Clinical Trial Software Development Services

Clinical trial software development services include design, development, modernization, support, and evolution of solutions for biomedical research organizations.

Essential components of clinical trial software ecosystems include a clinical trials management system (CTMS), electronic data capture (EDC) software, a clinical data management system (CDMS), statistical computing environment (SCE) software, electronic clinical outcome assessment (eCOA) software, and more.

Custom clinical trial software helps overcome the following limitations of ready-made systems:

• Inability to adapt ready-made software to complex protocol requirements or specific regulatory demands in the target countries.
• Inability to integrate all the required external data sources, for example, a specific oncology network’s HIE or oncology patient registries.
• Challenges with integrating the solution with other systems of a research organization, especially the legacy ones or those from different providers.

Why Entrust Your Clinical Trial Software Project to ScienceSoft

• Since 2005 in healthcare software development.
• 150+ successful projects in the domain.
• Since 1989 in data analytics, data science, and AI.
• Expertise in healthcare interoperability (FHIR, HL7, USCDI, CCDA), medical coding and terminology (ICD-10, CPT, LOINC, SNOMED, RxNorm), and medical imaging standards (DICOM).
• Experience in meeting GCP, FDA, HIPAA, HITECH, GDPR, and 21st Century Cures Act requirements.
• An in-house Architecture Center of Excellence to ensure the optimal balance of performance, cost, and scalability for clinical trial solutions.

Sample Architecture for a Clinical Trial Software Ecosystem

This example architecture of a clinical trial software ecosystem, designed by our principal solution architects on Amazon Web Services (AWS) technologies, is modular, cloud-native, microservices-driven, and follows a compliance-first approach. It ensures quick integration with a wide range of data sources, supports full automation of trial activities, and offers workflow adaptability to protocol amendments.

User access and trial workflow orchestration

Investigators, site staff, a trial sponsor’s or CRO’s staff, and patients access clinical trial software systems via secure web dashboards or mobile apps. Through these interfaces, they can view trial data, perform role-specific tasks, and submit both structured (forms, metadata) and unstructured data (scanned documents, medical images, PDFs, etc.).

All frontend traffic enters the clinical trial systems through Amazon CloudFront. It accelerates the delivery of user interface assets by caching them at geographically distributed edge locations and also routes API requests to backend services, which helps reduce latency. Then the traffic is routed by the Application Load Balancer (ALB) to backend services. By applying intelligent routing logic, the ALB ensures high availability and elastic scaling during periods of high load, such as site activation waves or last-patient-out (LPO) data processing.

From the load balancer, frontend API requests are routed to Amazon API Gateway, which acts as the controlled entry point into the backend. The gateway checks if the requests are properly structured, enforces request rate limits for each client to prevent overuse, and directs calls to the correct backend services.

Business logic execution is handled by AWS Lambda functions deployed in the private subnet. Each function performs an isolated step in the trial workflow and is triggered either by incoming API requests or event notifications from other system components.

Amazon EventBridge serves as a unified event bus for triggering backend automation. It receives structured events from both internal services (e.g., new patient enrollment) and external platforms (e.g., new lab results) and forwards them to Lambda for processing. This triggers the execution of atomic workflow steps. For example, submitting a site setup form can trigger a sequence that registers the new site in the CTMS, archives site documentation, and schedules the required site monitoring visits.

Through the use of event-driven triggers, isolated atomic backend tasks, and real-time data flows, highly efficient automation becomes possible, which accelerates effort-intensive trial tasks like data reconciliation or site monitoring.

Core trial operations

Core system backend includes modular services, each executing an isolated trial automation step in response to specific triggers. Together, they cover the core functionality of all components in the clinical trial software ecosystem, such as a CTMS, an IRT, an EDC, an eTMF, etc.

Each service is deployed and runs in isolation, without sharing infrastructure or state, which means trial designers can easily modify data processing rules, add logic tailored to specific countries, or create new forms without affecting unrelated components. This ensures short setup times and streamlined mid-trial changes in line with protocol amendments.

Services exchange data with other components via API calls or event messages. They use DynamoDB as a low-latency operational store. For example, when IRT assigns patients to trial arms, it immediately writes the group assignment results to DynamoDB so that downstream services have real-time access to allocation data. Some backend services store structured records in Amazon RDS when fast and frequent SQL queries are required. For instance, this might be used for document search in an eTMF or task tracking in a CTMS.

To support downstream analytics, audit traceability, and regulatory reporting, backend services forward all key clinical and operational data (e.g., regarding trial state, subject progress, site performance, document readiness) to the central trial data lake (Amazon S3).

Data ingestion from internal and external sources

The platform supports ingestion of operational and clinical data from diverse sources, such as central lab systems, commercial SaaS tools (e.g., eConsent or logistics software), partner CRO platforms, and EHRs. This includes data sources that rely on legacy and on-premises databases.

