- Shawn Thomas
- 6 minutes read
Why digital health technologies matter now
Clinical trial operations are being pushed to move faster while managing growing complexity and change. A Tufts CSDD benchmarking study found the share of protocols with amendments rose from 57% (2015) to 76% (recent years), and the average time from identifying the need to amend to last oversight approval was about 260 days, illustrating how operational friction can compound timelines.
At the same time, regulators are providing clearer expectations for modern approaches. FDA’s final guidance on decentralized elements frames decentralized activities as trial-related activities occurring remotely at locations convenient for participants, including telehealth visits and other distributed approaches. These pressures and guardrails together create the “why now”: digital tooling can reduce participant burden, improve continuity, and strengthen oversight—if implemented with quality-by-design and fit-for-purpose governance.
Participant-centered design is the first “technology decision”
The most effective technology stacks start with human behavior: what makes participation easier, clearer, and more trustworthy. CISCRP’s 2023 Perceptions & Insights “Participation Experiences” study collected experiences from thousands of participants and focuses directly on what participants find burdensome and which supports can help overcome those challenges.
FDA’s decentralized elements guidance reinforces this perspective by emphasizing participant convenience and practical implementation considerations, not just tooling. In practice, participant-centered design usually means:
- fewer unnecessary site visits (when appropriate),
- clear consent and communication between visits,
- flexible channels (phone, SMS, web portals) rather than a single “app-only” path,
- rapid escalation to human support when participants report symptoms, side effects, or barriers.
This is where “platform thinking” can help: some programs use integrated engagement and workflow platforms to manage omnichannel outreach, referral workflows, and real-time reporting. Serva Health’s ServaCore is one example of a built-for-purpose patient recruitment and retention platform that includes study websites with pre-screeners, mobile engagement options, call-center capability, and dashboards—useful to reference as a pattern (without assuming any single-vendor approach).
Core technologies that show up in high-performing trials
Below is a practical summary of four common technology categories and what to evaluate for each.
| Technology | Where it helps | Key considerations for real-world use |
|---|---|---|
| eConsent | Improves access, consistency, and documentation of informed consent | Must meet applicable informed consent/IRB expectations; ensure identity verification, comprehension support, and compliant documentation of consent. |
| Wearables and other Digital Health Technologies (DHTs) | Enables remote data acquisition (activity, physiologic signals, symptom capture) and supports decentralized elements | Choose fit-for-purpose devices, validate performance for the intended context, plan for missing data and usability, and document data handling end-to-end. |
| eSource | Reduces transcription, improves traceability, and streamlines source review and retention | Focus on reliability, quality, integrity, and traceability of electronic source data; define access controls and audit readiness. |
| Interoperability standards (FHIR, CDISC) | Makes data reusable across systems, supports automation, and reduces reconciliation burden | Use FHIR to exchange healthcare/research data when appropriate; ensure submission-bound data aligns with FDA expectations for standardized study data (commonly CDISC-based). |
Two notes are worth highlighting.
First, FDA’s eConsent Q&A guidance explains that electronic informed consent can use multiple electronic media (text, graphics, audio, video, interactive websites) and ties expectations back to applicable FDA requirements for electronic records/e-signatures and informed consent/IRB regulations.
Second, FDA’s DHT guidance defines DHTs broadly as hardware and/or software used to acquire data remotely from participants and provides recommendations for using these technologies in clinical investigations evaluating medical products.
Interoperability for scale: FHIR for exchange, CDISC for submission
Interoperability is where many clinical trial digitization efforts succeed or fail. When systems cannot exchange data cleanly, trial teams pay for it in re-entry, reconciliation, and monitoring effort.
For exchange, HL7 FHIR is increasingly used as a common format to share and reuse data. ClinicalTrials.gov highlights that study data can be viewed or downloaded in HL7 FHIR format, describing FHIR as a standard that enables sharing and reuse of scientific data. NIH’s Data Science initiative similarly encourages the development and use of clinical data standards such as HL7 FHIR to enable interoperability of datasets and exchange clinical/healthcare information for research purposes.
For regulatory submission, FDA’s guidance on standardized study data explains that certain study data in specified submission types must be submitted electronically in a format FDA can process, review, and archive, supported by FDA’s Study Data Standards Catalog and related technical specifications. In other words:
- FHIR helps move and standardize data across clinical/operational environments.
- CDISC-oriented standards (as reflected in FDA’s study data standards resources and catalog framework) help make datasets reviewable and submittable.
A practical best practice is to plan interoperability early: decide which data must be submission-ready, which data is operational only, and where mapping/transformation will occur. Then validate those pipelines.
Governance: risk-based monitoring, data integrity, Part 11, and AI guardrails
Technology only creates value in clinical research if it is governed to protect participants and produce reliable results.
Risk-based monitoring is a cornerstone of modern oversight. FDA’s risk-based monitoring guidance emphasizes focusing sponsor oversight on the most important aspects of study conduct and reporting to enhance subject protection and data quality. FDA’s Q&A guidance expands practical recommendations for sponsors to plan and implement risk-based monitoring approaches.
Data integrity and electronic records requirements should be treated as baseline design constraints, not afterthoughts. 21 CFR Part 11 describes the criteria under which FDA considers electronic records and electronic signatures to be trustworthy, reliable, and generally equivalent to paper records and handwritten signatures. ICH E6(R3) Good Clinical Practice further sharpens expectations for computerized systems in trials, emphasizing fit-for-purpose systems and risk-proportionate validation and controls. FDA’s more recent Q&A on electronic systems, records, and signatures in clinical investigations provides additional practical clarity on implementing electronic solutions in regulated studies.
On privacy and cloud, HHS guidance on HIPAA and cloud computing explains how covered entities and business associates can use cloud services while complying with HIPAA protections for electronic protected health information, including the need for appropriate agreements and safeguards.
Finally, AI governance is rapidly becoming part of trial tech governance. NIST’s AI Risk Management Framework (AI RMF 1.0) provides a structured approach to managing AI risks and promoting trustworthy AI. In clinical trials, a conservative, audit-friendly posture is to use AI first for low-risk productivity and operational enablement (for example, categorization, summarization, triage support) while requiring human review for decisions that could affect participant safety, endpoint interpretation, or protocol compliance, consistent with broader GCP and computerized system control expectations.