Team - Rivella - The Digitalisation of Business Processes in Clinical Trials

• Team Members
• Coaches
• Introduction
• Project Goals
• Process Flow
• How to run
• To-Be Process
• Make Scenarios
• Flask API - acting as EHR database
• Technologies
• Conclusion
• References
• Disclaimer

Team Members👨🏻‍⚕️👨🏼‍⚕️👩🏻‍⚕️👨🏽‍⚕️

Name	Email
Prerna Duhan	[email protected]
Marco De Luca	[email protected]
Gabriel Flückiger	[email protected]
Tim Frietsch	[email protected]

Links🔗

• Deepnote (External worker): https://deepnote.com/workspace/flask-project-testing-ad2085cb-80f7-4457-9e62-3f73d24fce59/project/Main-Code-c719ecc6-7add-42e5-96f8-239e9c0d87b1/notebook/External%20Worker%20-%20fetch%20data-6c15afbbea1341b09f7ad9b7d193e2f8
• Deepnote (API EHR): https://deepnote.com/workspace/flask-project-testing-ad2085cb-80f7-4457-9e62-3f73d24fce59/project/Main-Code-c719ecc6-7add-42e5-96f8-239e9c0d87b1/notebook/Flask%20-%20API-f1da699b5c6548e2963b2d788a440f18
• Make: https://eu2.make.com/198153/team/dashboard
• Google Forms (Informed Consent Form): https://docs.google.com/forms/d/e/1FAIpQLSclmsXFrDT53H1n6qVILp1mwzuchJ3oonDq3I2rf-nfsjKRQg/viewform?usp=sf_link

Coaches

• Charuta Pande
• Andreas Martin

Introduction 🔍

Clinical research involves the examination of various elements related to a disease, such as its symptoms, risk factors, and pathophysiology. Clinical Trials are interventional investigations conducted on human volunteers and patients to evaluate the effectiveness of a therapetuic drug or medical device (Kandi & Vadakedath, 2023). Clinical trials include various phases starting from Phase 0, performing pharmacokinetic and pharmacodynamic analysis. Phase 1 is conducted in 10-100 healthy volunteers to to establish safety and the maximum tolerated dose (MTD). Phase 2 trials is conducted in a sample size of 100-300 patients, to establish the efficacy of the medical intervention. Phase 3 trials are conducted on 1000-3000 patients to confirm the safety and efficacy, and establish the efficacy of the drug. Finally, in Phase 4 post-marketing studies, the drug effect is monitered in the real world to determine drug-drug interactions and drug-disease interactions.

Business process modelling has been recognized as an important means in the reorganization of management processes, and there are considerable opportunities to automate Clinical trial workflows. Through our research, we have determined that optimizing and digitalizing clinical trials could potentially:

• Improve Flexibility
• Increase Patient Recruitment
• Reduce paper and manual workload
• Shorten Clinical Trial Timelines
• Reduce Costs

A major contributor to clinical trial delays and a failure to reach clinical research targets is not reaching patient recruitment targets. The research indicates that leveraging digital strategies for patient recruitment in clinical trials can expedite the recruitment process and potentially mitigate trial delays (Katz et. al., 2019). Digitalized patient recruitment is not only more time-efficient but also more cost-effective compared to traditional offline methods (Brøgger-Mikkelsen et.al., 2020).

Project Goals 🎯

Automation and optimization of the patient screening and patient recruitment process in Phase 1 clinical trials by leveraging data from the patient's electronic health record (EHR).

Process Flow

How to run 🚀

1. Run both files "External Worker - fetch data.ipynb" and "Flask API - EHR Database.ipynb"

   To run the external worker script involving External Worker - fetch data.ipynb, ensure you have both External Worker - fetch data.ipynb and cam.py in the same directory. Open your platform and execute the external worker file.

   For Flask API - EHR Database.ipynb and its interdependency with CSV files lab_results_10_tests_patients_1_15_NA.csv and patient_information.csv, place all files in a directory. Open your platform and run Flask API - EHR Database.ipynb. As the API is dependent on the CSV files, ensure those files are correctly referenced or imported within the script for seamless operation. Make sure to specify the URL endpoint of the API, ensuring it's accurately referenced to establish the connection and retrieve data from the API.

2. Deploy the Camunda process on https://digibp.herokuapp.com/engine-rest and start the process "ClinicalTrialCheck"

3. Claim the first task. Input username and password for a random patient (sample access information can be found in patient_information.csv) and add the URL endpoint described in the previous step. Select the clinical trial you intend to check the eligibility for.

4. Claim task "Confirm patient data and eligibility". The external worker returned basic information about the patient and information about the eligibility for the specified clinical trial. Briefly check the information and click on complete.

5. An email will be sent to the specific patient informing him/her about the eligibility for the clinical trial. If the patient is not eligible the process will end here.

6. If the patient is eligible wait for the patient to fill out the informed consent form.

7. Once the patient has given its consent, an external task will add the patient to the clinical trial database. You will be notified about it in the task "Show message". A brief message about the patient_id and when this patient was added to the database will appear. Click on complete. The process ends.

Issues with the current Clinical Trial process 🐛

1. Patient Engagement and Communication:

• Limited patient awareness and engagement in clinical trials.
• Ineffective communication between researchers, sponsors, and participants.