To achieve this, the following ingestion paths are used:

•  AWS Direct Connect is used to integrate internal on-premises legacy systems. It establishes a dedicated private path into the AWS-hosted ecosystem, forming a hybrid network that covers both cloud-native and on-premises infrastructure. This enables the secure transfer of large data volumes from internal systems that require consistent throughput and cannot use the public internet due to compliance reasons.
• Amazon AppFlow ingests structured and semi-structured data (e.g., HL7 messages, FHIR resources, or other JSON-based payloads) from modern EHRs and SaaS platforms (e.g., lab software) via built-in cloud connectors. Then it applies transformation rules before forwarding this data to downstream services or the data lake.
• VPN Gateway creates an encrypted tunnel to connect with legacy hospital servers or on-site databases running in private local networks without public internet access. This connection enables secure import of exported files, scanned documents, and structured historical patient data from systems that can’t be integrated via API or cloud connectors.
• AWS PrivateLink allows secure point-to-point integration with trusted vendor systems (those hosted within AWS, with certified PrivateLink endpoints, or approved by the study sponsor). In this case, data is exchanged over private AWS channels rather than the public internet, which ensures secure transfer of sensitive trial information.

Ingested structured or unstructured data may be forwarded to Amazon S3 (the central trial data lake) for long-term storage and later processing, or routed directly to backend services, as when updates about investigational drug delivery are sent straight to the IRT.

Patient records ingested from EHR platforms in HL7 messages or FHIR resources are sent to AWS HealthLake. It restructures the data (such as diagnoses, vitals, and prescriptions) into a semantically indexed model to optimize it for querying by downstream services, custom search, and AI analytics.

Beyond accommodating both modern SaaS tools and legacy systems, this architecture enables researchers to integrate new data sources without redesigning and to connect region-specific infrastructure or new vendor platforms when needed.

Data processing and analytics

All incoming and processed data is sent to a centralized Amazon S3-based Data Lake. Raw data lands in a staging zone and then moves through AWS Glue pipelines for cleaning, transformation, and normalization. Processed outputs are loaded into Amazon Redshift, a scalable analytics store optimized for structured querying. For example, it enables automated generation of trial KPIs, such as enrollment rates and site risk scores.

Amazon SageMaker trains and applies machine learning (ML) models using data from Redshift and the data lake. For example, it can predict how quickly potential sites will recruit patients to support feasibility assessment. The outputs can be written back to Redshift or routed to operational systems such as CTMS for further action (for example, to initiate the site activation process).

Clinical teams, operations leads, and QA staff gain access to all aggregated trial metrics and ML model outputs through the analytics dashboards, which offer detailed reports and custom search capabilities. Users can also get direct SQL-based access to raw S3 files using Amazon Athena. For example, thanks to Athena’s serverless querying, auditors can instantly extract the original protocol deviation logs without the need to preload or preprocess the data.

Compliance and security

All operational and clinical data flows are tracked and controlled by a dedicated compliance layer, which is aligned with the major regulatory standards in clinical research.

Amazon CloudFront ensures all user traffic enters through encrypted TLS connections and provides built-in DDoS protection. It integrates with AWS Web Application Firewall (WAF) to block malicious payloads, unauthorized access attempts, and IPs violating regional policies. Together, they form a perimeter layer that satisfies HIPAA and 21 CFR Part 11 requirements for controlled, filtered system access.

All backend services operate inside a logically isolated Virtual Private Cloud (VPC). The API layer and other internet-facing endpoints are deployed in the public subnet. As for the sensitive compute and data services (e.g., Lambda, backend logic), they reside in the private subnet, which has no direct internet access. This segmentation ensures that only explicitly exposed interfaces are reachable externally, as required by GCP guidelines.

Amazon Cognito manages user authentication for trial participants, site investigators, and sponsor or CRO staff. It supports multi-factor authentication (MFA) and federated login, and generates time-bound tokens to enforce secure session control.

Secure ingestion of external data is achieved using AWS PrivateLink and VPN Gateway. PrivateLink enables connection with certified third-party tools without traversing the public internet. VPN Gateway establishes encrypted IPsec tunnels to isolated hospital infrastructure or legacy archives.

All data in transit is encrypted using TLS, and all data at rest (in Amazon S3, Amazon Redshift, Amazon RDS, and DynamoDB) is protected with AWS Key Management Service (KMS). The system uses either AWS-managed keys or customer-managed KMS keys with audit tracking and access revocation.

Access to data stored in S3 is governed by AWS Lake Formation, which applies fine-grained access controls. Only authorized services and IAM identities can read or write specific datasets. Redshift and Athena queries are also subject to row- and column-level permissions, ensuring patient-level data isolation across trials or sponsor roles.

Audit trails are automatically collected for all API calls, data access attempts, and configuration changes using AWS CloudTrail. These logs ensure traceability of actions such as dataset export, user access, or protocol updates, and can be provided for inspection during audits.

Amazon GuardDuty monitors network activity and service behavior for signs of potential security threats, like compromised credentials or unauthorized access attempts. It receives triggers from EventBridge and logs from system components, enabling it to detect threats in real-time and provide timely alerts to administrators.

Together, these controls ensure trial data integrity, full auditability, and regulatory readiness.