With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in data and model design that can harm certain demographic groups based on factors such as age, gender, and race. This study proposes an artificial intelligence framework, grounded in software engineering principles, for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions, and machine learning methods are suggested to prevent such biases. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.

This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria on the MIMIC III dataset based on attributes such as gender, ethnicity, language, and insurance group. The results identify disparities in the model's performance across different groups and highlights the need for better fairness and bias mitigation strategies. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.

Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model design can result in disparities for certain protected groups and amplify existing inequalities in healthcare. To address this challenge, this study summarizes seminal literature on ML fairness and presents a framework for identifying and mitigating biases in the data and model. The framework provides guidance on incorporating fairness into different stages of the typical ML pipeline, such as data processing, model design, deployment, and evaluation. To illustrate the impact of biases in data on ML models, we present examples that demonstrate how systematic biases can be amplified through model predictions. These case studies suggest how the framework can be used to prevent these biases and highlight the need for fair and equitable ML models in public health. This work aims to inform and guide the use of ML in public health towards a more ethical and equitable outcome for all populations.

Editor's note: Michael Sanky is global industry lead of healthcare and life sciences at Databricks, an enterprise software company. As pervasive health disparities in the U.S. continue to widen, data and artificial intelligence offer the potential to help close that gap. New technologies can analyze large, diverse data sets, informing the work of researchers, decision makers and policymakers across healthcare. If done correctly, AI can ultimately improve care delivery, advance proactive healthcare planning and predictive treatments, reduce clinician burnout and drive better patient outcomes. To this end, we've already seen groundbreaking advancements that move the needle in healthcare.

Healthcare's comfort level with artificial intelligence and machine learning models – and skill at deploying them across myriad clinical, financial and operational use cases – continued to increase in 2023. More and more evidence shows that training AI algorithms on a variety of datasets can improve decision support, boost population health management, streamline administrative tasks, enable cost efficiencies and even improve outcomes. But there's still a lot work to be done to ensure accurate, reliable, understandable and evidence-based results that ensure patient safety and account for health equity. There's no doubt that AI's application in healthcare has gone beyond "real" in 2019 to significant investment by providers and payers last year. This year, we've reported on deeper industry discussions focused on trust and best practices.

Artificial intelligence and machine learning are key to unlocking patient data and solving some of healthcare's most complex problems. Even as the U.S. seeks to put the COVID-19 pandemic in the rearview mirror, many who survive the initial illness suffer debilitating long-term health impacts, especially those with underlying health conditions. Technology allows easier access to disparate data sources without compromising data privacy or integrity. In addition, advanced analytics deliver real-time insights, enabling providers to predict outcomes and diagnose illness early to intervene with patients at risk of developing long-term COVID and other chronic diseases. To delve deeper into these technologies and their ramifications in healthcare, Healthcare IT News spoke with Brett Furst, president of HHS Tech Group.

Over the course of the last two years, we interviewed mayors, city officials, urban planners, academics, and citizens in cities around the world to identify the trends that are making urban living more sustainable, affordable, and human. One theme that emerged was cities' increasingly important role in ensuring the health and well-being of their residents.4 Cities currently represent just 3% of the world's territory but harbor 55% of the world's population. By 2050, it's estimated that 70% of the world's population will live in urban centers.5 At an economic level, cities generate around 80% of the global GDP,6 and are responsible for 80% of energy consumption and more than 70% of carbon emissions and global waste.7

Wearable devices offering visibility into the functions of the human body can empower underserved communities. Victor Brown, founder and CEO of Xcellent Life, explains how. Want to get more stories like this one? Get daily news updates from Healthcare IT News. Your subscription has been saved.

By now, everyone understands the potential of AI in healthcare, especially in our fight against COVID-19. But because of the nature of the emerging technology, it has also thrown up some major questions that no single tech or healthcare provider can solve on their own. So, Microsoft is bringing together leading public, private, educational and research organizations across the U.S. healthcare and life sciences industries to form the Artificial Intelligence Industry Innovation Coalition (AI3C). The coalition list includes the top names from healthcare and technology, including Brookings Institution, Cleveland Clinic, Duke Health, Intermountain Healthcare, Novant Health, Plug and Play, Providence, UC San Diego, and the University of Virginia. "We at Providence feel that the responsible and equitable implementation of AI will be a critical element of healthcare transformation [to] achieve our vision of Health for a Better World. We applaud Microsoft for bringing this coalition together and are excited to partner with these esteemed institutions to drive progress on how AI can be a force multiplier for good in healthcare," said Brett MacLaren, chief data officer at Providence.

When I graduated from medical school and took the Hippocratic Oath, I vowed to not just treat the illness on a patient's medical history form but to treat the person behind the diagnosis. To do this well, clinicians need to understand the whole person and the context in which they live -- their race, gender identity, native language, socioeconomic status, or zip code, among other things -- to ensure equitable care. According to the CDC, health equity is reached when every person has the opportunity to attain his or her full health potential regardless of social position or other socially determined circumstances. Yet, health inequities abound in our healthcare systems. Research says that those Americans who live in rural communities have less access to care and subsequently worse health outcomes than those who live in non-rural communities.