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Mercy has a long history of engaging in clinical trials research and outcome-based research. These roots have helped to develop our health care services for the communities we serve. On July 1, 2016, Mercy pioneered a new model for community health-based research when Mercy Research was formed, consolidating all research activities across Mercy into a stand-alone, not-for-profit entity.
Working in tandem with like-minded organizations, we are growing the number of patients and communities benefiting from clinical and health services research in support of our mission:
To bring to life the healing ministry of Jesus through our compassionate care and exceptional service.
Mercy centralized all research activities occurring across our service area into a stand-alone, not-for-profit entity known as Mercy Research. This type of infrastructure is unusual, especially for community health systems. Consolidating clinical research support across the entire health system allows us to standardize clinical, business and regulatory processes and use shared tools across a broad geography. This leads to increased compliance and improved risk management. It also increases stewardship by consolidating financials and allowing for better informed and more strategic decision-making.
The organizational structure of Mercy Research enables us to provide a unique research offering: the Supersite® study. This concept offers operational and clinical benefits to Mercy and Mercy Research by optimizing the use of resources and the deployment of operations to improve financial stewardship. It benefits the industry (e.g., device, pharmaceutical and other companies), allowing them to conduct research studies at multiple Mercy sites using Mercy Research’s centralized and dedicated clinical, business and regulatory operation resources.
With a Supersite® study, industry organizations can open a study in more than one Mercy location and/or state, with only one contract and one budget rather than multiple contracts and budgets for each location. This allows organizations to conduct more efficient and cost-effective clinical studies. It also allows for more diverse patient populations to be accessed for clinical study participation, which enhances the clinical value and reach of those clinical studies.
Mercy Research is one of the nation’s largest fully integrated, community health system-based research organization. We conduct research in all our available service lines, and we manage more than 400 clinical trials with data from locations throughout our system. We offer:
Dr. Johnson Thomas was named Mercy Researcher of the Year for 2019. He is honored for his dedication to research and for exemplifying:
He is section chair of the Department of Endocrinology at Mercy Hospital in Springfield, MO. Dr. Thomas completed his endocrinology fellowship and internal medicine residency at Nassau University Medical Center in New York, where he received the Kenneth Simcic Award for Academic Excellence in Endocrinology. He received his medical degree from TD Medical College in Kerala, India.
Thyroid nodules are common. About half of all people develop them by age 60, and more than 90% of nodules are benign. An artificial intelligence (AI) tool researched and developed by Dr. Johnson Thomas, section chair of the Department of Endocrinology at Mercy Hospital Springfield and our 2019 Researcher of the Year, can potentially reduce the number of unnecessary biopsies.
Dr. Thomas presented his study, “AiBx, a Deep Learning Model to Classify Thyroid Ultrasound Images,” at the American Thyroid Association’s 89th Annual Thyroid Conference in fall 2019—and his research is being published in Thyroid Journal.
AiBx is an AI model that allows physicians to compare ultrasound images of undiagnosed and diagnosed thyroid nodules that share similar characteristics. This adds objectivity to the diagnostic process, but doctors still determine whether a biopsy is needed. “From a physician standpoint, I can use these images to see why the AI is telling me that a nodule is cancer or not cancer, and then make my decision,” Dr. Thomas says. “Many medical AI algorithms are black boxes, but our tool highlights the reasons behind the predictions, which increases physicians’ trust.”
AiBx was built using Epic ultrasound images of thyroid nodules from 482 Mercy patients who underwent either biopsy or thyroid surgery from 2012 to 2017. Later, 103 thyroid nodules were tested against the AiBx model to see how accurately it predicts thyroid cancer. The model performed well at predicting both negative (benign) and positive (cancerous) nodules.
American Thyroid Association (ATA) and American College of Radiology (ACR), the AiBx model has similar negative predictive value but greater sensitivity, specificity and positive predictive value. “The ATA and ACR systems both have low positive predictive values. That means when they say a nodule is cancer, many times it’s actually not cancer. With these guidelines, we’re still doing a lot of unnecessary biopsies. AI models like ours with high positive and negative predictive values can help avoid that,” Dr. Thomas says.
In addition to the AiBx study, Dr. Thomas was selected as Researcher of the Year for his significant contributions to other research on treatment-resistant thyroid cancer and indeterminate thyroid nodules.
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