researcher accesses clinical database software to view patient information shown overlaying the image

6 Trends in Clinical Database Software that Researchers Need to Know

The landscape of clinical research is undergoing a significant transformation, influenced by technological advancements and evolving methodologies in data security, management, and analysis. Clinical database software stands at the heart of this change, providing the infrastructure needed to manage vast amounts of data generated from clinical trials.

It’s clear that this software will not only become more sophisticated but will also need to adapt to new trends such as artificial intelligence (AI), enhanced data security, cloud-based solutions, interoperability, patient-centric data collection, and notably, the rise of decentralized clinical trials. Here’s how these developments are shaping the future of clinical database software.


Contents: AI/ML Integrations | Data Security Trends | Cloud Services | Interoperability | Decentralized Clinical Trials | Patient-Centric Data Collection


Artificial Intelligence and Machine Learning Integration with Clinical Database Software

Artificial Intelligence (AI) and Machine Learning (ML) are set to transform clinical database software in several key ways. Through automation of routine data processes, these technologies significantly reduce the time and labor involved in data management. This automation extends beyond facilitating data entry to include complex data analysis, with pattern and correlation identification abilities that might not be apparent to human researchers. These data capabilities are invaluable in the context of clinical trials, where vast amounts of data must be sifted through to draw meaningful conclusions.

Additionally, ML algorithms can help reduce the inefficiencies inherent in the preclinical process. This includes analyzing previous studies and their databases to anticipate pitfalls in future trial designs, choosing valuable treatment regiments, and optimizing patient population selection. This enables more informed decision-making early in the development process.

AI and ML use also holds the promise of more adaptive protocols during trial, which can increase the diversity of data collected and reduce the burden on both patients and sites. For instance, ML approaches can analyze clinical databases with the intent to facilitate more efficient and equitable participant identification, recruitment, and retention. It can also help facilitate patient monitoring, identifying non-compliance risks, automating case report forms, and processing data from wearable devices.

At the moment, there is a notable concern regarding the lack of regulation surrounding the use of AI and ML tools in clinical research. As these technologies continue to evolve, regulators are tasked with developing frameworks for clinical trials and studies that establish ethical and effective use. Ensuring that AI and ML tools are deployed responsibly and with sufficient oversight is crucial to maintaining trust in clinical research processes and outcomes.


Enhanced Data Security and Privacy

With the growing emphasis on data privacy and security, future clinical database software will incorporate advanced security features. Expect developments like data security regulations, sophisticated encryption techniques, blockchain technologies for immutable data records, and enhanced privacy measures to ensure compliance with global regulations and safeguard sensitive patient information. This evolution is in response to growing concerns over data breaches and the misuse of personal data.

For example, as of 2023, researchers receiving NIH grants are required to have and update a Data Management and Sharing (DMS) plan that aligns with the latest NIH guidelines. These guidelines are designed to implement FAIR (Findable, Accessible, Interoperable, and Reusable) principles in modern research while ensuring patient data stays private.

Blockchain-based solutions are particularly promising for preserving privacy and security in electronic health records, which currently have centralized architecture. The decentralized nature of blockchain prevents central failure from occurring, preserving electronic records and reducing risk in clinical database software.


Cloud-Based Clinical Database Software

The shift to cloud-based clinical database software for study platforms is enhancing the way clinical research is conducted, offering solutions that are secure, scalable, flexible, and globally accessible.

Cloud services facilitate real-time collaboration among dispersed research teams, a crucial feature as clinical trials are decentralizing more and more. By leveraging cloud-based clinical database software, researchers can share and access data securely across different regions, making the management of complex clinical research data more streamlined and efficient.

Adopting cloud services comes with security and compliance benefits as well. Cloud-based clinical database software employs sophisticated security measures alongside Virtual Private Networks (VPNs) and VLAN segmentation, ensuring the protection of sensitive data during transmission. Regular maintenance is easily managed to uphold the software’s security and performance. The addition of fully managed backup services, along with network-hardened managed firewalls, web application firewalls, and mechanisms for blocking cybercrime IP reputations, reinforces the security framework. This comprehensive approach makes cloud-based clinical database software a robust, secure, and efficient tool for managing critical data in clinical studies.


Data Interoperability and Standardization for Clinical Applications

Clinical database software is adopting an emphasis on the importance of interoperability between diverse research tools and platforms. By using standardized data formats and communication protocols, clinical database software will ensure seamless data exchange, enhancing collaboration and efficiency across the clinical research ecosystem.

Interoperability encompasses several key dimensions, including:

  • Technical Interoperability: Ensures different systems can communicate
  • Syntactic Interoperability: Focuses on the structure of data exchange
  • Semantic interoperability: Ensures that the meaning of exchanged information is preserved and understood across systems

Challenges to be overcome include aligning a wide variety of data formats, custom specifications, and ambiguous semantics in current clinical databases. This diversity complicates integration, compounded by the increasing reliance on unstructured data stored in non-relational databases and data lakes. While unstructured data can be valuable, their complexity necessitates labor-intensive data cleaning and pre-processing before analysis can be conducted. The result is a scenario where, despite the potential of modern algorithms to extract useful information from unstructured data, the effort required for data preparation hampers the efficiency of clinical research processes. The path forward involves a concerted effort to embrace data interoperability and standardization within clinical database software.


Decentralized Clinical Trials

A particularly transformative trend is the rise of decentralized clinical trials (DCTs), which conduct research remotely or through local healthcare providers, rather than at central sites. DCTs offer the potential to make clinical trials more accessible, diverse, and efficient by leveraging digital technologies for remote monitoring, electronic consent, and patient engagement.

In a press release from 2023, the FDA announced draft guidance providing recommendations for sponsors, investigators and other stakeholders about the implementation of DCTS in medical research, building on agency recommendations initially issued in 2020.

“By reducing barriers to participation, we expect that DCTs will increase the breadth and diversity of participants in clinical trials and improve accessibility for those with rare diseases or mobility challenges. We anticipate that this approach will facilitate the development of drugs including in areas of medical need, resulting in more treatment options and improved patient outcomes.”

The adoption of clinical database software with decentralized clinical trials is crucial for managing the unique data challenges posed by this approach. This includes ensuring the software can handle data from various sources, such as wearable devices and electronic health records, while maintaining data integrity and compliance with regulatory standards. Additionally, software solutions for DCTs must be user-friendly to accommodate participants and researchers who may not have extensive technical expertise.


Patient-Centric Data Collection

Advancements in wearable technology and mobile applications are enabling more patient-centric approaches to data collection in clinical trials. Future clinical database software will need to support diverse data types and sources, ensuring comprehensive insights into patient experiences and outcomes.

Tactics of patient-centric data collection include:

  • Bring Your Own Device (BYOD): An approach where clinical study participants use their own phone, tablet, or computer to report study data remotely.
  • Electronic Patient-Reported Outcome (e PRO): Questionnaires accessible via mobile devices used to report subjective health statuses and coping metrics.
  • Remote Patient Monitoring (RPM): The approach to using virtual monitoring methods like televisits and remote screening to limit unnecessary stress and infection exposure.

Challenges that come with adoption of patient-centric data collection include structuring and integrating data from ePro questionnaires and addressing inequity challenges in BYOD clinical studies.


Our Clinical Database Software Solutions

LabKey Server SDMS is a cloud-based clinical database software system designed to help research organizations manage and analyze their scientific data. Server SDMS is highly flexible and customizable to meet the data management, analysis, security and compliance needs of researchers. Scientific data of all types can be captured and aligned to assemble a full picture of your research that can then be further explored with built-in analysis and reporting tools or third-party integrations.

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