As the extent of health disparities in the USA has been revealed, particularly during the COVID-19 pandemic, physicians have increasingly attended to their roles as advocates for their patients and communities. This article presents "spheres of influence" as a concept that can help physicians think strategically about how to build upon their clinical work and expertise to promote equity in medicine. The physician's primary sphere of influence is in direct patient care. However, physicians today often have many other roles, especially within larger health care institutions in which physicians often occupy positions of authority. Physicians are therefore well-positioned to act within these spheres in ways that draw upon the ethical principles that guide patient care and contribute materially to the cause of equity for colleagues and patients alike. By making changes to the ways they already work within their clinical spaces, institutional leadership roles, and wider communities, physicians can counteract the structural problems that undermine the health of the patients they serve.

Spurred on by recent health care reforms and the Triple Aim's goals of improving population health outcomes, reducing health care costs, and improving the patient experience of care, emphasis on population health is increasing throughout medicine. Population health has the potential to improve patient care and health outcomes for individual patients. However, specific population health activities may not be in every patient's best interest in every circumstance, which can create ethical tensions for individual physicians and other health care professionals. Because individual medical professionals remain committed primarily to the best interests of individual patients, physicians have a unique role to play in ensuring population health supports this ethical obligation. Using widely recognized principles of medical ethics-nonmaleficence/beneficence, respect for persons, and justice-this article describes the ethical issues that may arise in contemporary population health programs and how to manage them. Attending to these principles will improve the design and implementation of population health programs and help maintain trust in the medical profession.

Background

Accountable care organizations (ACOs) are proliferating as a solution to the cost crisis in American health care, and already involve as many as 31 million patients. ACOs hold clinicians, group practices, and in many circumstances hospitals financially accountable for reducing expenditures and improving their patients' health outcomes. The structure of health care affects the ethical issues arising in the practice of medicine; therefore, like all health care organizational structures, ACOs will experience ethical challenges. No framework exists to assist key ACO stakeholders in identifying or managing these challenges.

Methods

We conducted a structured review of the medical ACO literature using qualitative content analysis to inform identification of ethical challenges for ACOs.

Results

Our analysis found infrequent discussion of ethics as an explicit concern for ACOs. Nonetheless, we identified nine critical ethical challenges, often described in other terms, for ACO stakeholders. Leaders could face challenges regarding fair resource allocation (e.g., about fairly using ACOs' shared savings), protection of professionals' ethical obligations (especially related to the design of financial incentives), and development of fair decision processes (e.g., ensuring that beneficiary representatives on the ACO board truly represent the ACO's patients). Clinicians could perceive threats to their professional autonomy (e.g., through cost control measures), a sense of dual or conflicted responsibility to their patients and the ACO, or competition with other clinicians. For patients, critical ethical challenges will include protecting their autonomy, ensuring privacy and confidentiality, and effectively engaging them with the ACO.

Discussion

ACOs are not inherently more or less "ethical" than other health care payment models, such as fee-for-service or pure capitation. ACOs' nascent development and flexibility in design, however, present a time-sensitive opportunity to ensure their ethical operation, promote their success, and refine their design and implementation by identifying, managing, and conducting research into the ethical issues they might face.

Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.