Southeast Asian start-up leverages generative AI to simplify complex health data

A Malaysia and Singapore-based startup offers a platform that leverages generative AI to simplify complex health data into understandable content.

Based on a press release, SayHeart has developed an algorithm that can seamlessly translate medical jargon, health reports, and complex imagery into “easily digestible visual content.”

It is currently in the minimum viable product development stage – with advanced text-to-visual translation, differential diagnosis and predictive ability for various health conditions – and will launch early next month, in August. Later, the startup plans to test and refine its product using data from local community health reports.

---

Fujifilm revamps Medical Systems units

Fujifilm Corporation is undertaking a reorganization of its Medical Systems group companies in Japan to strengthen its business structure.

Fujifilm Healthcare Corporation’s business strategy and R&D will be consolidated into the umbrella company Fujifilm Corporation, which intends to rapidly introduce new medical products and services.

Meanwhile, Fujifilm Healthcare’s domestic sales functions will be integrated into Fujifilm Medical, Fujifilm’s local medical equipment sales company in Japan. The maintenance service functions of subsidiary Fujifilm Healthcare Systems Co. will also be transferred to Fujifilm Medical Service Solution Co., a subsidiary of Fujifilm Medical.

The reorganization is expected to be completed by the second half of 2024.

---

South Korea accelerates commercialization of AI gastric cancer diagnostic solution

An AI-based diagnostic assistance solution for gastric cancer diagnosis has been named as the latest innovative medical device in South Korea.

South Korea’s Ministry of Food and Drug Safety announced the designation of Waycen’s WAYMED Endo ST CS as an innovative medical device, accelerating its commercialization in the medical field.

The technology uses AI to analyze gastroscopic images and spot areas indicative of stomach cancer. Waycen recently won contracts to supply its product to local hospitals, including Gangwon State Gangneung Medical Center, National Health Insurance Service Ilsan Hospital and Veterans Health Service Medical Center, as well as St. Paul’s Hospital in Vietnam.

---

HD expands access to surgery in Thailand with Johnson & Johnson MedTech

HD, an online surgery marketplace in Southeast Asia, has entered into a collaboration agreement with Johnson & Johnson MedTech (Thailand) to increase access to innovative, high-tech surgical procedures.

Their collaboration also aims to increase the surgical skills of healthcare providers and surgeons operating on HDmall and HDcare, HD’s latest platform that aims to increase the capacity utilization of hospital and clinic operating rooms.

Tech giant Google announced in March that it additional fall detection capabilities to his Pixel Watchwhich uses sensors to determine if a user has taken a hard fall.

If the watch does not detect a user’s movement for approximately 30 seconds, it vibrates, sounds an alarm, and displays prompts allowing the user to select whether they are fine or need assistance. The watch notifies emergency services if no response is chosen after one minute.

In part one of our two-part series, Edward Shi, Product Manager in the Android and Pixel Personal Safety Team at Google, and Paras Unadkat, Product Manager and Fitbit Product Manager for Wearable Health/Fitness Sensing and Machine Learning at Google, sat down with MobiHealthNews to discuss the steps they and their teams took to create Pixel’s drop detection technology.

MobiHealthNews: Can you tell me about the fall detection development process?

Paras Unadkat: It was certainly a long trip. We started this a few years ago, and the first thing was, how can we even think of collecting a data set and understanding just the revenue from a motion sensor perspective. What does a fall look like?

So in order to do that, we consulted quite a number of experts who worked in a few different university labs in different places. We kind of consulted on the mechanics of a fall. What is biomechanics? What does the human body look like? What are the reactions like when someone falls?

We collected a lot of data in controlled environments, just like self-induced falls, having people strapped into harnesses and just, like, loss of balance events and seeing what that looked like. So that kind of kicked us off.

And we were able to start that process, building that initial dataset to really understand what falls look like and really break down how we actually think about detection and what kind of analysis of fall data.

We also started a major multi-year data collection effort, and it was collecting sensor data from people doing other activities without falling. The key is to distinguish between what is a fall and what is not.

And then we also kind of, in the process of developing this, we had to figure out how we can actually validate that this thing works? So one thing we did was we went to LA, and we worked with a stunt team and we just had a group of people take our finished product, test it, and use it basically to validate that in all these different activities that people were actually participating in the falls.

And they were trained professionals, so they didn’t hurt themselves to do it. We were actually able to detect all of these different types of things. It was really cool to see.

MNH: So you worked with stuntmen to see how the sensors worked?

Unadkat: Yes, we did. So we had a lot of different types of falls that we had people do and simulate. And, in addition to the rest of the data that we collected, it gave us this kind of validation that we could see this thing working in real-life situations.

