Presented by
Jordan Feder
Nominated by
Technion Institute of Technology
MedAware harnesses patterns from thousands of physicians treating millions of patients to identify and alert on prescription errors in real-time. Its machine learning technology has proven to be much more effective than existing rules-based solutions, which by definition, only detect a small fraction of potential errors – only those that trigger a rule and generate an alert. Moreover, since current solutions are not patient specific or self-adaptive, they suffer from unacceptably high false alarm rates, and are therefore often disregarded by providers. The literature suggests that this occurs in up to 96% of alerts. MedAware’s system solves these shortcomings, dramatically reducing healthcare costs while improving patient safety, outcomes, and experience.
The company’s flagship Rx Alert solution is the first in a suite of decision support solutions that transform real physician practice data into actionable knowledge for the payer, provider, and consumer markets. In order to address the key shortcomings of current solutions, MedAware analyzes millions of patient records, harnessing the medical practice patterns of thousands of physicians to determine if a specific medication is appropriate for an individual patient at a specific time and place, taking into account both patient and institutional context. Also, MedAware automatically tracks changes in an individual patient’s clinical status over time, and will render an alert asynchronously as well.