Over the past decade, business models of digital health such as remote patient monitoring (RPM), virtual cares, and telemedicine has been advancing rapidly improving the access, quality, and affordability of healthcare in general. A decentralized and personalized service model, which enables gathering patient health data, diagnostics data outside traditional clinical settings, adds a lot of benefits and values.
On the patient side, medical imaging services, testing services that are delivered to their homes have the benefits of:
Better outcome: continuous monitoring of patients, leading to early detection of health issues. This can result in timely interventions and improved patient outcomes.Cost-saving: reduce the need for frequent in-person visits, which can be expensive and time-consuming. It can also help prevent hospital readmissions, which is costly for both patients and healthcare providers.Enhanced patient engagement: patients are more engaged in their healthcare when they can actively monitor their health data and have a better understanding of their conditions. This can lead to better compliance with treatment plans.
On clinician, healthcare provider side, it also opens up new avenues of revenue, new patient base, new ways to make decisions, and diagnosis:
Access to a broader patient base: telehealth and remote monitoring technologies break down geographical barriers, allowing healthcare providers to reach patients in rural or underserved areas.Data-driven decision making: enabling physicians and healthcare providers to make evidence-based decisions for individual clinical care as well as population health by having a lot more diagnostic data, information available.New revenue streams: healthcare organizations can monetize remote monitoring services, creating new revenue streams. This is especially significant as the healthcare industry continues to evolve.
In particular, many of the patients with neurological disorders such as Parkinson’s disease, multiple sclerosis, or epilepsy, have mobility issues, cognitive impairments, or other health concerns that make it difficult to travel to a healthcare facility for testing. Home-based services offer a much better patient-centered approach to care.
One of the largest in-home medical data collection to date at real world setting is for detecting SAR-CoV-2 exposure [1]. Within more or less a year, data of total 282,831 subjects are collected all outside of a healthcare or clinical setting. Large datasets like this, is invaluable for understanding the pandemic and informing strategies to combat it. Imagine how costly and time-consuming to schedule 282,831 patients for hospital visit across the whole country, through multi-site activations, patient visit time coordination, sampling, plus potential consent forms signing and execution, so on and so forth.
Another good example is Quest Diagnostics, which offers home delivery services for certain types of diagnostic testing. They have a program known as “QuestDirect” that allows individuals to order a range of lab tests online and have the necessary testing materials delivered to their home. Quest definitely sees Direct-to-Consumer (DTC) testing as a promising market taking off post-pandemic.
Another example, MRI, which has traditionally been used as a high-end modality mostly a hospital or center-based modality. In 2022, researchers from Brown University (Sean C L Deoni et al.) published a paper [2] describing a mobile platform providing MRI scanning services, denoted as “Scan-a-van”. A low magnetic-field portable MRI scanner, Hyperfine Swoop [3], is used in this mobile platform as shown below. Certain level of mechanical modifications had to be performed to ensure safety and operation effectiveness.
Mechanical modifications to the van to accommodate loading and uploading the portable MRI scanner.Mobile MRI platform, Scan-a-van.
According to Deoni’s research group, operating costs includes initial upfront cost of the van, customization, remote power supply, and MRI scanner purchase (fixed asset of $110,000), recurring costs include insurance (fleet insurance, $1200), maintenance ($600), petrol ($1680).
All members of the research team had relevant Collaborative Institutional Training Initiative (CITI training), and first aid training. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent form (ICF) and assent was also obtained to take, use, and publish photos of their homes, children, and self in print and online open-access publications. Total acquisition time of a scan was approximately 17 minutes, including pre-scan calibration and localizer scans.
What does this study indicate to the traditional MRI market?
The average cost of an MRI scan at an inpatient facility is $2,250, and every year approximately 30 million patients in US undergo MRI procedures. This is easily a billion-dollar market just in the US, and just on the data collection part, not even mentioning the downstream value nowadays combined with AI-driven diagnose, predictive use cases, so on and so forth.
A typical in-hospital MRI station costs a few million dollars (1 M USD per Tesla of magnetic field). For countries such as United States and Germany, the standard follows with a rate of about 35 MRI scanners per one million of its inhabitants. However, according to a 2008 report from the World Health Organization 90% of the world does not have access to MRI. Reduced unit cost, modality sharing model could all potentially unlock a huge demand of market.
