Usability

AHRQ’s usability research focuses on how to design and implement electronic health records (EHRs) so that they are more intuitive to use and more readily support clinical workflow. Two areas of research, supported by the AHRQ projects below, are how to effectively reduce documentation burden for physicians and how to make data within EHRs more usable for clinical decisionmaking.

Related Funded Projects

Displaying 1 - 10 of 115

Related Publications

Displaying 31 - 40 of 528
Publication Name Publication Year Project Name Contract/Grant Number Principal Investigator
Informatics opportunities to involve patients in hospital safety: a conceptual model. 2019 Patients as Safeguards: Understanding the Information Needs of Hospitalized Patients R01 HS022894 Pratt, Wanda
Information flow during pediatric trauma care transitions: things falling through the cracks. 2019 Care Transitions and Teamwork in Pediatric Trauma: Implications for Health Information Technology Design R01 HS023837 Gurses, Ayse Pinar
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes. 2019 Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients R21 HS024977 Dexheimer, Judith W.
Knowledge among patients with heart failure: A narrative synthesis of qualitative research. 2019 Power to the Patient: Design and Test of Closed-Loop Interactive IT for Geriatric Heart Failure Self-Care R21 HS025232 Holden, Richard
mHealth interventions for disadvantaged and vulnerable people with type 2 diabetes. 2019 Engaging Diverse Patients in Using an Online Patient Portal, Improving Diabetes and Depression Self-management Via Adaptive Mobile Messaging R01 HS025429 Lyles, Courtney
mHealth interventions for disadvantaged and vulnerable people with type 2 diabetes. 2019 Engaging Diverse Patients in Using an Online Patient Portal, Improving Diabetes and Depression Self-management Via Adaptive Mobile Messaging R01 HS025429 Aguilera, Adrian
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. 2019 Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation R21 HS024983 Pratap, Jayant
NLP to Improve Accuracy and Quality of Dictated Medical Documents - Final Report 2019 NLP to Improve Accuracy and Quality of Dictated Medical Documents R01 HS024264 Zhou, Li
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients - Final Report 2019 Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients R21 HS024977 Dexheimer, Judith W.
Optimizing the Electronic Health Record for Cardiac Care - Final Report 2019 Optimizing the Electronic Health Record for Cardiac Care R01 HS022110 Windle, John