Citations

Abstract Objectives Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. Materials and methods This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. Results We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply–demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. Discussion This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. Conclusion Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.

Background While use of artificial intelligence (AI) in healthcare is increasing, little is known about how patients view healthcare AI. Characterizing patient attitudes and beliefs about healthcare AI and the factors that lead to these attitudes can help ensure patient values are in close alignment with the implementation of these new technologies. Methods We conducted 15 focus groups with adult patients who had a recent primary care visit at a large academic health center. Using modified grounded theory, focus-group data was analyzed for themes related to the formation of attitudes and beliefs about healthcare AI. Results When evaluating AI in healthcare, we found that patients draw on a variety of factors to contextualize these new technologies including previous experiences of illness, interactions with health systems and established health technologies, comfort with other information technology, and other personal experiences. We found that these experiences informed normative and cultural beliefs about the values and goals of healthcare technologies that patients applied when engaging with AI. The results of this study form the basis for a theoretical framework for understanding patient orientation to applications of AI in healthcare, highlighting a number of specific social, health, and technological experiences that will likely shape patient opinions about future healthcare AI applications. Conclusions Understanding the basis of patient attitudes and beliefs about healthcare AI is a crucial first step in effective patient engagement and education. The theoretical framework we present provides a foundation for future studies examining patient opinions about applications of AI in healthcare.

AbstractAs pharmacogenomic (PGx) testing increases in popularity, lay concepts of drug‐gene interactions set the stage for shared decision making in precision medicine. Few studies explore what recipients of PGx results think is happening in their bodies when a drug‐gene interaction is discovered. To characterize biobank participants’ understanding of PGx research results, we conducted a focus group study, which took place after PGx variants conferring increased risk of dihydropyrimidine dehydrogenase (DPD) deficiency were disclosed to biobank contributors. DPD deficiency confers an increased risk of adverse reaction to commonly used cancer chemotherapeutics. Ten focus groups were conducted, ranging from two to eight participants. Fifty‐four individuals participated in focus groups. A framework approach was used for descriptive and explanatory analysis. Descriptive themes included participants’ efforts to make sense of PGx findings as they related to: (1) health implications, (2) drugs, and (3) genetics. Explanatory analysis supplied a functional framework of how participant word choices can perform different purposes in PGx communication. Results bear three main implications for PGx research‐related disclosure. First, participants’ use of various terms suggest participants generally understanding their PGx results, including how positive PGx results differ from positive disease susceptibility genetic results. Second, PGx disclosure in biobanking can involve participant conflation of drug‐gene interactions with allergies or other types of medical reactions. Third, the functional framework suggests a need to move beyond a deficit model of genetic literacy in PGx communication. Together, findings provide an initial evidence base for supporting bidirectional expert‐recipient PGx results communication.

As genomic sequencing expands to screen larger numbers of individuals, offering genetic counseling to everyone may not be possible. One approach to managing this limitation is for a genetic counselor to communicate clinically actionable results in person or by telephone, but report other results by mail. We employed this approach in a large genomic implementation study. In this paper, we describe participants’ experiences receiving genomic screening results by mail. We conducted 50 semi-structured telephone interviews with individuals who received neutral genomic screening results by mail. Most participants were satisfied receiving neutral results by mail. Participants generally had a good understanding of results; however, a few participants had misunderstandings about their genomic screening results, including mistaken beliefs about their disease risk and the comprehensiveness of the test. No one reported plans to alter health behaviors, defer medical evaluations, or take other actions that might be considered medically problematic. Reporting neutral results by mail is unlikely to cause recipients distress or generate misunderstandings that may result in reduced vigilance in following recommended preventive health strategies. Nonetheless, some individuals may benefit from additional genetic counseling support to help situate their results in the context of personal concerns and illness experiences.

Purpose Recent advances in DNA sequencing have led to the development of breast cancer susceptibility gene panels for germline genetic testing of patients. We assessed the frequency of mutations in 17 predisposition genes, including BRCA1 and BRCA2, in a large cohort of patients with triple-negative breast cancer (TNBC) unselected for family history of breast or ovarian cancer to determine the utility of germline genetic testing for those with TNBC. Patients and Methods Patients with TNBC (N = 1,824) unselected for family history of breast or ovarian cancer were recruited through 12 studies, and germline DNA was sequenced to identify mutations. Results Deleterious mutations were identified in 14.6% of all patients. Of these, 11.2% had mutations in the BRCA1 (8.5%) and BRCA2 (2.7%) genes. Deleterious mutations in 15 other predisposition genes were detected in 3.7% of patients, with the majority observed in genes involved in homologous recombination, including PALB2 (1.2%) and BARD1, RAD51D, RAD51C, and BRIP1 (0.3% to 0.5%). Patients with TNBC with mutations were diagnosed at an earlier age (P < .001) and had higher-grade tumors (P = .01) than those without mutations. Conclusion Deleterious mutations in predisposition genes are present at high frequency in patients with TNBC unselected for family history of cancer. Mutation prevalence estimates suggest that patients with TNBC, regardless of age at diagnosis or family history of cancer, should be considered for germline genetic testing of BRCA1 and BRCA2. Although mutations in other predisposition genes are observed among patients with TNBC, better cancer risk estimates are needed before these mutations are used for clinical risk assessment in relatives.