Twice Sahar Arshad’s father-in-law went to an emergency room in Pakistan complaining of frequent headaches. Twice doctors sent him home with a diagnosis of allergies.
Turns out he was suffering from a subdural hematoma — bleeding inside the head. Following the second misdiagnosis, he went into a coma and required emergency brain surgery (and made a full recovery.)
Arshad and her husband, Tashfeen Suleman — both computer scientists living in Bellevue, Wash., at the time — afterwards tried to get to the root of the inaccurate diagnoses. The hematoma turned out to be a side effect of a new medication Suleman’s father had been prescribed a couple weeks prior. And he lacked physical symptoms like slurred speech and difficulty walking, which would have prompted doctors to order a CT scan and detect the bleeding earlier.
Too Much Data, Too Little Time
It’s a common problem, Arshad and Suleman found. Physicians often have to rely on limited information, either because there’s insufficient data on a patient or because there’s not enough time to analyze large datasets.
The couple thought AI could help address this challenge. In late 2014, they together founded CloudMedx, a Palo Alto-based startup that develops predictive healthcare models for health providers, insurers and patients.
A member of the NVIDIA Inception virtual accelerator program, CloudMedx is working with the University of California, San Francisco; Barrow Neurological Institute, a member of Dignity Health, a nonprofit healthcare organization; and some of the largest health insurers in the country.
Its AI models, trained using NVIDIA V100 Tensor Core GPUs through Amazon Web Services, can help automate medical coding, predict disease progression and determine the likelihood a patient may have a complication and need to be readmitted to the hospital within 30 days.
“What we’ve built is a natural language model that understands how different diseases, symptoms and medications are related to each other,” said Arshad, chief operating officer at CloudMedx. “If we’d had this tool in Tashfeen’s father’s case, it would have flagged the risk of internal head hemorrhaging and recommended obtaining a CT scan.”
Working with an AI to Risk Assessment
The CloudMedx team has developed a deep neural network that can process medical data to provide risk assessment scores, saving clinicians time and providing personalized insight for patients. It’s trained on a dataset of 54 million patient encounters.
In a study to evaluate its deep learning model, the clinical AI tool took a mock medical exam — and outperformed human doctors by 10 percent, on average. On their own, physicians scored between 68 to 81 percent. When taking the exam along with CloudMedx AI, they achieved a high score of 91 percent.
The startup’s AI models are used in multiple tools, including a coding analyzer that converts doctor’s notes into a series of medical codes that inform the billing process, as well as a clinical analyzer that evaluates a patient’s health records to generate risk assessments.
CloudMedx is collaborating with UCSF’s Division of Gastroenterology to stratify patients awaiting liver transplants based on risk, so that patients can be matched with donors before the tumor progresses too far for a transplant.
The company is also working with one of the largest health insurers in the U.S. to better identify congestive heart failure patients with a high risk of readmission to the hospital. With these insights, health providers can follow up more often with at-risk patients, reducing readmissions and potentially saving billions of dollars in treatment costs.
Predictive Analytics for Every Healthcare Player
Predictive analytics can even improve the operational side of healthcare, giving hospitals a heads-up when they might need additional beds or staff members to meet rising patient demand.
“It’s an expensive manual process to find additional resources and bring on extra nurses at the last minute,” Arshad said. “If hospitals are able to use AI tools for surge prediction, they can better plan resources ahead of time.”
In addition to providing new insights for health providers and payers, these tools save time by processing large amounts of medical data in a fraction of the time it would take humans.
CloudMedx has also developed an AI tool for patients. Available on the Medicare website to its 53 million patient beneficiaries, the system helps users access their own claims data, correlates a person’s medical history with symptoms, and will soon also estimate treatment costs.
NVIDIA Inception Program
As members of the NVIDIA Inception program, the CloudMedx team was able to reach out to NVIDIA developers and the company’s healthcare team for help with some of the challenges they faced when scaling up for cloud deployment.
Inception helps startups during critical stages of product development, prototyping and deployment with tools and expertise to help early-stage companies grow.
Both Suleman and Arshad have spoken at NVIDIA’s annual GPU Technology Conference, with Arshad participating in a Women@GTC healthcare panel last year. The conference has helped the team meet some of their customers, said Arshad, who’s also a finalist for Entrepreneur of the Year at the 2020 Women in IT Awards New York.
Check out the healthcare track for GTC, taking place in San Jose, March 22-26.