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The purpose of trial monitoring is to verify that:
- The rights and well-being of human subjects are protected
- The reported trial data are accurate, complete, and verifiable from source documents
- The conduct of the trial complies with the currently approved protocol/amendment(s), GCP, and the applicable regulatory requirements.
Artificial intelligence (AI) and machine learning (ML) approaches could drastically change how businesses operate, how healthcare is delivered, and what people do daily. These approaches can make clinical trials more efficient and effective, but more work must be done to integrate them into the clinical trial process.
What are the Benefits of Automated Trial Monitoring?
- Reduce work hours. There is an increasing trend of using real-world electronic health records (EHRs) to support clinical trials. Critical methodological components that are not traditionally reported need to be included when results are disseminated. It can be much work for employees to collect data for studies, but some of this data can be automatically generated from existing records. This would reduce the amount of work needed. The collection of baseline characteristics can be taken directly from structured data elements.
- Get accurate data. Even though there are benefits to using automation in research when it is regulated, we need to check that the information we get from electronic health records is accurate. The FDA believes we can get much helpful information from electronic data systems in healthcare settings. But we need to have a plan to make sure that the information we obtain from these electronic health records is high quality and valid if we want to use it to make decisions about labeling.
- Capture End Points in Real-Time. AI can be used to automate the process of adjudicating trial endpoints, which would improve efficiency and reduce costs. Smartphones can help monitor patients and detect when they have been hospitalized. You can create a virtual fence around a location, like a hospital, and if a person who has agreed to be part of a study spends a certain amount of time in that location, the phone will ask if they have been hospitalized. In studies that have been done to test this idea, it works.
- Monitor the ongoing treatment. Advanced analytics can help identify which participants are more likely to need extra help to stick to the study treatment plan. This can be done by using smartphone data, which can be analyzed to identify patterns in speech, voice, and facial expressions.
What Should You Switch to Remote Trial Monitoring?
Although the process is different for every organization, there are three actions required to move from traditional monitoring to remote monitoring for a clinical study:
- Risk Assessment Review – Ask the investigation team to redefine/reaffirm the purpose of the investigation, reassess the risk, and address mitigation actions.
- Redefining Critical Data and Processes – Identify factors for remote and centralized verification to avoid gaps that impact risk and quality.
- Documentation updates – Monitoring changes can be flagged for inspection and audit. As you build your monitoring strategy, have a rationale that reflects your risk assessment.
To learn more about remote patient monitoring, read more here in the FAQ and this overview.
Stay tuned for the last chapter on the value of clinical informatics in every clinical trial stage!