Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governance surrounding its use and integration is often poorly understood. What the Health AI Partnership (HAIP) aims to do in this research is to better define the requirements for adequate organizational governance of AI systems in healthcare settings and support health system leaders to make more informed decisions around AI adoption. To work towards this understanding, we first identify how the standards for the AI adoption in healthcare may be designed to be used easily and efficiently. Then, we map out the precise decision points involved in the practical institutional adoption of AI technology within specific health systems. Practically, we achieve this through a multi-organizational collaboration with leaders from major health systems across the United States and key informants from related fields. Working with the consultancy IDEO.org, we were able to conduct usability-testing sessions with healthcare and AI ethics professionals. Usability analysis revealed a prototype structured around mock key decision points that align with how organizational leaders approach technology adoption. Concurrently, we conducted semi-structured interviews with 89 professionals in healthcare and other relevant fields. Using a modified grounded theory approach, we were able to identify 8 key decision points and comprehensive procedures throughout the AI adoption lifecycle. This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States. We hope these findings can inform future efforts to build capabilities to promote the safe, effective, and responsible adoption of emerging technologies in healthcare

Evidence-based AI Ethics

With the rise in prominence of algorithmic-decision making, and numerous high-profile failures, many people have called for the integration of ethics into the development and use of these technologies. In the past five years, the field of “AI Ethics” has risen to prominence to explore questions such as 'how can ML algorithms be more fair' and 'are are tradeoffs when incorporating values such as fairness or privacy into models.' One common trend, particularly by corporations and governments, has been a top-down, principles-based approach for setting the agenda. However, such efforts are usually too abstract to engage with; everyone agrees models should be fair, but there is often disagreement on what "fair" means. In this work, I propose a bottom-up alternative: Evidence-based AI Ethics. Learning from other influential movements, such as Evidence-based Medicine, we can consider specific projects and examine them for "evidence." We draw from complementary critical lenses, one based on utilitarian ethics and on from intersectional feminism to analyze five case studies I have worked on, ranging from automatically-generated radiology reports to tech worker organizing.Ph.D

Quantifying racial disparities in end-of-life care

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 75-80).There are established racial disparities in healthcare, particularly during end-of-life care when poor communication and historical inequities can lead to suboptimal options and outcomes for patients and their families. Previous work has suggested that medical disparities can reflect higher rates of mistrust for the healthcare system among black patients. When the doctor-patient relationship lacks trust, patients may believe that limiting any intensive treatment is unjustly motivated, and demand higher levels of aggressive care. While there are clinical examples of exemplary end-of-life care, studies have highlighted that aggressive care can lead to painful final moments, and may not improve patient outcomes. In this thesis, I demonstrate that racial disparities which have been reported previously are also present in two public databases. I explore the notion that one underlying cause of this disparity is due to mistrust between patient and caregivers, and develop a multiple trust metric proxies to measure such mistrust more directly. These metric demonstrate even stronger disparities in end-of-life care than race does and statistically significant higher levels of mistrust for black populations. I hope that this work will serve as a useful view for bias and fairness in clinical data, and that future work can better understand mistrust so that its underlying factors (e.g. poor communication and perceived discrimination) can be addressed.by William Boag.S.M

Quantifying racial disparities in end-of-life care

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 75-80).There are established racial disparities in healthcare, particularly during end-of-life care when poor communication and historical inequities can lead to suboptimal options and outcomes for patients and their families. Previous work has suggested that medical disparities can reflect higher rates of mistrust for the healthcare system among black patients. When the doctor-patient relationship lacks trust, patients may believe that limiting any intensive treatment is unjustly motivated, and demand higher levels of aggressive care. While there are clinical examples of exemplary end-of-life care, studies have highlighted that aggressive care can lead to painful final moments, and may not improve patient outcomes. In this thesis, I demonstrate that racial disparities which have been reported previously are also present in two public databases. I explore the notion that one underlying cause of this disparity is due to mistrust between patient and caregivers, and develop a multiple trust metric proxies to measure such mistrust more directly. These metric demonstrate even stronger disparities in end-of-life care than race does and statistically significant higher levels of mistrust for black populations. I hope that this work will serve as a useful view for bias and fairness in clinical data, and that future work can better understand mistrust so that its underlying factors (e.g. poor communication and perceived discrimination) can be addressed.by William Boag.S.M

Entrance to old Cumbooquepa, the residence of Thomas Blacket Stephens, South Brisbane, ca. 1873. Site of the present day Somerville House School

T.B. Stephens, the owner of old Cumbooquepa, was a Member of the Queensland Legislative Assembly, a former Mayor of Brisbane,and (for ten years) proprietor of the Brisbane Courier newspaper. He also owned fellmongeries and wool scourers at Cleveland and Ekibin and acquired extensive landholdings in the Nerang district in the 1870s. His home was one of the grandest in the South Brisbane area, and remained so until ca. 1890, when it was demolished to make way for the South Coast railway line. His eldest son William Stephens then erected a larger house on a higher site nearby. The original Cumbooquepa with its decorative barge-boards, brick chimneys and substantial outbuildings stood on 16 acres of land and had a large garden stocked with banana plants, hoop-pine and prickly pear. The man standing behind the front gate was probably the gardener. In the bottom right is a white surveyors’ mark. Writing on the the photo indicates TB Stephens, Miss Laura Stephens and Tom C. Stephens were some of the people in this photograph

Scotty Howard & Carl Graham Collection

Photograph of tornado damage in Bigheart, OK. Photo by William J. Boag, Pawhuska, OK, April 12, 1911

Scotty Howard & Carl Graham Collection

Photograph of tornado damage in Bigheart, OK. Photo by William J. Boag, Pawhuska, OK, April 12, 1911

Scotty Howard & Carl Graham Collection

Photograph of tornado damage in Bigheart, OK. Photo by William J. Boag, Pawhuska, OK, April 12, 1911

Scotty Howard & Carl Graham Collection

Photograph of tornado damage to the High School Bigheart, OK. Photo by William J. Boag, Pawhuska, OK, April 12, 1911

Stanthorpe school building, children circa 1872

Stanthorpe State School constructed as a large slab hut