A Blog by Jonathan Low

 

Feb 2, 2021

Is AI That Corrects Medical Records A Good Idea?

Even if information has to be approved by a physician before being added to a patient's medical record, it is prudent to assume that, especially under conditions such as the current pandemic, a tired or stressed physician could permit evaluations that arent appropriately vetted. 

Without oversight from third parties with legal authority to mandate reviews and changes, the potential for mistakes or abuse remains high. JL

Kyle Wiggers reports in Venture Beat:

Health care organizations’ analytics might lead to a 25% reduction in annual costs in coming years. Better diagnosis and disease predictions, enabled by AI, can lead to cost reduction by decreasing hospital readmission rates. A product analyzes doctors’ notes and lab results to diagnose patients and writes diagnoses, including billing data, back into the medical record. Analysis reviews patient data to determine where diagnoses and documentation might be missing. Algorithms monitor patients, providing physicians with “data-driven” notes. All information must be approved by the physician before it can be added into the medical record.HealthTensor, a Los Angeles, California-based startup creating software to augment medical decision-making, today announced it has raised $5 million. The company says the funds will be used to scale up operations and acquire new customers. 
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.” 
HealthTensor’s approach isn’t unlike that of Ferrum, a startup developing an AI patient safety platform to prevent medical errors. There’s also Health Data Analytics Institute, whose its AI-powered platform analyzes over a billion patient encounters to improve health care outcomes, and Lumiata, a company providing AI-powered predictive analytics for managing health care costs. 
Since HealthTensor made its platform publicly available in 2020, the company says it’s gone live in three hospitals, with plans for 70 additional locations. To date, HealthTensor claims it has helped write more than 2,000 notes and identified overlooked medical conditions in 95% of cases. 
“We think of HealthTensor as an AI-powered medical resident that is focused specifically on the tedious, data-driven aspects of medicine, which is what computers do best,” Ben-Joseph said. “Many doctors are forced to spend a majority of their day focused on data aggregation from medical records, which leads to missed diagnoses, patient dissatisfaction, and physician burnout. HealthTensor frees up the physician to focus on the conceptual and emotional aspects of medicine, which is what humans do best.” 
Calibrate Ventures, TenOneTen Ventures, and Susa Ventures led the seed funding round. The company said the round saw participation from “top-rated” hospitals and physicians, including a medical officer at Amazon Health.

2 comments:

Ram said...

The current pitfalls of EHRs hamper efforts to use artificial intelligence to glean important insights, but AI might itself provide a possible solution. One of the main drawbacks of the existing EHR systems, doctors say, is the time it takes to document a visit—everything from the patient’s complaint to the physician’s analysis and recommendation. StraitsResearch

Ram said...

Artificial intelligence might also help clinicians make better, more sophisticated decisions. “We think of the decision support in a computer system as an alert,” says Jacob Reider, a physician and CEO at Alliance for Better Health, a New York–based health care system that works to improve the health of communities. That alert might be a box that pops up to warn of a drug allergy. But a more sophisticated system might list the likelihood of a side effect with drug option A versus drug option B and provide a cost comparison. From a technological standpoint, developing such a feature is “no different from Amazon putting an advertisement or making you aware of a purchasing opportunity,” Martechlive says.

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