Clinical trial recruitment changes under the wave of digitalization: Drug Trial Circle launches a new "Internet AI" recruitment model
2024-08-13
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Patient recruitment is one of the most important links in clinical research. In most cases, this process takes months or even years. Research data shows that more than 80% of clinical research projects cannot complete patient enrollment within the specified time. How to break the shackles of traditional recruitment models and recruit patients in a more innovative, efficient, stable and compliant way has become a proposition that all sponsors must consider.
The emergence of Drug Trial Net (www.trialnet.cn) is expected to provide a new solution to this problem.
Compared with the inherent defects of the traditional recruitment model, such as geographical restrictions, single channels, and information communication difficulties, the Drug Testing Circle relies on the Internet platform to carry out "Internet + AI" recruitment, effectively expanding the scope of recruitment, so that potential patients can also retrieve project information through the Internet channel, so as to carry out integrated collaborative processes such as understanding, screening, online informed, voluntary registration, evaluation data upload, project CRC review, etc. This innovative model not only brings a wider range of patient resources to the sponsor, but also significantly improves the efficiency and compliance level of recruitment, making the recruitment progress transparent and visible in real time.
Recently, around this innovative model, we interviewed Ms. Ma Fan, Director of R&D Subject Recruitment at Shengfang Pharmaceutical, and had an in-depth exchange on the detailed explanation of "Internet + AI" recruitment, the deep integration of AI technology, and its important value to the applicant.
The following is the main content of the interview:
1Why do we need to do "Internet+AI"recruit?
Ma Fan: Traditional recruitment means that recruitment specialists recruit subjects around the area where the research center is located. Taking a certain advanced liver cancer project as an example, if it is necessary to recruit advanced liver cancer patients, the traditional way is to send staff to the hospital oncology department for publicity, which may be in the form of placing roll-up banners, posters, and posting on WeChat Moments. If a patient wants to participate, he can find a recruitment specialist to sign up, and the recruitment specialist will sort out the patient's medical history according to the project requirements and conduct preliminary screening (some large companies will hand over the screening work to medical specialists). After screening out patients who meet the project requirements, they will be arranged to go to the research department for a face-to-face consultation with the research doctor, and the informed consent form will be signed, examined, and medication will be administered. When it comes to medication, the recruitment work is completed.
2What kind of skills do you need to have in order to do a good job in recruitment?
Ma Fan: Traditional recruitment has very high requirements for practitioners. Although the entry threshold is low, it requires a wide range of knowledge, a lot of repetitive work, and many emergencies, and it requires very high comprehensive personal capabilities.
First, from the perspective of medical and pharmaceutical knowledge, just the recruitment of anti-cancer drugs involves hundreds of diseases and corresponding pharmacology. To have a basic and comprehensive understanding of this knowledge, most people need at least 5 years of experience accumulation. And this is only the accumulation of the medical part.
Secondly, there are also requirements at the regulatory level, such as the "Specifications for the Management of Clinical Trials of Drugs", "Measures for the Management of Medical Advertisements", "Declaration of Helsinki", etc. As a practitioner, you cannot ignore these. You must have the professionalism of a practitioner and cannot make rash moves or talk nonsense.
In addition, the participation methods for each project are different; the research center's processes and screening cycles will also be dynamically updated. All this information requires close collaboration and clear knowledge between the recruitment staff and the external company team in order to be effectively communicated and connected. Otherwise, it may lower or raise the expectations of the subjects, causing confusion in arrangements and disputes.
Finally, the vast majority of subjects are prejudiced against clinical research, believing themselves to be "guinea pigs" or having unreasonable imaginations about the efficacy of drugs; at the outset, recruiters need to objectively introduce the background of clinical research.
These are hard skills that can be acquired through training.
As practitioners, you also need soft power. Many subjects are patients and are sensitive. When emergencies occur, qualified practitioners must also know how to communicate and coordinate in a compliant and efficient manner to solve problems while protecting the rights and interests of multiple parties.
