Prostate CancerNCT06561230
Leveraging EA8191 to Assess AI-Augmented EHR Abstraction
An observational study of Prostate Cancer, run by University of Pennsylvania at 1 site in United States.
Trial summary
The goal of this prospective study is to assess the performance of AI (artificial intelligence) augmentation (compared against historical controls) to identify oncology patients who meet inclusion criteria for a clinical trial. Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostate Cancer
- What is being tested?
- Chart review
- How many people
- 300 participants
- Where
- 1 siteUnited States
- Study dates
- 4 Apr 2025to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
Study population: Patients with a diagnosis code of prostate cancer and with an upcoming genitourinary radiation oncology or medical oncology appointments at the Perelman Center for Advanced Medicine
Who may be able to join
- Patient must be male and >= 18 years of age.
- Patient must have had a radical prostatectomy (RP) as definitive therapy for histopathologically-proven prostatic adenocarcinoma
- Patient must have biochemical recurrence (BCR) after RP, defined as follows:
- If time to BCR, defined as time to first detectable PSA ( > lower limit of normal for assay used) after RP, is < 12 months, a minimum PSA level of >= 0.2 ng/mL and a confirmatory reading of >= 0.2 ng/mL is required, per the American Urological Association (AUA) definition (Note: patients with a persistent PSA reading of at least 0.2 ng/mL are eligible)
- If time to BCR, defined as time to first detectable PSA (> lower limit of normal for assay used) after RP, is >= 12 months, a minimum absolute PSA of 0.5 ng/mL is required
- If the patient has a detectable PSA (> lower limit of normal for assay used) at any time after RP AND has an eligible baseline SOC PET (PET1) with at least one positive lesion in any location, then there is no minimum PSA requirement
Show all 30Show fewer
- Patients must have no definite evidence for extrapelvic metastatic disease by conventional imaging modalities (CIM) (CT abdomen/pelvis or MRI abdomen/pelvis AND bone scintigraphy, or equivalent), within 26 weeks prior to Step 0 registration. If a patient only has a study-eligible PET/CT or PET/MR (i.e., PET done without prior CIM): if the PET is negative for extrapelvic lesions, then baseline CIM is NOT required. If the PET positive for extrapelvic lesions, then patient should have a baseline CT/MRI for soft tissue lesions and/or a bone scan for osseous lesions
- Study eligible = PET using FDA-approved radiotracer and performed within 16 weeks prior to study registration
- Extra-pelvic metastases is defined as any osseous metastases and/or any extrapelvic soft tissue, lymph nodes and organ metastases; extra-pelvic is defined as superior to common iliac bifurcation, outside of standard prostate bed + whole pelvis nodal RT fields. Baseline PET/CT or PET/MR scan (PET1) is eligible for this study if the SOC PET scan is completed with an FDA approved radiotracer for prostate cancer after Step 0 registration and prior to Step 1 randomization OR up to 16 weeks prior to Step 0 registration
- Patient must be a candidate for SOC post-prostatectomy radiation therapy (RT) to the prostate bed and pelvic nodes with androgen deprivation therapy (ADT)
- Patient must have the ability to understand and the willingness to sign a written informed consent document. Patients with impaired decision-making capacity (IDMC) who have a legally authorized representative (LAR) or caregiver and/or family member available will also be considered eligible
- Patient must have an Eastern Cooperative Oncology Group (ECOG) performance status 0-2
- Patient must not have started ADT for biochemical recurrence prior to baseline PET (PET1) imaging. A short course of low-dose anti-androgen such as bicalutamide, given after baseline study PET/CT but prior to study registration, is permitted as a brief temporizing measure in advance of starting protocol-approved SOC ADT.
