Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06561217
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-08-16
Brief Title:
Assessing the Performance of Artificial Intelligence AI-Augmented Electronic Health Record EHR Data Abstraction for Clinical Trial Patient Screening
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Assessing the Performance of Artificial Intelligence AI-Augmented Electronic Health Record EHR Data Abstraction for Clinical Trial Patient Screening
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Identifying eligible patients is a key process in the clinical trial enterprise Currently this process relies on time-intensive manual chart review creating a rate-limiting step for trial participation
The integration of AI technology into the trial screening process has potential to improve participation rates This study aims to assess the performance accuracy speed of AI-augmented patient identification and inform optimal integration into clinical research screening processes
The integration of AI technology into the trial screening process has potential to improve participation rates This study aims to assess the performance accuracy speed of AI-augmented patient identification and inform optimal integration into clinical research screening processes
Detailed Description:
The objective of this study is to assess and compare the accuracy and speed of three different approaches to abstracting clinical data used to identify oncology patients who meet the inclusion criteria for participation in clinical trials The three approaches under evaluation include 1 an autonomous AI algorithm Mendel AI developed by artificial intelligence startup company Mendel which analyzes patient medical records to extract relevant clinical facts AI-alone 2 a human researcher who manually reviews patient charts as per the current normpractice Human-alone and 3 a human researcher utilizing AI augmentation HumanAI where Mendel AI serves as a supportive tool in the decision-making process by providing the researcher a list of elements abstracted by the AI algorithm and a rank-order list of patients most likely to meet inclusion criteria for a trial
The study primarily aims to compare 1 the accuracy of the HumanAI collaboration relative to Human-alone given the relevance of this comparison for real-world clinical workflows defined by the proportion of pre-identified chart elements classified correctly compared against a predetermined gold standard and 2 the speed of the HumanAI vs Human-alone arms defined by the volume of completed prescreens per unit time
Our hypotheses are 1 the HumanAI arm will be non-inferior in accuracy when compared to the Human-alone arm in relation to a predetermined gold standard and 2 that a HumanAI arm will be superior in speed of abstraction when compared to Human-alone screening
The identification of eligible patients for clinical trials is a critical component of clinical research as it directly impacts patient recruitment study enrollment and the generalizability of research findings Currently the process of identifying eligible patients often relies on manual chart review by clinical research staff which can be time-consuming labor-intensive and prone to human error Consequently eligible patients may be overlooked and opportunities for trial participation may be missed The integration of AI technology into the patient identification process has the potential to enhance the accuracy and efficiency of this critical task leading to improved clinical trial recruitment and outcomes
This study holds important implications for the field of clinical research by evaluating the effectiveness of AI-augmented patient identification compared to traditional manual methods and autonomous AI algorithms By examining the strengths and limitations of each approach the study will provide valuable insights into the optimal integration of AI technology in clinical research processes Furthermore the results of this study have the potential to benefit patients by improving their access to clinical trials and increasing awareness of available treatment options For clinical research institutions enhancing the efficiency of patient identification can lead to more effective use of research resources and the potential for accelerated clinical trial timelines Ultimately the findings of this study may contribute to advancements in clinical research practices promoting more equitable access to trials and facilitating the development of innovative treatments for patients with cancer
The study primarily aims to compare 1 the accuracy of the HumanAI collaboration relative to Human-alone given the relevance of this comparison for real-world clinical workflows defined by the proportion of pre-identified chart elements classified correctly compared against a predetermined gold standard and 2 the speed of the HumanAI vs Human-alone arms defined by the volume of completed prescreens per unit time
Our hypotheses are 1 the HumanAI arm will be non-inferior in accuracy when compared to the Human-alone arm in relation to a predetermined gold standard and 2 that a HumanAI arm will be superior in speed of abstraction when compared to Human-alone screening
The identification of eligible patients for clinical trials is a critical component of clinical research as it directly impacts patient recruitment study enrollment and the generalizability of research findings Currently the process of identifying eligible patients often relies on manual chart review by clinical research staff which can be time-consuming labor-intensive and prone to human error Consequently eligible patients may be overlooked and opportunities for trial participation may be missed The integration of AI technology into the patient identification process has the potential to enhance the accuracy and efficiency of this critical task leading to improved clinical trial recruitment and outcomes
This study holds important implications for the field of clinical research by evaluating the effectiveness of AI-augmented patient identification compared to traditional manual methods and autonomous AI algorithms By examining the strengths and limitations of each approach the study will provide valuable insights into the optimal integration of AI technology in clinical research processes Furthermore the results of this study have the potential to benefit patients by improving their access to clinical trials and increasing awareness of available treatment options For clinical research institutions enhancing the efficiency of patient identification can lead to more effective use of research resources and the potential for accelerated clinical trial timelines Ultimately the findings of this study may contribute to advancements in clinical research practices promoting more equitable access to trials and facilitating the development of innovative treatments for patients with cancer
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None