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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: Image created by the Authors/Free Mockup Zone; Copyright: Image created by the Authors/Free Mockup Zone; URL: http://freemockupzone.com/free-man-holding-elegant-smartphone-mockup/; License: Licensed by JMIR.

    Characteristics and Symptoms of App Users Seeking COVID-19–Related Digital Health Information and Remote Services: Retrospective Cohort Study

    Abstract:

    Background: Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice. Objective: The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19–related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools. Methods: This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)–driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19–related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician. Results: During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department. Conclusions: Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

  • Source: freepik; Copyright: yanalya; URL: https://www.freepik.com/free-photo/businesswoman-looking-important-contact-phone_3938519.htm#page=2&query=person+using+smartphone&position=1; License: Licensed by JMIR.

    Health-Related Quality of Life Improvements in Systemic Lupus Erythematosus Derived from a Digital Therapeutic Plus Tele-Health Coaching Intervention:...

    Abstract:

    Background: Systemic lupus erythematosus (SLE), a systemic autoimmune disease with no known cure, remains poorly understood and patients suffer from many gaps in care. Recent work has suggested that dietary and other lifestyle factors play an important role in triggering and propagating SLE in some susceptible individuals. However, the magnitude of influence of these triggers, how to identify pertinent triggers in individual patients, and whether removing these triggers confers clinical benefit is unknown. Objective: To demonstrate that a digital therapeutic intervention, utilizing a mobile app that allows self-tracking of dietary, environmental, and lifestyle triggers, paired with telehealth coaching, added to usual care, improves quality of life in patients with SLE compared with usual care alone. Methods: In this randomized controlled pilot study, adults with SLE were assigned to a 16-week digital therapeutic intervention plus usual care or usual care alone. Primary outcome measures were changes from baseline to 16 weeks on 3 validated health-related quality of life (HRQoL) tools: Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Brief Pain Inventory-Short Form (BPI-SF), and Lupus Quality of Life (LupusQoL). Results: A total of 50 patients were randomized (23 control, 27 intervention). In per-protocol analysis, the intervention group achieved significantly greater improvement than the control group in 9 of 11 domains: FACIT-F (34% absolute improvement for the intervention group vs –1% for the control group, P<.001), BPI-SF-Pain Interference (25% vs 0%, P=.02), LupusQoL-Planning (17% vs 0%, P=.004), LupusQoL-Pain (13% vs 0%, P=.004), LupusQoL-Emotional Health (21% vs 4%, P=.02), and LupusQoL-Fatigue (38% vs 13%, P<.001) were significant when controlling for multiple comparisons; BPI-SF-Pain Severity (13% vs –6%, P=.049), LupusQoL-Physical Health (17% vs 3%, P=.049), and LupusQoL-Burden to Others (33% vs 4%, P=.04) were significant at an unadjusted 5% significance level. Conclusions: A digital therapeutic intervention that pairs self-tracking with telehealth coaching to identify and remove dietary, environmental, and lifestyle symptom triggers resulted in statistically significant, clinically meaningful improvements in HRQoL when added to usual care in patients with SLE. Trial Registration: ClinicalTrials.gov NCT03426384; https://clinicaltrials.gov/ct2/show/NCT03426384

  • Source: Unsplash; Copyright: Van Tay Media; URL: https://unsplash.com/photos/47GH60NB-Rc; License: Licensed by JMIR.

    Patients’ Convergence of Mass and Interpersonal Communication on an Online Forum: Hybrid Methods Analysis

    Abstract:

    Background: Patients are increasingly taking an active role in their health. In doing so, they combine both mass and interpersonal media to gratify their cognitive and affective needs (ie, convergence). Owing to methodological challenges when studying convergence, a detailed view of how patients are using different types of media for needs fulfillment is lacking. Objective: The aim of this study was to obtain insight into the frequency of reported convergence, how convergence affects what posters write online, motives for posting, and the needs posters are trying to fulfill. Methods: Using a hybrid method of content analysis and supervised machine learning, this study used naturally available data to fill this research gap. We analyzed opening posts (N=1708) of an online forum targeting cancer patients and their relatives (Kanker.nl). Results: Nearly one-third of the forum opening posts contained signs of convergence in mass or interpersonal media. Posts containing mass media references disclosed less personal information and were more geared toward community enhancement and sharing experiences compared to posts without convergence. Furthermore, compared to posts without signs of convergence, posts that included interpersonal media references disclosed more personal information, and posters were more likely to ask for the experiences of fellow users to fulfill their needs. Within posts containing signs of convergence, posts including interpersonal media references reported fewer shortages of information, disclosed more information about the disease, and were more active in seeking other posters’ experiences compared to posts containing mass media references. Conclusions: The current study highlights the intertwining of media platforms for patients. The insights of this study can be used to adapt the health care system toward a new type of health information–seeking behavior in which one medium is not trusted to fulfill all needs. Instead, providers should incorporate the intertwinement of sources by providing patients with reliable websites and forums through which they can fulfill their needs.

