Precision Innovative Network (PIN) and Splice Machine to Present Predictive Healthcare Application During the American Academy of Neurology (AAN) Annual Meeting

SAN FRANCISCO, April 30, 2019 /PRNewswire/ — Splice Machine, provider of the first and only operational artificial intelligence (AI) data platform, today announced the unveiling of a new clinical application prototype that uses machine learning and multi-dimensional data to drive predictive healthcare, resulting in reduced costs, improved communication between doctors and patients, and ultimately, improved patient care and outcomes. The presentation coincides with the American Academy of Neurology (AAN) annual meeting taking place May 4-10, 2019 in Philadelphia, PA.

Splice Machine co-founder and CEO Monte Zweben will be presenting along with three renowned neurologists from Precision Innovative Network (PIN), Dr. Allen Gee, MD, PhD, FAAN;, Dr. Daniel Kantor, MD, FAAN;, and Dr. Mark Gudesblatt, MD. PIN is a physician-centric and Group Purchasing Organization designed to address the unmet needs of patients by using large amounts of networked data to improve their care.

Using Machine Learning to Drive Predictive Healthcare
The Ritz-Carlton, Philadelphia
10 Ave Of The Arts, Philadelphia, PA 19102
May 6-8, 2019

At the event, PIN and Splice Machine will reveal an early look at the PIN Population Data Platform. Working together, the two companies have developed an early predictive model using machine learning on a population of 300 multiple sclerosis (MS) patients, using multi-dimensional data to train the model. The advisory application takes an early look at how neurology clinics can use machine learning in their everyday workflow to predict the best treatment therapies and trajectory of patient conditions.

“Being able to capture the granular, robust data about a patient and contextualize it is a significant factor in the success of precision medicine,” said Gee, MD, a neurologist at Frontier NeuroHealth. “If we understand the functionality of a patient’s nervous system by looking at data around gait, cognition, and dexterity, we can support the highest quality of medical decision-making and deliver the right therapies in the continuum of care.”

The application uses Splice Machine’s operational AI data platform to store the network’s data, curate it, analyze it, and make the information assets available to other partnering organizations. Pharmaceutical companies will be able to subscribe to PIN to improve the clinical trial process, and payers will use the data to improve patient outcomes and delay the burden of economic disability.

“Traditional approaches to patient evaluation and treatment are no longer sufficient in a time-cost ratio to optimize the assessment of care needs and treatment responses,” said Mark Gudesblatt, MD, a board-certified neurologist and the medical director of the Comprehensive MS Care Center at South Shore Neurologic Associates in Patchogue, NY. “Leveraging machine learning and AI, the Population Data Platform is offering clinicians an incredible opportunity to enhance care outcomes and reduce the stress of the decision and disease management process.”

“Improving patient care and improving the communication between patients and doctors is paramount to better health outcomes,” said Kantor, President Emeritus of the Florida Society of Neurology and Founder of the Medical Partnership 4 MS (MP4MS) in Coconut Creek, FL and the Neurology Residency Program Director at Florida Atlantic University’s Schmidt College of Medicine. “By leveraging the data we are already collecting in our daily medical practice plus the data we receive from PIN, we can better predict patient outcomes and improve doctor decision-making, thereby reducing unnecessary medical tests and procedures, which will save on healthcare costs for everyone, including the patient, the practice and health insurer.”

“Together with PIN, we are working to pioneer a path to improved patient care and outcomes, by allowing doctors to take control over disease and treatment decisions in a data-driven manner,” said Zweben. “I’m proud to be part of the growing movement to improve patient care by empowering clinicians to leverage real-time data to determine and justify the optimal treatments for each person with confidence.”

Large neurologic clinics, data-driven pharmaceutical researchers, and other parties seeking population data in an effort to drive predictive healthcare and who are interested in attending the event should contact Brian Zweben at bzweben@pin.care.

For more information on Splice Machine, visit www.splicemachine.com. For more information on PIN, visit www.pin.care.  

About Splice Machine
Splice Machine is the operational AI data platform to simplify digital transformation. Unlike other data platforms that require duct taping separate systems together, the Splice Machine data platform is a scale-out SQL RDBMS, data warehouse and machine learning management solution in one. The Splice Machine platform powers intelligent, mission-critical applications that are woven into the operational fabric of companies in the financial services, healthcare, industrial and consumer verticals to improve operational efficiency, eliminate unnecessary costs and deliver superior service. The Splice Machine data platform can be deployed on-premise or as a fully-managed cloud service.

Splice Machine is a trademark of Splice Machine, Inc. All other trademarks are the property of their respective registered owners. Trademark use is for identification only and does not imply sponsorship, affiliation, or endorsement.

About PIN
PIN (Precision Innovative Network) is a physician-centric organization focused on strengthening the patient-physician relationship by making it easier to practice. As a Group Purchasing Organization (GPO), PIN is able to offer its members reduced practice costs and is also able to improve reimbursement for services provided by its members. With a focus on objective digital testing of our patients, PIN is designed to address the unmet needs of patients by using large amounts of networked data to improve their care.

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