Pew Research HOSA BD

Pew Research Center Note

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  • 60% of Americans uncomfortable with AI in their healthcare

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  • Study on Americans' views of AI in health and medicine

  • Surveyed 11,004 U.S. adults from Dec. 12-18, 2022

  • Participants from American Trends Panel (ATP)

  • Survey weighted to be representative of the U.S. adult population

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  • Americans uncomfortable with AI in their healthcare

  • Majority unconvinced AI would improve health outcomes

  • Survey conducted Dec. 12-18, 2022

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  • Americans see both positives and negatives of AI in healthcare

  • Concerns about impact on patient-provider relationship and security of health records

  • Majority concerned about the pace of AI adoption in healthcare

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  • Caution prevalent in public views on AI in healthcare

  • Majority concerned about healthcare providers moving too fast with AI adoption

  • Concerns shared across different groups in the public

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  • Majority of U.S. adults uncomfortable with health care provider relying on AI

    • 46% of men comfortable with AI in health care, 54% uncomfortable

    • 66% of women uncomfortable with AI in health care

    • Higher education, income, and younger adults more open to AI in health care

  • Discomfort with own health care provider relying on AI prevalent across all groups

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  • Those familiar with AI more comfortable with AI in health care

    • 50% comfortable among those who know a lot about AI

    • 63% uncomfortable among those who know a little about AI

    • 70% uncomfortable among those who know nothing about AI

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  • Modest share of Americans see AI improving patient outcomes

    • 38% believe AI in health care would lead to better patient outcomes

    • Men, younger adults, and higher education levels more positive about AI impact

    • Those familiar with AI more optimistic about AI impact on patient outcomes

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  • Americans anticipate positive and negative effects of AI in health care

    • 40% believe AI would reduce mistakes by health care providers

    • 57% expect patient-provider relationships to deteriorate with AI use

    • Concerns about data security and quality of care with AI in health care

    • Public divided on AI's impact on quality of care for individuals

Source: PEW RESEARCH CENTER, Survey conducted Dec. 12-18, 2022.

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  • Majority of Americans see bias based on race or ethnicity in health and medicine as a major or minor problem

    • Black adults are especially likely to view bias as a major problem

    • Smaller share of White adults see bias as a major problem

  • Survey shows percentages of U.S. adults who perceive bias and unfair treatment based on race or ethnicity in health and medicine

  • Black adults are most concerned about bias based on patients' race or ethnicity

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  • Those who see bias in health and medicine believe AI can improve the situation

    • Majority think increased use of AI would help reduce bias and unfair treatment

  • Larger shares among different racial groups believe AI would make bias better than worse

    • Black adults are slightly less optimistic compared to other groups

  • Reasons for optimism include AI being more objective and neutral than humans

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  • People believe AI can improve bias in health and medicine by being more neutral and consistent

  • Half of those who see bias believe AI can help address the issue

  • Survey explores reasons why people think AI can improve bias in health and medicine

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  • Some believe bias in health and medicine would stay the same with AI due to existing biases in AI design and data

  • Others think AI would make bias worse due to reflecting human bias and the need for human judgment in medicine

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  • Survey explores views on specific applications of AI in health and medicine

  • Americans are more open to AI-based skin cancer detection compared to other AI-driven applications

  • Public awareness of AI in health and medicine is still developing

Note: The survey was conducted from Dec. 12-18, 2022, by the PEW RESEARCH CENTER.

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  • AI-based skin cancer screening

    • AI can scan images of skin to detect potential cancer areas

    • Majority of U.S. adults (65%) want AI for their skin cancer screening

    • 55% believe AI would make skin cancer diagnoses more accurate

    • Only 13% think it would lead to less accurate diagnoses

    • Americans view AI in skin cancer screening as a medical advance

      • 52% see it as a major advance

      • 27% consider it a minor advance

      • 7% say it is not an advance for medical care

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  • Demographics and AI in skin cancer screening

    • Men, younger adults, and those with higher education levels are more enthusiastic about AI screening

    • Men (72%) more likely than women (58%) to want AI in skin cancer screening

    • Black adults (57%) less likely than White (65%) and Hispanic (69%) adults to want AI for screening

    • Younger adults and college graduates more open to AI in screening

    • Awareness of AI in skin cancer screening increases willingness for AI use

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  • AI for pain management recommendations

    • AI used to help prescribe pain medication and minimize addiction risks

    • 31% of Americans want AI for pain management, while 67% do not

    • Views on AI-based pain management: 26% say it would improve, 40% see little difference, 32% think it would worsen

    • Familiarity with AI-based pain management influences openness to AI in care plan

    • Those aware of AI in pain management more likely to consider AI in their own care

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  • Familiarity with AI-based pain management

    • Those familiar with AI-based pain management more open to using AI in their care plan

    • 47% of those familiar would want AI-based recommendations in post-op pain treatment

    • Demographic differences in willingness for AI in pain treatment are modest

    • Majority of most groups do not want AI to help decide their pain treatment

Source: PEW RESEARCH CENTER Survey conducted Dec. 12-18, 2022.

