Some years ago, a visit to a doctor’s office or to a traditional clinical trial setting involved securing a written assessment. That evolved to electronic health records (EHR), which allowed information to be sent to different outlets. In this scenario, the patient still had to physically go to the doctor.
Then came the COVID-19 pandemic. This is where the shift toward technology gained speed. When medical evaluation comes to the patient, it represents a significant change in health care. For device developers, it is important to remember that patient monitoring, remote diagnostics and the ensuing data collected is a means to the end of improving patient care, not the goal itself.
From Monitoring to Management
Building devices that provide medical accuracy, are easy for patients to use, and transmit data directly to a central location enable patients to receive quality care without the stress of travel.
This practice results in a wealth of quality data, which in turn requires the right processes to harness and effectively use that data for better patient care.
As part of this evolution in care, there has been a nuanced shift in how healthcare organizations use that data. Rather than just monitoring, providers want data that can enhance disease management. One example includes using an ECG patch to monitor heart rate and collect data in real time. That real-time data can then be used to look for medically meaningful anomalies or nuggets of information, which inform the clinician.
“Instead of going to a doctor for a medical checkup, some aspects of the examination could be done from home,” says Roy Grossberg, Head of Engineering, Digital Patient Solutions at AstraZeneca. “One potential use case involves lung issues. A peak expiratory flow (PEF) test measures air flowing out of the lungs. If we can combine telemedicine and spirometry so the test can be done on video with a doctor, and the data communicated in real time though a patient’s phone back to the provider, the doctor could provide guidance without the patient being on site. This could help with management of a lung disease.”
Keeping the Clinician in the Development Loop
The first step is using remote devices and technology to collect quality medical-grade data. Next is building a biometric platform capable of integrating data from different sources and preparing it for the machine learning necessary to generate the necessary insights to facilitate action. “Monitoring, collecting, and reviewing data is incredible and important, as a lot of data is needed to create viable algorithms. But let’s remember that data is a means to an end, not the goal itself,” says Grossberg. “We are exploring techniques, such as reinforced learning, to apply what we already know to new, smaller, data sets so we can train new algorithms faster and cheaper. Suppose we have a model that works well on one condition, how can we leverage it with new data and to try to help with another condition?”
It’s a lofty goal, but to understand how technology can really help digital health partners in particular therapeutic areas, you have to look at the big picture. There are multiple stakeholders—the patient, the technology solution provider, and the doctor. We must address the needs of the patient by ensuring the devices and the technology are comfortable, unobtrusive and easy to use. We must also remember the needs of providers.
“The solution provider that builds the device and creates the algorithm, should consider integration and accountability among multiple other challenges. But meeting the needs of the third element in the equation, the doctors, is key,” says Grossberg. “They have solution providers offering products with a range of features and interfaces. Yet no one has time for a lot of different systems and dashboards. They just want to practice medicine, so we must come up with a more efficient way to integrate all the systems. Every clinic is different, and building custom integration is time consuming and expensive.”
Finding a Balance
Creating a viable business model behind digital therapeutics is still an obstacle. The unfortunate reality is providers are reimbursed only when patients come in to be treated. They must be incentivized to accept a solution that doesn’t involve patient visits. One such incentive could be to develop a reimbursement model centered around prevention and early detection.
Combining medical-grade wearables with a seamless, integrated data platform takes us beyond just collecting data to facilitating AI algorithms to process the information. This, in turn, informs proactive patient management and real-time intervention. The entire process must be both useful and easy for both patients and doctors. An efficient patient engagement process combined with remote management technology ultimately garners data that advances life sciences and healthcare product development to create a more robust pharma and MedTech ecosystem.