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Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694; Impact Factor: 3.188) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a Pubmed/SCIE-indexed, top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.

Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), JMIR Med Inform has a slightly different scope (putting more emphasis on applications for clinicians and health professionals rather than consumers/citizens), publishes even faster, and also allows papers which are more technical or more formative than what would be published in JMIR.

JMIR Med Inform is indexed in PubMed Central/PubMed and has also been accepted for SCIE. JMIR Med Inform received an inaugural Journal Impact Factor for 2018 (released June 2019) of 3.188.

JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR (


Recent Articles:

  • Source: freepik; Copyright:; URL:; License: Licensed by JMIR.

    Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study


  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    A Good Practice–Compliant Clinical Trial Imaging Management System for Multicenter Clinical Trials: Development and Validation Study


    Background: With the rapid increase in utilization of imaging endpoints in multicenter clinical trials, the amount of data and workflow complexity have also increased. A Clinical Trial Imaging Management System (CTIMS) is required to comprehensively support imaging processes in clinical trials. The US Food and Drug Administration (FDA) issued a guidance protocol in 2018 for appropriate use of medical imaging in accordance with many regulations including the Good Clinical Practice (GCP) guidelines. Existing research on CTIMS, however, has mainly focused on functions and structures of systems rather than regulation and compliance. Objective: We aimed to develop a comprehensive CTIMS to meet the current regulatory guidelines and various required functions. We also aimed to perform computerized system validation focusing on the regulatory compliance of our CTIMS. Methods: Key regulatory requirements of CTIMS were extracted thorough review of many related regulations and guidelines including International Conference on Harmonization-GCP E6, FDA 21 Code of Federal Regulations parts 11 and 820, Good Automated Manufacturing Practice, and Clinical Data Interchange Standards Consortium. The system architecture was designed in accordance with these regulations by a multidisciplinary team including radiologists, engineers, clinical trial specialists, and regulatory medicine professionals. Computerized system validation of the developed CTIMS was performed internally and externally. Results: Our CTIMS (AiCRO) was developed based on a two-layer design composed of the server system and the client system, which is efficient at meeting the regulatory and functional requirements. The server system manages system security, data archive, backup, and audit trail. The client system provides various functions including deidentification, image transfer, image viewer, image quality control, and electronic record. Computerized system validation was performed internally using a V-model and externally by a global quality assurance company to demonstrate that AiCRO meets all regulatory and functional requirements. Conclusions: We developed a Good Practice–compliant CTIMS—AiCRO system—to manage large amounts of image data and complexity of imaging management processes in clinical trials. Our CTIMS adopts and adheres to all regulatory and functional requirements and has been thoroughly validated.

  • Source: freepik; Copyright: katemangostar; URL:; License: Licensed by JMIR.

    Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination


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    Developing a Standardization Algorithm for Categorical Laboratory Tests for Clinical Big Data Research: Retrospective Study


    Background: Data standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods. Methods: We developed a method called standardization algorithm for laboratory test–categorical result (SALT-C) that can process categorical laboratory data, such as pos +, 250 4+ (urinalysis results), and reddish (urinalysis color results). SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups (urine color, urine dipstick, blood type, presence-finding, and pathogenesis tests). Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned. Results: The performance of SALT-C was validated using 59,213,696 data points (167,938 unique values) generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% (123/126; urine color tests), 97.5% (198/203; (urine dipstick tests), 95% (53/56; blood type tests), 99.68% (162,291/162,805; presence-finding tests), and 99.61% (4643/4661; pathogenesis tests). Conclusions: The proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts.

  • A nurse and her equipment for blood transfusion management. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Implementation and Effectiveness of a Bar Code–Based Transfusion Management System for Transfusion Safety in a Tertiary Hospital: Retrospective Quality...


    Background: Large-scale and long-term studies are not sufficient to determine the efficiency that IT solutions can bring to transfusion safety. Methods: The BCTM system uses barcodes for patient identification, onsite labeling, and blood product verification, through wireless connection to the hospital information systems. Plan-Do-Study-Act (PDSA) cycles were used to improve the process. Process maps before and after implementation of the BCTM system in 2011 were drawn to highlight the changes. The numbers of incorrect labeling or wrong blood in tube incidents that occurred quarterly were plotted on a run chart to monitor the quality changes of each intervention introduced. The annual occurrences of error events from 2011 to 2017 were compared with the mean occurrence of 2008-2010 to determine whether implementation of the BCTM system could effectively reduce the number of errors in 2016 and whether this reduction could persist in 2017. Results: The error rate decreased from 0.03% in 2008-2010 to 0.002% in 2016 (P<.001) and 0.001% in 2017 (P<.001) after implementation of the BTCM system. Only one incorrect labeling incident was noted among the 68,324 samples for blood typing, and no incorrect transfusions occurred among 67,423 transfusion orders in 2017. Conclusions: This report demonstrates that continuous efforts to upgrade the existing process is critical to reduce errors in transfusion therapy, with support from information technology.

