ObjectiveThe 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.DesignSystematic review following the Preferred Reporting Items for Systematic Reviews 2020 checklist.Data sourcesPubMed, Embase and Scopus databases were searched for studies from January 2010 to March 2021.Eligibility criteriaInclusion criteria were studies employing codesign approaches to develop an EHTs, and the study population was aged 60 years and older.Data extraction and synthesisData 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.ResultsTwenty-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.ConclusionsWe 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 numberCRD42021240013.

We present evidence on the current state of utilizing co-design approaches involving older adults in developing electronic healthcare tools (EHTs). Research gaps were identified in defining the stages, involvement processes, and levels of participation using existing theoretical frameworks. Future studies should explore both involvement processes and levels of participation to optimally empower and collaborate with older adults in developing EHTs.

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Background The approval of novel therapies for patients diagnosed with hematologic malignancies have improved survival outcomes but increased the challenge of aligning chemotherapy choices with patient preferences. We previously developed paper versions of a discrete choice experiment (DCE) and a best-worst scaling (BWS) instrument to quantify the treatment outcome preferences of patients with hematologic malignancies to inform shared decision making. Objective We aim to develop an electronic health care tool (EHT) to guide clinical decision making that uses either a BWS or DCE instrument to capture patient preferences. The primary objective of this study is to use both qualitative and quantitative methods to evaluate the perceived usability, cognitive workload (CWL), and performance of electronic prototypes that include the DCE and BWS instrument. Methods This mixed methods study includes iterative co-design methods that will involve healthy volunteers, patient-caregiver pairs, and health care workers to evaluate the perceived usability, CWL, and performance of tasks within distinct prototypes. Think-aloud sessions and semistructured interviews will be conducted to collect qualitative data to develop an affinity diagram for thematic analysis. Validated assessments (Post-Study System Usability Questionnaire [PSSUQ] and the National Aeronautical and Space Administration’s Task Load Index [NASA-TLX]) will be used to evaluate the usability and CWL required to complete tasks within the prototypes. Performance assessments of the DCE and BWS will include the evaluation of tasks using the Single Easy Questionnaire (SEQ), time to complete using the prototype, and the number of errors. Additional qualitative assessments will be conducted to gather participants’ feedback on visualizations used in the Personalized Treatment Preferences Dashboard that provides a representation of user results after completing the choice tasks within the prototype. Results Ethical approval was obtained in June 2021 from the Institutional Review Board of the University of North Carolina at Chapel Hill. The DCE and BWS instruments were developed and incorporated into the PRIME (Preference Reporting to Improve Management and Experience) prototype in early 2021 and prototypes were completed by June 2021. Heuristic evaluations were conducted in phase 1 and completed by July 2021. Recruitment of healthy volunteers began in August 2021 and concluded in September 2021. In December 2021, our findings from phase 2 were accepted for publication. Phase 3 recruitment began in January 2022 and is expected to conclude in September 2022. The data analysis from phase 3 is expected to be completed by November 2022. Conclusions Our findings will help differentiate the usability, CWL, and performance of the DCE and BWS within the prototypes. These findings will contribute to the optimization of the prototypes, leading to the development of an EHT that helps facilitate shared decision making. This evaluation will inform the development of EHTs to be used clinically with patients and health care workers. International Registered Report Identifier (IRRID) DERR1-10.2196/39586

253 Background: Treatment decision making for older adults with acute myeloid leukemia (AML) is complex. An informed discussion between the patient and physician that incorporates patient values and preferences is crucial to arrive at a personalized treatment plan. We sought to understand the patient experience of treatment decision making and identify challenges to aligning care to patient preferences. Methods: We conducted in-depth interviews with newly diagnosed older (≥60 years) adults with AML and their caregivers following a semi-structured interview guide. Qualitative thematic analysis was used to summarize findings. Results: 16 in-depth interviews were conducted. Major themes emerged including: 1) Patients were overwhelmed and in shock (“I was shocked.” “... like my face was hit with a water hose turned all the way up.” “It’s like you have been hit by a truck.”) 2) Patients felt powerless, without true treatment options (“You don’t feel like you are in the driver seat.” “I didn’t see that there was a decision to be made.” “I mean, I had no choice.” “Either go through chemotherapy or go through dying.”) 3) Patients felt rushed and unprepared to make a treatment decision (“I don’t feel like we were prepared at all. It was all thrown in your face, and you had to decide.” “But the mindset when they went through the typical course of chemo... laying that all on me in a two-hour period and wanting me to decide right there and then, I think that that should not be allowed... you can’t make sound decisions in a two-hour period about your life and whether you are going to live or not.”) 4) Patients predominantly followed physician recommendations for treatment (“What went through my mind was I was told that I had a great doctor who was going to be looking at me and I leave stuff like that up to the professionals.” “They told me what needed to be done and I just let them do it.”) 5) Patients balanced many factors when making treatment decisions including survival, quality of life, and spending time with family (“Dr. [Name], I want to live long and strong.” “And both of us prefer quality for a shorter time than we do a longer time in agony.” “I’m not scared to die... it’s just the fact that I’ll miss everyone.”) 6) Patients desired greater input on care plans (“[I suggest] trying to let the patients have a little bit of input on their treatment plan.” “It has to be catered to the patients making those decisions.”). Conclusions: Older adults with newly diagnosed AML feel overwhelmed, unprepared, and rushed into treatment decisions. No one factor dominated treatment decision making highlighting the need for physicians to assess individual patient priorities to arrive at a treatment plan. Interventions to reduce distress, elicit patient preferences and values, and increase participation in treatment decision making would improve the quality of treatment decisions.