■ Healthcare
■ Healthcare
centers and learn the skills that they need to effectively navigate daily life stressors . Chrissy Buteas : HINJ remains committed to advancing health equity in all our advocacy efforts , ensuring that patients have fair and equal access to life-saving treatments and cures developed by our biopharmaceutical , medical technology , and diagnostic member companies . In 2025 , we are prioritizing key initiatives to address systemic barriers to healthcare access and affordability .
One critical focus is advocating for PBM ( Pharmacy Benefit Manager ) reform to ensure that the billions of dollars in rebates and discounts provided by manufacturers – intended to reduce the cost of brand medicines by 50 % or more – are directly passed on to patients . Currently , PBMs often require patients to pay based on the full price of medications , forcing them to pay more than intermediaries . Reforming this practice will help reduce out-ofpocket costs for patients , improving affordability and access .
Additionally , our companies are continuously working to expand diversity in clinical trial participation to better understand how treatments impact different patient populations . Through these actions , HINJ strives to eliminate disparities in healthcare , empower patients , and build a stronger , more equitable healthcare system for all . J . Cedar Wang : Holy Name offers community-based educational programming in various languages ( e . g . Korean , Spanish and Russian ) directly in the community setting at churches , senior centers , recreational facilities , and housing developments . Such programs include culturally sensitive education to raise awareness about diabetes , smoking cessation , heart disease and cancer .
Additionally , as an organization , we aim to train our future healthcare providers in issues related to health equity in both our nurse and physician residency programs .
Grill-Goodman : How do you think artificial intelligence and machine learning will impact health equity in the next five years ? Patrick Mattis : AI and ML are revolutionizing healthcare . AI / ML improves patient outcomes , including diagnostics , treatment , and patient prevention , and cost efficiency in health services . AI / ML analyzes enormous volumes of data to identify disease markers and trends and improve early disease detection .
However , AI / ML has challenges related to health equity . Biases are one of the most significant issues impacting health equity . Existing data and methods may have embedded types of biases . The lack of data on race , ethnicity , or social factors can create biases . This can result in people being under-represented in the dataset and underserved by public health .
Examples of intrinsic biases in AI are prioritization of care based on cost data instead of care needs , which adversely affects sicker marginalized patients . Current skin cancer detection
Natasha Carew , DNP , ACNP-BC , PMHNP-BC , Professor in the Ramapo College Nursing program
algorithms are less effective on darker skin tones . Algorithms predict lower health risks for populations without consideration for underserved populations with limited access to care . Rodgers : AI and ML can help address health disparities by predicting optimal treatments and identifying patients at risk of adverse effects , enabling tailored interventions . However , if existing biases in healthcare are embedded in algorithms , these technologies could also exacerbate health inequities . Denise Anderson : Artificial intelligence ( AI ) and machine learning ( ML ) can potentially reduce or perpetuate health inequities in the next five years . Their positive attributes include their ability to analyze large and complex data sets efficiently , forecast future health trends ; curate customized health interventions to individual needs , provide evidence-based / informed treatment plans , expand access to care through telehealth tools and virtual health assistants , translate multiple languages , automate burdensome health administrative processes , and more . However , AI and ML can perpetuate existing biases if the data feeding AI and ML are biased or incomplete . Further , the digital divide , or lack of access to the internet or a device , will continue to exclude underserved populations without access to or training on the use of AI and ML . Moreover , we will continue to grapple with data privacy and ethical concerns related to the use of AI and ML . Wang : AI will help us better identify health disparities and provide automated tools to extend the healthcare workforce by engaging individuals to promote health in marginalized and vulnerable populations . Gregoire : Over the next five years , AI and ML are poised to significantly improve health equity
Brigitte Johnson , Esq ., President and CEO of CarePlus NJ
by facilitating more personalized and efficient healthcare delivery . These technologies can identify at-risk populations through the analysis of social determinants of health , enabling targeted interventions . Additionally , AI can optimize administrative processes , thereby reducing wait times and enhancing access to care . Predictive analytics will guide resource allocation , ensuring underserved communities receive the necessary attention . However , it is vital to address potential biases within AI algorithms to prevent the exacerbation of existing disparities , ensuring these tools serve to advance health equity effectively . Andy Anderson : AI and ML will play a critical role in advancing health equity through their potential to enhance data-driven decision-making , address disparities , expand access to care , and personalize health care delivery and patient education .
AI allows providers to analyze large datasets to identify health risk and social determinants of health factors , enabling us to develop community-specific interventions , while targeting support for at-risk populations through predictive analytics . AI-powered telehealth solutions can bridge care gaps in underserved areas , addressing barriers like provider shortages and transportation challenges . Personalized AI-driven health education platforms cater to diverse literacy levels and cultures and ML can identify behavioral risks and support interventions , improving outcomes in populations with higher prevalence of chronic conditions . Of course , achieving these benefits will require a commitment to ethical standards , intentional design , prevention of algorithmic bias and diverse data inclusion .
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