Category Archives: Health Tech

Trusting Digital Health Technologies: Why Global Standards Are A Must

An editorial by Maria Palombini, director, healthcare and life sciences global practice lead, IEEE Standards Association, highlights that the clinical trials ‘industry’ needs to adopt the currently available and evolving digital tools that are integral to modernize and streamline the process of validating and discovering new treatment options and bring new drugs to market.

Clinical trials are not modernizing fast enough due, in part, to lagging global standards.

The need to digitally transformation the approach to patient recruitment and engagement, study design protocols, and overall trial management is mandatory. On a regular basis, shocking numbers are publicized as to the rising cost of evaluating and bringing a new drug to market, the declining rates of enrollment and patient retention, and the extended length of overall trials. “While these numbers often stir up emotions and debate, they are often undervalued as a symptom of the main problem: Clinical trials are not modernizing fast enough due, in part, to lagging global standards.”

Decentralized clinical trials (DCT) compels clinical operations to become more digitalized. 

Digitalization encompasses much more than electronic data capture and electronic data management systems. Digitalizing the entire process enables the use of the many exciting technologies in the digital health toolkit, including artificial intelligence (AI), machine learning (ML), blockchain/distributed ledger technologies (DLT), digital therapeutics, wearables, the Internet of Things (IoTs), biosensors, and many more. These following digital technologies present the opportunity to optimize the clinical trial management process from study design through review:

  • Wearables and sensors minimize, and potentially eliminate, the need for some patients to visit sites. Their continuous monitoring capability provides troves of data that can be further mined and used to improve clinical study design, protocols, recruitment, and more.
  • Digital therapeutics in the form of a smartphone app can be used to encourage patient adherence (i.e., medication reminders) or make participant feedback collections for eCOAs (electronic clinical outcome assessments) more efficient and effective.
  • Software as a Medical Device (SaMD) includes fixed or adaptive algorithms used for multiple purposes, including prescreening, screening, adjunctive clinical diagnosis, clinical diagnosis, medical monitoring for adverse events, and more.
  • AI/ML Artificial Intelligence and Machine Learning can be used throughout most steps of DCTs or modernized trials with the ability to predict patient retention rates, better analyze and implement participant feedback to reflect the patient’s perspective, better identify and target patient populations for recruitment, filter unstructured data from multiple patient record sources to gather insights, and more.

Can these technologies be trusted?

Some of these technologies are relatively new to a legacy process, which begs the inevitable question: Can they be trusted? How are they validated? Will they work the way they are intended? There are many technical and ethical considerations that clinical trial management teams need to understand and negotiate when applying these technologies to their processes while asking patients to use and trust them. This conundrum of trust in digital technologies can be the very obstacle to deciding whether a component or the entire trial will be decentralized.

Patients Are Ready, But Are Clinicians?

While trust can be the cornerstone of clinicians’ concerns, patients are seemingly ready to embrace more digital components.  A survey by Deloitte, reveal that 92% of clinical trial patients believe that improving experiences (such as through innovative technologies) should be a top priority for the healthcare industry. Patients are embracing digitalization quickly, and a great example is the growing wearables market: 

  • The number of connected wearable devices has increased dramatically since 2019.  The number of devices is forecast to reach more than 1 billion in 2022. (
  • It is estimated that there are over 52,400 healthcare and medical apps currently available on the Apple App Store.
  • According to Statistica, we are now nearly a decade into the Zettabyte Era, with the amount of health care data and information created, captured, copied, and consumed worldwide in 2020 was 64.2 zettabytes and projected to be 120 zettabytes in 2023.

At this rate of use, it can be argued that patients are ready for it. The growing use of digital technologies such as wearables, digital therapeutics, and other biosensors by patients provide benefits that were never available before to patients to participate in their wellness/health process.

However, scientists and clinicians are not engineers or technical gurus.

Therefore, how do they effectively evaluate what and which technologies should be used and trusted? The answer is not so simple. The process will take some time to learn from trial and error. However, it also will require communities of multidisciplinary experts including clinical professionals and patients to get involved with development of global industry technical standards to address these uncertainties in the application and/or technology itself.

Looking at the world of health sensors and wearable devices, there has been more development in global technical standards to mitigate concerns of data transfer and portability and open sharing of mobile health. However, more development is still needed in the space of cybersecurity, patient privacy, data governance, validating algorithms, data integration, and more.

In addition, technical standards very important. “On any given day, internet users trust that a mobile device will connect to Wi-Fi. The global technical standard helps enable seamless connectivity anywhere in the world. Wouldn’t it be just as great to have the same approach when digital technology is integrated into the clinical research process?”


[Reimagining clinical trials in the age of the digital patient (pdf)]

Mayo Clinic launches AI startup program to help early-stage companies get market-ready

Mayo Clinic launched its artificial intelligence startup program this week with four early-stage health tech companies. The program also features additional expert assistance from Google and electronic health records giant Epic.

This is a 20-week program called Mayo Clinic Platform_Accelerate. The aims are to help AI-based startups get market-ready by offering access to Mayo Clinic experts in regulatory, clinical, technology and business domains with a focus on AI model validation and clinical readiness.

According to John Halamka, M.D., president of the Mayo Clinic Platform, “Health tech startups are critical contributors to the cycle of innovation. We are excited to collaborate with these innovators to solve some of the most complex problems in medicine today.”

Mayo Clinic says its AI startup program is different from other typical accelerator programs. It focuses on helping drive a company’s success with valuable in-kind investments—from data sets to validation tools and from mentorship to clinical workflow planning, the company said on the program website.

As part of this in-kind investment, Mayo Clinic Platform will take an equity position in participating startups based on most recent valuations or a convertible note or SAFE, the organization said.

The four participating companies will work with data science experts to delineate AI model requirements, check for fairness and bias in their AI models and gain an understanding of the Food and Drug Administration clearance pathways.

The startups also will gain access to de-identified Mayo Clinic patient data, conduct model validation with guidance from data science experts, plan clinical validation studies such as clinical simulation or clinical trials and explore the potential to partner with Mayo.

Link: Mayo Clinic Platform_Accelerate