Unlocking the Potential of eConsent: Demystifying Electronic Signatures

Factors Shaping Study Timelines: Screenshots, Localized Content, and the Complex Role of IRB Submission Timing 

Case Studies

MODUS Outcomes Publications

Perspectives

Reports

THREAD Publications

THREAD is a software-as-a-service (SaaS) technology company providing a globally regulated and validated clinical research platform. The Platform is currently utilized in more than 60 countries (including the United States) that require numerous data integrity, security, and privacy requirements.

THREAD satisfies additional requirements of international regulations for security and data management, such as the General Data Protection Regulation (GDPR) within the United Kingdom and the European Union and the similar laws (“look-alikes") being added globally and by individual States in the United States. As our Platform supports a variety of randomized-controlled clinical trials (Phase Ib – III), post approval studies (Phase IIIb – IV), and registries, our focus on Computer System Validation regulations, security, data integrity, and privacy are central and critical components of our technology. The platform was developed with, and continues to utilize, a mature and stringent Software Development Life Cycle (SDLC) with multiple layers of segmentation to protect data integrity and security.

Regulatory Compliance and Quality Assurance

THREAD hosts audits from clients and third parties routinely and supports our clients during their own audits, whether those are from regulatory agencies and/or their own clients. We audit our own vendors to the rigor required for the service and/or tool provided. Additionally, we maintain an internal audit schedule to verify our internal controls and adherence to global regulatory requirements.

THREAD engages in continuous learning initiatives for quality assurance by providing ongoing training for global regulatory requirements, industry-expected and agencyrequired policies, procedures, and work instructions as well as utilizing a Quality Management System maintained by employees with deep industry experience.

THREAD maintains industry standard certifications as determined by qualified third-party auditors which are each available by request in a controlled virtual reading room and during scheduled audits.

While not all controls have an associated certification, where one is available it is noted below. The following list describes the most common controls reviewed during audits, but is not meant to be all-inclusive.

THREAD follows the industry standard Good Automated Manufacturing Practice (GAMP 5) and is a GAMP 5, Category 4: Configured Product. While we utilize Agile development for our Platform, we also engage in hybrid aspects that include waterfall methodologies where required for Computer System Validation (CSV).

The United States Food and Drug Administration (FDA) enforces Part 11 of Title 21 of the Code of Federal Regulations; Electronic Records; Electronic Signatures (21 CFR Part 11). Part 11 applies to records in electronic form that are created, modified, maintained, archived, retrieved, or transmitted under any records requirements set forth in Agency regulations.

Part 11 is comparable to Annex 11 of The Rules Governing Medicinal Products in the European Union, EudraLex, Volume 4, Good Manufacturing Practice, Annex 11: Computerised Systems. (Certification is not available for Annex 11 compliance.)

THREAD’s Quality Assurance and Risk Management processes are compliant with the appropriate International Conference on Harmonization Good Clinical Practice (ICH GCP) standards and guidance including ICH E6 (R2). 

SOC 2, Type II: (Certification Available) 

A SOC 2, Type II (“Type 2”) Report is a Service Organization Control (SOC) audit and internal controls report capturing how a company safeguards customer data and how well those controls are operating, specifically on how a cloud-based service provider handles sensitive information. It covers both the suitability of a company's controls and its operating effectiveness.

The Health Information Trust Alliance (HITRUST) Common Security Framework (CSF) is a comprehensive and certifiable security framework used by healthcare organizations and their business associates to efficiently approach regulatory compliance and risk management. HITRUST unifies recognized standards and regulatory requirements from NIST, HIPAA/HITECH, ISO 27001, PCI DSS, FTC, COBIT, and can be completed according to SOC 2 criteria, making it the most widely-adopted security framework in the U.S. healthcare industry and requires a certified assessor and licensed CPA firm to complete the certification process.

The Health Information Technology for Economic and Clinical Health Act (the HITECH Act) was enacted under Title XIII of the American Recovery and Reinvestment Act of 2009. While it was created to stimulate the adoption of electronic health records (EHR) and the supporting technology in the United States, it has become a standard for the expansion of privacy and security provisions that were included under HIPAA, holding not only healthcare organizations responsible for disclosing breaches, but holding their business associates and service providers responsible, as well.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law that requires the creation of national standards to protect sensitive patient health information from being disclosed. The privacy regulations governing individually identifiable health information specify any health care provider who transmits health information in electronic form in connection with transactions for which the Secretary of the United States Department of Health and Human Services (HHS) has adopted standards under HIPAA (the “covered entities”).

THREAD has a robust privacy program that meets all global requirements for every country in which we conduct business utilizing Privacy by Design to protect our clients and participants.

The General Data Protection Regulation (GDPR)

THREAD is fully compliant with The General Data Protection Regulation (GDPR) and has appointed an outsourced Data Protection Officer (DPO) with global reach and representatives in the EU and UK to fulfill GDPR Article 27 obligations. With offices in the US, UK, NL and Ireland, they maintain an active roster of local counsel in about 50 countries and act as DPO wherever THREAD conducts business. 

THREAD has also appointed a representative in the EU/UK called an “Article 27” rep. This is separate from a DPO (although often confused). The Article 27 rep serves as an agent for data access requests from individuals and as a point of contact for EU/UK regulators. This is a mandatory r equirement for all companies not established in the EU/UK. The Article 27 reps are in Ireland and the UK. They maintain local counsel and translation services in all 27 member countries.

The California Consumer Privacy Act of 2018 (CCPA) gives consumers more control over the personal information that businesses collect about them and the CCPA regulations provide guidance on how to implement the law. 

The CCPA and GDPR requirements share some common principles and THREAD defaults to the more conservative approach when available for studies that might have participants both in the US and UK/EU.

We have adopted a Data Processing Addendum with Standard Contractual Clauses (SCCs) that is current with the 4 Jun 2021 European Commission Annex on SCCs. We have conducted a Data Protection Impact Assessment (DPIA) and will assist in a DPIA conducted by our customer in alignment with the SCCs.

Even though the United States Privacy Shield was deprecated, THREAD has chosen to maintain our certification to demonstrate our commitment to those principles.

Privacy Policy
Learn more about privacy at THREAD.

Data Privacy Office (DPO)
Learn more about THREAD's Data Privacy Office.

THREAD aggressively protects our client data from threats using a variety of tools and procedures. 

Security Information and Event Management (SIEM) tools 

Our Security Information and Event Management (SIEM) includes the use of Crowdstrike, providing endpoint monitoring alerts if specific actions cause a trigger, and behavioral alerts provided through Azure triggered by abnormal behavior. These alerts notify the Security team based on relevant events for investigation and response. Our Chief Information Security Officer, Regulatory Compliance, and Corporate IT departments have utilized the same tools for more than four years with rigorously maintained standard operating procedures (SOPs). Our SIEM practices have been extensively audited by third parties and directly by Clients as part of our on-going security and compliance practices and controls. 

Penetration Testing and Enterprise Vulnerability Management 

THREAD utilizes Synack, a software tool utilized by the United States Department of Defense for penetration testing and vulnerability management along with several other tools. 

The combination of crowdsourced ethical hacking combined with our internal Enterprise Vulnerability Management SOPs defines our vulnerability assessment and management process to protect Information Systems / Information Technology assets. This combination also helps protect the privacy of individual employees, clients, and other entities with which THREAD has contractual obligations. 

THREAD complies with applicable laws and regulations regarding protection of systems and data and demonstrates compliance in both client and third-party audits. The timely and consistent application of security patches or mitigations of a reported vulnerability are critical components in protecting the THREAD Platform, systems, and data from damage or loss due to threats such as viruses or other types of external or internal attacks. 

The THREAD Platform is not hosted on premises at any THREAD location. 

The THREAD Platform is hosted in Amazon Web Services (AWS) data centers that have been certified as ISO 27001 and SOC 2 compliant. AWS is audited and certified by independent third parties and is a leader in cloud security with security and architectural documentation available through AWS portals. 

THREAD ensures the confidentiality and integrity of study data utilizing industry best practices and by adhering to global regulatory requirements with a robust and easily auditable Quality Management System. Our SOPs, Certifications, and all relevant documents are available to our Clients through a secure file share for easy accessibility.

THREAD conducts a variety of actions to secur ely develop and test against security threats to ensure the safety of our client data. THREAD employs third-party security experts to perform detailed penetration tests across our platform.

Secure Development within the Software Development Life Cycle (SDLC)

Various

Platform Integrity and Compliance

Evidence needed to confirm how DCT study designs were compared to traditional designs

To engage with an industry analytics firm to review and compare data

To identify studies and a timeline that could show pre and post pandemic impacts

To focus on key performance indicators to show comparisons between designs

To review pre and post pandemic data that could provide published evidence on the impact of recruitment and retention with DCT designs, THREAD offered:

the industries’ first publication on evidence of DCT performance versus traditional designs

an industry partnership in which THREAD data could have an outside company analyze DCT data against a large, traditional study data set (1,000+ studies)

focused data review and publication on the impacts of both designs on recruitment and retention

published report that highlights the challenges and areas of outperformance for balanced decision making

THREAD Supported DCTs Outperform Industry Benchmark in Patient Recruitment and Retention

Pitt CTSI approached THREAD seeking to bring scalable, patient-centric research technology to its portfolio

Provide a cost-effective solution that can be adopted by a wide range of researchers

Scale technology deployment across multiple organizations and research teams

Achieve portfolio ramp up within a three-year pilot period

THREAD deployed its no-code, fully configurable DCT & eCOA technology platform and implemented a self-service training curriculum to drive scaled adoption

Platform provides a mobile and web solution for UPitt researchers and participants to engage with their studies in a simple and accessible way

Teleresearch and electronic clinical outcomes assessments (eCOA) allow study participants to conduct study visits virtually, reducing participant burden

Core UPitt super user team trained on THREAD’s self-service, fully-configurable study builder, supported by THREAD experts to accelerate ramp up and scale quickly

Accelerating the Impact of Translational Research in an Academic Setting

How organizations capitalize on accelerated adoption of eSource to further improve outcomes of future trials.

Today’s clinical trial dashboards alert sponsors and sites to any developing data trends, highlight compliance issues, and provide insight into all study data, including lab and sensor data. This type of immediate alerting and reporting is available when collecting source data close to real-time. The Food & Drug Administration (FDA) defines electronic source data (eSource) as electronic source data initially recorded in an electronic format 

. In decentralized and hybrid clinical trials today, the participants may not visit the clinic or site for research-related data collection. The patient data is gathered electronically from various sources that do not need to be transcribed, verified, and double-checked. The participant may need to visit a lab or imaging center, and the resulting data from those facilities end up in a Decentralized Clinical Trial (DCT) platform. Digital endpoints are available in close to real-time. The required data provenance, or record of the origination and description of how and why the data ended up at its present location, indicates that no human has had to decipher handwriting, transcribe anything, or perform optical character recognition. Data streams from various sources such as sensors, electronic records, apps on a participant’s device, labs, and electronic investigation assessments are aggregated in a single platform. This central data repository is a result of utilizing eSource data, and the only barrier to this is not technology; it is resistance to change.

As an industry, we have graduated from paper diaries, and paper case report from data collection to acceptance of electronic case report form data (eCRF), Clinical Outcome Assessments (eCOA) including Patient Reported Outcomes (ePRO), Clinical Reported Outcomes (ClinRO), caregiver-reported outcomes (ObsRO), performance outcomes (PerfO), electronic patient diaries (eDiary), and electronic Device Reported Outcomes from sensors and wearables (eDRO). We can also include data obtained directly from the electronic health/medical record (EHR/EMR), and we have seen Real World Evidence (RWE) supporting a
clinical claim.

Clinical trials endured the COVID-19 pandemic, with some trials adapting to the travel and contact limitations by embracing technology that provides virtual visits via telehealth and data capture directly from the patient. During the pandemic, we saw trials on hold or delayed indefinitely. Stalled and delayed trials are problematic and cost the industry copious amounts of unrealized revenue; but most importantly, they delay lifesaving treatment to patients in need.

The trials that have thrived during the pandemic have been those not dependent on visits to a clinical site or institution.

The trials that thrived during the pandemic were those that were not dependent on visits to a clinical site or institution. Collecting data directly from a patient via their own device, having clinical staff use telehealth technologies to capture required data, and using home health services, as needed for assessments requiring a physical presence, are changing the way we conduct clinical research. All of these are examples of data collection in the moment, rather than recording information to be transcribed at a later date. This is called a decentralized approach, and it is supported and encouraged by regulatory authorities and eliminates unnecessary costly activities such as on-site source data verification and transcription.

The FDA has supported and encouraged the use of eSource as seen in the FDA eSource guidance released in 2013 (2). During the pandemic, both the FDA and the European Medicines Agency (EMA) released regulatory statements supporting electronic data sources. The FDA issued the FDA Guidance on Conduct of Clinical Trials of Medical Products during COVID-19 Public Health Emergency in March 2020 and has provided several updates (3). This guidance allowed alternative methods for safety assessments such as telehealth or virtual visits and alternative assessment types such as remote clinical outcome assessments (ClinRO) and remote Performance Outcome assessments (PerfO).

The FDA recommended using electronic informed consent in place of paper-based consent. Also recommended was optimizing the use of central and remote monitoring programs to maintain oversight of clinical sites.

The European Medicines Agency (EMA) issued 

GUIDANCE ON THE MANAGEMENT OF CLINICAL TRIALS DURING THE COVID-19 PANDEMIC 

in April 2020 and indicated that data collection should continue despite the absence of in-person interactions. This document gives guidance on how industry should evaluate the risk of protocol changes such as virtual visits and details how to document alternative methods of obtaining consent such as electronic consent (4).

The UK (United Kingdom) Medicines and Healthcare products Regulatory Agency (MHRA) issued initial guidance in March 2020 on managing clinical trials during the coronavirus pandemic, with several subsequent updates. The main item of interest in this guidance is that it allows for remote access to medical records if appropriate security is in place (5). The use of technology to obtain a response data directly from the patient has existed for many years, but this increased rapidly due to technological advances, and more recently out of necessity. Data from devices like the Apple Watch or continuous blood glucose monitoring systems are collected and viewed to better understand our current health endpoints. As of February 2020, clinicaltrials.gov shows that approximately 460 wearables studies are underway, and according to Kaiser Associates and Intel, 70% of clinical trials will incorporate sensors by 2025 (6).

When running a clinical trial, a single platform that collects data from all trial modalities and provides robust metrics is most efficient.

Many trials are collecting and processing data from wearables. Some, however, are collecting this data for exploratory endpoints or for use in proof-of-concept projects. In our industry’s next advance, we must leave this tentative approach behind. We must embrace these technologies for primary and secondary endpoints that directly affect the approval and disapproval of an Investigational Product (IP). To achieve this goal, a judicious selection of devices is necessary. There is a wide variety of approval approaches around the globe for devices. For direct primary and secondary endpoint collection, these devices must be validated for the specific data point and compared to the current gold standard collection of that endpoint to show they are equal or better.

This requires the industry knowledge to select mature vendors with solid scientific approaches that can show full validation of their devices and measurements.

We have seen the use of eSource with common endpoints like actigraphy and movement collection; also, the verticals within eCOA, wearables, and telehealth have grown vastly. Unfortunately, they have expanded within their own siloed verticals. However, there exists a new frontier of combining these data collection methods into one cohesive offering. These data platforms feed the hub of all the clinical data within a trial. The benefits of this approach are many, with being able to easily view metrics and analytics for all key risk indicators topping the list. A close second is the ability to see and resolve data discrepancies as they happen. Reducing the need for on-site monitoring by eliminating source data verification is third, followed by having signal detection and discovery enablement front and center when using one platform.

Questions always have existed around data from patients, in large part because many patients are eager to please clinicians, trials, and family. In the past, due to the siloed nature of clinical trial data cited above, detecting noise such as recall bias, observer expectancy effect, and response bias was more difficult. However, with the ability to collect data from many sources and view them from a single platform, it is far easier to identify, mitigate, and reduce these biases. This capability exists today and just needs adoption from industry to harvest the full benefit.

According to TransCelerate, eSource workstream categories fall into the four groups shown below (7).

The benefits of eSource are well documented as listed here from TransCelerate:

 and eliminate transcription of source data prior to entry into an electronic case report form (eCRF)

 of data; promote real-time access for data review

TransCelerate lists many key metrics from industry projects that quantify the value of eSource:

 between traditional EDC vs eSource, 1 FTE (Full Time Equivalent), 65% total keystrokes

The position in the TransCelerate eSource paper is that the combination of electronic source data with data stored in a unified platform will further increase these efficiencies.

THREAD's data analysis saw these efficiencies rise in 2020. The chart below shows a direct increase in the number of ePRO entries in the THREAD platform during a large long-term research study that ran throughout the COVID-19 pandemic. This spike in entries appears correlated to the confirmed COVID-19 cases (8), implying that patients turned to electronic means such as registry studies to improve their medical care at that time.

Offering participants, sites, and sponsors a way to provide their data electronically changes the landscape. In order to be successful, we need to make this data collection simple. For example:

 – Data such as diary data for eCOA assessments coming directly from the participant needs to be collected electronically or via a clinician interacting directly with the patient. Participants today can use their own devices Bring Your Device (BYOD), which is more convenient than carrying a study-provided device. Telehealth offers the ability to gather information directly from the patient while entering it into the source system. This entry method is preferred by regulatory agencies, reduces transcription, and source data verification (SDV), and limits data checking.

 – If the data already exists electronically, use that electronic source. An electronic source is always preferable. Today we can pull data from medical records, health devices such as blood pressure cuffs and thermometers, and many other
activity modalities.

 – Eliminate multiple systems with multiple passwords. Today clinical sites maintain access to many systems: RTSM, eCONSENT, eTMF, EDC, CTMS, EKG, Lab Providers, etc. If we consolidate this data into a single platform, we can reduce the site burden and improve quality by reducing transcription.

 – A study data flow diagram is worth a thousand words. Provide 

 showing all data streams, highlighting the ones for which they are responsible.

According to the Tufts-eClinical Solutions Data Strategies & Transformation Study, more than two-thirds of all sponsors are using or piloting at least four types of data in clinical trials (9). It is common today to collect patient/caregiver consent and assent electronically since this technology can ensure sites have the correct version and allows for multilingual deployment of multiple consents. Telehealth visits are scheduled to collect physician outcomes. Data is collected directly from the patient from electronic patient report outcomes (ePROs), or surveys or sensors.

Sponsors today are pre-qualifying a variety of sensors to develop a library of validated sensors prior to the study need. This is done by developing acceptance criteria, then piloting selected sensors while collecting scorecard results. Scorecard items may consist of:

Device characteristics such as use, battery life, etc.

Today we could create a trial that includes telehealth to assess and collect eCOA, mobile apps to enable ePROs on a participant’s phone to collect validated patient data, and the addition of a wearable to collect data that is more independent of bias. So now we not only can assess how a patient reports they feel or how they present themselves to a clinician, we also can add in wearable data to see how they are performing and feeling. Thus, comparing movement and activity ePRO questionnaire type data to the fact that the patient’s actigraphy has plunged in the past two weeks relative to baseline is incredibly valuable.

The adoption of technology has transformed training. We have seen virtual learning skyrocket out of necessity. Training in a clinical trial is no exception. Participants have access to training videos on every aspect of their clinical trial, from information about the trial to consenting details to videos on how to set up their wearables to video updates and reminders. These advances in training are a welcome improvement over in-person or paper-based training methods of the past. Throughout the patient training and study video visits with clinical staff, patients are encouraged to ask any questions they have about the study.

Training can be separated at an elevated level between patients and clinicians, and while learning new systems and approaches can be a hurdle, that hurdle gets lower each year with the pervasive adoption of technologies such as smartphones and streaming multimedia and an increasingly tech-savvy population.

For sites and clinicians, the onset of commonplace Electronic Data Capture (EDC) (10) started to accelerate around 2010, and most clinical site personnel are now familiar with these systems, as well as customary practice management systems. As a result, the training hurdle is low for sites to complete and use effectively. To mitigate any risk in tech barriers, choosing to deploy a single platform, opposed to disparate systems, and having a single point of contact for support and training, makes the already low hurdle even lower.

Here, the challenges are providing the proper materials to train the trainer on educating the patient and having the combined systems appear as one integrated platform for the patient. Technologies today can provide learning options when they are needed, not as a bolus upfront. These learning strategies are required to be successful in technology adoption.

With billions of people using social media, banking apps, and now health apps such as Apple HealthKit, user training has become much easier.

The patient, of course, is the other hurdle and a more exponentially impactful part of the equation. However, this hurdle gets lower each year as tech becomes more pervasive. It is easy to forget how rapidly consumers have adopted digital technology. For example, the original iPhone was released in the summer of 2007 with 300,000 orders; as of 2019, the iPhone has a global installed base of more than 900 million. This is only half of the equation: 91% of the 4.66 billion unique internet users worldwide access the web via smartphones (11). With billions of people using social media, banking apps, and health apps like Apple HealthKit, user training has become much. User interfaces in app design are simplifying and drastically decreasing adoption barriers. It is far easier to teach an app-savvy senior how to use their health app when using Facebook and Facetime daily to interact with the outside world.

Worldwide clinical trials today have seen the reduced need for provisioned devices, which lowers trial costs. More importantly,
it lowers the patient burden. Now, instead of using unfamiliar devices, more patients can use their own device. This has many benefits:

Since it is the patient’s own phone, the likelihood of their study device being left on the nightstand or kitchen table has
drastically reduced

Providing study data directly using a phone is eSource and thus doesn’t require source data verification and improves data
quality by eliminating transcription errors

Entry of critical study data can be tracked in real time allowing for increased compliance

Patient knowledge of technology, saturation of devices, and acceptance of sharing health data all have grown over the past ten years. Technology at clinical sites and institutions also has grown. All practices use some sort of practice management software and often use tablets for registration and EMR (Electronic Medical Record). Finally, eSource-related technology vendors have matured and are utilizing cloud-based storage and security linked to constantly connected devices and technology.

With the market dramatically consolidated, the need for provisioned devices is decreasing.

The last remaining need is for industry to accelerate adoption of proven technologies. The pandemic has forced our hand and we have seen the growth results. Industry adoption must grow at the rate of the customer – and the customer is the patient sitting in a clinical site waiting room using their smartphone for entertainment.

The data management role traditionally involves reviewing data to find transcription errors or site-reported data that is incomplete or has discrepancies. Data that comes directly from the source typically is left as it is received. There should be no need for queries or reconciliation since this data truly represents the activities where it was collected. The data management role shifts from finding incorrect data to orchestrating data of various data types into a single patient story. Data reviewchanges to check things such as whether all the data is in the correct timeframe, whether there are gaps in the data, and whether there are any data trends within the patient that need to be highlighted in the analysis. The role shifts from being a data manager issuing queries to becoming a data scientist looking at the validity and ensuring the quality of the data itself.

Integrity lets us see the data from the source to the final output, eliminating concerns about data manipulation. Data collected directly from the patient is considered the source and therefore cannot be changed by the participant. Certain data checks can be implemented to check for issues that may need to be explained in the analysis. The bottom line is that data collected directly from the electronic source has strong integrity and reduces the need for the type of data review in paper-based clinical trials.

When using an electronic source, the need for a verification step is eliminated, saving time and money, and improving quality. According to the U.S. Department of Health & Human Services, the typical SDV cost of a Phase 3 study is $400,173, or 3.5% of the overall cost of the trial (13). Eliminating or significantly reducing this SDV step will increase the database close and lock time while cutting costs and still providing high-quality data.

The adoption of Risk Based Quality Management is designed to improve data quality. Identifying study risks and proactively providing strategies to manage these risks will ensure a smoother data collection experience. For example, if a study is using a new sensor that the company is not familiar with, they may implement a proof-of-concept study to collect data from this sensor and comparator sensors for a brief period to close any gaps in processes, develop training materials, and ensure the output is as expected. Other risks may include improper storage of the clinical study drug. For this risk, the team can develop training and check-ins with the clinical site to evaluate that the procedures are being followed to safely store, distribute, and track the study drug.

is as expected. Other risks may include improper storage of the clinical study drug. For this risk, the team can develop training and check-ins with the clinical site to evaluate that the procedures are being followed to safely store, distribute, and track the study drug.

A big part of quality management is reviewing the data and metadata for items at risk throughout the study. Thresholds are provided around each area to help determine whether a risk is developing or if there is a quality issue. Take the example of enrollment: If a site is expected to enroll a certain number of patients per month, this is easily measured and identified as meeting expectations, becoming at risk for not meeting expectations, or not meeting expectations at all.

Typically, this risk-based approach is developed by a representative of each department in the clinical study process. Risks are identified, as well as proposed measurements and thresholds. Data around these risks updated on a regular basis, and the entire team meets to review all metrics throughout the life cycle of the study. This overall insight into the study benefits all departments and will result in fewer quality issues.

Analysis – Digital endpoints from electronic source data

The analysis phase of a study uses data from the patient to determine if the medicine or device is safe and effective. This data is so critical that we have devised many ways to gather it, enter it, and make sure it is accurate. Providing the statisticians digital electronically sourced data directly from the patient eliminates multiple steps, all of which may cause data integrity issues.

According to the U.S. Department of Health & Human Services, the typical SDV costs of a Phase 3 study is $400,173, or 3.5% of the overall cost of the trial. 

According to Stat News, The Digital Medicine Society recently released a crowdsourced collection of “pharma companies that have publicly disclosed they’ve collected digital endpoints in their trials.” Stat News writes: “The library details 38 unique industry-sponsored studies that evaluated digital endpoints in the new medical products or new applications of existing medical products. The digital tools used range from accelerometers to spirometers and microphones. To date, digital endpoints are being used in trials by 15 different sponsors; they include eight primary endpoints and seven Phase 3 studies.” (14)


This summary only represents those companies that chose to participate in the survey; there are many more. The accuracy and quality of this primary efficacy data is our end goal that eSource data collection provides.

Artificial Intelligence (AI) / Machine Learning (ML)

Paper collection requires data entry and human review to confirm validity. Both methods are impacted by error, delays, and rework. Deploying a trial model with ePRO, eCOA, and sensor data sources all on one platform allows for adoption of more robust AI and ML solutions. Merging paper diaries with EDC data is a manual task that takes time and validation to ensure accuracy.

Trial design focused on a single platform dramatically increases the power and capabilities of AI and ML models. Automatically notifying sites of visit inconsistencies, data anomalies, and compliance issues reduces trial duration and increases data quality. As adoption of these technologies grows, real-time notifications, data availability, and predictive risk modeling will increase, supporting cleaner data collected directly from the source with zero transcription error.

Dashboards and analytics provide clear and timely metrics of the health of a clinical trial and support real-time review and adjustments if required. Identifying and surfacing metrics of poor-performing sites will allow sponsors to amend recruitment strategies and optimize patient accrual timelines. Training algorithms across data sets will allow sites to monitor adverse events and concomitant medication logs across data sources in real-time. A patient may have identified difficulties with their daily activities in their ePRO but failed to mention them to site staff.

Fully integrated studies with eCOA, sensors, EDC, and real-world data in a single platform will allow computer algorithms to learn and evolve consistency evaluations, flag anomalies between ePRO and adverse events, and risk-score the reliability of data in real time as opposed to in retrospect. As the regulatory agencies increase acceptance of these technologies, and the industry deploys them to identify issues, learn patterns, and reduce human error, the more common these techniques will become, and the more efficient trials will become. AI and ML will allow sites to conduct more studies, Contract Research Organizations (CROs) to service more deliveries, sponsors to introduce more drugs, and more patients to benefit from an efficient and cost-effective data collection and management model.

This report highlighted the benefit of a completely paperless, electronic source ecosystem for clinical trials. In addition, the need for a single platform where all data and key performance indicators (KPIs) are displayed is critical for keeping a clinical study on track, monitoring compliance, and for the ability to identify any risks related to the study data. This industry has been historically slow to adopt any change in existing methodology due to the complexity of adding new processes, new Standard Operating Procedures (SOPs), providing training, and using innovative technology COVID-19 has pushed us into the digital age with the increased use of telehealth, virtual visits, data collected electronically by ePRO, electronic surveys and data provided directly by home health providers.

In the words of Scott Gottlieb, M.D., former FDA Commissioner, in a statement from the FDA: “Unfortunately, we have seen a continued reluctance to adopt innovative approaches among sponsors and clinical research organizations. In some cases, the business model adopted by the clinical trial establishment is incompatible with the positive but disruptive changes that specific innovations can enable. We appreciate that scientific and technical complexity is a real and ongoing challenge, but industry and academia also need to invest in and leverage these approaches and develop new incentives that reward collaboration and data sharing across the clinical research enterprise.” (15)

FDA Guidance for Industry, Electronic Source Data in Clinical Investigations, Sept 2013 https://www.fda.gov/media/85183/download

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/fda-guidance-conduct-clinical-trials-medical-productsduring COVID-19-public-health-emergency

 https://ec.europa.eu/health/sites/health/files/files/eudralex/vol-10/guidanceclinicaltrials_covid19_en.pdf

 https://www.gov.uk/guidance/managing-clinical-trials-during-coronavirus-COVID-19

Wearables & big Data In Clinical Trials – Where do we Stand? By Yvette Jansen, Grant Thornton:
https://www.clinicalleader.com/doc/wearables-big-data-in-clinical-trials-where-do-we-stand-0001

https://www.transceleratebiopharmainc.com/assets/eSource-solutions/

 COVID results: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/10162020/specimens-tested.html

Understanding eSource in the New Era of Clinical Trial Design

Opportunities and counterintuitive challenges for decentralized clinical trials to broaden participant inclusion

Resilient design: decentralized trials recovered faster from the impact of COVID-19 than traditional site-based designs

Innovation in clinical trial methodologies: Lessons learned during the Corona pandemic

Virtual visits: moving clinical trials visits from clinics to homes

Technology-enabled clinical trials: transforming medical evidence generation

Klassen AF, Grant C, Barr R, Brill H, Kraus de Camargo O, Ronen GM, et al. Development and validation of a generic scale for use in transition programs to measure self-management skills in adolescents with chronic health conditions: the TRANSITION-Q. Child: care, health and development. 2015;41(4):547-58.

Arnould B, Gauchoux R, Meunier J, Gilet H, Regnault A. Validation of ACCEPT, a new generic measure to assess how patients with chronic diseases balance between the advantages and disadvantages of following the recommended treatment regimen in real-life. ISPOR; Dublin, Ireland, 2013.

Chronic health conditions

Regnault A, Pompilus F, Ciesluk A, et al. Measuring patient-reported physical functioning and fatigue in myelodysplastic syndromes using a modular approach based on EORTC QLQ-C30. Journal of Patient-Reported Outcomes. 2021;5(1):1-10.

Pompilus F, Ciesluk A, Marquis P, Griebsch I, Voorhaar M. Understanding the Patient Experience in NUT Carcinoma: Qualitative Interviews with Patients and Caregivers to Develop a Conceptual Framework. Oncol Ther. Aug 6 2021.

Klassen AF, Kaur MN, Tsangaris E, et al. ASO Visual Abstract: Development and Psychometric Validation of BREAST-Q Scales Measuring Cancer Worry, Fatigue, and Impact on Work. Annals of surgical oncology. May 27 2021.

Klassen AF, Kaur MN, Tsangaris E, et al. Development and Psychometric Validation of BREAST-Q Scales Measuring Cancer Worry, Fatigue, and Impact on Work. Annals of surgical oncology. May 24 2021.

Kaur MN, Klassen AF, Xie F, et al. An international mixed methods study to develop a new preference-based measure for women with breast cancer: the BREAST-Q Utility module. BMC women's health. 2021;21(1):8.

Zeidner JF, Mazerolle F, Bell JA, et al. Randomized Phase 2 Trial of Pevonedistat Plus Azacitidine Versus Azacitidine in Higher-Risk Myelodysplastic Syndromes/Chronic Myelomonocytic Leukemia or Low-Blast Acute Myeloid Leukemia: Exploratory Analysis of Patient-Reported Outcomes. 62nd American Society of Hematology Annual Meeting; 2020.

Popat R, Lonial S, Voorhees P, et al. DREAMM-2: Single-Agent Belantamab Mafodotin (Belamaf) Effects on Patient-Reported Outcome (PRO) Measures in Patients with Relapsed/Refractory Multiple Myeloma (RRMM). 62nd American Society of Hematology Annual Meeting; 2020.

Eliason L, Loubert A, Gorsh B, Meunier J, Kleinman DM, Sapra SR, A. Methodology for Evaluating Meaningful Change Thresholds in Patient-Reported Outcome (PRO) Measures for Corneal Events in Patients with Relapsed/Refractory Multiple Myeloma (RRMM) Receiving Single-Agent Belantamab Mafodotin (BELAMAF). ISPOR; 2020.

Oza AM, Lorusso D, Aghajanian C, et al. Patient-Centered Outcomes in ARIEL3, a Phase III, Randomized, Placebo-Controlled Trial of Rucaparib Maintenance Treatment in Patients With Recurrent Ovarian Carcinoma. J Clin Oncol. 2020:Jco1903107.

Colombo N, Oza AM, Lorusso D, et al. The effect of age on efficacy, safety and patient-centered outcomes with rucaparib: A post hoc exploratory analysis of ARIEL3, a phase 3, randomized, maintenance study in patients with recurrent ovarian carcinoma. Gynecologic Oncology. 2020.

Klassen AF, Dominici L, Fuzesi S, Cano SJ, et al. Development and Validation of the BREAST-Q Breast-Conserving Therapy Module. Annals of surgical oncology. 2020.

Kaur M, Pusic AL, Cano SJ, et al. International phase 1 study protocol to develop a health state classification system for a preference-based measure for women with breast cancer: the BREAST-Q Utility module. BMJ Open. 2020;10(1):e034451.

Young-Afat DA, Gibbons C, Klassen AF, Vickers AJ, Cano SJ, Pusic AL. Introducing BREAST-Q Computerized Adaptive Testing: Short and Individualized Patient-Reported Outcome Assessment following Reconstructive Breast Surgery. Plast Reconstr Surg. 2019;143(3):679-684.

Regnault A, Pompilus F, Ciesluk A, Mazerolle F, Bejar R, Fram RJ, et al. Creating customized patient-reported outcome measures for rare disease clinical trials: Application of the EORTC Item Library to hematological stem cell disorders. ISOQOL; San Diego, CA, USA, 2019.

Pompilus F, Bell JA, Fram RJ, Faller DV, Marquis P. Identification of the most suitable patient-reported outcome instrument for trials in Myelodysplastic Syndromes, Chronic Myelomonocytic Leukemia, and Acute Myeloid Leukemia. MDS International Symposium; Copenhagen, Denmark, 2019.

Lee EH, Pusic AL, Cano SJ, Klassen AF. The FACE-Q Skin Cancer Module addresses post-resection aesthetic and quality of life outcomes. British Journal of Dermatology. 2019;180(4):953-4.

Cracchiolo JR, Klassen AF, Young-Afat DA, Albornoz CR, Cano SJ, Patel SG, et al. Leveraging patient-reported outcomes data to inform oncology clinical decision making: Introducing the FACE-Q Head and Neck Cancer Module. Cancer. 2019;125(6):863-72.

Colombo N, Oza AM, Lorusso D, et al. Patient-Centred Outcomes in the Phase 3 Study ARIEL3 of Rucaparib Maintenance Treatment in Patients with Platinum-Sensitive, Recurrent Ovarian Carcinoma: Post Hoc Exploratory Analyses by BRCA Mutation Status and Patient Age. European Society of Gynecological Oncology 21st congress, 2019; Athens, Greece.

Bell JA, Pompilus F, Ciesluk A, Mazerolle F, Scipione F, Bejar R, et al. Understanding and Measuring Fatigue in Higher-Risk Myelodysplastic Syndromes, Chronic MyelomonocyticLeukemia, and Acute Myeloid Leukemia: Mixed Methods Research. MDS International Symposium; Copenhagen, Denmark, 2019.

Bell JA, Galaznik A, Pompilus F, Strzok S, Bejar R, Scipione F, et al. A pragmatic patient-reported outcome strategy for rare disease clinical trials: application of the EORTC item library to myelodysplastic syndromes, chronic myelomonocytic leukemia, and acute myeloid leukemia. J Patient Rep Outcomes. 2019;3(1):35.

Bedel J, Lorusso D, Aghajanian C, Oaknin A, Dean A, Colombo N, Weberpals JI, Clamp AR, Scambia G, Leary A, Holloway RW, Amenedo Gancedo M, Fong PC, O’Malley DM, Meunier J, et al. Patient-Centered Outcomes in ARIEL3, a Phase 3, Randomized, Placebo-Controlled Study of Rucaparib Maintenance Treatment in Patients with Recurrent Ovarian Carcinoma. ISPOR; New Orleans, LA, USA, 2019.

Lee EH, Pusic AL, Cano SJ, Klassen AF. The FACE-Q Skin Cancer Module addresses post-resection aesthetic and quality of life outcomes. The British journal of dermatology. 2018.

Lee EH, Klassen AF, Cano SJ, Nehal KS, Pusic AL. FACE-Q Skin Cancer Module for Measuring Patient-Reported Outcomes Following Facial Skin Cancer Surgery. The British journal of dermatology. 2018.

Kaur M, Pusic A, Xie F, Bordeleau L, Matros E, Zhong T, et al. Development of health-state classification system for a new breast cancer-specific preference-based measure: the BREAST-Q. ISOQOL; 2018; Dublin, Ireland.

Bell JA, Pompilus F, Strzok S, Galaznik A, Scipione F, Fram RJ, et al. A pragmatic patient-reported outcome strategy for rare disease clinical trials: Application of the EORTC Item Library to Myelodysplastic Syndromes. ISOQOL; 2018; Dublin, Ireland.

Bell JA, Pompilus F, Galaznik A, Strzok S, Bejar R, Fram RJ, et al. Enhancing the Content of the EORTC QLQ-C30 to Maximize the Detection of Treatment Benefit in Hematological Stem Cell Disorders Using the EORTC Item Library. 60th American Society of Hematology Annual Meeting and Exposition; San Diego, CA, USA, 2018.

Bell JA, Pompilus F, Christian A, Mazerolle F, Scipione F, Bejar R, et al. Assessing Patient-Reported Outcomes in People with Myelodysplastic Syndromes: Can a Customized Selection of Items from the EORTC Library Enhance the EORTC QLQ-C30? 60th American Society of Hematology Annual Meeting and Exposition; San Diego, CA, USA, 2018.

Bell JA, Galaznik A, Pompilus F, Strzok S, Fram RJ, Faller DV, et al. Patient Perspectives on Symptom Experience and Impact on Health-Related Quality of Life in Higher-Risk Myelodysplastic Syndromes, Chronic Myelomonocytic Leukemia and Low-Blast Acute Myeloid Leukemia. 60th American Society of Hematology Annual Meeting and Exposition; San Diego, CA, USA, 2018.

Karagiannis T, Marquis P, Petersen J, Meyers OI, Chui SY, Campbell AK, et al. Understanding patients’ ability to function in order to inform clinical benefit in oncology studies. ISPOR; Glasgow, UK, 2017.

Karagiannis T, Marquis P, Petersen J, Meyers O, Chui SY, Campbell AK, et al. Beyond survival and tumor progression: Assessing patients' (pts') function to further define clinical benefit in oncology studies. American Societey of Clinical Oncology; Chicago, ILL, USA,2017.

Fuzesi S, Cano SJ, Klassen AF, Atisha D, Pusic AL. Validation of the electronic version of the BREAST-Q in the army of women study. Breast. 2017;33:44-9.

Bell JA, Pompilus F, Galaznik A, Strzok S, Bejar R, Fram RJ, et al. Enhancing the Content of the EORTC QLQ-C30 to Maximize the Detection of Treatment Benefit in Hematological Stem Cell Disorders Using the EORTC Item Library. American Society of Hematology annual meeting; Atlanta, GA, USA, 2017.

Bell JA, Galaznik A, Pompilus F, Strzok S, Fram RJ, Faller DV, et al. Patient Perspectives on Symptom Experience and Impact on Health-Related Quality of Life in Higher-Risk Myelodysplastic Syndromes, Chronic Myelomonocytic Leukemia and Low-Blast Acute Myeloid Leukemia. American Society of Hematology annual meeting; Atlanta, GA, USA, 2017.

Robinson DJ, Esseltine DL, Regnault A, Meunier J, Liu K, van De Velde H. The influence of baseline characteristics and disease stage on health-related quality of life in multiple myeloma: findings from six randomized controlled trials. Br J Haematol. 2016;174(3):368-81.

Lee EH, Klassen AF, Lawson JL, Cano SJ, Scott AM, Pusic AL. Patient experiences and outcomes following facial skin cancer surgery: A qualitative study. Australas J Dermatol. 2016;57(3):e100-4.

Lamarca A, Elliott E, Barriuso J, et al. Chemotherapy for advanced non-pancreatic well-differentiated neuroendocrine tumours of the gastrointestinal tract, a systematic review and meta-analysis: A lost cause? Cancer Treat Rev. 2016;44:26-41.

Regnault A, Ferrer L, Dinet J, Gabriel S, Pavel ME, Ruszniewski PB, et al. Health-Related Quality of Life in CLARINET, a phase III Trial of Lanreotide Autogel 120mg in Patients with Non-Functioning Entero-Pancreatic neuroendocrine Tumour: Analytical Challenges and Statistical Solutions. ISOQOL; Vancouver, Canada, 2015.

Klassen AF, Rosenberg-Yunger ZR, D'Agostino NM, Cano SJ, Barr R, Syed I, et al. The development of scales to measure childhood cancer survivors' readiness for transition to long-term follow-up care as adults. Health expectations : an international journal of public participation in health care and health policy. 2015;18(6):1941-55.

Klassen AF, Cano SJ, Sinha R, Shahbaz A, Klaassen R, Dix D. Is the Give Youth a Voice questionnaire an appropriate measure of teen-centred care in paediatric oncology: a Rasch measurement theory analysis. Health expectations : an international journal of public participation in health care and health policy. 2015;18(5):1686-97.

Socinski MA, Butts CA, Mitchell P, Thatcher N, Scagliotti G, Robinet G, et al. Exploration of patient health status as measured by the EQ-5D questionnaire alongside the START trial of tecemotide (L-BLP25) in NSCLC. World Conference on Lung Cancer; Sydney, Australia, 2013.

Pusic AL, Cemal Y, Albornoz C, Klassen A, Cano S, Sulimanoff I, et al. Quality of life among breast cancer patients with lymphedema: a systematic review of patient-reported outcome instruments and outcomes. Journal of cancer survivorship : research and practice. 2013;7(1):83-92.

Lee EH, Klassen AF, Nehal KS, Cano SJ, Waters J, Pusic AL. A systematic review of patient-reported outcome instruments of nonmelanoma skin cancer in the dermatologic population. Journal of the American Academy of Dermatology. 2013;69(2):e59-67.

Lee EH, Klassen A, Lawson JL, Cano SJ, Scott AM, Nehal KS, et al. A qualitative study to develop a conceptual framework to understand the health concerns of patients who undergo facial surgery for skin cancer. ISOQOL; Miami, FL, USA, 2013.

Lawson JL, Scott AM, Lee EH, Matros E, Albornoz CR, Price AN, et al. Quality of life and the impact of altered facial appearance in head and neck cancer patient populations: a qualitative study. ISOQOL; 2013; Miami, FL, USA.

Hoyt MA, Cano SJ, Saigal CS, Stanton AL. Health-related quality of life in young men with testicular cancer: validation of the Cancer Assessment for Young Adults (CAYA). Journal of cancer survivorship : research and practice. 2013;7(4):630-40.

Albornoz CR, Pusic AL, Reavey P, Scott AM, Klassen AF, Cano SJ, et al. Measuring health-related quality of life outcomes in head and neck reconstruction. Clinics in plastic surgery. 2013;40(2):341-9.

Scott AM, Ho AL, Klassen AF, Cano S, VanLaeken NY, Cordeiro P, et al. Patient-reported satisfaction with reconstructed breasts in the long-term survivorship period: Comparison of autologous and non-autologous breast reconstruction. American Society of Clinical Oncology; Chicago, IL, USA 2012.

Scott AM, Ho AL, Klassen A, Cano S, VanLaeken N, Cordeiro P, et al. Patient-reported satisfaction with reconstructed breasts in the long-term survivorship period: comparison of autologous and non-autologous breast reconstruction. ISOQOL; 2012; Budapest, Hungary

Pusic AL, Klassen AF, Scott AM, Cano SJ. Measuring and managing patient expectation for breast reconstruction: a new preoperative patient assessment tool. Appearance Matters 5; 2012; Bristol, UK

Pusic A, Klassen A, Scott AM, Cano S, Shouery M, Basch EM. Feasibility and acceptability of patient-reported outcomes data collection for clinical care following breast reconstruction. American Society of Clinical Oncology; 2012; Chicago, IL, USA

Pusic A, Cano S, Klassen A, Scott AM, Shouery M, Basch E, et al. Feasibility and acceptability of patient-reported outcomes data collection for clinical care following breast reconstruction. ISOQOL; Budapest, Hungary, 2012.

Price AN, Scott AM, Klassen A, Cano S, Mehrara BJ, Disa JJ, et al. Patient-reported satisfaction with psychosocial and sexual well-being and the nipple areola complex: comparison of preservation and reconstruction. ISOQOL; Budapest, Hungary, 2012.

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