When it comes to value-based care, 3M invests in clinical research to help leaders know what solutions to invest in and when.
“Our goal is to ensure that advanced therapies are used to improve patient care. We want to provide clinical evidence that supports our solutions,” explains Ron Silverman, senior vice president of clinical affairs and chief medical officer at 3M Health Care.
Data can help determine when a specific therapy or intervention may make the most impact.
Leah Griffin, global health economics and outcomes research leader at 3M Health Care, works with health care providers and institutions to conduct research to find insights clinicians can use.
“Continuous research on the efficacy and cost-effectiveness of products and treatment pathways is a necessity to the medical community. These partnerships are instrumental in ensuring we continue to make advances to provide patients with the best possible care,” says Leah.
One of Leah’s most recent collaborations was a peer-reviewed study with Duke University Medical Center and KēlaHealth. A risk-based prediction model was developed using a national database of 72,435 patients. The model was then applied to 370 vascular surgery patients at Duke University Medical Center.
The study revealed that when high-risk patients received closed incision negative pressure therapy (ciNPT) versus the regular standard of care, their risk of surgical site infection (SSI) dramatically decreased, from 20.9 to 6.8 percent. The study also forecast a 26 percent cost reduction, or $401 dollars per patient, with the appropriate use of ciNPT.
Now, Duke University Medical Center will implement the predictive model and KēlaHealth will make it available to hospitals via an electronic health record (EHR)-integrated platform that provides risk-based assessments tailored to each individual patient.
Studies like the one Leah facilitated can help lead care forward. With the predictive model, clinicians can receive alerts for patients who would be good candidates for ciNPT – right in the operating room. And while the EHR provides a paper trail of the patient journey, analytics and machine learning can guide clinical decisions in real time.
“As a trained surgeon, I know the complexity of decision-making in caring for surgical patients,” said Dr. Erich Huang, Chief Data Officer for Quality at Duke University Health System. “We began this work because we knew that an objective, risk-based stratification tool could help providers deliver the highest levels of care.”
Prospective studies monitor the future, while retrospective studies make sense of the past. Machine learning and data analytics apply lessons learned from past data, so the data can inform how care gets delivered today. These methods also allow for ongoing processing of data, so information is continually refined with data from new patients.
Dr. Bora Chang, CEO of KēlaHealth explains, “Our vision is to apply the lessons learned from millions of previous surgeries for the benefit of every patient undergoing a procedure. Patients and their families, clinicians, and hospitals deserve the assurance that the risks of any surgery will be safely navigated by surgical teams with the best information available to them at every point in the surgical journey. We are thrilled to have a stellar group of surgeons, hospital centers, investors, and advisors working with us to realize the opportunity of precision surgery."
Evidence can help ensure better, smarter, safer healthcare gets delivered when and where it matters most – and that’s the power of data at work.
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