Artificial intelligence (AI) and automation are unavoidable topics in clinical programming. With so much hype, it is easy to disregard emerging technology as a passing fad. However, this is a misguided approach. There is true value in automation, and when it comes to clinical data, exploring these tools can save time, money, and effort for your programming teams. In short, understanding how automation fits into your data strategy is no longer optional.
The Value of Automation, Beyond Buzzwords
There are many repetitive tasks involved in clinical programming, including reporting and validation. Study teams often require new information quickly, and there can be overlap between these various requests. The work needed to generate reports adds up rapidly, and automation has the potential to reduce workload, without sacrificing quality.
Use Cases for Clinical Programming Automation
Automation is best suited for repetitive processes. When used for these kinds of tasks, automation can enable faster completion of routine work, with human oversight over final deliverables.
Potential use cases include:
- Building listings and reports based on a standardized process
- Training models to provide support for complex clinical data reporting, with regular validation and adjustments from human teams
- Aligning datasets and outputs with necessary regulatory standards
- Building wireframes for CRF forms and other study requirements, with the ability for human customization and adjustment
- Gleaning insights from previous study data to see what has succeeded and failed, then using this information to enable more efficient trial design
- Creating a starting point for coding, allowing human experts to take a plug-and-play approach; many analytics tools are already integrating AI for this purpose
- Empowering non-programmers to access the information they need, while saving programmers’ time for requests where it is truly necessary
Common Automation Misconceptions
Although there is increasing interest in the benefits of automation, there are also many common misconceptions. Understanding these, and the truth behind them, can help you build a more efficient AI adoption strategy.
- Myth:Automation can fully replace humans in clinical programming.
- Fact: Automation can supplement human work, but it cannot replace the role of experts. This technology is best for routine or tedious work, freeing programmers to spend their time on complex problems where their expertise has the greatest value.
- Myth:Automating work will reduce quality.
- Fact:By maintaining human oversight, deliverables produced using automation can maintain necessary quality. When used strategically, AI tools can even improve quality by catching mistakes and learning from previous successes and failures.
- Myth:Automation will always add value when integrated into the programming process.
- Fact:Unlocking the full potential of automation requires a strategic approach. Simply using AI-enabled tools or automating processes without a clear plan in place is unlikely to result in true ROI and could do more harm than good.
Building Your Automation Adoption Plan
Like almost any process, building an effective plan to adopt AI and automation in clinical data operations starts with setting a clear goal. Consider what your current clinical programming process looks like. How do your programmers spend their time? Which processes are working well, and which could be more efficient? Questions like these can help you identify where automation has the potential to save time, improve programmers’ day-to-day experience by reducing repetitive work, and unlock greater insights from your data.
It’s also important that any automation works within your existing ecosystem. A one-size-fits-all approach is not feasible since the nuances of programming languages, analytical tools, and technology platforms all vary. Working with an experienced partner to guide AI adoption can help you account for these factors.
Similarly, you will need to consider the background of your team and provide training that addresses the topics that are more important for them. Programmers and non-programmers need to understand how to use modern technology for it to be worthwhile, and AI training programs need to adapt as tools continue to evolve. Again, working with an experienced partner can help you navigate this process.
Streamline Automation With an Experienced Partner
Automation and AI will only continue to increase in power and efficiency. If you have not already started to explore where they can streamline your clinical programming workflows, now is the time. Based on deep domain experience across the data life cycle, Ephicacy is ready to guide AI adoption. We utilize automation strategically in our workflows to unlock greater value for our clients, as well as designing and implementing AI analytics tools and training for both internal and external teams.
To discover how we can use automation to save you time and money, contact us today.