MNH: How can he tell the difference between someone playing with their child on the floor and hitting their hand against the floor, or something similar, and taking a substantial fall?

Unadkat: So there are several ways to do it. We use sensor fusion between a few different types of sensors on the device, including actually the barometer, which can actually indicate the change in altitude. So when you fall, you go from a certain level to another level, then to the ground.

We can also detect when a person has been stationary and lying down for a certain period of time. So these kind of foods in our exit from, like, okay, this person was moving, and they suddenly had a hard impact, and they weren’t moving anymore. They probably took a hard fall and probably needed help.

We also collected large datasets of people doing these kinds of things that we were talking about, like free living activities throughout the day, no drops, let’s add that into our machine learning model from these huge pipelines that we created to get all of this data and analyze all of it. And that, together with the other dataset on actual high-impact falls, we are able to use this to distinguish between these types of events.

MNH: Does the Pixel constantly collect data so that Google can see how it performs in the real world in order to improve it?

Unadkat: We have an opt-in option for future users where you know, if they sign up, when they get a fall alert, that we’re getting data on their devices. We will be able to take this data, incorporate it into our model and improve the model over time. But this is something that, as a user, you have to manually enter and press “I want you to do this”.

MNH: But if people do it, then it’s going to be continuously improved.

Unadkat: Yes exactly. It is the ideal. But we are continuously trying to improve all these models. And even continuing to collect data internally, continuing to iterate and validate it, increasing the number of use cases we are able to detect, increasing our overall coverage, and decreasing the type of false positive rate.

MNH: And Edward, what was your role in creating the fall detection capabilities?

Edward Shi: Working with Paras on all the hard work that he and his team have already done, basically, the Android Pixel security team that we have is really focused on protecting the physical well-being of users. And so there was a great synergy there. And one of the features we launched before was car crash detection.

And so, in many ways, they are very similar. When an emergency event is detected, in particular, a user may be unable to get help for themselves, depending on whether they are unconscious or not. How then can we escalate that? And then making sure, of course, that false positives are minimized. In addition to all the work the Paras team has already done to ensure that we minimize false positives, how from experience can we minimize this rate of false positives?

So, for example, we check with the user. We have a countdown. We have the haptics, and then we also have an audible alarm, the whole UX, the user experience that we designed there. And then, of course, when we actually call the emergency services, particularly if the user is unconscious, how do we relay the necessary information so that an emergency call taker can figure out what’s going on and then dispatch the appropriate help to that user? And so that’s the work that our team did.

And then we’ve also worked with emergency dispatch call receiving centers to kind of test our flow to validate, hey, are we providing the necessary information so they can sort? Do they understand the information? And would it be useful to them in an actual fall event, and we made the call for the user?

MNH: What kind of information would you be able to get from the watch to pass on to emergency services?

Shi: Where we come into play, it is essentially that the whole of the algorithm has already done its good job and said: “Okay, we detected a brutal fall. Then, in our user experience, we do not make the call as long as we did not give the user a chance to cancel it and to say:” Hey, that is fine. “So, in this case, now we assume that the user was.

So when we make the call, we’re actually providing a context to say, hey, the Pixel Watch has detected a potential hard drop. The user didn’t respond, so we can also share that context, and that’s the location of the particular user. So we keep it pretty succinct because we know that succinct and concise information is best for them. But if they have the context that the fall happened, and that the user may have been unconscious, and the location, hopefully they can send help to the user quickly.

MNH: How long did it take to develop?

Unadkat: I’ve been working on it for four years. Yeah, it’s been a while. It started a short time ago. And, you know, we had initiatives within Google to kind of understand the space, collect data and things like that long before that, but with this initiative, it kind of ended up being a little bit smaller and started growing.

In part two of our series, we’ll explore the challenges the teams faced during the development process and what future iterations of the Pixel Watch might look like.

The Federal Trade Commission joined the U.S. Office of Health and Human Services for Civil Rights this week in reminding healthcare organizations of their responsibilities regarding third-party disclosures of protected health information under HIPAA, the FTC, and the FTC’s Health Breach Notification Rule.

WHY IS IT IMPORTANT

While OCR has addressed the privacy and security risks of healthcare organizations knowingly or unknowingly using third-party tracking tools capable of analyzing, collecting, and sharing sensitive medical data with advertising partners under HIPAA, the FTC also uses its authority to protect consumer health information from “potential misuse and exploitation.”

“These tracking technologies collect identifiable information about users, usually without their knowledge and in ways that are difficult for users to avoid, when users interact with a website or mobile application,” the agencies said in their announcement on the joint letterposted on the HHS website on Thursday.

