Last year, DeepMind AI helped discover a solution to the protein folding problem — a problem scientists have tried to resolve for fifty years and would spend another fifty if not for machine learning. It will accelerate drug development, assist molecular biologists in finding more comprehensive info about proteins and diseases like Alzheimer’s that associate with them. More than half of American leaders in the healthcare system plan to put more money into the development of telemedicine services, with 36% of patients accessing telehealth in 2021 — compare this to 9% in 2020. Clinical labs are starting to pay attention to laboratory automation systems because it’s hard to keep up with the fact that more and more patients want to do diagnostic tests while fewer people want to work in labs. Tech helps healthcare work faster and better, and patients can get help from doctors on mobile apps. Medical app development — one of the core the digital transformation in the care industry won’t slow down anytime soon.
Shift to the On-Demand Healthcare
All patients need to get help for on-demand healthcare is a smartphone and an app. Telehealth vendors are already offering platforms with an option to use the pay-as-you-go model — when a user pays a doctor right after the session — instead of simply integrating traditional insurance models. (Despite the fact there are more of those now — and many fit the new digital reality of healthcare services.) That gives people who have been underserved within healthcare — BIPOC people, trans people, people with visibility — connect to providers they trust and don’t worry about payments.
Outside of telehealth, visibility on the Internet is now a must for healthcare institutions — patients are looking for services online and expect providers to deliver them there. 47% of them look for doctors, 38% — for hospitals or other medical facilities, and 77% are booking consultations online. Doctors have the opportunity to offer personalized, more detailed treatment plans — because, being contacted earlier (and, perhaps, after patients shared their symptoms and health data they have access to) they have more time to think about patient health history.
Application of Big Data-Driven Technologies
It’s hard to imagine the amount of information that the healthcare industry holds: medical data, reports, consultations, treatment plans, etc. The amount of information is increasing every year, so big data-focused analytics companies are coming up with tools to clean up, structure, process, analyze, and build predictions upon these volumes.
Confidence in AI-powered tech increased last year, and 98% of healthcare leaders have some AI strategy in mind. AI-powered insights allow healthcare institutions to deliver more precise, personalized diagnoses. Machine learning algorithms help to notice things humans can skip over — for instance, when analyzing imaging data. Hospitals and other care institutions are using it to monitor data from IoT tech to provide prompt and patient-focused virtual care; they apply it to analyze Big Data within clinical or therapeutic research (like PathAI is doing for cancer treatment); investigate targets for vaccines; and so on.
Virtual Reality-Enhanced Therapy and Training
Another technology that slowly gains popularity is virtual reality. Using VR headsets and other accessories, different specialists help patients get better by submerging them in simulated environments (and sometimes asking them to complete tasks they wouldn’t in real-world settings).
For instance, VR-enhanced pre-surgery environments help patients to get through the pain before the procedure. VR also proved to help treat post-traumatic stress disorders, various kinds of phobias, insomnia, and anxiety. It is also applied to help stroke patients recover muscle control and treat body dysmorphia. It helps some autistic people to improve their social skills.
Outside of point-of-care settings, VR tech helps doctors (surgeons in particular) train in a risk-free error-tolerant environment.
Wearable Medical Devices and Continuous Care
Wearable Medical Devices are becoming a significant part of our everyday lives. Heart rate sensors and motion trackers exist in varieties of brands, designs, prices, and functionality. Among less popular wearables, there are band-aids for measuring sugar in the blood called sweat meters; oximeters for oxygen blood screening, often used by patients with respiratory illnesses like chronic obstructive pulmonary disease or asthma; sleep and mood trackers; etc.
As it’s already been mentioned, wearables are a rich source of patient data — but apart from that, they help diagnose patients, detect anomalies in their conditions, devise data-driven treatment and determine its efficiency. When used within a continuous care monitoring environment, wearables can detect a spike in, for instance, heart rate — and call help early, preventing a patient’s condition from getting worse.
AI in Robots: Growth and Examples
This AI-driven robot Droid Moxi helps hospitals all around the world 24/7 by assisting clinical staff — for instance, it delivers supplies for patients, lab samples, and medications. Hospitals adopt chatbots to address different sorts of patients’ issues: now, they offer psychological support, offer pre-diagnosis info, and even suggest treatments. Intuitive’s da Vinci was the first non-human surgery assistant. It was approved for medical use 18 years ago — the robot features cameras, ‘arms’, and other accompanying tools to carry on in minimally invasive procedures. Da Vinci has assisted in more than five million operations.
Robotic process automation (RPA) (which is not an AI technology — it is less advanced, but it’s used to help people do the work they would take too much time to complete, too) helps reduce the number of human errors in the healthcare administrative software, assign codes for the right diagnoses and reimbursements within the EHRs and invoicing solutions.
There is no doubt the healthcare industry has changed. Now, the question tech providers and healthcare institutions need to resolve is how to use technology to enhance and boost treatment — not adopt it without adjusting, as a token or performative progress. After the pandemic ends — or takes a form we are more fit to fight — it will be the main challenge for vendors and care systems: to build tech-enabled care patients would choose when viable alternatives are present.
