My last blog dove into robotic process automation fixing mistakes in clinical data entry. This blog highlights enhancing slow recruitment with robotic process automation (RPA).
Even the most promising clinical sites can have trouble recruiting enough eligible subjects. Pressure from upper management or the sponsor to meet recruitment targets causes stress and anxiety for study managers, which in turns strains their relationships with underperforming sites.
This tension can be alleviated by helping sites:
- Broaden their pool of potential subjects
- Build a database of potential subjects
- Leverage the social networks of potential subjects for recruiting purposes
To illustrate how companies achieve this, consider the following scenario.
While surfing the web on his mobile phone, a young man receives an offer to apply to participate in a clinical trial. He thinks, “Why not?,” creates a user ID and password, and then follows a series of prompts to build out his profile. While there are no current trials that are a good fit for him, he continues to receive occasional helpful tips for managing the conditions he included in his profile.
Down the road, potentially after failing a screening visit, his profile is flagged as someone who is not himself a good fit for an upcoming study, but he is someone who might have people in his social network who may be. So, he receives a request to spread the word to his network, along with ready-to-use tweets and Facebook posts. A few people in his network are interested and create their own profiles, just like their friend, and the process restarts for each of them.
This modern, multi-faceted solution not only frees sites from the constraints of their existing patient population and physical location, but it makes it convenient for potential subjects to become and stay engaged over time. It casts a wider net than any one site can, involves more parties (even automated parties) in recruitment efforts, and leverages CX principles to connect potential subjects to sites.
Issues with Payments:
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
This is one of the biggest complaints from both sponsors and sites. Sponsors and CROs dedicate countless resources to researching and addressing payment questions and concerns raised by site personnel, and site personnel often feel frustrated, undervalued, and distrustful of sponsors and CROs that do not pay them as promised.
When a site is not paid on time, is not paid correctly, or is not acknowledged in a timely manner when payment issues are raised, it can trigger a sharp decline in site productivity, which negatively impacts a product’s overall time to market.
This negative outcome can be sidestepped by:
- Providing sites with a comprehensive history of payments that have been issued to them
- Equipping sites with self-service tools to open claims against payments that seem incorrect
- Giving sites the ability to see the status of open claims over time
- Automating claim investigations and resolutions, where possible and appropriate
Here is one of the more thoughtful solutions that incorporates these principles:
An investigator running a study site logs in to see an overview of her payment history. Something seems off, so she begins to review each payment in detail, and comes across what she believes to be the source of the problem – the withholding for her first payment seems higher than what was agreed upon in the contract she signed. The percentage was corrected for all subsequent payments, but the original payment was never adjusted.
Rather than picking up the phone, right there in the system she opens a claim against the payment in question and selects “Withholding” as the type of claim. She fills out the other required fields and submits the claim; the status changes to “Submitted.”
The field values the investigator selected trigger an automated resolution process in which the withholding percentage in the payment is compared to the details of the contract.
The automated workflow is unable to resolve the claim because the contract contains two different withholding percentages, so the clinical finance team is notified that human intervention is required. A clinical finance staff team member picks up the claim, which changes the status to “Under Review” The investigator can see that someone is working on his claim.
The clinical finance resource works with the legal team to draft an amendment to the contract that clarifies the correct withholding percentage for the investigator and routes the amendment for review and approval. After receiving the approval from the investigator, clinical finance issues a payment adjustment for the initial payment; the claim status changes to “Resolved.” Once the investigator receives the payment adjustment, she clicks the “Accept Resolution” button for the claim, and the claim is closed.
While it does take time and effort to map out the automated workflows for investigating payment claims, you can see the incredible value in doing so. The issue of payment problems is so prevalent in the industry and so detrimental to site and investigator productivity and relationships, that many companies make this particular solution one of their top priorities.
To learn more about specific clinical trial challenges concerning the pharma-investigator relationship and potential solutions using robotic process automation for subject recruitment, you can download the guide here, or fill out the form below.