  • Source: Unsplash; Copyright: Damir Spanic; URL: https://unsplash.com/photos/vwaTtIhCjVg?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink; License: Licensed by the authors.

    Determining if Telehealth Can Reduce Health System Costs: Scoping Review

    Abstract:

    Background: Telehealth represents an opportunity for Australia to harness the power of technology to redesign the way health care is delivered. The potential benefits of telehealth include increased accessibility to care, productivity gains for health providers and patients through reduced travel, potential for cost savings, and an opportunity to develop culturally appropriate services that are more sensitive to the needs of special populations. The uptake of telehealth has been hindered at times by clinician reluctance and policies that preclude metropolitan populations from accessing telehealth services. Objective: This study aims to investigate if telehealth reduces health system costs compared with traditional service models and to identify the scenarios in which cost savings can be realized. Methods: A scoping review was undertaken to meet the study aims. Initially, literature searches were conducted using broad terms for telehealth and economics to identify economic evaluation literature in telehealth. The investigators then conducted an expert focus group to identify domains where telehealth could reduce health system costs, followed by targeted literature searches for corresponding evidence. Results: The cost analyses reviewed provided evidence that telehealth reduced costs when health system–funded travel was prevented and when telehealth mitigated the need for expensive procedural or specialist follow-up by providing competent care in a more efficient way. The expert focus group identified 4 areas of potential savings from telehealth: productivity gains, reductions in secondary care, alternate funding models, and telementoring. Telehealth demonstrated great potential for productivity gains arising from health system redesign; however, under the Australian activity-based funding, it is unlikely that these gains will result in cost savings. Secondary care use mitigation is an area of promise for telehealth; however, many studies have not demonstrated overall cost savings due to the cost of administering and monitoring telehealth systems. Alternate funding models from telehealth systems have the potential to save the health system money in situations where the consumers pay out of pocket to receive services. Telementoring has had minimal economic evaluation; however, in the long term it is likely to result in inadvertent cost savings through the upskilling of generalist and allied health clinicians. Conclusions: Health services considering implementing telehealth should be motivated by benefits other than cost reduction. The available evidence has indicated that although telehealth provides overwhelmingly positive patient benefits and increases productivity for many services, current evidence suggests that it does not routinely reduce the cost of care delivery for the health system.

  • Source: Pexels; Copyright: Elijah O'Donnell; URL: https://www.pexels.com/photo/photo-of-woman-smoking-cigarette-3473525/; License: Licensed by the authors.

    Tailored Web-Based Smoking Interventions and Reduced Attrition: Systematic Review and Meta-Analysis

    Abstract:

    Background: The increasing number of internet users presents an opportunity to deliver health interventions to large populations. Despite their potential, many web-based interventions, including those for smoking cessation, face high rates of attrition. Further consideration of how intervention features impact attrition is needed. Objective: The aim of this systematic review is to investigate whether tailored web-based smoking cessation interventions for smokers are associated with reduced rates of attrition compared with active or passive untailored web-based interventions. The outcomes of interest were dropout attrition at 1-, 3-, 6-, and 12-month follow-ups. Methods: Literature searches were conducted in May 2018 and updated in May 2020 on MEDLINE (Medical Literature Analysis and Retrieval System Online), PsycINFO (Psychological Information), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulated Index to Nursing and Allied Health Literature), Scopus, and the Cochrane Tobacco Addiction Group Specialized Register with the following search terms: smoking cessation, tailored, or web- or internet-based. Included studies were published in English before or in May 2020 using a randomized controlled trial design. Studies were restricted to those with web-based delivery, a tailored intervention group, an untailored control group, and a reported outcome of smoking cessation. Studies were assessed for methodological quality using the Cochrane Risk of Bias tool. Two reviewers independently extracted the study characteristics and the number of participants lost to follow-up for each treatment group. Results: A total of 13 studies were included in the systematic review, of which 11 (85%) were included in the meta-analysis. Tailoring had no statistically significant effect on dropout attrition at 1-month (risk ratio [RR]=1.02, 95% CI 0.95-1.09; P=.58; I2=78%), 3-month (RR=0.99, 95% CI 0.95-1.04; P=.80; I2=73%), 6-month (RR=1.00, 95% CI 0.95-1.05; P=.90; I2=43%), or 12-month (RR=0.97, 95% CI 0.92-1.02; P=.26; I2=28%) follow-ups. Subgroup analyses suggested that there was a statistically significant effect of tailoring between the active and passive subgroups at 1-month (P=.03), 3-month (P<.001), and 6-month (P=.02) follow-ups but not at 12-month follow-up (P=.25). Conclusions: The results suggest that tailoring of web-based smoking cessation interventions may not be associated with reduced rates of dropout attrition at 1-, 3-, 6-, or 12-month follow-ups. Significant differences between studies that include untailored active and passive control groups suggest that the role of tailoring may be more prominent when studies include a passive control group. These findings may be because of variability in the presence of additional features, the definition of smokers used, and the duration of smoking abstinence measured. Future studies should incorporate active web-based controls, compare the impact of different tailoring strategies, and include populations outside of the Western countries.

  • Source: Freepik; Copyright: zaozaa09; URL: https://www.freepik.com/free-photo/doctor-holding-tablet-e-health-concept-business-concept_1178625.htm#page=1&query=ehealth&position=0; License: Licensed by JMIR.

    Identifying the Value of an eHealth Intervention Aimed at Cognitive Impairments: Observational Study in Different Contexts and Service Models

    Abstract:

    Background: Value is one of the central concepts in health care, but it is vague within the field of summative eHealth evaluations. Moreover, the role of context in explaining the value is underexplored, and there is no explicit framework guiding the evaluation of the value of eHealth interventions. Hence, different studies conceptualize and operationalize value in different ways, ranging from measuring outcomes such as clinical efficacy or behavior change of patients or professionals to measuring the perceptions of various stakeholders or in economic terms. Objective: The objective of our study is to identify contextual factors that determine similarities and differences in the value of an eHealth intervention between two contexts. We also aim to reflect on and contribute to the discussion about the specification, assessment, and relativity of the “value” concept in the evaluation of eHealth interventions. Methods: The study concerned a 6-month eHealth intervention targeted at elderly patients (n=107) diagnosed with cognitive impairment in Italy and Sweden. The intervention introduced a case manager role and an eHealth platform to provide remote monitoring and coaching services to the patients. A model for evaluating the value of eHealth interventions was designed as monetary and nonmonetary benefits and sacrifices, based on the value conceptualizations in eHealth and marketing literature. The data was collected using the Mini–Mental State Examination (MMSE), the clock drawing test, and the 5-level EQ-5D (EQ-5D-5L). Semistructured interviews were conducted with patients and health care professionals. Monetary data was collected from the health care and technology providers. Results: The value of an eHealth intervention applied to similar types of populations but differed in different contexts. In Sweden, patients improved cognitive performance (MMSE mean 0.85, SD 1.62, P<.001), reduced anxiety (EQ-5D-5L mean 0.16, SD 0.54, P=.046), perceived their health better (EQ-5D-5L VAS scale mean 2.6, SD 9.7, P=.035), and both patients and health care professionals were satisfied with the care. However, the Swedish service model demonstrated an increased cost, higher workload for health care professionals, and the intervention was not cost-efficient. In Italy, the patients were satisfied with the care received, and the health care professionals felt empowered and had an acceptable workload. Moreover, the intervention was cost-effective. However, clinical efficacy and quality of life improvements have not been observed. We identified 6 factors that influence the value of eHealth intervention in a particular context: (1) service delivery design of the intervention (process of delivery), (2) organizational setup of the intervention (ie, organizational structure and professionals involved), (3) cost of different treatments, (4) hourly rates of staff for delivering the intervention, (5) lifestyle habits of the population (eg, how physically active they were in their daily life and if they were living alone or with family), and (6) local preferences on the quality of patient care. Conclusions: Value in the assessments of eHealth interventions need to be considered beyond economic terms, perceptions, or behavior changes. To obtain a holistic view of the value created, it needs to be operationalized into monetary and nonmonetary outcomes, categorizing these into benefits and sacrifices.