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

    Common Data Elements for Acute Coronary Syndrome: Analysis Based on the Unified Medical Language System


  • Source: / Pexels; Copyright:; URL:; License: Licensed by JMIR.

    Development of an eHealth Readiness Assessment Framework for Botswana and Other Developing Countries: Interview Study


    Background: Electronic health (eHealth) readiness has been defined as the preparedness of health care institutions or communities for the anticipated change brought about by programs related to information and communication technology use. To ascertain the degree of such preparedness, an eHealth readiness assessment (eHRA) is needed. Literature on the existing eHRA frameworks and tools shows high inconsistency in content, definitions, and recommendations, and none have been found to be entirely suitable for assessing eHealth readiness in the context of developing countries. To develop an informed eHRA framework and tools with applicability to Botswana and similar developing countries, insight was sought from a broad spectrum of eHealth key informants in Botswana to identify and inform relevant issues, including those not specifically addressed in available eHRA tools. Objective: The aim of this study was to evaluate key informant (local expert) opinions on aspects that need to be considered when developing an eHRA framework suitable for use in developing countries. Methods: Interviews with 18 purposively selected key informants were recorded and transcribed. Thematic analysis of transcripts involved the use of an iterative approach and NVivo 11 software. The major themes, as well as subthemes, emerging from the thematic analysis were then discussed and agreed upon by the authors through consensus. Results: Analysis of interviews identified four eHealth readiness themes (governance, stakeholder issues, resources, and access), with 33 subthemes and 9 sub-subthemes. A major finding was that these results did not directly correspond in content or order to those previously identified in the literature. The results highlighted the need to perform exploratory research before developing an eHRA to ensure that those topics of relevance and importance to the local setting are first identified and then explored in any subsequent eHRA using a locally relevant framework and stakeholder-specific tools. In addition, seven sectors in Botswana were found to play a role in ensuring successful implementation of eHealth projects and might be targets for assessment. Conclusions: Insight obtained from this study will be used to inform the development of an evidence-based eHealth readiness assessment framework suitable for use in developing countries such as Botswana.

  • A clinician is working with artificial intelligence. Source: tmeier1964 / pixabay; Copyright: tmeier1964; URL:; License: Licensed by JMIR.

    Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review


    Background: Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. Methods: We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. Results: A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. Conclusions: Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians’ experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.

  • Source: Flickr; Copyright: Lung Cancer Research Foundation (LCRF); URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study


    Background: Liquid biopsies based on blood samples have been widely accepted as a diagnostic and monitoring tool for cancers, but extremely high sensitivity is frequently needed due to the very low levels of the specially selected DNA, RNA, or protein biomarkers that are released into blood. However, routine blood indices tests are frequently ordered by physicians, as they are easy to perform and are cost effective. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between multiple sets of test data and diseases. Objective: The aim of this study is to discover the potential association between lung cancer and routine blood indices and thereby help clinicians and patients to identify lung cancer based on these routine tests. Methods: The machine learning method known as Random Forest was adopted to build an identification model between routine blood indices and lung cancer that would determine if they were potentially linked. Ten-fold cross-validation and further tests were utilized to evaluate the reliability of the identification model. Results: In total, 277 patients with 49 types of routine blood indices were included in this study, including 183 patients with lung cancer and 94 patients without lung cancer. Throughout the course of the study, there was correlation found between the combination of 19 types of routine blood indices and lung cancer. Lung cancer patients could be identified from other patients, especially those with tuberculosis (which usually has similar clinical symptoms to lung cancer), with a sensitivity, specificity and total accuracy of 96.3%, 94.97% and 95.7% for the cross-validation results, respectively. This identification method is called the routine blood indices model for lung cancer, and it promises to be of help as a tool for both clinicians and patients for the identification of lung cancer based on routine blood indices. Conclusions: Lung cancer can be identified based on the combination of 19 types of routine blood indices, which implies that artificial intelligence can find the connections between a disease and the fundamental indices of blood, which could reduce the necessity of costly, elaborate blood test techniques for this purpose. It may also be possible that the combination of multiple indices obtained from routine blood tests may be connected to other diseases as well.

