The usability of electronic health records (EHRs) continues to be a major concern.^1-3 Usability challenges include suboptimal design of interfaces that have confusing layouts and contain either too much or too little relevant information as well as workflows and alerts that are burdensome. Suboptimal usability has been associated with clinician burnout and patient safety events, and improving the usability of EHRs is an ongoing need.^4,5

Objective The primary aim was to review and synthesise the current evidence of how older adults are involved in codesign approaches to develop electronic healthcare tools (EHTs). The secondary aim was to identify how the codesign approaches used mutual learning techniques to benefit older adult participants. Design Systematic review following the Preferred Reporting Items for Systematic Reviews 2020 checklist. Data sources PubMed, Embase and Scopus databases were searched for studies from January 2010 to March 2021. Eligibility criteria Inclusion criteria were studies employing codesign approaches to develop an EHTs, and the study population was aged 60 years and older. Data extraction and synthesis Data were extracted for analysis and risk of bias. We evaluated the quality of studies using the Agency for Healthcare Research and Quality Evidence-based Practice Center approach. Results Twenty-five studies met the inclusion criteria for this review. All studies used at least two involvement processes, with interviews and prototypes used most frequently. Through cross-classification, we found an increased utilisation of functional prototypes in studies reaching the ‘empower’ level of participation and found that studies which benefitted from mutual learning had a higher utilisation of specific involvement processes such as focus groups and functional prototyping. Conclusions We found gaps to support which involvement processes, participation levels and learning models should be employed when codesigning with older adults. This is important because higher levels of participation may increase the user’s knowledge of technology, enhance learning and empower participants. To ensure studies optimise participation and learning of older adults when developing EHTs, there is a need to place more emphasis on the approaches promoting mutual learning. PROSPERO registration number CRD42021240013.

Objective To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment. Methods Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS (n = 12) and Epic (n = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors. Results Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS (P < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic (P < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance (P < .01). Discussion Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes. Conclusion The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments.