At-Home Digital Diagnostics Is The Next Big Thing

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At-Home Digital Diagnostics Is The Next Big Thing

A New Era of Managing Health from Home

Written by Chiweon Kim, M.D., M.P.H.

One key vision of the digital healthcare industry is to transcend the limits of space - making it possible to do at home what was once only doable within hospital walls, using digital technology. Both telemedicine and digital therapeutics emphasize this aim.

The same holds true for diagnostics.

The goal is to allow people to perform tests at home using blood, urine, or saliva. Companies specializing in ‘digital biomarkers’ seek to provide diagnostic insights by collecting data gathered digitally. Many of these companies explore ways to gather diagnostic information using smartphones, for example. This type of diagnostic tool can be called “at-home digital diagnostics.”

Clear definition of purpose is essential when developing new healthcare tools. Healthcare diagnostic tools can be categorized as below:

As with many healthcare products and services, most diagnostic tools are still used within traditional hospital settings.

But some can potentially be used in the home. These include tools used for risk prediction and pre-screening, which occur prior to diagnosis, as well as post-treatment monitoring (evaluating treatment response or disease progression). Diagnostics involved in other stages are in most cases too professional to be done at home since they require advanced equipment like CT and MRI, or the close supervision of a doctor.

So then, how do we evaluate emerging at-home digital diagnostic tools?

Several factors play a key role:

1. Demonstrating Value

The most crucial business model in healthcare revolves around health insurance coverage. To gain insurance approval, it’s essential to demonstrate that using a product at a certain stage leads to improved patient outcomes.

Among the stages discussed earlier, demonstrating the value of “pre-screening” can be challenging. Even if an abnormality is detected, the follow-up diagnostic process may reveal no actual problem to be concerned of (i.e., low positive predictive value).

Monitoring, on the other hand, offers a clearer value proposition. Monitoring results can influence treatment decisions, potentially improving patient outcomes. Thus, monitoring tends to be more advantageous when it comes to proving value compared to pre-screening

In addition to proving the product’s value, the likelihood of physician involvement is another key consideration. Pre-screening happens before a hospital visit, while monitoring typically occurs after a diagnosis and treatment plan have been written up by a doctor. Tools used in tandem with physician involvement tend to have a higher chance of success, as they are more likely to be integrated into a patient's ongoing treatment.

2. Technical Accuracy

The required level of accuracy may vary depending on the tool’s intended purpose. For example, pre-screening tools need to identify specific conditions from a broad range of potential diseases, which can be more difficult. Monitoring tools, on the other hand, focus on tracking changes in an already identified condition, making the task somewhat simpler.

For instance, a digital biomarker tool designed to screen for depression or dementia would need to differentiate between conditions that present with similar symptoms (e.g., depression vs. anxiety, dementia vs. Parkinson's disease). Monitoring, however, only requires tracking changes in a confirmed diagnosis, which typically involves fewer challenges.

3. Passive vs. Active Measurement

Passive measurement is more advantageous for mass distribution than active measurement. For instance, automatic collection of data during spontaneous smartphone usage is much more convenient than requiring users to open an app every time a measurement is needed. Or consider the following, even more specific example: Detecting voice biomarkers automatically during phone calls is more seamless than a user manually dictating phrases into an app. (Though this does raise privacy concerns.)

4. Relevance to the Disease’s Pathophysiology

The connection between the biomarker and the disease’s ‘pathophysiology’ - the physiologic changes that happen inside the body resulting from a certain disease - is another critical factor. This is important for two reasons.

First, the stronger the connection to the disease’s underlying biology, the more likely healthcare providers are to adopt the tool.

Second, this also affects the tool’s accuracy. A stronger link to the disease’s pathophysiology makes it easier to distinguish between different conditions and make a differential diagnosis. For example, using smartphone data to pre-screen for depression by analyzing symptoms like decreased activity or increased insomnia may not be specifically related to the pathophysiology of depression. In this case, it may be hard to distinguish the patient’s condition from other similarly presenting conditions. In contrast, using sound to estimate sleep apnea or the urination sound to assess prostate enlargement is more directly related to the underlying condition, which holds less concern for such challenges.

Developing successful at-home digital diagnostics requires careful consideration of these factors: proving value in healthcare, achieving technical accuracy, and maintaining a strong connection to the disease’s biology. Evaluating these aspects is necessary to increase the chances of success. I hope to see more and more startups emerge to lead the way in making healthcare accessible from the comfort of home, in the near future.