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

    Core Data Elements in Acute Myeloid Leukemia: A Unified Medical Language System–Based Semantic Analysis and Experts’ Review


    Background: For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. Objective: This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. Methods: Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. Results: A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. Conclusions: Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

  • RFID chip with chip and antenna. Source: Wikimedia Commons; Copyright: Maschinenjunge; URL:,12,14,100,106,0%5d%7d&uselang=nl#/media/File:RFID_Chip_004.JPG; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    The Value of Radio Frequency Identification in Quality Management of the Blood Transfusion Chain in an Academic Hospital Setting


    Background: A complex process like the blood transfusion chain could benefit from modern technologies such as radio frequency identification (RFID). RFID could, for example, play an important role in generating logistic and temperature data of blood products, which are important in assessing the quality of the logistic process of blood transfusions and the product itself. Objective: This study aimed to evaluate whether location, time stamp, and temperature data generated in real time by an active RFID system containing temperature sensors attached to red blood cell (RBC) products can be used to assess the compliance of the management of RBCs to 4 intrahospital European and Dutch guidelines prescribing logistic and temperature constraints in an academic hospital setting. Methods: An RFID infrastructure supported the tracking and tracing of 243 tagged RBCs in a clinical setting inside the hospital at the blood transfusion laboratory, the operating room complex, and the intensive care unit within the Academic Medical Center, a large academic hospital in Amsterdam, the Netherlands. The compliance of the management of 182 out of the 243 tagged RBCs could be assessed on their adherence to the following guidelines on intrahospital storage, transport, and distribution: (1) RBCs must be preserved within an environment with a temperature between 2°C and 6°C; (2) RBCs have to be transfused within 1 hour after they have left a validated cooling system; (3) RBCs that have reached a temperature above 10°C must not be restored or must be transfused within 24 hours or else be destroyed; (4) unused RBCs are to be returned to the BTL within 24 hours after they left the transfusion laboratory. Results: In total, 4 blood products (4/182 compliant; 2.2%) complied to all applicable guidelines. Moreover, 15 blood products (15/182 not compliant to 1 out of several guidelines; 8.2%) were not compliant to one of the guidelines of either 2 or 3 relevant guidelines. Finally, 148 blood products (148/182 not compliant to 2 guidelines; 81.3%) were not compliant to 2 out of the 3 relevant guidelines. Conclusions: The results point out the possibilities of using RFID technology to assess the quality of the blood transfusion chain itself inside a hospital setting in reference to intrahospital guidelines concerning the storage, transport, and distribution conditions of RBCs. This study shows the potentials of RFID in identifying potential bottlenecks in hospital organizations’ processes by use of objective data, which are to be tackled in process redesign efforts. The effect of these efforts can subsequently be evaluated by the use of RFID again. As such, RFID can play a significant role in optimization of the quality of the blood transfusion chain.

  • Source: Flickr; Copyright: Medill DC; URL:; License: Creative Commons Attribution (CC-BY).

    Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis...


