Stories about diabetes, dementia, and more.
Researchers and physicians have been using data-driven techniques to help treatment decisions. This year, data were used in ways to help the opioid crisis, patients with diabetes and patients with undiagnosed dementia. There was also a push by the U.S. Food and Drug Administration to advance the use of real-world data and evidence across drug and biologic development efforts. And real-world data were found to replicate clinical trials, which highlights its usefulness in real-world studies.
With the help of our analytics, we compiled a list of seven of our most impactful stories about big data this year.
Researchers made data-driven guidelines based on patient and physician feedback to limit the amount of opioids they were prescribing. Based on the data collected, the protocol eliminated the circulation of more than 16,000 opioid tablets. The protocol yielded an overall reduction in opioids of 89 percent.
Big data algorithms could be used to consider the severity and duration of diabetic symptoms and conditions. The algorithms could also possibly identify optimal patient characteristics for a new drug therapy. Prediction models were used to make up for missing patient data to show risk of severe hypoglycemia.
The FDA recognized the first public database for genetic variants called ClinGen. ClinGen could open new doors for researchers, clinicians and med-tech startups. Developers can use the data to support clinical claims for their diagnostic tests. Use of the database could help reduce regulatory burdens and aid in the advancements in the implementation of precision medicine.
A model was trained on data collected from more than 100 clinics to help predict whether patients have dementia. The model was able to predict with 95 percent confidence that 2,000 patients were potentially living with undiagnosed dementia. Researchers hope to implement the model in general practices.
Big data and machine learning could radically change treatment decisions. And Leo Anthony Celi, M.D., uses data in ways that most clinicians do not. Celi used natural language processing to analyze doctors’ notes to find out what people were talking about before they went to the hospital.The algorithm was found to be highly accurate.
The increase in older patients and reduction of medical personnel will continue to change how healthcare is consumed and dispensed. Technological advances are a part of the shift to streamline processes in hospitals. Big data could help meet these challenges. The more data, the more insight and different approaches to treatment. Data mining could help physicians find information they didn’t even know existed. Some believe a combination of data analytics and technology can help overcome a shortage of physicians.
1. 38M Patients’ EHR Data Replicated Clinical Trial Results
A health tech company successfully leveraged real-world data on patients with diabetes to generate real-world evidence. The real-world evidence replicated results of randomized clinical trials. The data were gathered from electronic health records (EHRs) and helped detect risks of cardiovascular events for diabetic patients on certain medications. Study results proved that using real-world data could replicate results coming from more complex and costly studies.
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