Pain Points that can be fixed to enhance Automation in Clinical Data Management (CDM).

Standardization of data

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Interoperability of EHRs for automation

Although the use of EHRs has not been optimal, they have yielded great benefits at low costs and less time and presented significant possibilities for research. The collection, organization, exchange, and automation of data depends on the effective use of electronic health records (EHRs). However, EHRs have a history of poor interoperability and insufficient quality control and security of data. The way data is stored in these records often varies across institutions and organizations. Sharing the data becomes a struggle since there is no standard format for EHRs.

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Improvement in AI and automation

Artificial intelligence (AI) has great potential to identify eligible patients for clinical trials. However, the reality is quite different from expectations. The major problem has been the development of sophisticated algorithms. Other barriers include the unstructured format of data and how to integrate that data into the clinical workflow of stakeholders. Clinical trial stakeholders can indefinitely benefit from a data exchange network, particularly one established between clinical trial sites and sponsors. The network would collect and analyze data before sharing it with relevant stakeholders, improving overall quality. Sponsors shall be able to share important information with sites, including draft budgets and protocol documents. At the same time, sites shall be able to update sponsors in real-time on impending matters, such as patient registrations. This would ensure an unhindered flow of information through integrated systems. However, sites should remember that not all information can flow freely and should be careful while sharing protocol-specified data with sponsors. EHRs have protected health information (PHI) and non-protocol-specific data, which would put patients’ confidential data at risk if shared.

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