Study of UK GPs’ Views

    Date Submitted: Nov 13, 2018

    Open Peer Review Period: Nov 13, 2018 - Jan 8, 2019

    Background: The potential for machine learning to disrupt the medical professions is the subject of ongoing debate within biomedical informatics and related fields. Objective: To explore GPs’ opinio...

    Background: The potential for machine learning to disrupt the medical professions is the subject of ongoing debate within biomedical informatics and related fields. Objective: To explore GPs’ opinions about the potential impact of future technology on key tasks in primary care. Methods: Context and Setting: A web-based survey of 720 UK GPs’ opinions about the likelihood of future technology to fully replace GPs in performing six key primary care tasks; and if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. Qualitative descriptive analysis of written responses (‘comments’) to an open-ended question. Results: Comments were classified into three major categories in relation to primary care: (i) limitations of future technology; (ii) potential benefits of future technology; and (iii) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning, and the ability to provide value-based care as necessitating physicians’ judgements. Perceived benefits of technology included expectations about improved efficiencies in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls, and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. Conclusions: This study presents timely information on physicians’ views about the scope of artificial intelligence in primary care. Overwhelmingly, GPs considered the potential of artificial intelligence to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views. Clinical Trial: (Not applicable)

  • Assessment of CHA2DS2-VASc Score for the Risk Stratification of Hospital Admission in Patients with Cardiovascular Diseases Receiving a Fourth-Generation Synchronous Telehealth Program

    Date Submitted: Nov 11, 2018

    Open Peer Review Period: Nov 11, 2018 - Nov 21, 2018

    Background: The telehealth program is diverse with mixed results. A comprehensive and integrated approach is needed to evaluate who gets benefits from the program to improve clinical outcomes. Objecti...

    Background: The telehealth program is diverse with mixed results. A comprehensive and integrated approach is needed to evaluate who gets benefits from the program to improve clinical outcomes. Objective: The CHA2DS2-VASc score has been widely used for the prediction of stroke in patients with atrial fibrillation. This study adopts the predictive concept of the CHA2DS2-VASc score and investigated this score for risk stratification in hospital admission in patients with cardiovascular diseases receiving a fourth-generation synchronous telehealth program. Methods: This was a retrospective cohort study. We recruited patients with cardiovascular disease who received the fourth-generation synchronous telehealth program at the National Taiwan University Hospital between October 2012 and June 2015. We enrolled 431 patients who had joined a telehealth program and compared them with 1549 control patients. Cardiovascular hospitalization was estimated with Kaplan-Meier curves. The CHA2DS2-VASc score was used as the composite parameter to stratify the severity of the patients. The association between baseline characteristics and the clinical outcomes was assessed via the Cox proportional hazard model. Results: The mean follow-up duration was 886.1 ± 531.0 days in patients receiving the fourth-generation synchronous telehealth program and 707.1 ± 431.4 days in the control group. (p<0.0001). The telehealth group had more comorbidities at baseline than the control group. Patients with higher CHA2DS2-VASc score (≥ 4) were associated with a lower estimated rate of free from cardiovascular hospitalization (46.5% vs. 54.8%, log-rank test p = 0.0028). Patients receiving the telehealth program with CHA2DS2-VASc score ≥ 4 were less likely to be admitted for cardiovascular disease (61.5% vs. 41.8%, log-rank test p = 0.010). The telehealth program remains a significant prognostic factor after multivariable Cox analysis in patients with CHA2DS2-VASc score ≥ 4 (HR=0.36 [CI: 0.22 -0.62], p < 0.0001) Conclusions: A higher CHA2DS2-VASc score is associated with higher cardiovascular admission. Patients with CHA2DS2-VASc ≥4 benefits most for free from cardiovascular hospitalization after accepting the fourth-generation telehealth program. Clinical Trial: N/A

  • Tweet Classification Toward Twitter-Based Disease Surveillance: Overview of the MedWeb Shared Task

    Date Submitted: Nov 9, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: The amount of medical and clinical-related information on the Web is increasing. Among the various types of information on the Web, social media-based data obtained directly from people ar...

    Background: The amount of medical and clinical-related information on the Web is increasing. Among the various types of information on the Web, social media-based data obtained directly from people are particularly valuable and garnering much attention. To encourage medical natural language processing research exploiting social media data, the NTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering three languages (Japanese, English, and Chinese), and annotated with eight symptom labels (e.g., cold, fever, flu, and so on). Then, participants classify each tweet into one of two categories: those containing a patient’s symptom, and those that do not. Objective: We aim to present the results of groups participated in the Japanese subtask, the English subtask, and the Chinese subtask along with discussions, in order to clarify the issues that need to be resolved in the field of medical natural language processing. Methods: The performance of participant systems is assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. Results: In all, eight groups (19 systems) participated in the Japanese subtask, four groups (12 systems) participated in the English subtask, and two groups (six systems) participated in the Chinese subtask. The best system achieved .880 in exact match accuracy, .920 in F-measure, and .019 in Hamming loss. Conclusions: This paper presented and discussed the performance of systems participated in the NTCIR-13 MedWeb task. Because the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be applied directly to practical clinical applications.

