A Survey of Patient Perspectives on Approach to Health Care: Focus on Physician Competency and Compassion

Patients' Perceptions Toward Human-Artificial Intelligence Interaction in Health Care: Experimental Study

Background

It is believed that artificial intelligence (AI) will be an integral part of health care services in the near future and will be incorporated into several aspects of clinical care such as prognosis, diagnostics, and care planning. Thus, many technology companies have invested in producing AI clinical applications. Patients are one of the most important beneficiaries who potentially interact with these technologies and applications; thus, patients’ perceptions may affect the widespread use of clinical AI. Patients should be ensured that AI clinical applications will not harm them, and that they will instead benefit from using AI technology for health care purposes. Although human-AI interaction can enhance health care outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care.

Objective

The main objective of this study was to examine how potential users (patients) perceive the benefits, risks, and use of AI clinical applications for their health care purposes and how their perceptions may be different if faced with three health care service encounter scenarios.

Methods

We designed a 2×3 experiment that crossed a type of health condition (ie, acute or chronic) with three different types of clinical encounters between patients and physicians (ie, AI clinical applications as substituting technology, AI clinical applications as augmenting technology, and no AI as a traditional in-person visit). We used an online survey to collect data from 634 individuals in the United States.

Results

The interactions between the types of health care service encounters and health conditions significantly influenced individuals’ perceptions of privacy concerns, trust issues, communication barriers, concerns about transparency in regulatory standards, liability risks, benefits, and intention to use across the six scenarios. We found no significant differences among scenarios regarding perceptions of performance risk and social biases.

Conclusions

The results imply that incompatibility with instrumental, technical, ethical, or regulatory values can be a reason for rejecting AI applications in health care. Thus, there are still various risks associated with implementing AI applications in diagnostics and treatment recommendations for patients with both acute and chronic illnesses. The concerns are also evident if the AI applications are used as a recommendation system under physician experience, wisdom, and control. Prior to the widespread rollout of AI, more studies are needed to identify the challenges that may raise concerns for implementing and using AI applications. This study could provide researchers and managers with critical insights into the determinants of individuals’ intention to use AI clinical applications. Regulatory agencies should establish normative standards and evaluation guidelines for implementing AI in health care in cooperation with health care institutions. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI clinical applications.

High-risk surgery among older adults: Not-quite shared decision-making

BACKGROUND

Shared decision-making is critical to optimal patient-centered care. For elective operations, when there is sufficient time for deliberate discussion, little is known about how surgeons navigate decision-making and how surgeons align care with patient preferences. In this context, we sought to explore surgeons' approaches to decision-making for adults ≥65 years at high-risk of postoperative complications or death.

METHODS

We conducted semistructured in-depth interviews with 46 practicing surgeons across Michigan. Transcripts were iteratively analyzed through steps informed by inductive thematic analysis.

RESULTS

Four major themes emerged characterizing how surgeons approach high-risk surgical decision-making for older adults: (1) risk assessment was defined as the process used by surgeons to identify and analyze factors that may negatively impact outcome; (2) expectations and goals described the process of surgeons engaging with patients and families to discuss potential outcomes and desired objectives; (3) external and internal motivating factors outlined extrinsic dynamics (eg, quality metrics, referrals) and intrinsic drivers (eg, surgeons' personal experiences) that influenced high-risk decision-making; and (4) decision-making approaches and challenges encompassed the roles of patients and surgeons and obstacles to engaging in a true shared decision-making process.

CONCLUSION

Although shared decision-making is strongly recommended, we found that surgeons who perform high-risk operations among older adults predominantly focused on assessing risk and setting expectations with patients and families rather than inviting them to actively participate in the decision-making process. Surgeons also reported influences on decision-making from quality metrics, referrals, and personal experiences. Patient involvement, however, was seldom discussed suggesting that surgeons may not be engaging in true shared decision-making when benefits should be weighed against a high likelihood of harm.