Data they say is the new gold. All sectors of the economy continue to collect huge amounts of data to better serve humanity. And the healthcare sector, among other sectors, arguably tops the list in terms of data collection.
A lot of data collected in the healthcare sector come from various sources, and these can include radiology information, insurance portals, lab information systems, ERPs, and clinical trial applications. With so much information to be processed, integrating the flow of this information across all channels in the health sector has become labor-intensive and complicated. This ultimately has increased the consumption of resources, hence, reducing efficiency.
To tackle these challenges, a lot of healthcare companies are now bringing Robotic Process Automation (RPA) into the picture to automate repetitive and redundant procedures that are vital to the smooth functioning of their system.
One of those processes is Clinical Trials. In this article, we look at ways in which RPA can help in providing credible data and also reduce cost, workforce utility, enhance operational efficiency, and restrict the likelihood of errors.
Clinical Trials – How The Processes Look Like?
Clinical trials are research studies that aim to deduce if a treatment, device, or medical strategy is safe for consumption and use by humans. In general, clinical studies provide credible data and medical knowledge which assist in making healthcare guidelines and decisions.
The main objective of clinical trials is research. Trials are modeled to add to medical data and knowledge that pertains to the diagnosis, prevention, and treatment of disease conditions.
When it comes to healthcare research, a successful clinical trial needs a lot of accurate and precise data computations. No study or research is better than the quality of data used. The quality of data used in the stages of clinical trials is the most crucial factor to its success. To this end, a lot of healthcare companies continue to look for ways to achieve and use high-quality data in every stage of a clinical trial.
There’s been a continuous increase in effort in clinical data management and these efforts are reflected in related healthcare guidelines, books, and publications. However, as much as new efforts are being made, there are still several sources of errors in clinical trials regarding data entry, collection, and processing.
How automation and RPA help to improve the efficiency of clinical trials
Robotic Process Automation (RPA) continues to be an innovative and effective technology across many industries within the economy automating repetitive and manual processes. Given that RPA focuses on integrating several IT systems, improving employee productivity, and eliminating human error, RPA has become a useful technology in the Life Sciences industry, especially in the area of clinical trials.
As explained earlier, Randomized Clinical Trials (RCT) are the standards for evaluating new therapeutics in terms of safety when used in human subjects. Presently at a success rate between 40% to 80% across various phases, clinical trials are still considered to have significant failures due to patient recruitment.
Furthermore, with still many challenges to overcome when conducting RCTs, the cost of trials is still on the very high side. Some of the challenges that affect RCT include study design, trial sites, patient retention, availability of the patient, and principal investigators. However, with the integration of RPA, these challenges can be put to check thereby increasing the efficiency and success rate of clinical trials.
Robotic Process Automation can improve rule-based processes and workflows in clinical trials. This is because the bots are capable of data manipulation, transaction processes, and system calibration facilities while improving storage.
The Benefits of RPA to Clinical trials
Eliminate Monotonous Manual Data Collection
Robotic Process Automation (RPA) brings about infinite possibilities. Through the integration of RPA, Life Sciences companies can effectively speed up the duration to market for vaccines while enhancing regulatory measures and safety.
Through the integration of RP, both valuable times are saved during clinical trials which free up healthcare professionals to perform other tasks. RPA effectively promotes efficiency by providing a continuous, error restructured technique of data collection.
Automate Analysis To Show Extensive Effect Overview
With RPA, the Life Science industry can adequately carry out quick data integration to analyze and detect the overall effect of formulated drugs and vaccines. The patients will be able to notify RPA bots of all forms of side effects. These robots in turn then combine this data with medical historic data and external sources such as location-based data or weather to provide a general overview for additional analysis.
After the introduction of such a vaccine or drug into the market, the same RPA bots also assist in analyzing the side effects of such drugs that were not predetermined. These side effects including market sentiments are then collected daily and integrated with previous analysis. This is to continuously manage and monitor the entire effect of the vaccine introduced into the market.
At the end of it all, integrating RPA bots does not only enhance operational efficiency but also improves the management of clinical trial audit logs as well as compliance with pharmacovigilance regulations.
Assist In Validation Processes
RPA bots can be integrated to be involved in validation processes. However, the process of integration is not that simple. To program bots to validate various clinical processes, stringent regulations are required for each separate system that supports the development of medicine or vaccines.
Therefore to prevent an error of validation, setting up a well-structured robot configuration framework, change management, and validation is required. This is very crucial to the process of integrating RPA solutions.
Other benefits include:
- Facilitates quick response to patience which helps to improve the progress of both the doctor and the patient.
- Provides patients, with one-point access to their data profile
- Assist in scheduling appointments and make immediate reminders for both the patient and the doctor.
- Facilitates healthcare professional cross-platform access to information
RPA Clinical Trial Use-cases
Business endpoints that can be optimized using RPA are:
Creating a patient population with the use of dynamic exclusion and inclusion to evaluate the effect of a trial is usually an overwhelming task. However, the integration and use of RPA effectively perform this task. As a result, the eligible patient population can be easily enlarged by the study designer if there’s a need for modification.
By automating patient matching with RPA, the entire process is better and faster. RPA effortlessly increases recruitment speed by carrying out initial execution with potential patients before the decisive interaction with associates of the clinic. RPA assists in lessening the effort put into repetitive tasks at the patient recruitment stage for a clinical study.
Processing Pharma Co-vigilance Cases
Based on a study carried out by ClinicalResearchNewsOnline.com, it is estimated that big pharmaceutical companies on average process about 700,000 adverse incident cases every year. With this kind of pressure on big pharma companies, a lot of these companies plan to go lean to reduce cost while increasing the caseload and not jeopardizing the base cost.
Today, about 50% of the PV resources are utilized in managing cases that need data integration. However, according to RoboticsandAutomationNews.com the automation of these manual steps can reduce the time spent on PV by 45% hence saving big pharma companies a lot of money yearly.
Trial Master Process Management
Sponsors of clinical trials are known to record every activity carried out in a clinical study. This includes various sites in a master data repository called Trial Master File (TMF).
Presently, these data are still manually inputted in the TMF structures. As a result, sponsors with a lot of CROs have to create multiple TMFs which most times are not efficiently integrated. Errors during this integration are known to limit the level of insights drawn from the TMF. That’s not all, additional resources are needed to be trained and equipped to verify and maintain each of these systems.
However, with more healthcare companies integrating RPA, the trial master process management is carried out quickly and efficiently. This is because the upload of data and documents into the TMF becomes automatic. The integration of RPA has proven to lessen the time spent on data entry by a whopping 90%, therefore saving millions of dollars on each clinical trial every year.
Regulatory Submission Process
The process of submission for regulatory activities is usually slow. It involves pharmaceutical companies taking part in activities like the compilation of health records and monitoring the status of documents. The automation of this process via RPA has been observed to significantly reduce the time to market a product.
It has become obvious that RPA is the answer to increasing the efficiency of repetitive but vital administrative tasks in clinical processes. RPA is gradually enhancing the efficiency of pharmaceutical firms in focusing on producing effective and safe drugs at a very low cost to the market.
RPA now effortlessly assists in preventing delays in the introduction of drugs into the market. It also helps to redistribute tasks of higher values such as core research and development to employees. What we do at Flobotics is give a wide range of RPA consulting services, help recognize automation opportunities, analyze these healthcare processes, establish essential infrastructures, and develop sustainable and stable bots.