Currently, there is no widely adopted definition of Digital Health. At Phastar, we take a broad view and refer to Digital Health as the “use of technology in healthcare”, as it encompasses the use of computing platforms, connectivity, software, artificial intelligence, machine learning, and sensors for health care and related uses.
Just 20 years ago, clinical trials were primarily carried out on paper, with queries posted to the sites for resolution. This created significant delays between initial data entry and query resolution. Until 2015, Direct Data Capture (DDC) systems and Electronic Patient Reported Outcomes (ePROs) were rarely utilised in clinical trials.
Digital Health today is expanding at a rapid rate due to recent technological advances and increased adoption across the industry from health care systems, patients, regulatory authorities, and pharmaceutical companies around the globe. The recent rise in Digital Health is most notably due to the Covid-19 pandemic which led to many health services across the world converting to remote visits and assessments (where feasible) to reduce the risk of transmission of infection.
COVID-19, combined with the rise in smartphone ownership increasing access to the internet and acceptance of technology by patients and medical professionals, has led to a rise in the use of digital technology in the healthcare industry. The National Institute for Health (NIH) and the National Institute for Health and Care Excellence (NICE), have recently approved an application that utilises artificial intelligence (AI) to deliver cognitive behavioural therapy (CBT) for insomnia called Sleepio. This, and similar applications that utilise Machine Learning (ML) and AI to provide treatment, diagnostics, and monitoring are all increasing in usage.
This article will cover the current opportunities and challenges in Digital Health in medical research today.
There are numerous wearable devices and sensors that can be provisioned to patients on a clinical trial that have received regulatory approval and can monitor the safety and effectiveness of medications outside the healthcare setting. This often allows real time or frequent periodic monitoring of the patient, and provides a broader overview of the investigational medication’s effects on the patient. Wearables can also aid in detecting and monitoring non-compliance and can reduce the burden of participating by decreasing the number of hospital visits required.
Some examples of the benefits of these devices and sensors offer are:
- Patient Adherence: Electronic pill bottle and blister packs are available that can monitor when a patient opens the pack or bottle. There are also digital pills and video which monitor in real time and any non-compliance is flagged to the sites for investigation.
- Activity and movement: These can include step counters through apps on mobile phones, watches that measure activity, and orthopaedic devices that can measure range of motion in joints.
- Vital Signs: There are devices that can measure temperature, heart rate, blood oxygen levels, respiratory rate, and take electrocardiograms (ECGs).
A challenge with collecting wearable and sensor data is data privacy and data security which may be subject to different regulations across regions. Devices used to collect data need to be carefully selected to ensure their accuracy and ease of use to ensure patient safety and data quality. Comprehensive training for sites and patients must be available to ensure devices are used correctly.
In recent years, clinical trials have become more complex with innovative study designs and the number of data sources increasing from the use of wearables/sensors, ePRO, central and local labs, and radiology reports/images to name a few alongside the traditional Electronic Data capture. It is vital that all the data collected in the clinical trial can be amalgamated into one system and reviewed to ensure the safety and efficacy of the trial. The amalgamation of data real time analytics can be generated though dashboards and dynamic visualisations to locate and resolve data inconsistencies and anomalies and detect any device malfunctions in real time and prevent any data quality issues that could affect the integrity of the trial.
Electronic Health Records (EHR) to Electronic Data Capture (EDC) systems via integrations and APIs without the need from manual transcription from the site are beginning to be used for primarily standardised data, reducing the need for sites to transcribe data to EDC systems. EHR to EDC integration is in its infancy and further development is required to ensure that non-standard data is transferred correctly and that only data relevant to the clinical research is transferred to the EDC system.
DDC usage has been increasing and it allows staff to enter data directly onto an EDC system either at sites or at home healthcare visits. This saves site or home health care staff time as data does not need to be transcribed from the notes into the system later, and it allows sponsors almost instant access to the data which reduces data review timelines and allows faster safety review. The challenge is to ensure that site staff are trained to enter the data correctly and that a copy of the data entered on to the data capture system is available to be added to the patients’ clinical notes.
A major challenge for increasing technology usage in clinical trials has been digital accessibility as there are still populations that do not have access to either the internet or training opportunities to utilise these technologies. Organisations must ensure that certain populations are not excluded due to limited access to technology.
Three key considerations should be taken when planning a clinical trial with Digital Health devices:
- Intended trial population
- Use of provisioned devices or bring your own device (BYOD)
- Complexity and number of device and/or sensors required
There are numerous benefits both for the patient and researchers in using Digital Health in clinical research, but there are a few challenges the industry needs to overcome, including the review of big data, reducing digital exclusion, data security and privacy, and standardisation of health data for EHR to EDC transfers.
Operationalising Digital Health in clinical trials requires multidisciplinary teams including statisticians, data managers, data scientists, clinicians, regulatory experts, and project managers to ensure safety of the participants and data integrity is maintained. At Phastar, our experts are available to advise on and lead the advances in the digital health revolution.