Take United States as an example, to the first order, 10,000 MRI sites servicing 10 million user demands as is (1000 user demands per unit) is needed. And don’t forget incorporation of artificial intelligence and machine learning in MRI technology will enable more accurate and efficient diagnosis, as well as the ability to detect and diagnose conditions that were previously difficult to detect. These will all lead to more demand in the future.
Can the throughput of MRI be increased to 10 times more (10,000 uses per unit)? Uber once showed the average number of rides or services each Uber car (full-time) can provide is about 2000. Obviously, in-hospital MRI station is far less accessible than Uber cars. Scaling throughput is pretty much impossible with the current model and unit price. However, what if the 17-minutes-scan delivery and modality sharing model is used? Can more cares be delivered to patients?
In a traditional fee-for-service model, system of standardized codes for medical procedures (such as CPT codes in US which originated from radiology practice) can be effectively a big barrier to lower-cost, more efficient MRI exam protocols. The underlying logic treat these MRI stations as “profit centers” for big medical institution. While these comprehensive MRI exams are well-reimbursed under the fee-for-service model, it naturally lead to reduced economic motivation for developing lower cost, efficient MRI exam protocols. However with healthcare payment systems steadily shifting to bundled payment systems for entire healthcare encounters or population-based prospective payment models, MRI imaging becomes a “cost center” rather than a profit center, and creates new strong incentive to reengineer MRI practice, with an emphasis on high-value, low-cost protocols.
Ever since pandemic the rapid increase in non-laboratory-based tests, scans in both point-of-care (POC), at-home, and over-the-counter (OTC) settings, as well as other hybrid forms of tests, will continue to grow in the coming years. Technologies developed and lessons learned from highly-distributed testing will usher in both new expectations and new technological ways of using diagnostic data for years to come and for conditions beyond infectious disease.
FDA’s Diagnostic Data Program — including the Digital Diagnostics (OTC/POC) focus area — within Center for Devices and Radiological Health (CDRH) is working to support the testing community to develop innovative methods for collection, harmonization, transmission, and analysis of diagnostic data originating from tests, including those performed outside laboratories. This program is also working with stakeholders to inform regulatory review processes that will ensure safe, effective, and accurate diagnostics arising from new, emerging, and convergent technologies that are first to market in the U.S.
Another way CDRH is supporting innovation in diagnostics is through collaboration with other federal agencies, such as with NIH’s Independent Test Assessment Program (ITAP) and Rapid Acceleration of Diagnostics (RADx) Program. ITAP has facilitated the authorization of at-home tests by developing standardized evaluation protocols and data reporting mechanisms along with targeted outreach to developers of rapid antigen tests that were authorized in other markets. RADx has worked with the in-vitro diagnostics (IVD) community to accelerate the volume and variety of testing technologies available in the U.S. For modalities such as MRI, a special FDA 510K re-submission (https://www.fda.gov/media/116418/download) might be needed in order to cope with changes in user or use environment.
In conclusion, barriers to capturing diagnostic data from OTC, POC, at-home tests are not technological — developers and innovators are rapidly rolling out solutions that will potentially enable far better data harmonization, capture, and reporting than traditional lab-based systems. However, these developers need regulatory support and guidance that is timely and specialized to bring their innovations to market.
Decentralization and personalization is the future of healthcare which shifts the focus from provider-centric care to patient-centric care leading to better engagement and satisfaction. New opportunities, use cases will be unlocked as technology develops, such as the recent booming of Generative AI. Data can never be an afterthought in this process. Rather, it is the core fuel that powers the ability of a business to capture value from new technologies.
1. Humphreys, D.P., Gavin, K.M., Olds, K.M. et al. At-home sample collection is an effective strategy for diagnosis and management of symptomatic and asymptomatic SARS-CoV-2 carriers. BMC Infect Dis 22, 443 (2022). https://doi.org/10.1186/s12879-022-07377-42. Development of a mobile low-field MRI scanner, Sci Rep. 2022; 12: 5690. Published online 2022 Apr 5. doi: 10.1038/s41598–022–09760–23. Hyperfine Portable MRI https://hyperfine.io/products
