Journal of Medical Internet Research

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Lessons Learned From Developing Dashboards to Support Decision-Making for Community Opioid Response by Community Stakeholders: Mixed Methods and Multisite Study

Naleef Fareed, Ramona G Olvera, Yiting Wang, Michael Hayes, Elizabeth Liz Larimore, Peter Balvanz, Ronald Langley, Corinna A Noel, Peter Rock, Daniel Redmond, Jessica Neufeld, Sarah Kosakowski, Daniel Harris, Marc LaRochelle, Timothy R Huerta, LaShawn Glasgow, Emmanuel Oga, Jennifer Villani, Elwin Wu

JMIR Hum Factors 2024;11:e51525

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Implications for Electronic Surveys in Inpatient Settings Based on Patient Survey Response Patterns: Cross-Sectional Study

Megan E Gregory, Lindsey N Sova, Timothy R Huerta, Ann Scheck McAlearney

J Med Internet Res 2023;25:e48236

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Differential Effects of Outpatient Portal User Status on Inpatient Portal Use: Observational Study

Naleef Fareed, Pallavi Jonnalagadda, Sarah R MacEwan, Gennaro Di Tosto, Seth Scarborough, Timothy R Huerta, Ann Scheck McAlearney

J Med Internet Res 2021;23(4):e23866

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Metrics for Outpatient Portal Use Based on Log File Analysis: Algorithm Development

This approach is similar to the approach undertaken by Huerta et al [17], where a data model and procedure for processing log files from an inpatient portal were provided. However, given the differences between the outpatient portal and the inpatient portal log files, these data are idiosyncratic, affecting how they can be parsed; we note many of these differences in our Discussion section. We address this first study aim below in our Methods section.

Gennaro R Di Tosto, Ann Scheck McAlearney, Naleef Fareed, Timothy R Huerta

J Med Internet Res 2020;22(6):e16849

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Patients’ Perceptions of Portal Use Across Care Settings: Qualitative Study

Ann Scheck McAlearney, Cynthia J Sieck, Alice Gaughan, Naleef Fareed, Jaclyn Volney, Timothy R Huerta

J Med Internet Res 2019;21(6):e13126

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Patient Engagement as Measured by Inpatient Portal Use: Methodology for Log File Analysis

Timothy R Huerta, Naleef Fareed, Jennifer L. Hefner, Cynthia J Sieck, Christine Swoboda, Robert Taylor, Ann Scheck McAlearney

J Med Internet Res 2019;21(3):e10957

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Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

The overall model produced an adjusted R-squared of .91, suggesting a high model fit. Table 2 provides the estimates for the external motivation coefficient (p) and internal motivation coefficient (q) used in the final model (see Multimedia Appendix 2 for additional details). The two motivation coefficients show trends moving in opposite directions. For the earlier stages (ie, EMRAM Stages 1-3), the external influence is the primary motivation for EHR adoption.

Hadi Kharrazi, Claudia P Gonzalez, Kevin B Lowe, Timothy R Huerta, Eric W Ford

J Med Internet Res 2018;20(8):e10458

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