Artificial intelligence in primary care practice Qualitative study to understand perspectives on using AI to derive patient social data

To understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health. Qualitative study. Ontario, Canada. Semistructured, 30-minute virtual interviews were conducted wit...

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Published inCanadian family physician Vol. 70; no. 7-8; pp. e102 - e109
Main Authors Garies, Stephanie, Liang, Simon, Weyman, Karen, Durant, Steve, Ramji, Noor, Alhaj, Mo, Pinto, Andrew
Format Journal Article
LanguageEnglish
Published Canada College of Family Physicians of Canada 01.07.2024
Subjects
Online AccessGet full text
ISSN0008-350X
1715-5258
1715-5258
DOI10.46747/cfp.700708e102

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Abstract To understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health. Qualitative study. Ontario, Canada. Semistructured, 30-minute virtual interviews were conducted with eligible participants across Ontario wherein they were asked about their perceptions of using AI to derive social data for patients. A descriptive content analysis was used to elicit themes from the data. A total of 12 interviews were conducted with 7 family physicians, 3 clinical team members of various health professions, and 2 health system leaders. Five main themes described the current state of social determinants of health information, perceived benefits of and concerns with using AI to derive social data, how participants would want to see and use AI-derived social data, and suggestions for ethical principles that should underpin the development of this AI tool. Most participants were enthusiastic about the possibility of using AI to derive social data for patients in primary care but noted concerns that should be addressed first. These findings can guide the development of AI-based tools for use in primary care settings.
AbstractList To understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health.OBJECTIVETo understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health.Qualitative study.DESIGNQualitative study.Ontario, Canada.SETTINGOntario, Canada.Semistructured, 30-minute virtual interviews were conducted with eligible participants across Ontario wherein they were asked about their perceptions of using AI to derive social data for patients. A descriptive content analysis was used to elicit themes from the data.METHODSSemistructured, 30-minute virtual interviews were conducted with eligible participants across Ontario wherein they were asked about their perceptions of using AI to derive social data for patients. A descriptive content analysis was used to elicit themes from the data.A total of 12 interviews were conducted with 7 family physicians, 3 clinical team members of various health professions, and 2 health system leaders. Five main themes described the current state of social determinants of health information, perceived benefits of and concerns with using AI to derive social data, how participants would want to see and use AI-derived social data, and suggestions for ethical principles that should underpin the development of this AI tool.MAIN FINDINGSA total of 12 interviews were conducted with 7 family physicians, 3 clinical team members of various health professions, and 2 health system leaders. Five main themes described the current state of social determinants of health information, perceived benefits of and concerns with using AI to derive social data, how participants would want to see and use AI-derived social data, and suggestions for ethical principles that should underpin the development of this AI tool.Most participants were enthusiastic about the possibility of using AI to derive social data for patients in primary care but noted concerns that should be addressed first. These findings can guide the development of AI-based tools for use in primary care settings.CONCLUSIONMost participants were enthusiastic about the possibility of using AI to derive social data for patients in primary care but noted concerns that should be addressed first. These findings can guide the development of AI-based tools for use in primary care settings.
To understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health. Qualitative study. Ontario, Canada. Semistructured, 30-minute virtual interviews were conducted with eligible participants across Ontario wherein they were asked about their perceptions of using AI to derive social data for patients. A descriptive content analysis was used to elicit themes from the data. A total of 12 interviews were conducted with 7 family physicians, 3 clinical team members of various health professions, and 2 health system leaders. Five main themes described the current state of social determinants of health information, perceived benefits of and concerns with using AI to derive social data, how participants would want to see and use AI-derived social data, and suggestions for ethical principles that should underpin the development of this AI tool. Most participants were enthusiastic about the possibility of using AI to derive social data for patients in primary care but noted concerns that should be addressed first. These findings can guide the development of AI-based tools for use in primary care settings.
Objective To understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health. Design Qualitative study. Setting Ontario, Canada. Methods Semistructured, 30-minute virtual interviews were conducted with eligible participants across Ontario wherein they were asked about their perceptions of using AI to derive social data for patients. A descriptive content analysis was used to elicit themes from the data. Main findings A total of 12 interviews were conducted with 7 family physicians, 3 clinical team members of various health professions, and 2 health system leaders. Five main themes described the current state of social determinants of health information, perceived benefits of and concerns with using AI to derive social data, how participants would want to see and use AI-derived social data, and suggestions for ethical principles that should underpin the development of this AI tool. Conclusion Most participants were enthusiastic about the possibility of using AI to derive social data for patients in primary care but noted concerns that should be addressed first. These findings can guide the development of AI-based tools for use in primary care settings. Artificial intelligence (AI) has become a ubiquitous part of our society and use within health care settings has been increasing. One emerging application of AI is exploring ways to identify social determinants of health (SDoH) for patients in the health care system.1-3 Social determinants of health are the socioeconomic positions that can shape one's health status and include income, race or ethnicity, education, housing, occupation, gender, and other material or social factors.4 While the influence of social attributes and economic situations on health outcomes is well-established,4-7 this information is not routinely collected in clinical settings. Previous attempts to capture SDoH directly from patients have been relatively successful8; however, the COVID-19 pandemic disrupted this type of data collection as it is often gathered during in-person appointments. Artificial intelligence (specifically machine learning and natural language processing) could augment primary SDoH collection by developing models using large clinical and narrative data found in electronic medical records (EMRs) to create labels or phenotypes of patients on relevant social categories,1,9,10 rather than collecting this information directly from patients. An important prerequisite in the design, deployment, and use of AI systems in health care is ensuring that such tools are co-designed with a multidisciplinary team that includes end users.11 This can provide end users with a better understanding of how the algorithms work, which in turn fosters greater trust in AI and its outcomes.12 There are several factors that influence the extent to which clinicians will trust AI: the complexity of the algorithm, sensitivity of the data, personal cognitive biases, lack of subject (or AI) knowledge, and the role of AI in a particular task or organization.12 Clinician trust in AI and the likelihood of AI adoption can be enhanced if there is transparency, fairness, and robustness in the AI development process and in the interpretation of outcomes.12,13 This study builds on our team's ongoing work to derive SDoH information from primary care EMR data using machine learning and natural language processing. Building and deploying AI tools such as this is still relatively exploratory in Canadian health care settings, and our knowledge of the potential uptake and use of AI-based tools by clinicians is limited. This study is part of a participatory co-design process intended to understand the perspectives and preferences of family physicians, primary care teams, and health organization leaders around the use and presentation of AI-derived social data.
Author Weyman, Karen
Ramji, Noor
Durant, Steve
Liang, Simon
Alhaj, Mo
Pinto, Andrew
Garies, Stephanie
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StartPage e102
SubjectTerms Adult
Artificial Intelligence
Attitude of Health Personnel
Family physicians
Female
Health disparities
Humans
Interviews as Topic
Male
Middle Aged
Ontario
Practice nursing
Primary care
Primary Health Care
Qualitative Research
Social Determinants of Health
Socioeconomic factors
Technology adoption
Subtitle Qualitative study to understand perspectives on using AI to derive patient social data
Title Artificial intelligence in primary care practice
URI https://www.ncbi.nlm.nih.gov/pubmed/39122422
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Volume 70
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