Unexpected Inequality: Disparate-Impact From Artificial Intelligence in Healthcare Decisions.

Systemic discrimination in healthcare plagues marginalized groups. Physicians incorrectly view people of color as having high pain tolerance, leading to undertreatment. Women with disabilities are often undiagnosed because their symptoms are dismissed. Low-income patients have less access to appropriate treatment. These patterns, and others, reflect long-standing disparities that have become engrained in U.S. health systems. As the healthcare industry adopts artificial intelligence and algorithminformed (AI) tools, it is vital that regulators address healthcare discrimination. AI tools are increasingly used to make both clinical and administrative decisions by hospitals, physicians, and insurers--yet there is no framework that specifically places nondiscrimination obligations on AI users. The Food and Drug Administration has limited authority to regulate AI and has not sought to incorporate anti-discrimination principles in its guidance. Section 1557 of the Affordable Care Act has not been used to enforce nondiscrimination in healthcare AI and is under-utilized by the Office of Civil Rights. State level protections by medical licensing boards or malpractice liability are similarly untested and have not yet extended nondiscrimination obligations to AI. This Article discusses the role of each legal obligation on healthcare AI and the ways in which each system can improve to address discrimination. It highlights the ways in which industries can self-regulate to set nondiscrimination standards and concludes by recommending standards and creating a super-regulator to address disparate impact by AI. As the world moves towards automation, it is imperative that ongoing concerns about systemic discrimination are removed to prevent further marginalization in healthcare.

Clinical Decision Support: The Law, the Future, and the Role for Radiologists.

Clinical Decision Support (CDS) was designed as an interactive, electronic tool for use by clinicians that communicates Appropriate Use Criteria (AUC) information to the user and assists them in making the most appropriate treatment decision for a patient's specific clinical condition. Policymakers recognized AUC as a potential solution to control inappropriate utilization of imaging and made CDS mandatory in the Protecting Access to Medicare Act of 2014. In the years since Protecting Access to Medicare Act, data on the potential impact of CDS has been mixed and much of the physician community has expressed concern about the logistics of the program. This article aims to review the legislation behind the AUC program, the events that have transpired since, and some of the challenges and opportunities facing radiologists in the current environment.

What Physicians and Health Organizations Should Know About Mandated Imaging Appropriate Use Criteria.

The Appropriate Use Criteria Program, enacted by the Centers for Medicare & Medicaid Services in response to the Protecting Access to Medicare Act of 2014 (PAMA), aims to reduce inappropriate and unnecessary imaging by mandating use of clinical decision support (CDS) by all providers who order advanced imaging examinations (magnetic resonance imaging; computed tomography; and nuclear medicine studies, including positron emission tomography). Beginning 1 January 2020, documentation of an interaction with a certified CDS system using approved appropriate use criteria will be required on all Medicare claims for advanced imaging in all emergency department patients and outpatients as a prerequisite for payment. The Appropriate Use Criteria Program will initially cover 8 priority clinical areas, including several (such as headache and low back pain) commonly encountered by internal medicine providers. All providers and organizations that order and provide advanced imaging must understand program requirements and their options for compliance strategies. Substantial resources and planning will be needed to comply with PAMA regulations and avoid unintended negative consequences on workflow and payments. However, robust evidence supporting the desired outcome of reducing inappropriate use of advanced imaging is lacking.

Legal and ethical issues surrounding the use of crowdsourcing among healthcare providers.

As the pace of medical discovery widens the knowledge-to-practice gap, technologies that enable peer-to-peer crowdsourcing have become increasingly common. Crowdsourcing has the potential to help medical providers collaborate to solve patient-specific problems in real time. We recently conducted the first trial of a mobile, medical crowdsourcing application among healthcare providers in a university hospital setting. In addition to acknowledging the benefits, our participants also raised concerns regarding the potential negative consequences of this emerging technology. In this commentary, we consider the legal and ethical implications of the major findings identified in our previous trial including compliance with the Health Insurance Portability and Accountability Act, patient protections, healthcare provider liability, data collection, data retention, distracted doctoring, and multi-directional anonymous posting. We believe the commentary and recommendations raised here will provide a frame of reference for individual providers, provider groups, and institutions to explore the salient legal and ethical issues before they implement these systems into their workflow.

What Should Oversight of Clinical Decision Support Systems Look Like?

A learning health system provides opportunities to leverage data generated in the course of standard clinical care to improve clinical practice. One such opportunity includes a clinical decision support structure that would allow clinicians to query electronic health records (EHRs) such that responses from the EHRs could inform treatment recommendations. We argue that though using a clinical decision support system does not necessarily constitute a research activity subject to the Common Rule, it requires more ethical and regulatory oversight than activities of clinical practice are generally subjected to. In particular, we argue that the development and use of clinical decision support systems should be governed by a framework that (1) articulates appropriate conditions for their use, (2) includes processes for monitoring data quality and developing and validating algorithms, and (3) sufficiently protects patients' data.

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017.

INTRODUCTION: Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry. OBJECTIVE: Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017. METHODS: Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally. RESULTS: CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related "Big Data" sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of "app" ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions. CONCLUSION: Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.

Clinical Implementation of Pharmacogenomics for Personalized Precision Medicine: Barriers and Solutions.

