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DTSTAMP:20240325T185834Z
LOCATION:Salon C
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UID:HFESHCS_2024 International Symposium on Human Factors and Ergonomics i
 n Health Care_sess111_POST157@linklings.com
SUMMARY:HE7 - I, Doctor: Patient Preference for Medical Diagnostic Artific
 ial Intelligence
DESCRIPTION:Poster Presentation\n\nAutumn Charette and Chris Wickens (Colo
 rado State University) and Benjamin Clegg (Montana State University)\n\nAs
  the use of automation and artificial intelligence grows, it is important 
 to understand the benefits these tools can bring to healthcare. With the c
 urrent strain on the healthcare industry, implementing automation and AI m
 ay be able to bring some relief to the overworked employees and patients b
 y optimizing diagnosis timelines and streamlining patient appointments for
  routine procedures. The faster a diagnosis is received, the faster treatm
 ent can be implemented, which results in better overall outcomes. However,
  patients and providers may feel wary of the use of these technologies, as
  their newness may produce uncertainty and distrust, particularly for some
  segments of the population in need of healthcare treatment. In order for 
 automation and AI to be a productive tool in the medical field, it is pert
 inent for all parties involved to be able to work alongside and with the t
 echnology. This type of cooperation requires an understanding of the benef
 its this type of care can provide, such as increased diagnostic accuracy a
 nd efficiency, as well as decreased invasiveness compared to traditional a
 pproaches. Previous literature has examined attitudes towards automated he
 althcare, but primarily focused on demographic trends (Stai et al., 2020).
  While demographic trends may be informative when attempting to enhance th
 e relationship between automated healthcare and specific target population
 s, it fails to acknowledge the specific factors that the automation can ma
 nipulate in order to increase trust.  In this study, four variables that c
 ould impact an individual’s willingness to choose an AI or automated care 
 application were investigated: the accuracy, efficiency, and invasiveness 
 of the technology compared to the traditional method, and the risk level o
 f the overall health scenario. \n	In order to investigate these factors, a
 n online survey was implemented amongst 60 psychology undergraduate studen
 ts at Colorado State University, with the goal of examining levels and var
 iables that may impact preference for diagnostic artificial intelligence a
 nd automation. The survey was broken down into four separate hypothetical 
 medical scenarios, in which different versions of both a traditional medic
 al intervention and an AI or automated intervention were presented. The sc
 enarios included a possible skin cancer diagnosis, a routine test for tube
 rculosis, a heart disease screening, and a routine physical examination. E
 ach scenario was followed by two to three blocks of questions, with each b
 lock measuring a different factor that may influence the participant’s lik
 elihood to choose the AI diagnostic: the procedure’s Accuracy, Efficiency,
  or Invasiveness. \nRisk was measured between scenarios, as two scenarios 
 were deemed “high risk” (cancer diagnosis and heart disease screening with
  symptoms) while the other two were deemed “low risk” (routine tuberculosi
 s test and physical). Accuracy was measured across all scenarios, as well 
 as between variables, as each question indicated a varying level of accura
 cy of the traditional method (70%, 75%, 80%, or 90%) compared to the autom
 ation’s 70%, or indicated a varying level of the automation’s accuracy (70
 %, 75%, 80%, or 90%) compared to the traditional method’s 70%. The partici
 pant was asked to respond to a likert scale question on how likely they wo
 uld be to choose the automated or traditional intervention (0=extremely un
 likely, 5=extremely likely). Invasiveness and efficiency were measured by 
 adding a modifying statement presented after the scenario which suggested 
 that the AI method would be more efficient or less invasive than the tradi
 tional method before the above questions were asked. \nAfter analysis, it 
 was found that the variables of accuracy, efficiency, and risk all impacte
 d the participant’s preference for AI diagnostics over the traditional app
 roach. Participants preferred the AI diagnostic over the traditional human
  diagnostic method when it was said to produce more accurate results at ea
 ch level of accuracy (70%, 75%, 80%, 90%) compared to the traditional meth
 od’s 70%. In particular, the baseline preference, when both methods were 7
 0%, indicated a significant preference for the AI method, which may be par
 tially due to the younger demographics of the participant pool. Participan
 ts also significantly chose the AI diagnostic when it was more efficient t
 han the traditional method between and across all scenarios, indicating th
 e importance that individuals place on interventions that may save them ti
 me. \nIn regards to invasiveness, preference for diagnostic AI failed to b
 e overall significantly affected when the automation took a less invasive 
 approach than the traditional method, aside from the Tuberculosis scenario
 . While less invasiveness is an actual benefit of automation, as it can le
 ad to lower infection rates, this difference does not appear to greatly im
 pact the patient’s perception of the automation. However, it was found tha
 t the risk of the health scenario did significantly affect an individual’s
  preference for automation (Table 4). These findings suggest that high-ris
 k scenarios, such as receiving a cancer or heart disease diagnosis, decrea
 se an individual’s preference for AI or automated diagnostics compared to 
 low-risk scenarios, such as a routine TB test or physical exam. Understand
 ing the components that influence a patient’s perceptions of medical autom
 ation and AI is essential for the successful implementation of these tools
 , which have the capacity to positively impact the field of healthcare.\n	
 The intent of this study was to further the understanding of factors that 
 may lead to increased compliance and preference for automated healthcare. 
 However, the current study only depicted four types of medical diagnostic 
 AI, when there are hundreds of similar automated diagnostic tools in exist
 ence. Again, the current study found a significant preference for automate
 d healthcare when both intervention methods were at the same accuracy leve
 l of 70%. This preference is likely due to the younger population of the s
 tudy, as the average age was 19.8 years. Future studies should target a wi
 der age range of participants, specifically older populations that may hav
 e more hesitations around technology as a whole. These factors are essenti
 al to identify, as presenting them to patients may be enough to sway their
  opinions of these new technologies. \nReferences\nStai, B., Heller, N., M
 cSweeney, S., Rickman, J., Blake, P., Vasdev, R., Edgerton, Z., Tejpaul, R
 ., Peterson, M., Rosenberg, J., Kalapara, A., Regmi, S., Papanikolopoulos,
  N., & Weight, C. (2020). Public perceptions of artificial intelligence an
 d robotics in medicine. Journal of Endourology, 34(10), 1041–1048. https:/
 /doi.org/10.1089/end.2020.0137\n\nTrack: Digital Health, Simulation and Ed
 ucation, Hospital Environments, Medical and Drug Delivery Devices, Patient
  Safety Research and Initiatives
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