An Agent-Based Approach to Real-Time Patient Identification for Clinical Trials

Transcription

1 An Agent-Based Approach to Real-Time Patient Identification for Clinical Trials Gareth Tyson, Adel Taweel, Simon Miles, Michael Luck, Tjeerd Van Staa and Brendan Delaney King's College London, UK General Practice Research Database, UK

2 Overview Our goal... is to improve the procedure by which eligible patients are discovered and recruited to clinical trials

3 Overview Our approach... is to utilise agent-oriented engineering to actively discover and recruit patients in real-time

4 Overview Our motivation... money (and saving lives)

5 Overview Overview Background Principles of epcrn-idea epcrn-idea Architecture Conclusion

6 Background

7 Clinical Trials

8 Clinical Trials A Clinical Trial is a: Recruitment Process Set of procedures in medical research conducted to allow safety and efficacy data to be collected for health interventions Patient databases Newspaper and radio adverts Posters Personal Recruitment UK Medical Research Council found that 31% of trials actually recruited to their planned target 30 40% of costs arising during the recruitment phase

9 Clinical Trial Eligibility Criteria Clinical Trials require specific patients Defined through eligibility criteria encompassing: With specific characteristics Age Gender Problem Drug Treatment Lab Tests...and others. Can be expensive and complicated to compute Especially when involving human parties

10 Challenges for Recruitment

11 Challenges for Recruitment Locate eligible patients Deciding eligibility Is a patient truly eligible for a trial? Contacting eligible patients Where are patients who might be eligible located? How can (trusted) communications be formed between recruiters and patients? Persuading patients to participate How can a patient be persuaded into participation?

12 Agent-Oriented Engineering

13 Agent-Oriented Engineering What is an agent? Agents are: a computer system that is situated in some environment, and that is capable of autonomous action in their environment in order to meet its delegated objectives Independent software components Able to interact/observe their surroundings Goal-driven...in a similar vein to existing recruitment agents

14 Principles of epcrn-idea

15 Principles of epcrn-idea Apply agent-oriented engineering to clinical trial recruitment Replace existing human recruitment agents with software agents Intended to: Shorten recruitment life cycle Reduce errors and inaccuracies Reduce costs and overheads

16 Principles of epcrn-idea

17 Principles of epcrn-idea

18 Principles of epcrn-idea

19 Principles of epcrn-idea

20 Principles of epcrn-idea

21 Principles of epcrn-idea

22 Principles of epcrn-idea

23 Principles of epcrn-idea

24 Principles of epcrn-idea

25 epcrn-idea Architecture

26 epcrn-idea Central Control Service Clinical Trial Management System Practitioner's Computer

27 epcrn-idea Central Control Service Clinical Trial Management System LEPIS Practitioner's Computer

28 Central Control Service (CCS) Central Control Service Clinical Trial Management System LEPIS Practitioner's Computer

29 Central Control Service (CCS) A server that operates as a trial store Recruiters upload new trials Trial Description Eligibility Criteria Recruitment Description Implemented as a database back-end LEPIS agents provided with secure (remote) access

30 Local Eligible Patient Identification Service (LEPIS) Central Control Service Clinical Trial Management System LEPIS Practitioner's Computer

31 Local Eligible Patient Identification Service (LEPIS) Software agent that resides on practitioners' computers LEPIS detects when patient enters consultation Computes if patient is eligible for any trials Creates graphical pop-up Allows immediate recruitment

32 Local Eligible Patient Identification Service (LEPIS) Accessing Patient Information Accessing Trial Information E.g. discovering the eligibility criteria of trials Computing Eligibility E.g. discovering if a patient has a particular disease Deciding if a patient is eligible Notifying the practitioner

33 Local Eligible Patient Identification Service (LEPIS) Accessing patient Information Modified a prominent EHR: Vision Whenever patient enters clinic Whenever data is entered about patient Creates XML 'extract file' Read by LEPIS Modifies XML extract file. Accessible information is: Patient Identifier DOB Gender Problem Codes (Read Codes) Drug Codes (Multilex Codes)

34 Local Eligible Patient Identification Service (LEPIS) Accessing trial information To compute eligibility, LEPIS needs access to trials and their eligibility criteria However, not possible to store full repositories E.g. can scale to in excess of 100k trials Each LEPIS agent selects n trials p trials that include a known patient in the clinic c trials that include coded information previously encountered r random trials

35 Local Eligible Patient Identification Service (LEPIS) Computing eligibility Involves comparing eligibility criteria rules against patient characteristics E.g. check if patient is within an age range Notifying the practitioner LEPIS generates a pop-up Provides all information required to inform practitioner of trial Provides functionality to register a response Providers links to external services

36 Local Eligible Patient Identification Service (LEPIS)

37 Local Eligible Patient Identification Service (LEPIS)

38 Local Eligible Patient Identification Service (LEPIS)

39 Clinical Trial Management System (CTMS) Central Control Service Clinical Trial Management System LEPIS Practitioner's Computer

40 Clinical Trial Management System (CTMS) A web server that allows patients to be registered Offers back-end for researchers If interested in participation If they wish to opt-out Can access registered patient details Follow progress (e.g. counts) Then any further necessary steps can then be taken Contacting patients Monetary payments etc.

41 Clinical Trial Management System (CTMS)

42 Conclusion Discussed the current limitations of clinical trial recruitment Presented an agent-oriented solution LEPIS agents discover eligible patients Infrastructure supports their endeavours Currently under test in preparation for deployment Future Work Focussing on adding further agent intelligence Better selection of trials Load balancing Rolling out of two clinical trials

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