They then describe how tools embedded on hospital and telemedicine websites can not only return PHI information directly, but third parties like Google and Meta/Facebook can continue to track and collect patient information even after they leave.

Several lawsuits allege that online tracking companies share PHI with their advertising partners, who target the patient with advertisements and other content. Class action lawsuits can also seek to ensure that any profits that hospitals may have made from the sale of the data are paid to victimized patients, damages that some Louisiana hospitals may experience.

The letter reiterates that HIPAA rules apply when information a regulated entity collects through tracking technologies or discloses to third parties (e.g., tracking technology providers) includes PHI.

In December 2022, the OCR published a newsletter on the use of online tracking technologies by HIPAA-regulated entities and provides a general overview of how HIPAA rules apply.

The FTC adds a warning about consumer protection laws.

“Even if you are not covered by HIPAA, you still have an obligation to protect yourself from inadmissible disclosures of personal health information under FTC law and the FTC’s Health Breach Notification Rule.”

“This is true even if you have used a third party to develop your website or mobile application and even if you do not use the information obtained through the use of tracking technology for marketing purposes.”

THE GREAT TREND

When OCR has published guidelines on the use of online tracking toolsit reminded regulated entities of their obligations to comply with HIPAA’s privacy, security, and breach notification rules and explained the steps healthcare and other organizations must take to protect PHI on user-authenticated webpages and forms and other applicable webpages and forms.

“In these circumstances, regulated entities should ensure that disclosures made to these vendors are permitted by the privacy rule and enter into a trade association agreement with these tracking technology vendors to ensure that PHI is protected in accordance with HIPAA rules,” OCR said in the bulletin.

The OCR said it continues to be concerned about the disclosure of health information to third parties.

“While online tracking technologies can be used for beneficial purposes, patients and others should not have to sacrifice the privacy of their health information when using a hospital’s website,” OCR Director Melanie Fontes Rainer said in a statement regarding the joint letter with the FTC.

REGISTRATION

“When consumers visit a hospital’s website or search for telehealth services, they shouldn’t have to worry that their most private and sensitive health information could be leaked to advertisers and other anonymous and hidden third parties,” Samuel Levine, director of the FTC’s Office of Consumer Protection, said in a statement.

“The FTC again warns that companies should exercise extreme caution when using online tracking technologies and that we will continue to do everything in our power to protect consumer health information from potential misuse and exploitation.”

Andrea Fox is the editor of Healthcare IT News.
E-mail: [email protected]
Healthcare IT News is a HIMSS Media publication.

Family First, which provides employers and health plans with a technology platform to support the mental health and wellbeing of caregivers, secured $11 million in Series A funding.

RPM Ventures and Eos Venture Partners led the round with participation from Stephen Fromm and Wormhole Capital.

“Family First has a holistic and expert approach to solving caregiving issues,” Jonathan Kalman, founding partner of Eos Venture Partners, said in a statement. “And Family First’s leadership has the proven expertise and track record of scaling this kind of expert solution.”

---

Mental health start-up Uprising announced that it raised $10.7 million in Series A funding, bringing its total raise to $22 million.

New investor Ballast Point Ventures led the round with participation from Front Porch Ventures, Kapor Capital and existing investor B Capital.

The company will use the funds to expand its reach.

“We have long been interested in investing in the behavioral health segment of the healthcare industry, especially as we have seen access to mental health services become even more difficult during the pandemic,” Matt Rice, partner at Ballast Point Ventures, said in a statement. “We are inspired by UpLift’s team care approach, which combines therapy and psychiatry, and we look forward to supporting Kyle Talcott and his impressive team in their mission to improve access to affordable care that patients can trust.”

---

CathVision, a medical technology company specializing in electrophysiology solutions for clinical decision-making, has secured $9 million in funding, bringing its total raise to $30 million.

The Denmark-based company will use the funds to drive adoption of its ECGenius system, which consists of EP recording technology, and the development of Cardialytics, its AI-based analytics offering.

The money will also support its development of AI-powered analytics modules to provide providers with data during cardiac ablation procedures and help expand its sales team and presence in the United States.

“Despite challenging times in financial markets, CathVision remains a confident investment for stakeholders as we reach critical milestones that validate the need for our technology, revenue generation through successful commercialization, and our commitment to continued R&D,” CathVision CEO Mads Matthiesen said in a statement. “This round of investment will support accelerating commercial adoption through 2024 as we position CathVision to have a meaningful impact on how we in the United States help physicians more effectively treat cardiac arrhythmias.