Fast Facts: Digital Medicine - Measurement

A. Coravos, J.C. Goldsack, D.R. Karlin, C. Nebeker, E. Perakslis, N. Zimmerman, M.K. Erb, Andrea Coravos, Jennifer C. Goldsack, Daniel R. Karlin, Camille Nebeker, Eric Perakslis, Noah Zimmerman, M. Kelley Erb

• Publisher: S. Karger
• Year: 2020
• Print ISBN: 9783318067071
• eBook ISBN: 9783318067088
• Topic: Médecine
• Subtopic: Théorie, pratique et référence de la médecine

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier is the cross-disciplinary approach required to create digital tools, a process that requires knowledge from many people across a range of fields. 'Fast Facts: Digital Medicine – Measurement' aims to overcome that barrier, introducing the reader to core concepts and terms and facilitating dialogue. Contrasting 'clinical research' with routine 'clinical care', this short colorful book describes types of digital measurement and how to use and validate digital measures in different settings. And with the burgeoning development of digital medicine tools, the authors provide a timely overview of the security, ethical, regulatory and legal issues to be considered before a product can enter the market.

1	What is digital medicine?

Digital medicine describes a field concerned with the use of technologies as tools for measurement and intervention in the service of human health. Digital medicine products are driven by high-quality hardware and software products that support health research and the practice of medicine broadly, including treatment, recovery, disease prevention and health promotion for individuals and across populations (Figure 1.1).

Role of products

Digital medicine products can be used independently or in concert with pharmaceuticals, biologics, devices or other products to optimize patient care and health outcomes. Patients and healthcare providers are empowered with intelligent and accessible tools to address a wide range of conditions through high-quality, safe and effective measurements and data-driven interventions. Digital products are also used in health research to develop knowledge of the fundamental determinants of health and illness by examining biological, environmental and lifestyle factors. In observational and interventional research, digital technologies are increasingly used in the prevention and treatment of disease and to support health promotion.

As a discipline, digital medicine encapsulates both broad professional expertise and responsibilities concerning the use of these digital tools. Digital medicine focuses on evidence generation to support the use of these technologies. Measurement products include digital biomarkers (e.g. using a vocal biomarker to track changes in tremor for a Parkinson’s patient), electronic clinical outcome assessments (e.g. an electronic patient-reported outcome survey) and tools that measure adherence and safety (e.g. a wearable sensor that tracks falls and smart mirrors for passive monitoring in the home).¹

Figure 1.1 Digital medicine overview. Digital medicine uses software and algorithmically driven products to measure or intervene to improve human health.

Intervention products include digital therapeutics and connected implantables (e.g. an insulin pump). Digital therapeutics deliver evidence-based therapeutic interventions to patients that are driven by high-quality software programs to prevent, manage or treat a medical disorder or disease. They are used independently or in concert with medications, devices or other therapies to optimize patient care and health outcomes.² Digital intervention products are not the primary focus of this book, however.

Combination products both measure and intervene. For example, continuous glucose monitors (CGMs) for people with diabetes share data from a patient automatically with their doctor’s office using a companion app. The level of human involvement may vary in the cycle between measurement and intervention – for example, when a doctor diagnoses an abnormal heart condition from an electrocardiogram (EKG) reading off a smartphone. Over time, this cycle may become more of a closed loop, with less need for human intervention in response to routine changes. The development of the ‘artificial pancreas’ has combined the CGM with an insulin pump and a computer-controlled algorithm that allows the system to automatically adjust the delivery of insulin to reduce high blood glucose levels (hyperglycemia) and minimize the incidence of low blood glucose (hypoglycemia).³

Digital health

Similar to the way in which ‘wellness’ products differ from those used in medicine, ‘digital health’ differs from digital medicine. We use ‘digital health’ to describe products that consumers use to measure physical activity or sleep quality – things that might influence their personal wellbeing. Digital health products may include apps or wearable sensors (e.g. Fitbit, Oura ring). Digital health products are intended to be consumer-facing rather than used in clinical care; they often do not produce the evidence necessary to support medical use.

