HealthTensor raises $5 million for AI that augments and corrects medical records
On February 2, 2021, HealthTensor, a Los Angeles-based startup creating software to augment medical decision-making, announced it has raised $5 million. The company says the funds will be used to scale up operations and acquire new customers. HealthTensor was part of the second class at the Cedars-Sinai Accelerator powered by Techstars in 2017.
The global market for big data analytics in health care was valued at $16.87 billion in 2017 and is projected to reach $67.82 billion by 2025, according to a recent report from Allied Market Research. It’s believed that health care organizations’ implementation of big data analytics might lead to a more than 25% reduction in annual costs in the coming years. Better diagnosis and disease predictions, enabled by AI and analytics, can lead to cost reduction by decreasing hospital readmission rates, among other factors.
HealthTensor was founded by three longtime friends. Eli Ben-Joseph and Thomas Moulia met at MIT, and they met Nate Wilson via a mutual friend. All three had been on the medical school track at some point, but they realized that technology was playing an increasingly important role in health care and decided to lean into tech.
HealthTensor aims to build a suite of AI-powered health care solutions, beginning with “clinically validated” diagnostic algorithms. Its product analyzes doctors’ notes and lab results to diagnose patients and writes diagnoses, including required billing data, back into the medical record. A retrospective analysis feature reviews historic patient data to determine where diagnoses and documentation might be missing. The platform can also be used in outpatient settings to automate the diagnosis and documentation of chronic conditions. Algorithms continuously monitor patients, providing physicians with “data-driven” notes.
“HealthTensor works with a team of physicians from leading institutions to ensure that bias is not introduced into any algorithms. This is done by a thorough six-step development process, where the data used to build the models is taken from a large and randomized pool and is then always manually vetted by the team of physicians,” CEO Ben-Joseph said in a statement. “Any algorithm that is in production has hit an accuracy level of at least 90%. Furthermore, HealthTensor has developed unique features that ensure the physician does not blindly copy over any suggested information. All information must be approved by the physician before it can be added into the medical record.”
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