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TME7/366: Borderless Teleradiology with CHILI

U Engelmann, A Schroeter, M Schwab, U Eisenmann, H P. Meinzer

J Med Internet Res 1999;1(suppl1):e114

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Evaluation of Mobile Apps Targeted to Parents of Infants in the Neonatal Intensive Care Unit: Systematic App Review

no statistically significant difference in overall quality of apps reported in the MARS between the apps that were deemed trustworthy (mean 3.65, SD 0.75) compared with those that were not (mean 3.26, SD 0.57) in the Trust It or Trash It tool, t16=−1.20, P=

Brianna Richardson, Justine Dol, Kallen Rutledge, Joelle Monaghan, Adele Orovec, Katie Howie, Talia Boates, Michael Smit, Marsha Campbell-Yeo

JMIR Mhealth Uhealth 2019;7(4):e11620

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The Impact of Nonpharmacological Interventions on Patient Experience, Opioid Use, and Health Care Utilization in Adult Cardiac Surgery Patients: Protocol for a Mixed Methods Study

Alexander A Brescia, Julie R Piazza, Jessica N Jenkins, Lindsay K Heering, Alexander J Ivacko, James C Piazza, Molly C Dwyer-White, Stefanie L Peters, Jesus Cepero, Bailey H Brown, Faraz N Longi, Katelyn P Monaghan, Frederick W Bauer, Varun G Kathawate, Sara M Jafri, Melissa C Webster, Amanda M Kasperek, Nickole L Garvey, Claudia Schwenzer, Xiaoting Wu, Kiran H Lagisetty, Nicholas H Osborne, Jennifer F Waljee, Michelle Riba, Donald S Likosky, Mary E Byrnes, G Michael Deeb

JMIR Res Protoc 2021;10(2):e21350

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Assessment of Adaptive Engagement and Support Model for People With Chronic Health Conditions in Online Health Communities: Combined Content Analysis

Brian M Green, Katelyn Tente Van Horn, Ketki Gupte, Megan Evans, Sara Hayes, Amrita Bhowmick

J Med Internet Res 2020;22(7):e17338

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Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

We briefly summarized the experimental results as follows: The results we obtained showed that the use of combined dataset was better than the use of either source individually (Table 1) for all the models and is statistically significant (at P=.04).

Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig

JMIR Med Inform 2017;5(4):e51

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Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Kenji Ikemura, Eran Bellin, Yukako Yagi, Henny Billett, Mahmoud Saada, Katelyn Simone, Lindsay Stahl, James Szymanski, D Y Goldstein, Morayma Reyes Gil

J Med Internet Res 2021;23(2):e23458

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Digital Information Technology Use and Patient Preferences for Internet-Based Health Education Modalities: Cross-Sectional Survey Study of Middle-Aged and Older Adults With Chronic Health Conditions

b Significantly (P c Significantly (P d Significantly (P e Significantly (P f Significantly (P g Significantly (P h Significantly (P i Significantly (P Table 2 also shows that across all age groups, noncollege graduates are significantly (P Approximately 62.5%

Nancy P Gordon, Elizabeth Crouch

JMIR Aging 2019;2(1):e12243

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Media Use Among Students From Different Health Curricula: Survey Study

Results were considered significant with an error probability of 5% (P Students from the various schools showed some similarities, as shown in Figure 1.

Michaela Zupanic, Patrick Rebacz, Jan P Ehlers

JMIR Med Educ 2019;5(2):e12809

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Prevalence and Factors Influencing Use of Internet and Electronic Health Resources by Middle-Aged and Older Adults in a US Health Plan Population: Cross-Sectional Survey Study

Elizabeth Crouch, Nancy P Gordon

JMIR Aging 2019;2(1):e11451

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Differences in Access to and Preferences for Using Patient Portals and Other eHealth Technologies Based on Race, Ethnicity, and Age: A Database and Survey Study of Seniors in a Large Health Plan

Nancy P Gordon, Mark C Hornbrook

J Med Internet Res 2016;18(3):e50

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