There are times when it may be appropriate to use consumer-grade tools for measurement in clinical research. For example, using an accelerometer manufactured for the consumer market to measure physical activity among research participants enrolled in a clinical trial is common. However, this would require a reasonable body of evidence to support this use (see chapter 9 on verification and validation).

Digital medicine product manufacturers commit to undergo rigorous randomized controlled clinical studies for their products. Unlike digital health products, digital medicine products demonstrate success in clinical trials.⁴ In this book, we outline digital products that measure and intervene in all areas of the practice of medicine, extending to and including behavioral health, public health and population health management.

Usage. We have decided not to use the term ‘digital health’. While it is one of the buzziest catchphrases in the industry today, it has been so broadly used and misinterpreted that it has no real meaning. Instead, we use digital medicine as the term to describe evidence-based digital products that measure and intervene, including those intended for health promotion and disease prevention. Digital medicine products are evidence-based tools that support health research and the practice of medicine. Digital medicine describes this broad, evidence-based field and does not refer to the narrow use of the term ‘medicine’, which is sometimes interpreted as the drug (‘medicine’) that is administered to the patient.

Algorithms, machine learning and artificial intelligence

The recent explosion of machine learning and artificial intelligence methods, driven in large part by the availability of massive datasets and inexpensive computation, has played an important role in enabling digital medicine products.⁵ Whereas traditional health measures represent a snapshot in time – a lab value, a diagnostic image, a blood pressure reading or a note in a medical record – connected digital devices offer a longitudinal and highly personalized window into human health.

A key component of these systems is the transformation of raw physiological or environmental signals into health indicators that can be used to monitor and predict aspects of health and disease. These data (e.g. from a sensor) are processed, transformed and used to build computational models with output that represents the health indicators of interest. Computational approaches range from simple statistical models like linear regression, to signal processing methods like the Fourier transform, to time series analyses like additive regression models, or machine learning methods like support vector machines or convolutional neural networks.

For example, an algorithm is required to transform the raw data from a three-axis accelerometer into the more widely usable health indicator of step counts. There are a variety of different approaches to this task, yielding a variety of different performance characteristics.⁶ Importantly, the more examples of real-world walking that the algorithm has access to – by people of different shapes and sizes, under different conditions – the greater the opportunity to improve the accuracy of the model.

Digital measurements in medicine today

Some digital measurements are already well established in routine clinical care – ambulatory EKG monitoring, for example, is used to detect arrhythmias in cardiac patients.⁷ Similarly, remotely monitoring patients with implanted heart devices allows doctors to better follow their cardiac patients, detecting abnormal heart rhythms and problems with the device sooner.

Digital measures are also used in clinical research to better monitor patients and more efficiently assess safety and efficacy. For example, in-hospital ambulatory cardiac monitoring has existed for many years, enabling real-time monitoring of EKG signals. Similarly, portable EKG technologies have also existed; these recorded signals for later analysis. The digital medicine solutions for cardiac monitoring include non-obtrusive patch-based cardiac monitors that may be worn for days at a time, while ambulatory and remote from the hospital, and which send real-time signals.

Across therapeutic areas and technologies, digital medicine solutions can solve weaknesses of existing solutions, and can come to market with more patient-friendly packages. Some examples of products used in clinical care are described below.

Recovery, performance and treatment selection. In patients recovering from orthopedic surgery, app-enabled wearable sensors are increasingly being used during rehabilitation. Digital measurements, such as range of motion and step count, allow remote monitoring of a patxient’s progress. More sophisticated measurements can monitor in real-time if a patient is doing their rehab exercises.

Real-time safety monitoring. Digital fall detection systems allow remote monitoring of elderly and frail individuals. Such monitoring often relies on wearable sensors, cameras, motion sensors, microphones ...

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• Acknowledgments
• Glossary
• Introduction
• 1: What is digital medicine?
• 2: Where does digital medicine fit?
• 3: Regulatory considerations
• 4: Ethical principles and our responsibilities
• 5: Ethics in practice
• 6: Security, data rights and governance
• 7: Digital biomarkers and clinical outcomes
• 8: Measurement in clinical trials
• 9: Verification and validation
• 10: The future of digital medicine
• Useful resources
• Index