  • SMS-based family planning communication and its association with modern contraception and maternal healthcare use in selected low-middle-income countries

    Date Submitted: Nov 9, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: In recent years, there has been a growing interest surrounding mobile phone-based health communication and service delivery methods especially in the areas of family planning (FP) and repr...

    Background: In recent years, there has been a growing interest surrounding mobile phone-based health communication and service delivery methods especially in the areas of family planning (FP) and reproductive health. However, little is known regarding the role of SMS-based FP communication on the utilisation of modern contraception and maternal healthcare services in low-resource settings. Objective: The objectives of this study were to 1) measure the coverage of SMS-based family planning (FP) communication, and 2) its association with modern contraception and maternal healthcare services (MHS) among mothers. Methods: Cross-sectional data on 94,675 mothers (15-49 years) were collected from the latest Demographic and Health Surveys on 14 low-and-middle-income countries. The outcome variables were self-reported use of modern contraception and basic MHS (timely and adequate use of antenatal care, and of facility delivery services). Data were analysed using multivariate regression and random effect meta-analyses. Results: The coverage of SMS-based FP communication for the pooled sample was 5.4% (95%CI=3.71, 7.21), and was slightly higher in Africa (6.04, 95%CI=3.38, 8.70) compared with Asia (5.23, 95%CI=1.60, 8.86). Among the countries from sub-Saharan Africa, Malawi (11.92, 95%CI=11.17, 12.70) had the highest percent of receiving SMS while Senegal (1.24, 95%CI=1.00, 1.53) had the lowest. In the multivariate analysis, SMS communication shown significant association with the use of facility delivery only (2.22 (95%CI=1.95, 2.83). The strength of the association was highest for Senegal (OR=4.70, 95%CI=1.14, 7.33) and lowest for Burundi (OR=1.5; 95%CI=1.01, 2.74). Meta analyses revealed moderate heterogeneity both in the prevalence and the association between SMS communication and the utilisation of facility delivery. Conclusions: Although positively associated with using facility delivery services, receiving SMS on FP does not appear to affect modern contraceptive use and other components of MHS such as timely and adequate utilisation of antenatal care.

  • The association between usage and outcomes of an online intervention for depression: how optimal dosage can help establish adherence

    Date Submitted: Nov 9, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: Internet interventions are able to easily generate objective data about program usage. Increasingly, more studies are exploring the relationship between usage and outcomes but they often r...

    Background: Internet interventions are able to easily generate objective data about program usage. Increasingly, more studies are exploring the relationship between usage and outcomes but they often report different metrics of use and the findings are mixed. Thus, current evaluations fail to demonstrate which metrics should be considered and if it is possible to determine an optimal dose-response relationship which can inform thresholds for adherence and clinically meaningful change. Objective: This study aimed to explore the relationship between several usage metrics and outcomes; and determine an optimal dose of usage of an internet intervention for depression. Methods: This is a secondary analysis of data from a Randomized Controlled Trial that examined the efficacy of an internet-based Cognitive-Behavioral Therapy (iCBT) program for depression (Space from Depression) in an adult community sample. Space from Depression is a seven-module, supported intervention delivered over a period of 8 weeks. Supporters were trained volunteers who provided feedback to participants on a weekly basis. Different usage metrics (i.e. time spent, modules and activities completed, percentage of program completion) were automatically collected by the platform and composite variables from these (e.g. activities per session) were computed. A breakdown of the usage metrics was obtained by weeks. For the analysis, the sample was divided into those who obtained a reliable change (RC) (Beck Depression Inventory [BDI-II] change >8) and those who did not. Results: Data from 216 users who used the intervention and completed pre and post-treatment outcomes were included in the analyses. 89 participants obtained a RC and 127 did not. Those in the RC group significantly spent more time, had more logins, used more tools, viewed a higher percentage of the program and got more reviews from the supporter compared to those who did not obtain a RC. Differences between groups in usage was observed from first week in advance across the different metrics although they vanished over time. In the RC group, the usage was higher during the first four weeks and then a significant decrease was observed. ROC Curve analyses showed that the optimal cut-points for different usage metrics were 7 hours total time spent, 15 sessions, 30 tools used and 50% of program completion. Conclusions: Overall, the results showed that those individuals who obtained RC after the intervention had higher levels of exposure to the platform. The usage during the first half of the intervention was higher and differences between groups were observed from the first week. This study also suggests that it is possible to determine an optimal dose and this can be used to inform the minimal usage to establish adherence. These results will help to better understand how to use internet interventions and what optimal level of engagement can most affect outcomes. Clinical Trial: The trial is registered as a controlled trial with ISRCTN (ISRCTN03704676).

  • Digital Health Starts with Interoperable EHRs

    Date Submitted: Nov 7, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: 16 years of working in Digital Health Objective: Improve health outcomes through harnessing of digital technologies. Methods: Accumulating experience with current trends in Digital Health...

    Background: 16 years of working in Digital Health Objective: Improve health outcomes through harnessing of digital technologies. Methods: Accumulating experience with current trends in Digital Health Results: The paper Conclusions: Guidelines on EHR integrated implementation should be considered part of healthcare policy.