  • Source: Pexels; Copyright: Erik Karits; URL: https://www.pexels.com/photo/macro-photography-of-insect-in-yellow-flower-3760420/; License: Licensed by JMIR.

    Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018

    Abstract:

    Background: Borreliosis is the most frequently transmitted tick-borne disease in Europe. It is difficult to estimate the incidence of tick bites and associated diseases in the German population due to the lack of an obligation to register across all 16 federal states of Germany. Objective: The aim of this study is to show that Google data can be used to generate general trends of infectious diseases on the basis of borreliosis and tick bites. In addition, the possibility of using Google AdWord data to estimate incidences of infectious diseases, where there is inconsistency in the obligation to notify authorities, is investigated with the perspective to facilitate public health studies. Methods: Google AdWords Keyword Planner was used to identify search terms related to ticks and borreliosis in Germany from January 2015 to December 2018. The search volume data from the identified search terms was assessed using Excel version 15.23. In addition, SPSS version 24.0 was used to calculate the correlation between search volumes, registered cases, and temperature. Results: A total of 1999 tick-related and 542 borreliosis-related search terms were identified, with a total of 209,679,640 Google searches in all 16 German federal states in the period under review. The analysis showed a high correlation between temperature and borreliosis (r=0.88), and temperature and tick bite (r=0.83), and a very high correlation between borreliosis and tick bite (r=0.94). Furthermore, a high to very high correlation between Google searches and registered cases in each federal state was observed (Brandenburg r=0.80, Mecklenburg-West Pomerania r= 0.77, Saxony r= 0.74, and Saxony-Anhalt r=0.90; all P<.001). Conclusions: Our study provides insight into annual trends concerning interest in ticks and borreliosis that are relevant to the German population exemplary in the data of a large internet search engine. Public health studies collecting incidence data may benefit from the results indicating a significant correlation between internet search data and incidences of infectious diseases.

  • Source: iStock by Getty Images; Copyright: ipopba; URL: https://www.istockphoto.com/photo/medicine-doctor-touching-electronic-medical-record-on-tablet-dna-digital-healthcare-gm1165067633-320441314; License: Licensed by the authors.

    Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation

    Abstract:

    Background: Fractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years. Objective: The goal of this study was to develop and evaluate an algorithm for the identification of patients at risk of fracture in a subsequent 1- to 2-year period. In order to address the aforementioned limitations of current prediction tools, this approach focused on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions. Methods: Using retrospective electronic health record data from over 1,000,000 patients, we developed Crystal Bone, an algorithm that applies machine learning techniques from natural language processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the signature of the patient’s journey is similar to that of a typical future fracture patient. A holdout set with 192,590 patients was used to validate accuracy. Experimental baseline models and human-level performance were used for comparison. Results: The model accurately predicted 1- to 2-year fracture risk for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC] 0.81). These algorithms outperformed the experimental baselines (AUROC 0.67) and showed meaningful improvements when compared to retrospective approximation of human-level performance by correctly identifying 9649 of 13,765 (70%) at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians. Conclusions: These findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the health care system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.

  • Source: Pixabay; Copyright: Free-Photos; URL: https://pixabay.com/de/photos/smartwatch-gadget-technologie-smart-828786/; License: Licensed by JMIR.

    Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey

    Abstract:

    Background: Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. Objective: The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. Methods: Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. Results: About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. Conclusions: The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

  • Source: Freepik; Copyright: pressfoto; URL: https://www.freepik.com/free-photo/unrecognizable-female-doctor-with-laptop-holding-medication-writing-prescription_5577146.htm; License: Licensed by JMIR.

    Impact of Electronic Health Record Interface Design on Unsafe Prescribing of Ciclosporin, Tacrolimus, and Diltiazem: Cohort Study in English National Health...

    Abstract:

    Background: In England, national safety guidance recommends that ciclosporin, tacrolimus, and diltiazem are prescribed by brand name due to their narrow therapeutic windows and, in the case of tacrolimus, to reduce the chance of organ transplantation rejection. Various small studies have shown that changes to electronic health record (EHR) system interfaces can affect prescribing choices. Objective: Our objectives were to assess variation by EHR systems in breach of safety guidance around prescribing of ciclosporin, tacrolimus, and diltiazem, and to conduct user-interface research into the causes of such breaches. Methods: We carried out a retrospective cohort study using prescribing data in English primary care. Participants were English general practices and their respective EHR systems. The main outcome measures were (1) the variation in ratio of safety breaches to adherent prescribing in all practices and (2) the description of observations of EHR system usage. Results: A total of 2,575,411 prescriptions were issued in 2018 for ciclosporin, tacrolimus, and diltiazem (over 60 mg); of these, 316,119 prescriptions breached NHS guidance (12.27%). Breaches were most common among users of the EMIS EHR system (breaches in 18.81% of ciclosporin and tacrolimus prescriptions and in 17.99% of diltiazem prescriptions), but breaches were observed in all EHR systems. Conclusions: Design choices in EHR systems strongly influence safe prescribing of ciclosporin, tacrolimus, and diltiazem, and breaches are prevalent in general practices in England. We recommend that all EHR vendors review their systems to increase safe prescribing of these medicines in line with national guidance. Almost all clinical practice is now mediated through an EHR system; further quantitative research into the effect of EHR system design on clinical practice is long overdue.

  • Source: Image created by the Authors; Copyright: The Authors; URL: https://www.jmir.org/2020/10/e22628; License: Creative Commons Attribution (CC-BY).

    Knowledge, Awareness, and Attitudes Relating to the COVID-19 Pandemic Among Different Populations in Central China: Cross-Sectional Survey

    Abstract:

    Background: The COVID-19 pandemic has threatened the health systems of many countries worldwide. Several studies have suggested that the pandemic affects not only physical health but also all aspects of society. A lot of information has been reported about the disease since the beginning of the outbreak. For that reason, it is essential to investigate the attitudes and level of knowledge and awareness that different populations had regarding COVID-19 during the critical period of the outbreak. Objective: This study aimed to assess the knowledge and awareness of and attitudes toward the COVID-19 pandemic among different populations in Central China during the critical period of the outbreak. Methods: A cross-sectional web-based survey was conducted in Central China from February to March 2020. The study participants included three different populations: medical workers, students, and those with other occupations. In this study, a questionnaire was designed to collect information on the following four aspects: sociodemographic information, knowledge related to COVID-19, awareness of COVID-19, and attitude toward COVID-19. The chi-square test and Fisher test were used for comparison among groups. The level of significance was set at P<.05. Results: This study enrolled a total of 508 participants. Among them, there were 380 students (74.8%), 39 medical workers (7.7%), and 89 people with other occupations (17.5%). Most of the participants were female (n=272, 53.5%), lived in rural areas (n=258, 50.8%), and were single (n=423, 86.9%). The majority of the respondents had attended college (n=454, 89.4%). Most of the participants said they had heard about COVID-19 by January, and most of them looked for information on social media (Sina Weibo, 84.7%), and WeChat and QQ groups (74.2%). The participants showed an adequate level of knowledge about COVID-19 with no significant differences among the groups. However, medical workers demonstrated a slightly advanced knowledge in their responses to professional questions such as the potential susceptible population, possible host, treatment of COVID-19, and disease category. A higher proportion of medical workers (71.8%) and those in the other occupations group (52.8%) were highly concerned about the COVID-19 pandemic. More than 43% of the participants stated that the lockdown of their village/city had a significant impact on their lives. Nevertheless, the majority of respondents had an overall optimistic attitude toward the control of the disease (92.1% of students [n=350], 94.9% of medical workers [n=37], and 92.3% of those in other occupations [n=83]). Conclusions: All three groups reported an adequate background knowledge about COVID-19 but medical workers showed a slightly advanced knowledge in their responses to professional questions. Most of the participants were highly concerned about COVID-19 during the critical period of the outbreak. The majority of respondents declared that the village/city lockdown policy had a significant impact on their daily life but most of them held an optimistic attitude toward the control of COVID-19.

  • Source: Freepik; Copyright: jcomp; URL: https://www.freepik.com/free-photo/girl-wearing-mask-striped-shirt-playing-smartphone_8351631.htm; License: Licensed by JMIR.

