Harry Reyes Nieva About CV Projects Publications Presentations Teaching News
PhD Candidate in Biomedical Informatics (on the job market)
I am currently a PhD Candidate at Columbia University where I am advised by Prof. Noémie Elhadad, Chair of the Department of Biomedical Informatics. For most of my PhD, I was also a Visiting Postgraduate Research Fellow at Harvard Medical School. My long-term goal is to pursue a career in academia as a professor and advance health equity science. Building on two decades of domestic and international experience in clinical research and public health informatics, my research focuses on human-centered artificial intelligence (AI) and development of systematic, scalable data-driven approaches to promote health equity. My work usually examines and applies methods such as machine learning, natural language processing, and spatiotemporal analysis in addition to traditional biostatistics and epidemiology.
My scholarship is informed by a longstanding commitment to justice, ethics, diversity, and inclusion (JEDI). This is largely demonstrated by many years of service on JEDI-oriented research studies and working groups, capacity-building efforts devoted to resource-limited settings and marginalized communities, and voluntary civil service. Among these experiences, perhaps one of the more formative was my three-year term appointment as a Commissioner of Human Rights for the city of Cambridge, Massachusetts. During that time, I presided over hearings and facilitated conciliations regarding complaints of discrimination against protected classes related to housing, employment, education, and public accommodation. While Vice Chair of the Commission, I also led efforts to engage the Massachusetts State Legislature on issues of digital and pay equity, protections related to domestic violence, and anti-discrimination legislation for sexual and gender minority populations.
As a first-generation college graduate, I have benefitted greatly from many excellent mentors in my academic and personal journey. I am also an active mentor/mentee of the Biomedical Science Careers Program. Before starting my PhD program, I held a number of positions at Brigham and Women’s Hospital (BWH), Harvard Medical School (HMS), and the Harvard T.H. Chan School of Public Health. While at BWH and HMS, I served on more than a dozen research studies and pursued my own projects with generous support from Harvard Catalyst. Prior to pursuing a career in biomedical informatics and health services research, I was a member of the Strategic Information division of the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) at Harvard University which aimed to rapidly expand antiretroviral therapy for people living with HIV/AIDS in sub-Saharan Africa.
Enabling equitable learning health systems and precision public healthMy work embraces the learning health system (LHS) paradigm, which routinely integrates informatics and advanced data science methods into care practices to improve patient health and the healthcare system as a whole. LHS involves continuous aggregation and analysis of data while incorporating what is learned into the improvement of future care as part of a natural feedback loop. By ensuring equity comprises an essential component of LHS efforts, over time routine care processes become equity promoting. To date, I have focused especially on leveraging AI, multimodal data, and an intersectional lens to reduce diagnostic delay and related disparities through earlier detection of conditions such as acute myocardial infarction, endometriosis, and mpox. This work has been presented at various scientific fora including the Symposium on AI in Learning Health Systems (SAIL). My research also seeks to extend the LHS paradigm to enable a learning public health system by leveraging existing sources of real-world data (e.g., EHR data spanning multiple health systems) through health information exchange, data harmonization, integration of open government datasets, and geospatial analysis.
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Promoting health data equity and creating knowledge basesMy work promoting data equity in healthcare seeks to improve documentation of demographics and the social determinants of health to support culturally competent care and research of particular interest to minoritized populations. While I often employ advanced data science methods to extract information from existing data sources (e.g., natural language processing of clinical narratives in the electronic health record [EHR]) or integrate community-level information from large public datasets (e.g., national survey data), my work has also involved expanding primary data collection during clinical care. While co-chair of the Brigham and Women's Hospital (BWH) LGBTQ and Allies Employee Resource Group, colleagues and I lobbied Mass General Brigham (MGB), one of the largest and leading academic health systems in the country, to include sexual orientation and gender identity (SOGI) among the core set of demographics routinely collected in the EHR. Subsequently, I was invited by MGB to serve on the working group that oversaw design and implementation plans impacting care for millions of patients. These efforts resulted in MGB becoming one of the first US health systems to collect SOGI in the EHR in a standardized, scalable fashion. For my contributions, I received the inaugural LGBTQ Leadership Award from BWH and a Partners in Excellence Award for Leadership and Innovation from MGB. My research in this area has been featured in Harvard Medicine Magazine and the Think Research podcast.
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Elucidating health inequity and creating tools to facilitate further discoveryMy research also leverages real-world data sources to conduct comprehensive large-scale characterization studies, spotlighting the myriad factors that influence the health outcomes of people from diverse backgrounds, or applies advanced data science techniques to uncover hidden areas of health inequity across intersectional dimensions of lived experience and group identity. For example, an investigation of terminations of patient care revealed a practice that disproportionately harms people from historically marginalized groups. Our study found that “no-shows” were cited as the cause for termination in more than a third of cases. Study findings led to policy changes in the health system, including a new mandate that clinicians can no longer “fire” patients for missing or failing to cancel clinical appointments. Notably, terminations were also formalized via EHR functionality that made it both possible and relatively easy to prevent patients from scheduling new appointments, exacerbating barriers to healthcare access.
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Ethical and justice considerations in AI, clinical practice, and digital healthMy research on ethical considerations, perceptions, responsibilities, and implications in the digital age of healthcare often examines stakeholders and their relationships to one another, notions of data and algorithmic justice, and transparency and trust in clinicians and AI. It also involves encoding human values and goals into AI via a process known as alignment. Most recently, my colleagues and I wrote a book of educational case studies focused on justice, ethics, diversity, and inclusion (JEDI) in health-related AI which is soon to be released for wide dissemination. We also conducted a scoping review of ethical considerations in clinical natural language processing to spotlight the promise and perils of its use in healthcare.
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