3, the “Internet+AIHow is the recruitment model different from the traditional recruitment model?
Ma Fan: The process is slightly different, and the tools used are also different.
Traditional recruitment still uses WeChat, posters, Excel spreadsheets, PDF documents, etc. to recruit and manage subjects. For practitioners, there is a lot of repetitive work, and information details are easily missed when communicating and collaborating across enterprises. It is difficult to trace and take over the subject situation when personnel leave. For subjects, they cannot get answers to project-related questions in the first place; for management and reporting, integrating the situation of each project and reviewing it takes a lot of working hours, resulting in waste of resources, reduced work efficiency, and poor experience.
The entire drug trial circle is managed by a digital system, and AI provides great help in many tasks.
For example, for subjects who register for clinical trials online, our system will automatically guide them to submit their medical history information. AI will immediately analyze the information uploaded by the subject and give feedback on whether the subject matches the registered trial. Before the staff contacts the subject, the subject has already passed the "intelligent matching" and has a preliminary understanding of the projects he can participate in. After that, the communication efficiency will be very high, which greatly saves communication time.
For patients who sign up on the traditional Internet, they must first introduce their company through telephone communication to establish initial trust, then add the subject on WeChat to collect his medical history information. The staff will then sort out his medical history and find a project suitable for the subject in the project library, and then give the subject a preliminary introduction to the project... The conversion rate of the entire process is very low, and the subject experience will also be compromised.
In addition to the scenarios where subjects sign up on their own, we have also applied AI in scenarios such as project reporting, data management, and collaborative work, which has greatly reduced repetitive work. Because of the upgrade of tools, our partners can be competent in more work scenarios, grow faster, reduce the workload of partners, increase collaboration efficiency, make project management smoother, and provide subjects with a better experience.
4, the drug testing circle is actually2022We only started recruiting subjects around 2000, but there were already a number of10It is generally believed that the fastest growing period of the industry is14to19In 2017, it is difficult to increase recruitment, but your enrollment data is growing very fast. How did you do it?
Ma Fan: Yes, the data in the drug testing circle has grown very rapidly in recent years, exceeding many people's expectations.
In 2022, I started to get involved in the subject recruitment industry. Although there were already leading companies at that time, recruitment was still the main reason why clinical research was difficult to advance. Many projects had to open more centers, postpone trials, or even abandon them midway due to recruitment difficulties, resulting in resource loss and waste, and also preventing many drugs from entering the market and being used by patients. We think we can do better, so we want to challenge this difficulty.
Although 2014-2019 was a period of rapid growth and high profits for the traditional model, that was the growth brought about by the development of the market from 0 to 1. The model has not changed for so many years, and many small companies have also begun to recruit gradually in the same model, causing serious internal competition in the industry. Therefore, from the beginning, we did not intend to use the human sea tactics to grab the market.
We have been using technology and process optimization to solve the problems faced by the industry. We have spent a lot of effort developing a drug trial circle subject recruitment system, which is equipped with many AI technologies, such as disease knowledge graphs, OCR, LLM, etc.
When choosing clinical research registration, if ordinary cancer patients do not have the help of doctors and cannot understand hospital lists and clinical research projects, we use AI to help them write medical records and find projects, and give a result that everyone can understand. For example, Zhang San has a 60% match with project A, which is 456km away from the center, and a 100% match with project B, which is only 2km away from the center. This is something that everyone can understand and choose independently.
When reporting with the sponsor, our PM does not need to spend time organizing data, making tables, sending report emails, calculating prices... The data in the system can automatically generate (BI) various visual reports, analysis charts, price lists, and the robot can also report and send project progress.
