Research shows that health information technology has the potential to improve quality of care and patient safety and to help eliminate disparities.
Harnessing health technology to eliminate disparities
HIGH-TECH HEALTH: From left, Zai, Lopez, Megan Renfrew, DSC senior project coordinator, Joseph Betancourt, MD, DSC director, and Alexander Green, MD, MPH, DSC associate director
Research shows that health information technology has the potential to improve quality of care and patient safety and to help eliminate disparities. To address this topic, the Disparities Solutions Center (DSC) hosted a Nov. 29
seminar, “Bridging the Digital Divide in Health Care: The Role of Health Information Technology in Addressing Racial and Ethnic Disparities,” as part of its “Keeping Current” series.
Lenny Lopez, MD, MPH, MDiv, a DSC senior staff member and faculty member at the Mongan Institute for Health Policy, explained that health information technology can be used to help patients self-manage chronic diseases,
provide clinicians with decision support and help manage large-scale patient populations. Lopez highlighted an example of technology being developed at the MGH known as the “e-navigator” – a web-based, self-management tool for Spanish-speaking Latinos with diabetes. Through the website, patients upload their daily glucose readings and receive guidance based on their results.
“Traditionally, patients kept written logs that could be spotty or hard to read,” Lopez said. “This is a way of getting real-time data that is correct and can be used to manage patients between visits.”
Adrian Zai, MD, PhD, MPH, clinical director of Population Informatics at MGH, also offered insight into the field by describing a cancer screening monitoring system known as TopCare. The system identifies patients who are due for cancer screening and flags the information for the appropriate clinician or automatically sends customized letters to alert the patients. If patients are still due for a screening after four months, they are linked with a navigator who provides additional support.
“We’re moving in a direction where computers provide a decision support system that takes into account race, ethnicity and language to optimize interventions and recommendations,” Zai said. “Right now interventions tend to be based on published results of existing disparities; however, I think the best way to address disparities is to have an active system, one that automatically catches patients who might otherwise fall through the cracks.”
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