    Background: Most US health care providers have adopted electronic health records (EHRs) that facilitate the uniform collection of clinical information. However, standardized data formats to capture social and behavioral determinants of health (SBDH) in structured EHR fields are still evolving and not adopted widely. Consequently, at the point of care, SBDH data are often documented within unstructured EHR fields that require time-consuming and subjective methods to retrieve. Meanwhile, collecting SBDH data using traditional surveys on a large sample of patients is infeasible for health care providers attempting to rapidly incorporate SBDH data in their population health management efforts. A potential approach to facilitate targeted SBDH data collection is applying information extraction methods to EHR data to prescreen the population for identification of immediate social needs. Objective: Our aim was to examine the availability and characteristics of SBDH data captured in the EHR of a multilevel academic health care system that provides both inpatient and outpatient care to patients with varying SBDH across Maryland. Methods: We measured the availability of selected patient-level SBDH in both structured and unstructured EHR data. We assessed various SBDH including demographics, preferred language, alcohol use, smoking status, social connection and/or isolation, housing issues, financial resource strains, and availability of a home address. EHR’s structured data were represented by information collected between January 2003 and June 2018 from 5,401,324 patients. EHR’s unstructured data represented information captured for 1,188,202 patients between July 2016 and May 2018 (a shorter time frame because of limited availability of consistent unstructured data). We used text-mining techniques to extract a subset of SBDH factors from EHR’s unstructured data. Results: We identified a valid address or zip code for 5.2 million (95.00%) of approximately 5.4 million patients. Ethnicity was captured for 2.7 million (50.00%), whereas race was documented for 4.9 million (90.00%) and a preferred language for 2.7 million (49.00%) patients. Information regarding alcohol use and smoking status was coded for 490,348 (9.08%) and 1,728,749 (32.01%) patients, respectively. Using the International Classification of Diseases–10th Revision diagnoses codes, we identified 35,171 (0.65%) patients with information related to social connection/isolation, 10,433 (0.19%) patients with housing issues, and 3543 (0.07%) patients with income/financial resource strain. Of approximately 1.2 million unique patients with unstructured data, 30,893 (2.60%) had at least one clinical note containing phrases referring to social connection/isolation, 35,646 (3.00%) included housing issues, and 11,882 (1.00%) had mentions of financial resource strain. Conclusions: Apart from demographics, SBDH data are not regularly collected for patients. Health care providers should assess the availability and characteristics of SBDH data in EHRs. Evaluating the quality of SBDH data can potentially enable health care providers to modify underlying workflows to improve the documentation, collection, and extraction of SBDH data from EHRs.

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  • Exploring the Knowledge Structure of Research on Internet of Medical Things: Bibliometric and Visualization Analysis

    Date Submitted: Aug 28, 2019

    Open Peer Review Period: Aug 28, 2019 - Oct 23, 2019

    Background: In the last few decades, the literature related to Internet of Medical Things (IoMT) has grown rapidly. Obviously, widespread application of IoMT is helpful for considerably improving the...

    Background: In the last few decades, the literature related to Internet of Medical Things (IoMT) has grown rapidly. Obviously, widespread application of IoMT is helpful for considerably improving the efficiency of medical services. In addition, IoMT enables economies and societies to develop in a sustainable way to some extent. However, little is known about the panorama of IoMT-related research based on a visualization approach and bibliometrics. Objective: The purpose of this study was to deeply detect the knowledge structure, including literature distribution, clustering of keywords and topic evolution of IoMT-related research, by analyzing the current publication outputs related to IoMT. Methods: We conducted various bibliometric analyses on IoMT-related literature, including publication growth analysis, and the distribution of core countries and journals. Then, keyword co-occurrence analysis, as well as temporal visualization bar, was performed based on the bibliographic records during the period from 1997 to 2018. Results: A total of 6415 bibliographic records related to IoMT were identified. The United States and China take the lead in the publication output related to IoMT, followed by Korea, United Kingdom, and India. Furthermore, the collaboration between continents as well as countries is uneven; North America and Asia have the greatest frequent cooperation with other continents, mainly owing to the great contribution of the United States and China. There are 6 important research directions identified, with an imbalanced state. Moreover, these 6 topics can be further categorized into 3 areas: (1) Technologies and devices of IoMT, (2) Healthcare applications, and (3) Security and privacy. Finally, the evolution of topic were identified, namely from “basic research” through “hardware and software updates” to “application of new technologies”. Conclusions: This study provides important insights into the knowledge structure of IoMT. Cooperation among countries is not balanced, and the main research content of IoMT is the application of new technologies in recent years. Moreover, user security and privacy issues are also hotspots in the future. All of these are helpful for scholars and institutions worldwide to obtain the basis for comprehensive understanding and potential guidance in future research in this field, which can further enhance the sustainable development of countries.

  • Feasibility and Acceptability of Video narratives intervention to Promote Medication Understanding and Use Self-Efficacy: A Pilot Study on Post-Stroke Patients

    Date Submitted: Aug 21, 2019

    Open Peer Review Period: Aug 21, 2019 - Oct 16, 2019

    Background: A large number of the world’s post-stroke survivors suffers from moderate to severe disability. Long-term uncontrolled stroke risk factor led to unforeseen recurrent stroke and a growing...