These applications can truly make users feel convenient and surprised, so they have also brought us rapid growth in performance. Our patient registration volume is very large. Taking psoriasis as an example, the number of new registrations per day is more than 100. After the registered patients use our APP themselves, they have a preliminary understanding and choice of clinical research, and their enthusiasm for participation will also increase. There are also many traditional recruitment partners who are willing to cooperate with us as long as they can use our AI system. The recruitment speed of subjects for many chronic diseases and tumors far exceeds customer expectations.
Not only that, we are also trying to apply it in more scenarios. The popular LLM is iterating rapidly and constantly brings us many surprises. We are also following closely and gradually practicing it. In the near future, we will launch smarter products and services.
In the future, with the iteration of technology and the reduction of cognitive barriers through AI, more and more ordinary people will be able to understand clinical research, eliminate prejudice, and participate in clinical research. This is also the market growth point that we value most.
5, Could you please introduce us to your team and business scale?
Ma Fan: Our team is currently composed of two parts: production and research and operations. The operations team has 6 people, responsible for the acceptance and delivery of subject projects, subject follow-up management, etc. The production and research team is responsible for product design, R&D and online launch, etc., with about 10 people. Of course, there are also many robots responsible for follow-up, reporting, medical matching, Q&A and other matters.
So far, we have undertaken more than 900 subject recruitment projects, and now about 100 subjects are enrolled every month.
Do you think that in the future, the traditional recruitment model may be replaced by AI intelligent recruitment?
Ma Fan: The subject recruitment workflow is lengthy, and AI cannot replace some patient services and humanistic care work for the time being. However, in 3-5 years, teams that are not good at applying new tools may be gradually eliminated by the market due to internal circulation and lack of growth.
7Data security and patient privacy protection have been issues that have received a lot of attention in recent years. How does the drug testing circle solve this problem?
Ma Fan: The drug testing circle has always attached great importance to compliance work. Around this issue, we have obtained many certifications and built a comprehensive data security and patient privacy protection system.
When patients sign up, they need to sign a privacy authorization with the platform, agreeing that their personal medical data will be used for project screening and matching of all clinical research on the platform.
After the platform obtains the patient data, in addition to encrypting the personal privacy part of the registration, AI will also automatically code the part of the photo data involving the patient's privacy, such as name, ID number, mobile phone number, etc., to ensure that the software user cannot see the patient's personal information when viewing the patient's information.
The regulations in my country regarding the ownership of patient data that are currently more widely recognized are: data related to intellectual achievements such as patient treatment results belong to the hospital and the patient, and the patient's test results belong to the patient ("Electronic Medical Record Application Management Specifications (Trial)"). In order to participate in clinical research, patients take photos of their disease information and upload them to the platform and make relevant authorizations. As long as there are no problems with the platform's data security and privacy protection, and the scope of application does not exceed the agreed limits, the process is compliant.
Yaoshiquan has passed international and national certifications including ISO/IEC 27001 Information Security Management System, Level 3 Security Certification, GDPR General Data Protection Regulation, HIPAA Health Insurance Portability and Accountability Act, etc. These certifications not only demonstrate our professionalism, but also our emphasis on data security and privacy protection.
At this point, the interview about the intelligent recruitment business of Yaoshiquan has come to an end.
In the transformation of clinical research subject recruitment under this wave of digitalization, the exploration of the new "Internet + AI" recruitment model initiated by the drug trial circle has allowed us to see another possibility and opportunity for the implementation of new technologies in the field of clinical research. It can be foreseen that the future AI technology will achieve a qualitative leap in accuracy, adaptability, and intelligence, from disease prediction and diagnostic assistance in the medical industry to intelligent driving and traffic flow optimization in the transportation field; from quality inspection and process automation in industrial production to smart homes and personalized services in daily life. The application space of AI technology is extremely broad, injecting new impetus into social development and bringing unprecedented convenience and innovation to people's lives.
The Drug Testing Circle looks forward to working with all parties involved in the industry and will continue to use AI technology to help the development of innovative drugs in my country. (Xianning News Network)