2. Technological Integration:

• The need for better integration of technology in the clinical trial process, including electronic health records (EHRs) and digital data collection methods.

3. Costs and Resource Allocation:

• High costs associated with clinical trial conduct and management.
• Efficient allocation of resources, including personnel and funding.

4. Complex Protocols:

• Overly complex study protocols that can lead to challenges in execution and understanding.

To-Be Process

Make Scenarios

1. Sending an Email: Informing patient/volunteer about eligibility

   An email is sent to a potential volunteer informing him/her about the eligibility to the clinical trial. If the volunteer is still available to participate he/she can click on the google forms link and give its informed consent.

2. Adding volunteer to database

   Once the informed consent of the volunteer is confirmed, their patient_id is inserted in to a google sheet (acting as database in this case).

3. Checking the database for a specific patient

   The external task retrieves patient_id from the Camunda process and checks if the volunteer has been inserted in to the google sheets. If that is the case it sends back a complete post request to the process.

Flask API - acting as EHR database

Simulates EHR database, providing patient medical records to users with password access. The API routes are secured by JWT authentication. Using JWT authentity the user only receives the medical data he's allowed to access. Getting basic patient information such as last name, first name, date of birth can be achieved via "/patient_data". Lab data is accessed via "/lab_data".

External worker - check study eligibility based on EHR data

The external worker is provided with a username, password, EHR API URL and clinical trial number through camunda input. The external worker executes a post request to retrieve the user-specific access token from the EHR API. This authentication token is then used to access and retrieve patient-specific EHR data. In a second step, the patient's medical history is extracted and compared with the specific requirements of the clinical trial. If the requirements of the study are met, the patient is classified as eligible. Basic patient information (patient ID, name, date of birth) and study eligibility are sent back to the camunda process as variables.

Depending on the clinical trial number, this worker can quickly and easily assess patient eligibility.

Benefits ✅

1. Potentially shortens clinical trial timelines
2. Increases volunteer and patient engagement in clinical trials
3. Reduces clinical trial management and conduct costs
4. Leverages technology

Technologies 🌐

• Camunda
• Make
• Google Sheets
• Google Forms
• Deepnote (for Python programming - Flask API / External worker)

Conclusion

Through this project, under the leadership of our coaches Andreas Martin and Charuta Pande, our team aimed to optimize and shorten Phase 1 clinical trial timelines by improving patient recruitment timelines. The current clinical trial process requires a lot of manual documentation and screening patients singularly in a time consuming manner. To overcome these challenges, we introduced an automated and digitalized patient screening and patient recruitment workflow, which is cost-effective and easy to integrate. This ease would push for online recruitment campaigns that would be primary strategies to maximize recruitment rates. We hope to integrate this model for larger Phase 2, Phase 3 and Phase 4 clinical trials, additionally automating other clinical trial documentation and regulatory documentation. This would create a centralized system for clients and regulators to integrate in their companies, ensuring effective communication between patients and stakeholders.

In conclusion, a digitalized and automated patient screening and recruitment process can effectively shorten clinical trial timelines, providing patients with efficacious drugs in a timely manner.

Acknowledgments 🙏

We are grateful to our incredible coaches and mentors, Andreas Martin and Charuta Pande. Thank you for generously sharing your knowledge, wisdom, and expertise, helping us improve our process through each stage.

References 📚

1. Kandi, V., & Vadakedath, S. (2023). Clinical trials and clinical research: A comprehensive review. Cureus, 15(2). https://doi.org/10.7759/cureus.35077
2. Rohilla, A., Singh, R. K., Sharma, D., Keshari, R., & Kushnoor, A. (2013). Phases of clinical trials: A review. Ijpcbs.com. https://www.ijpcbs.com/articles/phases-of-clinical-trials-a-review.pdf
3. Katz, B. E., Aleksander, E., Valerija, M., & Zibert, J. R. (2019, January 4). Optimize clinical trial recruitment with digital platforms. Dermatology Times. https://www.dermatologytimes.com/view/optimize-clinical-trial-recruitment-digital-platforms
4. Brøgger-Mikkelsen, M., Ali, Z., Zibert, J. R., Andersen, A. D., & Thomsen, S. F. (2020). Online patient recruitment in clinical trials: Systematic review and meta-analysis. Journal of Medical Internet Research, 22(11), e22179. https://doi.org/10.2196/22179
5. Pufahl, L., Zerbato, F., Weber, B., & Weber, I. (2022). BPMN in healthcare: Challenges and best practices. Information Systems, 107(102013), 102013. https://doi.org/10.1016/j.is.2022.102013

Disclaimer 📝

1. This project is a digitalized clinical trial project that involves the collection, processing, and storage of sensitive volunteer and patient data.
2. All data collected as part of this clinical trial is treated with the utmost confidentiality and is securely stored. Participation in this clinical trial is voluntary, and participants have the right to withdraw at any stage.By participating, volunteers and patients acknowledge and consent to the collection and processing of their sensitive data for research purposes.
3. Data collected as part of this clinical trial is used solely for research purposes and will not be sold or shared with third parties for commercial purposes.
4. For questions or concerns regarding data privacy and security, participants can contact [email protected]