Noah's research merges health economics with innovations in machine learning to investigate the role of social factors in the delivery of healthcare with the goal of better targeting policy solutions to disparities in health. He also investigates how the application of artificial intelligence in healthcare can benefit patients as well as its potential to perpetuate and even worsen existing inequities. His disease focus centers on cancer and cardiac health. Noah is a member of numerous interdisciplinary research teams that include economists, computer scientists, biostatisticians, informaticists and clinicians.
Areas of Expertise (5)
Prostate Cancer Disparities
Inclusion of nonrandomized studies of interventions in systematic reviews of interventions: updated guidance from the Agency for Health Care Research and Quality Effective Health Care programJournal of Clinical Epidemiology
Ian J.Saldanha, et. al
We developed guidance to inform decisions regarding the inclusion of nonrandomized studies of interventions (NRSIs) in systematic reviews (SRs) of the effects of interventions. The guidance workgroup comprised SR experts and used an informal consensus generation method. Instead of recommending NRSI inclusion only if randomized controlled trials (RCTs) are insufficient to address the SR key question, different topics may require different decisions regarding NRSI inclusion.
An evaluation of copy and paste events in electronic notes of patients with hospital acquired conditionsInternational Journal of Medical Informatics
Ruixuan Wang, et. al
The increased use of the copy and paste function (CPF) in Electronic Health Records (EHRs) has raised concerns about possible clinician miscommunication and adverse patient outcomes. This study investigated the prevalence and extent of CPF in the EHRs of patients diagnosed with Hospital-acquired Conditions (HACs). We also examined the association between the use of CPF and patient characteristics.
A scoping review of the use of machine learning in health economics and outcomes research: part 2—data from nonwearablesValue in Health
Woojung Lee PharmD, et. al
Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included.