Confronting complexity: toward equitable healthcare technology design and evaluation for socioeconomically...

Investigate how patients at a Federally-Qualified Health Center (FQHC) perform with, and perceive, complex telehealth tasks. Determine which complexity dimensions most affect patients' performance and perceptions, and compare this to researcher-evaluators' complexity walkthrough results.

A novel complexity walkthrough inspection method was implemented by researcher-evaluators (n = 8) to identify theory-informed complexity dimensions in 6 FQHC-required telehealth tasks. Remote user testing (n = 24) where FQHC patients were observed performing tasks, then completed newly-developed complexity-focused interviews and surveys. Descriptive statistics regarding complexity dimension presence, task performance, cognitive load, and perceived difficulty were integrated with qualitative data analyzed using inductive and deductive coding.

Patients completed 33.9% of the required subtasks without issues. Cognitive load and perceived difficulty were high for 2 tasks. Complexity dimensions of ambiguity (unclear inputs/processes; new concepts/words) and relationship (context switching; deep navigational hierarchies) most affected patient-perceived difficulty. Patients spent twice as long as walkthrough evaluators on tasks, and encountered broader complexity dimensions: new concepts/words, and errors. Many patients ended tasks early, asserting that they would abandon them outside of a study or had previously done so.

Technology-mediated task complexity may explain some telehealth uptake inequities. The complexity dimensions that challenge patients extend known usability heuristics by enhancing their equity sensitivity. Complexity walkthroughs surface design patterns that challenge patients, but complexity-focused user testing with patients reveals additional difficulties. Findings support complexity reduction of tasks via structuring, familiar concepts/words, feature integration, and shallow/broad navigation.

This paper's novel, theoretically-grounded complexity-focused methods and findings may inform future equitable design and evaluation of technology-mediated tasks for socioeconomically marginalized patients.