    Background: A large number of the world’s post-stroke survivors suffers from moderate to severe disability. Long-term uncontrolled stroke risk factor led to unforeseen recurrent stroke and a growing number of stroke occurrence across ages in Malaysia. This situation has led to research works tapping into patient education, especially related to the self-efficacy of understanding and taking medication appropriately. Video narratives integrated with Health Belief Model (HBM) constructs have displayed potential impact as an aide to patient education efforts. Objective: This pilot study aimed to investigate the feasibility and acceptability of study procedures based on a trial protocol of video narratives intervention among post-stroke patients. In this paper, we report the preliminary efficacy of video narratives on medication understanding and use self-efficacy (MUSE). Methods: A parallel-group, randomized controlled study was conducted at the neurology outpatient clinic on post-stroke patients (N=54). Baseline data included patients’ socio-demographics, medical information, and all outcome measures. Post measurement of MUSE and blood parameters were done during 3 months follow-up. Feasibility of the study included recruitment and study completion rate with patients’ feedback on the burden of study procedures and outcome measures. Whereas, acceptability of study were analyzed qualitatively. Few statistical analyses were applied to ascertain preliminary results of the video narratives. Results: The recruitment rate was (60/157) 38%. Nevertheless, the dropout rate of (6/60) 10% was within the acceptable range. Patients were aged between 21 and 74 years. Over 85% of them had adequate health literacy and exposure to stroke education. Most patients (> 80%) were diagnosed with ischemic stroke whereby the majority were primarily hypertensive. The technicality of randomization and patient approach were carried out with minimal challenge and adequate patient satisfaction. However, the burden of outcome measures received some concerns. The video contents received good responses about its comprehension and simplicity. Moreover, an in-depth phone interview with 8 patients found the video narratives useful and inspiring. This findings also paralleled significant preliminary improvement in MUSE (P<0.05). Conclusions: The queries and feedback noted from each study’s phase in the pilot study had been acknowledged, and thus, convincingly would be taken forward to the full trial. Clinical Trial: Universal Trial Number (UTN) U1111-1201-3955

  • Model-driven processing of cardio-respiratory data using dynalet transform

    Date Submitted: Aug 8, 2019

    Open Peer Review Period: Aug 12, 2019 - Oct 7, 2019

    This paper proposes a new data processing approach tailored to a model of the complex biological system studied. We will take as example the cardiac system made of a stimulator (represented for the sa...

    This paper proposes a new data processing approach tailored to a model of the complex biological system studied. We will take as example the cardiac system made of a stimulator (represented for the sake of simplicity by the sinoatrial node), controlled by the respiratory oscillator, synchronizing a collection of sinoatrial node myocytes (pacemaker cells) and propose a mathematical model of the global system built from two van der Pol differential systems historically proposed for giving a first approximation of the cardiac and the respiratory activity. Then, we develop the observed cardiac signal on a functional base of approximate analytic solutions of the model. Finally, we propose perspectives to use it in the early detection of the drowsiness.

  • Applied practice and possible leverage points for information technology support for the recruitment for clinical trials: A qualitative study

    Date Submitted: Aug 2, 2019

    Open Peer Review Period: Aug 5, 2019 - Sep 30, 2019

    Background: One of the most challenging and most meaningful designs in medical research are clinical trials (CTs). One essential step before a CT can start is recruitment, i.e. to identify patients wh...

    Background: One of the most challenging and most meaningful designs in medical research are clinical trials (CTs). One essential step before a CT can start is recruitment, i.e. to identify patients which fulfill the inclusion and do not fulfill the exclusion criteria. The recruitment for CTs might be supported by means of modern information technologies. Objective: The aims of the present work were 1) to evaluate which (not necessarily electronical) tools are actually used in clinical routine and 2) to evaluate in which way and of which kind electronic support would be helpful for the clinical staff. Methods: Semi-standardized interviews were performed in five wards (cardiology, gynaecology, gastroenterology, nephrology, and palliative care) in a German university hospital. Four of the interviewees were directly involved in patient recruitment. Three of them were clinicians, one was a study nurse, and one was a research assistant. Results: All interviewees reported that either feasibility estimations as well as recruitment is mostly done from memory, although there would be many possibilities where IT support could assist. However, all participants reported some kind of IT support. Searches in ward-specific patient registers (data bases) and searches in Clinical Information Systems were reported. Furthermore, free text searches in medical reports were mentioned. No preference whether active or passive systems would be desired for potentially future applications was reported. However, all interviewees stated that, besides IT support, the personal motivation is the most relevant factor for successful recruitment. Conclusions: Overall, IT support has a minor standing in the recruitment for CTs today. The lack of IT usage and the estimations from memory that were reported by all of the participants, might bind cognitive resources which might distract from clinical routine. We conclude that the recruitment for CT is still a challenge for electronic support and that education of the clinic staff about the possibilities is compellingly necessary.