    Online Health Information Seeking Using “#COVID-19 Patient Seeking Help” on Weibo in Wuhan, China: Descriptive Study

    Abstract:

    Background: First detected in Wuhan, China in December 2019, the COVID-19 pandemic stretched the medical system in Wuhan and posed a challenge to the state’s risk communication efforts. Timely access to quality health care information during outbreaks of infectious diseases can be effective to curtail the spread of disease and feelings of anxiety. Although existing studies have extended our knowledge about online health information–seeking behavior, processes, and motivations, rarely have the findings been applied to an outbreak. Moreover, there is relatively little recent research on how people in China are using the internet for seeking health information during a pandemic. Objective: The aim of this study is to explore how people in China are using the internet for seeking health information during a pandemic. Drawing on previous research of online health information seeking, this study asks the following research questions: how was the “#COVID-19 Patient Seeking Help” hashtag being used by patients in Wuhan seeking health information on Weibo at the peak of the outbreak? and what kinds of health information were patients in Wuhan seeking on Weibo at the peak of the outbreak? Methods: Using entity identification and textual analysis on 10,908 posts on Weibo, we identified 1496 patients with COVID-19 using “#COVID-19 Patient Seeking Help” and explored their online health information–seeking behavior. Results: The curve of the hashtag posting provided a dynamic picture of public attention to the COVID-19 pandemic. Many patients faced difficulties accessing offline health care services. In general, our findings confirmed that the internet is used by the Chinese public as an important source of health information. The lockdown policy was found to cut off the patients’ social support network, preventing them from seeking help from family members. The ability to seek information and help online, especially for those with young children or older adult members during the pandemic. A high proportion of female users were seeking health information and help for their parents or for older adults at home. The most searched information included accessing medical treatment, managing self-quarantine, and offline to online support. Conclusions: Overall, the findings contribute to our understanding of health information–seeking behaviors during an outbreak and highlight the importance of paying attention to the information needs of vulnerable groups and the role social media may play.

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    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.

  • Documenting Social Media Engagement as Scholarship: A New Model for Assessing Academic Accomplishment for the Health Professions

    Date Submitted: Oct 16, 2020

    Open Peer Review Period: Oct 16, 2020 - Oct 23, 2020

    Background: The traditional model of promotion and tenure in the health professions relies heavily on formal scholarship through teaching, research, and service. Institutions then consider how much we...

    Background: The traditional model of promotion and tenure in the health professions relies heavily on formal scholarship through teaching, research, and service. Institutions then consider how much weight is given to activities in each of those three areas and determine a threshold for advancement. With the emergence of social media, scholars can now engage wider audiences in more creative ways and have a broader impact. Conventional metrics like the h-index do not account for social media impact. Social media engagement is poorly represented in most current curricula vitae (CV) and therefore is undervalued in promotion and/or tenure reviews. Objective: This paper presents crowdsourced guidelines on how to cite scholarly productivity on social media. These guidelines describe a process and structure for documenting and describing a scholar’s general impact on a social media platform, as well as methods of documenting individual social media contributions exemplifying innovation, education, mentorship, advocacy, and dissemination. Methods: To create a set of unifying guidelines, we created a crowdsourced process that capitalized on the strengths of social media and generated a case example of successful use of such a medium for productive academic collaboration. The primary author created a brief draft of the guidelines and then sought input from users on Twitter via a publicly accessible Google Document. There was no limitation on who could provide input and the work was done in a democratic, collaborative fashion. Contributors edited the draft over a period of one week (September 12-18, 2020). The primary and secondary authors then revised the draft to make it more concise. The guidelines and manuscript were then distributed to the list of contributors for edits and adopted by the group. All contributors were given the opportunity to serve as co-authors on the publication and were told upfront that authorship would be dependent upon whether they were able to document the ways in which they met the four International Committee of Medical Journal Editors authorship criteria. Results: The authorship team developed two sets of guidelines: “Guidelines for Listing All Social Media Scholarship Under Public Scholarship (in Research/Scholarship Section of CV)” and “Guidelines for Listing Social Media Scholarship Under Research, Teaching, and Service Sections of CV.” The content of both sets of guidelines is identical: institutions can choose which set fits their existing CV format. The authorship team was notably diverse: 18% identified as a person of color and/or underrepresented minority, 38% identified as LGBTQ+, 73% used she/her pronouns, and 23% identified as a person with a disability. Conclusions: With more uniformity, schools and scholars alike can better represent the full scope and impact of their work. These guidelines are not intended to dictate how individual institutions should weigh social media contributions within promotion and tenure cases. Instead, by providing an initial set of guidelines, we hope to provide scholars and their institutions with a common format and language to describe what is becoming more and more ubiquitous among academics.

  • 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.

  • Using “Xuexi Tong Platform”as the Major Approach to Explore of Teaching Models of “Histology and Embryology”and “Pathology” in the Time of COVID-19

    Date Submitted: Oct 12, 2020

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

    Background: The Coronavirus Disease 2019 (COVID-19) epidemic has suddenly swept the world, and online courses have completely replaced offline courses. This is a new type of teaching practice in China...