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  • AI-driven robots in development for surgical procedures

    • Expected to increase precision and consistency

    • Americans cautious about using surgical robots for their own care

      • 40% would want AI-based robotics for their surgery

      • 59% would not want this

    • Public familiarity with AI-based surgical robots higher than other health applications

      • 59% have heard at least a little about this development

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  • Views on AI-driven robots in surgery based on awareness

    • Unfamiliar individuals more likely to not want them in their care

    • Men more inclined than women to want AI-based robots for surgery

    • Higher education levels more open to this technology

    • Little difference in views between age groups

  • Preferences for AI applications vary across demographic groups

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  • AI chatbots for mental health support using AI

    • Public reactions to AI chatbots for mental health support are negative

      • 79% of U.S. adults would not want to use an AI chatbot for mental health support

    • Support for limits on availability or use of AI chatbots for mental health

      • 46% say chatbots should only be used by people seeing a therapist

      • 28% say chatbots should not be available to people at all

      • 23% say chatbots should be available regardless of therapy

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  • Majority of U.S. adults across groups lean away from using AI chatbots for mental health support

  • Even among those familiar with chatbots, 71% would not want to use one for mental health support

  • Public opinion on AI chatbots for mental health support still developing

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  • Acknowledgments to The Pew Charitable Trusts for the report

  • Primary research team and contributors listed

  • Project guidance from Pew Research Center methodology team and feedback from other members

Methodology Overview

  • The American Trends Panel (ATP) by Pew Research Center is a nationally representative panel of randomly selected U.S. adults.

    • Panelists participate via self-administered web surveys.

    • Panelists without internet access are provided with a tablet and wireless internet.

    • Interviews are conducted in English and Spanish.

    • Managed by Ipsos.

  • Data in the report is from a panel wave conducted from Dec. 12-18, 2022.

  • Response rate was 88% with 11,004 out of 12,448 sampled panelists responding.

  • Margin of sampling error for the full sample is +/- 1.4 percentage points.

Panel Recruitment

  • ATP created in 2014 with the first cohort invited after a national random-digit-dial survey.

  • Additional recruitments in 2015 and 2017.

  • Switched to address-based recruitment in August 2018.

  • Panelists are removed if inactive or request to be removed.

Address-Based Recruitment

  • Invitations sent to a stratified, random sample of households from the U.S. Postal Service’s Delivery Sequence File.

  • Incentives offered for completing surveys.

  • Subset of adults received follow-up mailings with incentives to join the ATP.

  • 23,176 adults invited, with 20,341 agreeing to join the panel.

Sample Design

  • Target population: non-institutionalized persons ages 18 and older in the U.S.

  • All active panel members invited to participate in the survey wave.

Questionnaire Development and Testing

  • Questionnaire developed by Pew Research Center in consultation with Ipsos.

  • Rigorously tested on PC and mobile devices before launching the survey.

Incentives

  • Post-paid incentives offered to all respondents.

  • Amounts ranged from $5 to $20 based on the group's survey response propensities.

Data Collection Protocol

  • Field period: Dec. 12-18, 2022.

  • Postcard experiment conducted among ATP non-tablet household panelists.

  • Two separate launches: Soft Launch and Full Launch.

  • Invitations sent via email and SMS with reminders.

Invitation and Reminder Dates

  • Soft Launch: Dec. 12, 2022

  • Full Launch: Dec. 13, 2022

  • Reminders sent on Dec. 15 and Dec. 17, 2022.

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  • Data quality checks:

    • Researchers checked for patterns of satisficing like leaving questions blank or always selecting the first or last answer.

    • Eight ATP respondents were removed due to this checking.

  • Weighting process:

    • ATP data is weighted in a multistep process considering sampling stages and nonresponse.

    • Base weights are adjusted for changes in recruitment survey design.

    • Weights are calibrated to align with population benchmarks for nonresponse correction.

    • Additional weighting dimensions are applied within Black adults.

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  • Calibration of weights:

    • Weights are calibrated to align with population benchmarks and trimmed at 1st and 99th percentiles.

    • Sampling errors and statistical significance tests consider the effect of weighting.

  • Sample sizes and margins of error:

    • Unweighted sample sizes and margins of error at the 95% confidence level for different groups are provided.

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  • Dispositions and response rates:

    • Final dispositions for ATP Wave 119 are listed.