Clinical implementation of pharmacogenomics (PGx) leads to personalized medicine, which improves the efficacy, safety, and cost-effectiveness of treatments. Although PGx-based research has been conducted for more than a decade, several barriers have slowed down its widespread implementation in clinical practice. Globally, there is an imbalance in programs and solutions required to empower the clinical implementation of PGx between countries. Therefore, we aimed to review these issues comprehensively, determine the major barriers, and find the best solutions. Through an extensive review of ongoing clinical implementation programs, scientific, educational, ethical, legal, and social issues, information technology, and reimbursement were identified as the key barriers. The pace of global implementation of genomic medicine coincided with the resource limitations of each country. The key solutions identified for the earlier mentioned barriers are as follows: building of secure and suitable information technology infrastructure with integrated clinical decision support systems along with increasing PGx evidence, more regulations, reimbursement strategies for stakeholder's acceptance, incorporation of PGx education in all institutions and clinics, and PGx promotion to all health care professionals and patients. In conclusion, this review will be helpful for the better understanding of common barriers and solutions pertaining to the clinical application of PGx.

Partnering With Your Health System to Select and Implement Clinical Decision Support for Imaging.

Recent legislation mandates the documentation of appropriateness criteria consultation when ordering advanced imaging for Medicare patients to remain eligible for reimbursement. Implementation of imaging clinical decision support (CDS) is a solution adopted by many systems to automate compliance with the new requirements. This article is intended to help radiologists who are employed by, contracted with, or otherwise affiliated with systems planning to implement CDS in the near future and ensure that they are able to understand and contribute to the process wherever possible. It includes an in-depth discussion of the legislation, evidence for and against the efficacy of imaging CDS, considerations for selecting a CDS vendor, tips for configuring CDS in a fashion consistent with departmental goals, and pointers for implementation and change management.

Regulatory Framework for Clinical Decision Support Software: Present Uncertainty and Prospective Proposition.

US regulators have been slow to provide meaningful guidance to industry participants on the issue of clinical decision support (CDS) software. It is crucial that regulators soon clarify the differences between regulated medical devices and unregulated health management software that nevertheless has the potential to affect patient care. Future CDS regulation in the United States should aim to reduce ambiguity by establishing detailed and simple criteria for manufacturers to use in deciding if a CDS product will be regulated. Clear standards will help ensure the safety of CDS that is brought to market. In addition, clarification will facilitate technological innovation, delivering clinical benefits to needy patients. To this end, the regulatory framework implemented in the United States with respect to CDS should consider the "substantial dependence" standard.

Clinical decision support for imaging in the era of the Patient Protection and Affordable Care Act.

Imaging clinical decision support (CDS) systems provide evidence for or against imaging procedures ordered within a computerized physician order entry system at the time of the image order. Depending on the pertinent clinical history provided by the ordering clinician, CDS systems can optimize imaging by educating providers on appropriate image order entry and by alerting providers to the results of prior, potentially relevant imaging procedures, thereby reducing redundant imaging. The American Recovery and Reinvestment Act (ARRA) has expedited the adoption of computerized physician order entry and CDS systems in health care through the creation of financial incentives and penalties to promote the "meaningful use" of health IT. Meaningful use represents the latest logical next step in a long chain of legislation promoting the areas of appropriate imaging utilization, accurate reporting, and IT. It is uncertain if large-scale implementation of imaging CDS will lead to improved health care quality, as seen in smaller settings, or to improved patient outcomes. However, imaging CDS enables the correlation of existing imaging evidence with outcome measures, including morbidity, mortality, and short-term imaging-relevant management outcomes (eg, biopsy, chemotherapy). The purposes of this article are to review the legislative sequence relevant to imaging CDS and to give guidance to radiology practices focused on quality and financial performance improvement during this time of accelerating regulatory change.

Group-based and personalized care in an age of genomic and evidence-based medicine: a reappraisal.

This article addresses the philosophical and moral foundations of group-based and individualized therapy in connection with population care equality. The U.S. Food and Drug Administration (FDA) recently modified its public health policy by seeking to enhance the efficacy and equality of care through the approval of group-specific prescriptions and doses for some drugs. In the age of genomics, when individualization of care increasingly has become a major concern, investigating the relationship between population health, stratified medicine, and personalized therapy can improve our understanding of the ethical and biomedical implications of genomic medicine. I suggest that the need to optimize population health through population substructure-sensitive research and the need to individualize care through genetically targeted therapies are not necessarily incompatible. Accordingly, the article reconceptualizes a unified goal for modern scientific medicine in terms of individualized equal care.

A model-driven privacy compliance decision support for medical data sharing in Europe.

OBJECTIVES: Clinical practitioners and medical researchers often have to share health data with other colleagues across Europe. Privacy compliance in this context is very important but challenging. Automated privacy guidelines are a practical way of increasing users' awareness of privacy obligations and help eliminating unintentional breaches of privacy. In this paper we present an ontology-plus-rules based approach to privacy decision support for the sharing of patient data across European platforms. METHODS: We use ontologies to model the required domain and context information about data sharing and privacy requirements. In addition, we use a set of Semantic Web Rule Language rules to reason about legal privacy requirements that are applicable to a specific context of data disclosure. We make the complete set invocable through the use of a semantic web application acting as an interactive privacy guideline system can then invoke the full model in order to provide decision support. RESULTS: When asked, the system will generate privacy reports applicable to a specific case of data disclosure described by the user. Also reports showing guidelines per Member State may be obtained. CONCLUSION: The advantage of this approach lies in the expressiveness and extensibility of the modelling and inference languages adopted and the ability they confer to reason with complex requirements interpreted from high level regulations. However, the system cannot at this stage fully simulate the role of an ethics committee or review board.