Adapting A Unified Electronic Health Record Usability Framework for Evaluation of Connected Health Care Technologies Linking Mobile Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Evidence-based, objective, and systematic usability evaluation is key to successful connected health care technologies.  The increases in patient-facing mobile health technologies not only offer convenience for patients in managing their own health and/or chronic conditions, but also offer the opportunity for health care providers to access patient behaviors and patient-centered outcomes at ease. Thus, it is of significance to link and present patient-facing mobile device data to their health care providers in a secure and uninterrupted way that will facilitate workflow and promote patient provider communication, rather than drawing providers away from patients. This prompts increasing efforts developing connected health care technologies linking mobile data to electronic health record systems guided by user-centered design and redesign principles. However, lack of scientific, objective, and systematic usability evaluation put connected health care technologies at risk for low adoption and eventual failure. Objective: Learning from lessons in electronic health record usability evaluation, we propose to adapt an existing unified framework, TURF, for electronic health record usability evaluation to guide the design, redesign, and usability evaluation of connected health care technologies linking mobile data to electronic health record systems or other provider-facing Web-based evaluation tools. Methods: TURF, a unified framework of electronic health record usability, involves three dimensions: useful, usable, and satisfying; and four key components: task, user, representation, and function. Each dimension and component is described with theoretical underpinnings along with examples of how usability can be measured. Results: Specific adaptation of TURF that’s unique for connected health technologies include (1) user analysis for “satisfying” dimension will need to include both users using and mobile health users who’s feeding data into the system; (2) function analysis for “useful” dimension will need to consider functions/data wanted by the providers, functions actually used in real activities, functions/data available from mobile devices and with agreement from patients, functions/data available in interfaces within connected health care technologies ; (3) representation analysis for “usable” dimension need to consider correct representation of data from mobile devices in connected interface; (4) task analysis for “usable” dimension will highlight learnability, efficiency (time on task, steps on task, task success, mental effort), and error prevention and recovery (occurrence rate, error recovery rate). Real world interruptions, team dynamics, and multitasking should also be considered during evaluation of connected health care technologies. Conclusions: An adapted framework is proposed to offer objective, evidence-based, and systematic usability evaluation to guide the design and redesign of interfaces connecting mobile data with electronic health record systems and Web-based evaluation tools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it