Search

Online symptom checkers are increasingly popular health technologies that enable patients to input their symptoms to produce diagnoses and triage advice. However, there is concern regarding the performance and safety of symptom checkers in diagnosing and triaging patients with life-threatening conditions. This retrospective cross-sectional study aimed to evaluate and compare commercially available symptom checkers for performance in diagnosing and triaging myocardial infarctions (MI). Symptoms and biodata of MI patients were inputted into 8 symptom checkers identified through a systematic search. Anonymised clinical data of 100 consecutive MI patients were collected from a tertiary coronary intervention centre between 1st January 2020 to 31st December 2020. Outcomes included (1) diagnostic sensitivity as defined by symptom checkers outputting MI as the primary diagnosis (D1), or one of the top three (D3), or top five diagnoses (D5); and (2) triage sensitivity as defined by symptom checkers outputting urgent treatment recommendations. Overall D1 sensitivity was 48±31% and varied between symptom checkers (range: 6-85%). Overall D3 and D5 sensitivity were 73±20% (34-92%) and 79±14% (63-94%), respectively. Overall triage sensitivity was 83±13% (55-91%). 24±16% of atypical cases had a correct D1 though for female atypical cases D1 sensitivity was only 10%. Atypical MI D3 and D5 sensitivity were 44±21% and 48±24% respectively and were significantly lower than typical MI cases (p

In recent years, technology has been increasingly incorporated within healthcare for the provision of safe and efficient delivery of services. Although this can be attributed to the benefits that can be harnessed, digital technology has the potential to exacerbate and reinforce preexisting health disparities. Previous work has highlighted how sociodemographic, economic, and political factors affect individuals' interactions with digital health systems and are termed social determinants of health [SDOH]. But, there is a paucity of literature addressing how the intrinsic design, implementation, and use of technology interact with SDOH to influence health outcomes. Such interactions are termed digital determinants of health [DDOH]. This paper will, for the first time, propose a definition of DDOH and provide a conceptual model characterizing its influence on healthcare outcomes. Specifically, DDOH is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy [DHL], and other forms of digital technology. A better appreciation of DDOH by the various stakeholders at the individual and societal levels can be channeled towards policies that are more digitally inclusive. In tandem with ongoing work to minimize the digital divide caused by existing SDOH, further work is necessary to recognize digital determinants as an important and distinct entity.