Clinical Decision Support Tools Transform Point Of Care Delivery

Can we predict which patients are at high risk of hospitalization? How can we reduce this risk? Debra Patt, MD, MPH, MBA, executive vice president of policy and strategy at Texas Oncology, posed these questions during the ACCC 46th Annual Meeting and Cancer Center Business Summit.

“We need ways to be better, to be more efficient. Can we predict which patients are at high risk of hospitalization, and how can we reduce this risk?” asked Debra Patt, MD, MPH, MBA, executive vice president of policy and strategy at Texas Oncology; medical director of analytics, McKesson Specialty Health; clinical professor, Dell Medical School, University of Texas at Austin; and editor-in-chief, JCO Clinical Cancer Informatics. “How many of you use clinical decision support systems that are integrated within your electronic health record to make therapy choices at the point of care? There’s an opportunity to do better.”

During “Applied Informatics in Oncology” at the ACCC 46th Annual Meeting and Cancer Center Business Summit held March 5 and 6 in Washington, DC, Patt detailed Texas Oncology’s experience using clinical informatics to guide treatment practices and decisions, which she believes can increase both the value and quality of care.

Using clinical informatics and decision support can help with guideline adherence, clinical and patient education, and predictive analytics. Having these tools helps to ensure quality by facilitating evidence-based decision making. This is especially important with the increasing numbers of long-term cancer survivors and the growing complexity of cancer care in regard to more cancer subtypes, treatments, combination therapies, and targeted treatments, especially immunotherapy, Patt pointed out.

Between 1991 and 2016, there was a 27% reduction in the overall cancer death rate, equating to more than 2.6 million lives saved, according to data Patt presented. And from 1971 to 2030, there is estimated to be a more than 7-fold increase—from 3 million to 22.1 million—in total cancer survivors.

“This is a totally different field than it was 10 years ago,” she pointed out. “When you have complexity, it’s useful to have something like decision support to help you manage the complexity.”

She emphasized that in order to be successful, these integrated solutions need to be patient-centric and help patients and their physicians to make better, more-informed treatment decisions. This can be accomplished through the use of iterative solutions and high-quality pathways that are expert- and outcomes-driven, evidence-based, patient-focused, and comprehensive, as well as that promote research and continuous quality improvement.

Patt illustrated how clinical decision support tools bolster care delivery, explaining that the shift from volume-based to value-based care that took place under the Oncology Care Model (OCM) that was meant to improve quality and increase service value in oncology care necessitates their use.

Before the OCM, the care delivery model consisted of a consult, financial counseling if paying out of pocket for treatment, chemotherapy education, treatment start and conclusion, and a survivorship visit, depending on diagnosis, she illustrated. However, with OCM providers required to institute 13-point care plans, the additional information required by the OCM to prove the worth of a service makes applied informatics necessary.

It’s all about being more efficient and effective at the point of care, of using that information to improve care delivery.

“The OCM has been a catalyst for a lot of changes in oncology in a system that is changing dramatically. I think the only way we are going to get better is if we share information with each other, with regards to the strength and limitations of what we do. We’ll get there better, faster, and safer,” she concluded.

Related Videos
View All
© 2023 MJH Life Sciences
All rights reserved.