1. Provide the citation and attach a pdf of the article.

Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021). Exploring perceptions of healthcare technologies enabled by artificial intelligence: An online, scenario-based survey. BMC Medical Informatics and Decision Making, 21(1),1-15. https://doi.org/10.1186/s12911-021-01586-8

Exploring perceptions of healthcare technologies enabled by artificial intelligence

2. What is the abstract of the article?  

Background

• Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants’ level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions.

Methods

• We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently.

Results

• Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 [+ or -] 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions.

Conclusions

• Participants’ openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research.

3. Was the study experimental or non-experimental? Explain, tell us what made that clear.

• Non-experimental. The study uses a survey to determine individuals’ perceptions of AI in healthcare and is focused more on their attitudes or beliefs towards a certain topic. The participants were not randomized, and all answer the same set of questions. There is no control group as all participants are exposed to the same information, and questions.

 

• The researchers are attempting to draw correlations between variables to see if they impact openness to AI in healthcare. Variables such as socio-demographic variables, health, and psychosocial variables were observed to see how they played a part in an individual’s attitude towards AI (Antes et al., 2021, p.3). These variables were measured based on the participant’s survey answers, allowing the researchers to plug in the data and see if there were any correlations or relationships between the variables. They were able to draw some correlations between the data such as people with better access to healthcare were more open to the benefits of AI, while those with a poorer health status were more concerned about AI in healthcare (Antes et al., 2021, pg.9). If there is a relationship between two variables, then typically as one is increased or decreased then it impacts the other variable as well. The researchers were trying to see what impacted individual perceptions specifically by identifying a few possible variables and performing correlation tests to see if there were any relationships present between two or more variables.

 

• If the study was experimental then there would be a control group present that remains the same, and a group that receives a treatment. However, since this is focused more on perceptions and correlations rather than it being a control group versus an experimental group that received the treatment along with the lack of randomization, this points more towards a non-experimental design.

4. Was the research qualitative or quantitative? Again, explain.

• The study used a large sample size rather than a small one and utilized ratios and percentages to determine the amount of concern towards AI integration. If the research was qualitative then the sample size would be much smaller and would be more focused on interviews and observations. Another indicator was the usage of the Likert scale and a number-based rating system in the participant surveys.

5. What was the population studied? Why do you say that?

• The population studied were individuals 18 years of age and older who were also current residents in the U.S. This represents the population that the study was trying to survey, but they cannot survey every individual in the U.S. This meant limiting their sample to who they could recruit who met the criteria of being 18 years of age or older and lived in the U.S at the time of the study.

6. What sample was used for this study? Explain.

• A total of 936 individuals living in the U.S took part in the survey; they were mostly white individuals in college. Participants on average were between 30-40 years of age. The crowdsourcing platform, MTurk, was able to keep track of the number of participants who had taken part in the study. This allowed the researchers to determine their sample size.

7. What was the method of measurement? If the research was quantitative, was the measurement scale used, Nominal, Ordinal, Interval, or Ratio?

• 7-point and 5-point Likert scales were used, and they are considered interval scales. The 7-point Likert scales were used to measure trust in technology, and how certain statements influenced how participants perceived technology in healthcare. The 5-point Likert scales measured a participant’s openness to technology in healthcare.  Nominal scales were used when individuals were asked to select B or C on the survey to note which technologies could be seen as beneficial (B) or concerning (C). Nominal scales were utilized to classify the demographic data. A conservatism interval scale was numbered from 0-100 to also measure conservatism.

8. What was the method of analysis? (10 pts)

• Researchers used stepwise regression, Pearson’s r, Cronbach’s alpha, exploratory factor analysis (EFA), and the confirmatory factor analysis (CFA) in their analyses.

9. What was the conclusion of the study? (10 pts)

• The study found that young adults in the U.S were moderately open to AI technologies performing in healthcare and taking part in patient care scenarios. Some of the significant factors that play a part in an individual’s acceptance of AI technologies comes from demographic variables, the patient’s own health, the individual’s mental state, and their current environment. The researchers called for additional research to be done in the future in addition to their study.

10. Why is this study useful to you? Explain in detail.

• This study was useful to me because it shows patients feel about AI technologies being integrated into their care routine. Many seemed open to it, but there were some that were concerned about the technology. I know I would be too because there is always the chance that something always could go wrong. Many people expect a golden scenario to occur, where nothing can go wrong because many scenarios were accounted for before a new device’s release.  The reality behind it is that every variable cannot be accounted for and some things are just beyond an individual’s own control.
• I recall hearing about a machine that delivered doses of radiation to patients in hospitals, and the machine often gave too much of a dose or gave out a patient’s dose but failed to record it. This led to many people getting a dose from the machine twice or getting too much to the point that they experienced radiation poisoning. Even technology can be prone to errors, so it’s important that everything is not left to technology alone. It is important to ensure that technology works in collaboration with humans to enhance the patient experience.

11. What would be the next logical step in extending this study?

• The study itself is new and approaches openness to AI. The prior studies involving AI technologies in healthcare had not studied how open participants are to AI technologies being used for patient care. The next logical step could be to observe how open participants are to new AI innovations over time instead of focusing on how they feel at a single moment in time.
• Adapt the scenarios to real-life AI technologies present in healthcare instead of trying to come up with potential innovations to gauge how participants feel about AI.
• A study could be done to further identify the cause of uncertainty in individuals who were less open to AI-related innovations in healthcare.