    Background: The Coronavirus Disease 2019 (COVID-19) epidemic has suddenly swept the world, and online courses have completely replaced offline courses. This is a new type of teaching practice in China and even the world,which needs our exploration and research. “Histology and Embryology” and “Pathology” are both basic medical morphology disciplines. Thus, the previous teaching mode is no longer suitable for teaching in this special period. Objective: The purpose of this research is to explore a new teaching mode of “Histology and Embryology” and “Pathology”under the circumstances of the Coronavirus Disease 2019 (COVID-19) epidemic, and to study its application effects. Methods: From March to July 2020, our teaching team conducted research on 512 students in 10 classes of Grade 2019 undergraduate clinical medicine students who study “Histology and Embryology” and Grade 2018 undergraduate clinical medicine students who study “Pathology”, applying the “Internet + education” approach. The teaching team adopted a new teaching mode involving diverse online teaching methods, carefully designed and selected teaching contents, and various teaching activities, which included: 1. New flipped classroom; 2. Screen-to-screen experimental teaching; 3. Drawing competitions; 4. A writing activity themed “The You I Know-During the Coronavirus Disease 2019 (COVID-19) epidemic”. When the teaching was about to end, the “Questionnaire Star” is used to make investigation about the feedback of students on the theoretical teaching mode and the use of the experimental platform. Results: Students accepted “Seven-in-One” teaching mode of “video + materials + chapter tests + interaction + homework + live broadcast + case analysis/discussion”. The guidance of online learning which integrated six online applications namely “Xuexi Tong Platform + Tencent Conference Live + Chinese University MOOC/school online MOOC + medical morphology digital teaching platform + WeChat/QQ platform + questionnaire star survey” was also well received by students. The teaching team received 244 and 246 valid questionnaires for the two grades respectively, and the number of students’with satisfaction response reached 71.28%. Conclusions: This study shows that this teaching mode, which proved feasible and acceptable during the epidemic, laid a solid foundation for future online and offline hybrid teaching.

  • Short-range forecasting of coronavirus disease 2019 (COVID-19) during early onset at county, health district, and state geographic levels: Comparative forecasting approach using seven forecasting methods

    Date Submitted: Oct 10, 2020

    Open Peer Review Period: Oct 10, 2020 - Dec 5, 2020

    Background: Modeling approaches have utilized variations on susceptible, infected, and recovered (SIR), susceptible, exposed, infected, and recovered (SEIR), and machine learning models to estimate th...

    Background: Modeling approaches have utilized variations on susceptible, infected, and recovered (SIR), susceptible, exposed, infected, and recovered (SEIR), and machine learning models to estimate the spread of coronavirus disease 2019 (COVID-19) based on the identified virus characteristics. Forecasting methods rely on real-time numbers of confirmed case and death counts to create forecasts based on the characteristics of the trends and averages of prior data. COVID-19 forecasting studies have varied in geographic scales from global, country, and state levels. These studies support the need to implement mitigation strategies to slow the spread, flatten the peak, inform policy, and indicate medical capacity burdens. Objective: Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. Using publicly available real-time data provided online, we evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts are evaluated based on how well they can forecast one-. three-, and seven-days forward when utilizing one-, three-, seven-, or all-prior days’ cumulative case counts during early onset of case spreading. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. Methods: One-, three-, and seven-days forecasts are created at the county, health district, and state levels using: (1) a naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Forecasts rely on 3,463 observations from Virginia’s county-level cumulative case counts as reported by The New York Times. 95% confidence of Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics of the resulting 216,698 forecasts are used to identify statistically significant differences. Results: Single-day MA forecast with three-day lookback obtained the lowest MdAE and statistically significantly differs from 39 (66.1%) to 53 (89.8%) of alternatives at each geographic level using P value equal to 0.05. Methods assuming stationary means of prior days’ counts outperform methods with assumptions of weak- or non-stationarity means. MdAPE results reveal statistically significant differences across geographic levels. Conclusions: For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset: (1) MA is effective for forecasting one-, three-, and seven-days’ cumulative case counts; (2) assumptions of stationarity of means in prior-observations are more effective than assumptions of weak- or non-stationarity means; and (3) geographic resolution is a factor in forecasting method selection. (This work received no external funding.)

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