A5006479392- Ethics in Clinical Research
- Artificial Intelligence in Healthcare and Education
- Electronic Health Records Systems
- Long-Term Effects of COVID-19
- Clinical Reasoning and Diagnostic Skills
- Scientific Computing and Data Management
- AI in cancer detection
- Medical Malpractice and Liability Issues
- Digital Radiography and Breast Imaging
- Digital Mental Health Interventions
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Cardiac Health and Mental Health
- Heart Failure Treatment and Management
- Ethics in medical practice
- Data Quality and Management
- Ethics and Social Impacts of AI
- Mobile Health and mHealth Applications
- Research Data Management Practices
- COVID-19 epidemiological studies
- Health, Environment, Cognitive Aging
- Privacy, Security, and Data Protection
- Child Nutrition and Water Access
- Machine Learning in Healthcare
- Empathy and Medical Education
- Artificial Intelligence in Law
NorthShore University HealthSystem
2015-2024
University of Washington
2024
University of Iowa
2024
Oregon Health & Science University
2024
University of Houston - Clear Lake
2024
Lurie Children's Hospital
2024
Johns Hopkins University
2024
Boston University
2024
University School
2024
Research Network (United States)
2020-2023
Stratification of patients with post-acute sequelae SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, COVID is incompletely understood and characterised by a wide range manifestations that are difficult to analyse computationally. Additionally, the generalisability machine learning classification COVID-19 outcomes has rarely been tested.
Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to complex acute presentation that can affect multiple organ systems, increasing evidence points long-term sequelae being common and impactful. The worldwide scientific community is forging ahead characterize range outcomes associated with infection; however underlying assumptions in these studies have varied so widely resulting data are difficult compareFormal definitions...
Abstract Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced this domain. Self and social stigma contribute to under-reported symptoms, under-coding worsens ascertainment. Health disparities algorithmic bias. Lack reliable biological clinical markers hinders model development, explainability impede trust among users. In perspective, we describe these discuss design recommendations overcome them intelligent...
Recent advances in the science and technology of artificial intelligence (AI) growing numbers deployed AI systems healthcare other services have called attention to need for ethical principles governance. We define provide a rationale that should guide commission, creation, implementation, maintenance, retirement as foundation governance throughout lifecycle. Some are derived from familiar requirements practice research medicine healthcare: beneficence, nonmaleficence, autonomy, justice come...
Abstract Background Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- post-deployment evaluation AI tools regarding crucial attributes transparency, performance monitoring, adverse event reporting makes this situation challenging. Objectives This paper aims make...
The Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) represents an unprecedented collaboration across diverse healthcare institutions including private, county, and state hospitals health systems, a consortium of Federally Qualified Health Centers, two Department Veterans Affairs hospitals. CAPriCORN builds on the strengths our to develop cross-cutting infrastructure for sustainable patient-centered comparative effectiveness research in Chicago. Unique aspects include with...
The COVID-19 pandemic response in the United States has exposed significant gaps information systems and processes that prevent timely clinical public health decision-making. Specifically, use of informatics to mitigate spread SARS-CoV-2, support care delivery, accelerate knowledge discovery bring forefront issues privacy, surveillance, limits state powers, interoperability between systems. Using a consensus-building process, we critically analyze informatics-related ethical light across 3...
Abstract Accurate stratification of patients with post-acute sequelae SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history COVID is incompletely understood and characterized by an extremely wide range manifestations that are difficult to analyze computationally. In addition, generalizability machine learning classification COVID-19 outcomes has rarely been tested. We present a method for computationally modeling PASC...
Pulmonary fibrosis is characterized by lung parenchymal destruction and can increase morbidity mortality. commonly occurs following hospitalization for SARS-CoV-2 infection. As there are medications that modify pulmonary risk, we investigated whether distinct pharmacotherapies (amiodarone, cancer chemotherapy, corticosteroids, rituximab) associated with differences in post-COVID-19 incidence.
Abstract The active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, implementing learnings from can improve both processes outcomes. Adopting a science approach the practice precision medicine clinical care will require healthcare providers, researchers, institutions address number technical, organizational, scientist collaboration issues. Some changes be made relative ease, while...
Objective: Asthma exacerbations are associated with significant morbidity, mortality, and cost. Accurately identifying asthma patients at risk for exacerbation is essential. We sought to develop a prediction tool based on routinely collected data from electronic health records (EHRs).Methods: From repository of EHRs data, we extracted structured gender, race, ethnicity, smoking status, use medications, environmental allergy testing BMI Control Test scores (ACT). A subgroup this population...
Empirically investigate current practices and analyze ethical dimensions of clinical data sharing by healthcare organizations for uses other than treatment, payment, operations. Make recommendations to inform research policy protect patients' privacy autonomy when with unrelated third parties.
To assess the potential to adapt an existing technology regulatory model, namely Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).
<h3>Importance</h3> Past studies have showed associations between antibiotic exposure and child weight outcomes. Few, however, documented alterations to body mass index (BMI) (calculated as in kilograms divided by height meters squared) trajectory milestone patterns during childhood after early-life exposure. <h3>Objective</h3> To examine the association of use first 48 months life with BMI milestones a large cohort children. <h3>Design, Setting, Participants</h3> This retrospective study...
Abstract Authentic inclusion and engagement of behavioral health patients in their care delivery the process scientific discovery are often challenged system. Consequently, there is a growing need to engage with better serve needs patients, particularly by leveraging information technologies. In this work, we present rationale strategies for improving patient population research clinical care. First, describe potential creating meaningful patient–investigator partnerships allow cocreation...
Background Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction have been developed to optimize impact while sustaining sufficient performance. Objective We aimed derive for hospital mortality, 180-day 30-day readmission, implement these within our electronic health record prospectively validate use across an entire system. Materials & methods developed, integrated into validated three using logistic regression from data...
Abstract Background Inefficient electronic health record (EHR) usage increases the documentation burden on physicians and other providers, which cognitive load contributes to provider burnout. Studies show that EHR efficiency sessions, optimization sprints, reduce burnout using a resource-intense five-person team. We implemented sprint-inspired one-on-one post-go-live training sessions (mini-sprints) as more economical option directed at providers. Objectives evaluated mini-sprint...