Clinical Data Management (CDM) is an integral part of clinical trials and enables clinical trial staff to extract valuable, accurate, high-quality data during the clinical trial process. The final results of a clinical trial are only good if the data collected is reliable. Incorrect data can result in the wrong conclusions being made which can be a risk to participants and can tarnish the reputation of Contract Research Organisations (CROs) and clinical trial sponsors. 

The CDM field has faced many changes over the years from data entry to electronic data capture (EDC), to the adoption of digital-enabled trials and the increased usage of data sources like biosensors, wearable devices and Electronic Health records (EHR). 

With the advancements in technology including machine and artificial learning, the Clinical Data Management sector is overall excited to explore the digital age of real-time data, collection, analysis and management. 

Clinical Data Management in the Past 

Data management has its beginnings in the 1920s after healthcare professionals realised that documenting patient care was a huge benefit as they were better able to treat patients that had an accurate medical history. Patient records established the treatments, details, complications and outcomes of patient care.  

In the past (the 1920s-1970s), Clinical Data Management was paper-based with all participant data being in one centralised location. This was made possible by CDM data entry staff who entered data from various sources including diaries, scales, lab reports and paper case report forms (CRFs). Having all the information in one place made the analysis of the data easier, although carrying out a manual analysis was time-consuming compared to the time it takes today. 

Clinical Data Managers would need to cross-check reviews, and write Structured Query Language (SQL) queries to check the validity and consistency of the data and review listings with knowledge of the therapeutic area. All this relied on the skill and experience level of the person performing the reviews. The consistency of the review and data validation could be difficult to achieve which is why such intricate quality control checks were always carried out. 

Emerging technologies during the 1980s introduced the idea of a new system and the merging of written records and computers. Although this made the whole data management process easier, accessibility was an issue as data could only be accessed at a specific facility which limited the software’s usefulness. The price of using computers and computer performance limitations were other downsides to this new technology. 

Further developments in the 1980s meant that patients were able to benefit from a more efficient electronic check-in process. The 1980s also saw the introduction of the Master Patient Index (MPI) - a database of patient information used across the healthcare industry. However, there were still limitations, as computers couldn’t communicate with one another. 

Clinical Data Management in Present Times 

In the present day, there are a record number of software programs that support smart data management which means that data managers are multi-tasking between different tools. Choosing between streamlining and flexibility means that there are different solutions for different studies. The solution is one of the biggest innovations in data management - centralisation. 

Sponsors are centralising their data management and the technology they use as well. This allows for everyone to be on the same page, while smaller companies are outsourcing more of their work to a Functional Service Provider (FSP). Implementing the right software and having a partnership with an FSP creates an efficient and productive working environment, and having an effective data management system creates an organised analytics-based approach. 

There are currently multiple data sources used in CDM including Electronic Data Capture (EDC), Electronic Clinical Outcome Assessment (eCOA), central laboratory data and an electrocardiogram (ECG). Biosensors, wearable devices and Electronic Health Records (EHRs), have also started to be utilised over recent years. This allows for a participant’s statistical data to be kept up-to-date and information to be accessed in real-time. 

Clinical trials in the present day require real-time data analysis, modelling and simulation that supports data-driven decision-making and reduces costs, development time and research failures. 

The Future of Clinical Data Management 

With technological advancements like artificial intelligence (AI) and machine learning (ML), CDM is moving more and more towards a digital age of real-time data, collection and management. This means that more data is being collected than ever before, resulting in a need for a centralised place for the information to be collected, validated and analysed. 

In future, data management team members will need to: 

  • Align the capture and review of data 
  • Automate data reviews and improve the quality 
  • Take more of a leadership role
  • Deliver more efficient processes for real-time data collection and processing and technology optimisation 
  • Ensure data alignment and integration with other clinical data 
  • Utilise AI and ML to go through the data and pick up on any potential errors 
  • Practice a risk-based, validation approach when it comes to data 
  • Introduce a Key Risk Indicator (KRI) that can point out any areas of concern in the data 
  • Continue to optimise AI and ML to reduce manual tasks, allowing the team to focus their attention on critical thinking activities 
  • Provide input on the data collection strategies during the study design step 
  • Oversee standards and compliance throughout the end-to-end data life cycle 
  • Not only need to collect data but will also need to be able to process, analyse and deliver the data to any device at any time 

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