Healthcare is constantly evolving, but here’s one thing that’s consistent: It’s better to detect a heart condition when it first begins than to let it evolve into a cardiac event later on. Prevention and/or early detection is certainly preferable for the patient, who can seek treatment and make lifestyle changes to prolong their life. It’s also preferable to self-insured employers since prevention is the key to limiting high-value claims. Early detection also keeps patients out of emergency rooms and ICUs, controlling healthcare costs for the insurers who cover them. For instance, about one in six healthcare dollars spent in the U.S. is spent on cardiovascular disease alone. Since it’s such a pervasive issue for Americans, there’s a huge amount of research in this area. So how can the collection of patient data contribute to this work and ultimately improve patient outcomes?
Cardiovascular disease (CVD) is common, deadly and expensive to treat, so many researchers and healthcare tech companies are currently focused on creating more sensitive diagnostic tools that detect developing heart problems. A non-invasive electrocardiogram (ECG) tests the heart’s electrical activity and gives doctors a great deal of diagnostic information about the patient’s past and current heart health. When ECG technology is refined using artificial intelligence, the test can also potentially predict the future—specifically, whether a patent is likely going to experience heart failure.
Artificial intelligence, or machine learning, is at the forefront of healthcare innovation across many fields of medicine. One big catch: AI requires a ton of data. These systems need past patient data to learn from before they can read and analyze new patient data.
To support innovation, some healthcare systems are partnering directly with tech companies to license their patient data and improve patient care. For example, in 2016 the Mayo Clinic made a deal with a company that makes mobile ECG products. Mayo shared millions of patient ECGs, which the tech company analyzed to create a wearable ECG device that patients could use to track their heart data in real time. (All the shared data was de-identified and couldn’t be linked to specific patients—though new data-sharing arrangements like these still pose questions about medical ethics and patient consent.)
Mayo isn’t the only health system to share its patient data with outside entities. There’s tremendous demand from healthcare tech companies for this valuable resource. Innovators can’t build products that deliver a better patient experience without patient data. As demand grows and technology evolves, patient data is being collected, analyzed and used in new ways. The hope is that all this data will drive advancements that improve patient care and lower healthcare costs, two top priorities for self-insured employers.
Innovations Involving Patient Data
In the digital age, it’s safe to guess that any new healthcare innovation will ultimately touch patient data in some way. Certain developing technologies and systems are already having a direct effect on the way patient data is collected and used.
AI-driven ECG technology like Mayo supported is just one example of what machine learning looks like within healthcare right now. AI systems are currently being developed to detect and locate strokes, analyze medical images, identify patients at risk of domestic violence, automate patient intake, streamline provider operations and claims processing, and much more.
Cloud-based data collection.
There are certainly still corners of the American healthcare landscape in which patient data is stored in dusty filing cabinets. But now that most patient data is digitized, cloud-based data collection is becoming the norm. Using cloud-based data collection and cloud-based electronic medical records is much cheaper for healthcare systems than it is to store patient data on their own servers. Patient data stored in the cloud can be accessed and analyzed from anywhere in the world by health care providers and authorized researchers working in real time.
The beginning of the coronavirus pandemic brought increased attention to the benefits of telemedicine. (An April 2020 survey of health practitioners found that 51 percent of their patent interactions were being done via telehealth, up from 9 percent before the pandemic began.) Patients meeting with their providers remotely must also share their data virtually, whether it’s through a secure web portal or over a video chat. The ease of sharing data with your provider may be a bonus for the patient, but there are also legitimate security concerns around keeping data secure from cyberattacks.
Wearable health technology includes any electronic device that’s designed to be worn on the body. This is a broad category that includes things like fitness trackers, sleep trackers, augmented reality smart glasses and skin patches that monitor vitals and dispense medication. These devices are capable of capturing a massive amount of patient data throughout the course of a single day.
While social media as a concept no longer seems revolutionary—Facebook is more than 15 years old—healthcare entities are constantly finding new ways to use it. If you’ve ever scrolled through a social media feed, you know that many users openly share highly personal information. People on social media share their medical symptoms, solicit treatment recommendations, write detailed posts about their mental health conditions, and so on. Researchers who do data collection through social media can mine these sites and use all that public data to identify trends, track contagious diseases and even predict behavior.
Again, these are just a few of the innovations that are set to change the healthcare landscape over the next decade or so. I’m especially interested in the way healthcare innovation will affect/improve stop-loss for the self-insured employers that rely on it. The ability to track and analyze your own data is one of the benefits of self-funding… but at a time when patient data has real financial value to so many people, protecting that data is more important than ever.
I’m always watching healthcare advancements so I can advise my self-insured clients about the next best steps for their coverage. Do you have questions or concerns about your stop-loss insurance or how your self-insured company is managing its healthcare data? Contact me at Stop-Loss Insurance, Inc. today.