Transitioning from a corrective healthcare ecosystem to a more predictive and preventive one
Most of the global healthcare systems are focused on diagnosing and treating already existing diseases, with very few players working on a preventive approach instead. For a significant number of patients landing up at tertiary care hospitals, the disease could have been prevented, or perhaps detected earlier on, and fixed at a significantly lower cost. And the list of chronic diseases is only growing – diabetes, cardiovascular diseases, renal diseases and liver illnesses, neuromuscular issues, and cognitive disorders. One thing is clear — we are waking up too late, and this type of a lackadaisical approach has far-reaching consequences.
Not only are patients suffering in pain, but they also have to deal with loss of productive time, and expensive treatments. Additionally, the public health systems are pressed to spend more, the insurance companies to settle more, and the care providers to deal with more and more cases. Now, a heavy burden on healthcare providers may have a domino effect as the increased workload may lead to a delay in treating other patients in need of critical care. It is thereby clear that the current healthcare approach is leading to acute losses and is in need of an essential overhaul and radical change.
If we are to tackle this problem, we need to get to the root of it. This analysis will also help us design a plan with a proactive-preventive approach. With a deep dive analysis, we will realize that most healthcare issues are a result of our lifestyles; these are also the diseases which contribute heavily to the care-providers’ burden.
This is where technology steps in.
Leveraging technologies with an intricate network of customers and a global reach can be one effective way of achieving a large-scale, more permanent behavioral change.
Take the example of monitoring individual patients. Although monitoring is often considered the first step to evaluate a patient’s symptoms and situation, we soon realize that this is not enough. More importantly, we need to get to the root of the problem — the “why quotient” — to help diagnose the problem. Data and analytics can fast-forward this process immensely. A sizeable amount of health-related data over time can be used by machine learning (ML) and artificial intelligence (AI) to build models that can predict possibilities and answer the question “What is likely to happen in the near future?”
Imagine knowing why a person may develop a certain symptom or illness before it even happens. Imagine understanding the possibility and predicting the chances of an individual actually falling victim to a disease. Well, with IoT, AI and machine learning, this is now a possibility, and can pave the way to preventive healthcare measures. This journey of monitoring diagnostics to prediction and eventually prevention is built on the solid foundation of useful data.
How IoT, telecommunication and AI/ML are playing a critical role in the healthcare landscape
As we ponder on how modern-day technology can actually translate into a better healthcare system and a stronger preventive approach, here are some questions we need to answer:
- How do we start capturing the required data?
- How do we know if this may be deemed useful or not?
- How do we make sense of it all, especially when the amount of data and real-life complications demand multi-factorial analysis, contextual interpretations, and a rapid response?
To tackle these questions, we have to handpick the right enabling technologies and employ them to work in conjunction with domain experts with a profound product knowledge.
The entire journey of data is enabled by three technology components — the Internet of Things (IoT), Telecommunications, and AI/ML. The advances in IoT, including sensors, wireless connectivity such as BLE, and edge computation, combined with modern telecommunication (4G, 5G & Private LTE) and AI/ML offer an end-to-end toolset for a true proactive-preventive healthcare. Here’s how they work.
Let’s start with capturing the data.
Rapidly decreasing prices and increased power of sensors and edge devices are making the task of monitoring individuals and devices easier and more affordable than ever before. These may be individuals, hospitalized patients, or connected sensors measuring critical health metrics like temperature, heart rate, blood oxygen saturation (SpO2), blood pressure, urine output, and blood glucose. Or this could be nutritional data, including the farm-to-fork tracking of nutrient contents in the food and their shelf life. This data could come from various sources such as wearables like activity monitors which may provide the much-needed impetus for individuals to adopt a regular exercise regime.
Combined with energy-efficient edge computation, this information can be processed and securely transmitted to gateways using BLE, WiFi, NFC, etc. At times, hand-held devices like smartphones can also act as data gateways, transmitting the data securely to cloud servers. Modern telecommunication systems allow near-real-time, nearly-location-independent transmission of data, 24×7, reliably, and securely.
Making sense of the data is the next critical part.
After all, any data is only as useful as the actionable insight we can generate out of it. Data visualization for better human analytics, and smartly employed ML/AI to build models on huge datasets aid tremendously in interpreting the large expanse of data. Intuitive UIs with carefully chosen data representations can help a human expert draw critical insights that can go a long way in preventive healthcare. It first provokes the right questions, and then provides the tools to start answering them.
While the insight and experience of the human experts is unparalleled, there is also a great need of assistance from the machines in uncovering hidden patterns in the data. Various ML algorithms can help here, identifying data clusters, quantifying relatedness, and bringing out correlations, allowing for a deeper data analysis at a much faster pace. This further allows construction of predictive models, and care plans can be crafted using those. Experts and caregivers can utilize such models and algorithms simultaneously in multiple geographies, across time-zones, and tweak it to their own patients as per the context.
The final phase of prevention can be successful when such predictive models are used to build wellness and care plans which put individuals on the path to health and wellness.
Preventive Healthcare in Action
GS Lab’s innovative teams have delivered cutting-edge engineering solutions for our customers’ needs. One such effort resulted in developing groundbreaking voice-analytics to detect asthma, clinical depression, and screen populations for early signs of Covid-19. The simplicity of the end application belies the technological complexity of what goes into designing and delivering such a complex solution: digital signal processing, data engineering, building machine learning models, and providing all of this seamlessly to the end user via a secure, scalable cloud architecture and an intuitive UI.
For another customer, we developed a medical implant and wearable miniature device to monitor body vitals like body temperature, pulse rate, respiration rate, oxygen saturation and body activity. The collection of data was valuable for users/patients as it would warn them of any potential health issues, allowing them to seek adequate medical assistance in time.
In yet another project, the team developed smart connected devices to help prevent opioid overdoses. Patients on long-term drug treatments battle multiple issues, and drug overuse is a rapidly worsening problem. The smart connected device helps tackle this by enabling patient-caregiver communication in case of a varied schedule and changes in dosage.
The future of tomorrow is already here, at our fingertips. All we need to do is embrace the technological genius available and we can make the much-needed transition from a corrective healthcare system to a preventive one using predictive analytics. After all, prevention is better than cure!
Author
Mandar Gadre | Director of Engineering – Healthcare & Manufacturing
Mandar Gadre serves as Director of Engineering – Healthcare & Manufacturing for GS Lab. Mandar holds B.Tech from IIT Bombay, and a Ph.D. in engineering from Arizona State University, USA. He brings deep expertise and experience in crafting industrial solutions, leading technology teams, while contributing technically to sensor technology, hardware and control solutions, and data analytics. Mandar has helped numerous organizations implement IIoT and delivered results that have shaped new business models for those organizations.