  • Perceived Level of Interest in Health Research among Those Who View Health-Related YouTube Videos: A Secondary Data Analysis from the Health Information National Trends Survey

    Date Submitted: Jul 31, 2019

    Open Peer Review Period: Aug 5, 2019 - Sep 30, 2019

    Background: More and more, people are using internet resources, such as YouTube, as a primary source of health-related information. While evidence exists of how this behavior affects the patient-physi...

    Background: More and more, people are using internet resources, such as YouTube, as a primary source of health-related information. While evidence exists of how this behavior affects the patient-physician relationship and the clinician perspective, it is still uncertain how it affects patient engagement in research. Objective: The aims of this study were to (1) determine if an association exists between watching health-related YouTube videos and being interested in patient engagement in research and (2) explore if any associations exist between sociodemographic characteristics, health-related YouTube use, and interest in patient engagement in research. Methods: We analyzed data from the 2013 Health Information National Trends Survey (n = 3039). Our independent variable of interest was whether individuals had watched health-related videos in the las 12 months; our dependent variable of interest was whether respondents were interested in patient engagement in research. Analysis included bivariate analyses and multivariate logistic regression modeling between sociodemographic characteristics, YouTube viewing, and being interested in patient engagement in research. Results: Interest in patient engagement in research was significantly associated with watching a health-related video on YouTube, after adjustment for relevant covariates. Individuals who watched a health-related video on YouTube, had a 2.11-fold increased odds ratio of being interested in patient engagement in research, compared to those who did not watch health-related videos (OR = 2.11, 95% CI = 1.40, 3.18, P <.001). We did not find any statistically significant associations between being interested in patient engagement in research and gender, age, race/ethnicity, or education. Conclusions: YouTube has the potential to be used as a tool to increase interest in patient engagement in research. Future studies could use YouTube to evaluate its effectivity promoting participation in research of underrepresented communities.

  • Perceived Level of Interest in Health Research from Patients Who Use Social Networking Sites: A Secondary Data Analysis of HINTS Data

    Date Submitted: Jul 31, 2019

    Open Peer Review Period: Aug 5, 2019 - Sep 30, 2019

    Background: There is a need to address the factors associated with underrepresentation of socioeconomically disadvantaged groups in research participation. The growth of social networking sites over t...

    Background: There is a need to address the factors associated with underrepresentation of socioeconomically disadvantaged groups in research participation. The growth of social networking sites over the past decade provides an opportunity to engage and educate patients from underrepresented populations about health information and research. Objective: To use the National Cancer Institute’s Health Information National Trends Survey (HINTS) to determine if there is an association between social networking site use and interest in patient engagement in research, and to identify sociodemographic disparities between social networking site use and interest in patient engagement in research. Methods: Data from the 2013 administration of HINTS were analyzed. Descriptive statistics were generated for all items, and bivariate analyses were conducted between sociodemographic variables and interest in participating in patient engaged research. Multivariate logistic regression modeling was used to examine the effects of each independent variable on respondent interest in patient-engaged research. Results: There was a statistically significant association between social networking site use for reading/sharing a medical topic (P< .001) and being interested in patient engagement in research, after adjusting for relevant covariates (OR=3.17; 95% CI: 2.04, 4.90). Respondents who had some college education (OR=3.13; 95% CI: 1.56, 6.27) or were college graduates (OR=3.98; 95% CI: 2.19, 7.24) had higher odds of interest in patient engagement in research, as compared to respondents with less than a high school education (P=.002). Among respondents who indicated using social networking sites for medical topics, males (P=.006) showed increased interest in patient engagement in research, as compared to females (OR=1.56; 95% CI: 1.13, 2.17). Interest in patient engagement in research did not differ significantly between different races/ethnicities, irrespective of their social networking site use (P<.001). Conclusions: The relationship found between social networking site use and increased interest in patient engagement in research gives researchers an avenue to overcome barriers that have limited participation among different groups. Our study found no significant difference in this association among race/ethnicity, suggesting that social networking could be a tool to address the underrepresentation of certain groups regarding participation in research.