{"id":143435,"date":"2025-04-15T10:53:41","date_gmt":"2025-04-15T15:53:41","guid":{"rendered":"https:\/\/uwm.edu\/news\/?p=143435"},"modified":"2025-04-22T13:18:01","modified_gmt":"2025-04-22T18:18:01","slug":"using-artificial-intelligence-to-tease-out-answers-to-health-care-disparities","status":"publish","type":"post","link":"https:\/\/uwm.edu\/news\/using-artificial-intelligence-to-tease-out-answers-to-health-care-disparities\/","title":{"rendered":"Using artificial intelligence to tease out answers to health care disparities"},"content":{"rendered":"

Without computers, it would take Jake Luo a lifetime to sort through the sheer amount of data that is integral to his work. An associate professor in 51ÁÔÆæ\u2019s Zilber College of Public Health and College of Engineering & Applied Science, Luo focuses on identifying patterns in massive, sprawling electronic health records to highlight disparities in care.<\/p>\n

One dataset in particular, from the National Inpatient Sample, contains the data of 7 million patients across multiple years, Luo said. Even regular computers struggle to process this amount of information. \u201cSometimes, if the data set is too large, you can\u2019t get a result because of the memory limitation or the CPU limitations,\u201d he explained.<\/p>\n

That\u2019s why artificial intelligence is a powerful companion for Luo\u2019s work. AI was designed to handle huge amounts of data and identify patterns. With resources from 51ÁÔÆæ\u2019s High Performance Computing Center, Luo employs advanced AI computing techniques to efficiently process massive datasets.<\/p>\n

\u00a0These AI models can identify subtle patterns in how different patient populations access and experience health care services and their health outcomes, helping us understand where disparities exist and how to address them. The end result is organized information that researchers can leverage to draw conclusions about the state of health care \u2014 and build a roadmap for improvement.<\/p>\n

Finding the gaps<\/h2>\n

Access to health care varies widely in the United States. Income level, insurance status, location, race, sex and level of education can affect each person\u2019s experience with preventive and emergency care.<\/p>\n

Making health care more equitable starts with addressing the disparities. But to prompt real change, professionals need to identify the gaps and who is most affected.<\/p>\n

Luo, who directs the 51ÁÔÆæ Center for Health Systems Solutions, approaches this widespread problem by digging into the data. As an expert in bioinformatics, he sifts through electronic medical records in order to spot patterns. These databases are huge and have many data points on each patient.<\/p>\n

\u201cAll the details about the patient \u2014 what kind of treatment they had, what kind of drug they’ve been taking, what kind of diagnosis and the (clinician) notes are in the electronic health record,\u201d Luo said. \u201cWe leverage this particular dataset to do a lot of different kinds of research.\u201d<\/p>\n

Highlighting disparities<\/h2>\n

In many projects, Luo begins by collaborating with clinical investigators \u2014 physicians who directly work with patients \u2014 to suss out patterns and develop hypotheses.<\/p>\n

\u201cFor example, they might observe that certain patient groups have lower response to certain treatments, and some patient groups are not adhering to the treatment \u2026 protocol as well as other patient groups,\u201d Luo said.<\/p>\n

Then, using patient data, the researchers determine if the hypothesis is true or not. \u201cClinical investigators give us some hint about potential gaps and challenges,\u201d Luo said. \u201cAnd then we basically drill into those areas and look into the pattern to see if that’s true or not.\u201d<\/p>\n

Other times, Luo\u2019s group works backward; they get access to a large dataset but have to use machine learning to detect patterns within it. Such was the case when they studied disparities in telemedicine during the COVID-19 pandemic. Using sophisticated machine learning algorithms, they analyzed several factors simultaneously, from clinical outcomes and treatment patterns to socioeconomic indicators, to identify which patient populations may be underserved. For example, when studying telemedicine adoption during COVID-19, their AI systems processed millions of patient interactions to detect usage patterns across different demographic groups, revealing previously unknown disparities in virtual care access.<\/p>\n

\u201cWe pooled all the patients who used telemedicine and then generated a control group who did not use telemedicine and looked into the pattern of those patients to see, for example, whether a specific group actually adopted telemedicine better than the other groups,\u201d Luo said.<\/p>\n

Some of the data confirmed their hypothesis \u2013 that more educated patients were more likely to use telemedicine. Other patterns were less obvious and more surprising, Luo says. For example, female patients were more likely to meet with their doctor virtually than male patients, as the team revealed in a 2021 paper in the journal Applied Clinical Informatics<\/a>.<\/p>\n

In another project, Luo is working on an initiative with the Medical College of Wisconsin called OTO Clinomics. It aims to help researchers better understand individual risk factors for otolaryngologic diseases and treatment to provide better care. (Otolaryngology includes conditions like head and neck cancer, tonsillitis, reflux and hearing loss.)<\/p>\n

In 2021, Luo contributed to a report in OTO Open<\/a>\u00a0 about the socioeconomic factors that correlate with a chronic rhinosinusitis diagnosis at a specialized clinic (as opposed to the emergency room). His team found that patients at the clinic tended to be proportionally older, educated, white and female. Conversely, clinics saw fewer patients who were Black, male, and had lower income and education levels.<\/p>\n

These findings correlated with national trends related to race and socioeconomic status in health care access. In this case, Luo\u2019s team won\u2019t be the one to address the gaps with potential solutions, but drawing attention to these disparities can set the stage for other researchers to explore ways to help different patient populations.<\/p>\n

Improving the patient experience<\/h2>\n

In other projects, Luo is working on more direct improvements for the patient experience. Under a grant from the National Institutes of Health, he helped design an AI-enabled voice system to help patients report data.<\/p>\n

When patients start a new medication regimen or join a clinical trial, they\u2019re not always diligent about reporting their health data. For example, a person undergoing diabetes treatments might need to log their glucose levels every day in an online portal. Clinicians rely on this data to determine if a treatment is working, yet patients aren\u2019t necessarily consistent when it comes to recording their biomarkers.<\/p>\n

So Luo\u2019s team is working with a small cohort of participants who agreed to bring home an Amazon Alexa device that can talk to them when it\u2019s time for a check-in. \u201cIt provides a very natural interface for the patient to do this task,\u201d Luo says.<\/p>\n

Instead of requiring patients to sit down at a computer and type in information each day, it\u2019s a lot easier to just chat with the device on the go. The AI-enabled software can have a conversation with the patient, prompting them to share incremental health details as needed. Unlike simple reminder systems, this AI can engage in more sophisticated interactions. For example, asking clarifying questions if a patient reports concerning symptoms or offering encouragement when they’re consistently tracking their health data. The goal is to create an easier reporting system that streamlines data collection for patients and clinicians alike.<\/p>\n","protected":false},"excerpt":{"rendered":"

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