“Meaningful clinical analysis is only possible when the right data is collected.”

Sarah Iqbal, Head of Digital Life Sciences, Biotaware

When a clinical trial is being designed, it is important to plan how data will be collected and captured during the trial. Data in a clinical trial is generated and collected by the investigator, study staff and directly by patients. Apart from the clinical protocol, the next important document that is acquired from a clinical trial is its data. Meaningful clinical analysis is only possible when the right data is collected.

New technologies for collecting data

Depending on the clinical study design and protocol, there are various ways to collect clinical data in this digital age. Data such as patient questionnaires, eDiaries, case report forms, physiological data and digital biomarkers can be collected digitally using smart phones, tablets, wearable technology, medical grade devices and sensors. In addition, by using point-of-care testing devices, algorithms and cloud computing, innovative digital health solutions enable clinicians to perform tests in near-patient settings and receive answers in real-time.Collecting data with these innovative technologies is the way to move forward in clinical trials, but is not necessarily the issue. Most, if not all, of these technologies can collect data effectively if used correctly. Collecting data effectively depends on the choice of outcome measured. This is because different data has different requirements and priorities. Hence, usage of the innovative technologies needs to be placed at the right point in the clinical protocol to enable the right data to be collected.Initial data collected from these techs is usually the raw data. The key is to transform raw data that is captured into insights and map and associate patterns and behaviours from multiple data sets collected from various sources. This generates insights that are specific to the study trial or the patient, enabling the opportunity to mine patient data to improve future efficiency and effectiveness.

6 benefits of new data technologies

1. Convenience
Patients can input data remotely using mHealth technology from the comfort of their own home. Clinicians can use smartphones and tablets to keep tabs on the scientific literature, track their experiments remotely and stay in contact with laboratory members. Investigators can have online access to lab results, improve patient satisfaction and provide more timely access to clinical results.2. Data security
A digital data capture system is usually hosted online with data entry completed on either a mobile or web-based interface. Given the nature of the data collected in a digital ecosystem, software vendors make sure the data is protected and backed up. Each user account (i.e. investigator, study staff or patient) has designated permissions, so most actions can only be carried out by certain roles.3. Data consistency & accuracy
Capturing data digitally improves data quality. There are options to add constraints or perimeters on a form that prevents inaccurate or illogical values from being entered. Using a computerised or a digital health system enables legible entries and automatic calculations for cleaner, more accurate data.4. Quicker access to data
Capturing data using digital technology such as smartphones, tablets, wearables, biosensors and clinical grade mobile devices can save a significant amount of time with real-time access to data and less time spent on query management. This also saves time at the end of a study allowing quicker availability of the data for analysis. While it can take substantial time to initially learn how to use a specific system, some are so intuitive that only a few hours of training is sufficient.5. Organised and efficient
The use of a digital health data capture system increases efficiency of clinical trials due to its user-friendly design and navigation. Search options allow you to easily find and filter exactly what you need, and store everything in one location with greater visibility.6. Cost-effectiveness
The cost of a digital health end-to-end solution varies depending on the complexity of the study. Adopting an end-to-end digital health ecosystem can seem like a large investment, but it should save money in the long run.

Challenges that still need to be overcome

Adoption of revolutionary advancements in healthcare has not been without challenges. Some of the major challenges that still need to be overcome are:

Identifying and communicating the value that digital technologies offer

Defining reimbursement models for these digital solutions from a payer, provider, or consumer perspective

Challenging difficult for adopting organizations, especially sponsors, to determine which digital health company to partner with have done their due diligence to assure proper healthcare regulation compliance

Digital health companies need to learn how to become certified in designing, developing, and validating data from unregulated devices in clinical trials since these companies are less familiar with the regulated industry

There are other easier challenges that need to be overcome, most with feasible solutions, including:

Higher technical effort (therefore more expensive than paper)

Not all investigators, study staff or patients are familiar with modern technology

As with every electronic hardware or software, there can be failures and break-downs

Online connectivity or wireless networks need to be available at all times

Data breach in sensitive clinical and health data

ABOUT THE AUTHOR: Sarah Iqbal is a scientist with a background in biopharmaceuticals and business entrepreneurship. She is currently the Head of Digital Life Sciences at Biotaware Ltd, a connected health company with expertise in mobile app design and development, wearables integration and cloud server tech in clinical trials and consumer well-being industry.