    • Cumulative response rates and weighted response rates are detailed.

  • Adjusting income and defining tiers:

    • Income tiers are created based on adjusted 2021 family incomes.

    • Upper-, middle-, and lower-income tiers are defined.

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  • Adjusting income and defining income tiers:

    • Upper-, middle-, and lower-income tiers are created based on adjusted family incomes.

    • Income ranges for each tier are specified.

  • Note about Asian adult sample:

    • The survey includes a sample of 371 English-speaking Asian adults.

    • Views of Asian adults are reported but may not be fully representative.

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  • Additional charts and tables:

    • Majority across groups would not want to use an AI chatbot for mental health support.

    • Data on U.S. adults' willingness to use AI chatbots for mental health support is presented.

Note: The information provided is based on a survey conducted by the Pew Research Center.

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  • Survey conducted by PEW RESEARCH CENTER on American Trends Panel Wave 119

  • Questions on risk-taking behavior, comfort with AI in healthcare, creativity, and technology use

    • Participants were asked to rate how well certain phrases describe them

    • Participants were asked about their comfort level with AI in healthcare

    • Ratings were provided on comfort with taking risks, being a creative thinker, and enjoying new technology

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  • Participants were asked about the potential outcomes of using AI in health and medicine

    • Views on whether AI would lead to better or worse health outcomes

    • Concerns about the speed of adoption of AI in healthcare

  • Survey included questions on bias and unfair treatment in health based on race or ethnicity

    • Participants were asked about the extent of bias in healthcare based on race or ethnicity

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  • Participants were asked about the impact of AI on healthcare quality, mistakes, patient-provider relationships, fairness in treatment, and data security

    • Views on how AI would affect bias and unfair treatment based on race or ethnicity in healthcare

  • Ratings were provided on the quality of healthcare, mistakes made by providers, patient-provider relationships, fair treatment for all races and ethnicities, and patient data security

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  • Reasons for the belief that bias and unfair treatment based on race or ethnicity would improve with AI use in healthcare

    • Factors included the neutrality of AI, absence of human bias, and AI's ability to make accurate decisions

  • Reasons for the belief that bias and unfair treatment based on race or ethnicity would worsen with AI use in healthcare

    • Factors included AI reflecting human biases, concerns about inequality, and potential reinforcement of biases by medical providers

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  • Reasons for the belief that bias and unfair treatment based on race or ethnicity would remain the same with AI use in healthcare

    • Factors included existing biases in people, training, or data, skepticism about AI's impact on bias, and the belief that racial and ethnic bias is not a significant issue in healthcare.

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  • AI for Skin Cancer Detection

    • AI used to review images of skin with and without cancer to detect patterns

    • Survey on awareness and opinions about AI for skin cancer detection

    • Questions on familiarity, perception of advancement, and personal preference for AI in screening

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  • AI Chatbots for Mental Health Support

    • AI chatbots designed to support mental health by responding to users' texts

    • Survey on awareness and opinions about AI chatbots for mental health support

    • Questions on familiarity, perception of advancement, personal preference for using AI chatbots, and opinions on their availability

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  • AI for Pain Medication Management

    • AI used to determine pain medication dosage post-surgery to prevent abuse

    • Survey on awareness and opinions about AI for pain medication management

    • Questions on familiarity, perception of advancement, personal preference for AI involvement, and impact on treatment

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  • AI for Pain Medication Management (cont.)

    • Survey continues on opinions about AI's role in pain medication management

    • Questions on perception of AI's impact on treatment effectiveness

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  • AI Surgical Robots

    • Development of AI-powered robots for performing surgical tasks

    • Survey on awareness and opinions about AI surgical robots

    • Questions on familiarity, perception of advancement, personal preference for AI surgical robots

Overall, the survey covers public awareness, perceptions, and preferences regarding the use of artificial intelligence in skin cancer detection, mental health support through chatbots, pain medication management, and surgical procedures.

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  • AI in Biology

    • AI used to predict protein structures in cells for developing medical treatments.

    • Survey on awareness of AI in predicting protein structures.

  • AI in Agriculture

    • AI used to recommend plant varieties for drought and heat resistance in crops.

    • Survey on awareness and perception of AI in producing drought and heat-resistant crops.

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  • AI in Weather Forecasting

    • AI used to predict extreme weather occurrences like heavy rainfall and storms.

    • Survey on awareness and perception of AI in predicting extreme weather events.

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  • AI in News Reporting

    • AI used to write news articles based on news events and information.

    • Survey on awareness and perception of AI in news article writing.

  • AI in Visual Arts

    • AI used to generate visual images from keywords, including complex artistic images.

    • Survey on awareness and perception of AI in producing visual images from keywords.