- Patient must not be enrolled in another therapeutic clinical trial
- Patient must be able to lie flat and still for approximately 20-30 minutes or otherwise tolerate a PET scan and radiation treatment planning and delivery
- Patients undergoing a PET/MR must meet local institutional safety guidelines for MRI
- Patient must not have history of seizures or known condition that may cause predisposal to seizures (e.g., stroke or head trauma resulting in loss of consciousness) within 1 year prior to registration
- Patient must not have history of inflammatory bowel disease or any gastrointestinal disorder affecting absorption that is expected to increase risk of complication from radiotherapy
- Hemoglobin (Hgb) >= 9.0 g/dL (independent of transfusion and/or growth factors within 3 months prior to Step 0 registration) (obtained within 8 weeks prior to Step 0 registration)
- Leukocytes >= 3,000/mcL (obtained within 8 weeks prior to Step 0 registration)
- Absolute neutrophil count >= 1,500/mcL (obtained within 8 weeks prior to Step 0 registration)
- Platelets >= 100,000/mcL (obtained within 8 weeks prior to Step 0 registration)
- Total bilirubin < 1.5 x institutional upper limit of normal (ULN) (patients with Gilbert's syndrome, if total bilirubin is > 1.5 x ULN, must have a direct bilirubin of < 1.5 x ULN to be eligible) (obtained within 8 weeks prior to Step 0 registration)
- Aspartate aminotransferase (AST) (serum glutamic oxaloacetic transaminase [SGOT])/alanine aminotransferase (ALT) (serum glutamate pyruvate transaminase [SGPT]) =< 2.5 x institutional ULN (obtained within 8 weeks prior to Step 0 registration)
- Creatine < 1.5 x institutional ULN (or measured creatinine clearance > 30 mL/min)
- Human immunodeficiency virus (HIV)-infected patients on effective anti-retroviral therapy with undetectable viral load within 6 months are eligible for this trial
- Patients with known history or current symptoms of cardiac disease, or history of treatment with cardiotoxic agents, should have a clinical risk assessment of cardiac function using the New York Heart Association Functional Classification. To be eligible for this trial, patients should be class I or II (by patient symptoms) or A or B (by objective assessment)
- Patient must not have completed a course of prior pelvic radiation therapy for any reason
- Patient must agree not to father children while on study
- Patient must be English or Spanish speaking to be eligible for the QOL component of the study
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Pennsylvania
Philadelphia, Pennsylvania, 19104
About this study
Official title: Leveraging a Penn-based Cancer Trial (EA8191) to Assess the Prospective Performance of Artificial Intelligence Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening and Selection
The goal of this prospective study is to assess the performance of AI (artificial intelligence) augmentation (compared against historical controls) to identify oncology patients who meet inclusion criteria for a clinical trial. The study staff will leverage a natural language processing (NLP)-based AI algorithm that rank-orders patients most likely to meet inclusion criteria for a trial. We hypothesize that this collaborative Human+AI workflow can improve the efficiency, accuracy, and diversity of trial prescreening.
Read the detailed description
The objective of this prospective study is to assess the accuracy and efficiency of a clinical research coordinator utilizing AI augmentation to identify oncology patients who meet the inclusion criteria for participation in clinical trials. The clinical research coordinator utilizing AI augmentation ("Human+AI") will leverage an autonomous natural language processing (NLP)-based AI algorithm (Mendel AI) developed by artificial intelligence startup company Mendel.ai. The algorithm Mendel AI serves as a supportive tool in the decision-making process by providing the clinical research coordinator a rank-ordered list of patients most likely to meet inclusion criteria for a trial, as well as a list of elements abstracted by the AI algorithm for each patient. The performance of "Human+AI" would be compared against historical control from the selected clinical trial: EA8191/INDICATE (Penn IRB #848795), a national, Phase III, randomized, prostate cancer clinical trial on the use of PET scan findings to direct local and systemic treatment intensification in patients with post-prostatectomy biochemical recurrence.
The electronic health records (EHRs) reviewed as part of this clinical trial screen would draw from randomly-selected Penn patients with upcoming genitourinary radiation oncology and medical oncology appointments at the Perelman Center for Advanced Medicine. Given a randomly selected batch of EHRs from these patients viable for prescreening, the research team aims to determine how well (and also if better, how much better) clinical research staff leveraging Mendel's AI algorithm can identify those patients who met the eligibility criteria for the EA8191 trial (as compared to historical averages for the EA8191).
The study primarily aims to compare (1) the efficiency of the Human+AI collaboration relative to historical efficiency from a Human-alone workflow, (2) the accuracy of the Human+AI collaboration relative to historical accuracy from a Human-alone workflow and (3) the diversity of eligible patients identified by the Human+AI prescreening workflow, compared to historical diversity from a Human-alone workflow.
Our central hypothesis is that workflows that merge traditional CRC-driven prescreening with automated AI - "Human+AI" workflows - can improve the efficiency, accuracy and diversity of trial prescreening.
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.
- Conditions
- Prostate Cancer
- Being tested
- Chart review
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Chart reviewOther
Patients eligible for prescreening will be identified using structured criteria (e.g., anyone with an appointment in the upcoming 2 months with a relevant provider). All individual charts will be de-identified. De-identified EHRs will then be transferred to Mendel's secure data abstraction platform, where trial eligibility criteria will be abstracted. Patients will be rank-ordered by number of eligibility criteria met, with patients meeting the most eligibility criteria at the top of the queue. The study team will then review eligibility criteria and flag eligible patients.
| Study group | Role | What they receive |
|---|---|---|
| Human + AI | Not described Receives: Other: Chart review |
What is being measured
The primary outcome is the main question the study is designed to answer.
Efficiency
The number of eligible patients per 10 charts prescreened
Measured: 1 year
Secondary outcomes (2)
Accuracy
The proportion of total charts identified correctly as eligible
Measured: 6 months
Diversity
The proportion of eligible individuals who identified from groups historically underrepresented in clinical trials
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Aug 2024
- Study start4 Apr 2025
- Primary completionOct 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate CancerStudies of the same condition, open and under way.
- More trials run by University of PennsylvaniaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Pennsylvania
- Sponsor type
- University, hospital or other
- Collaborators
- Mendel AI
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06561230
- Sponsor's protocol ID
- 854974
- Status last verified
- Jun 2026
- First submitted
- 16 Aug 2024
- Last updated
- 17 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No individual participant data will be shared.
Study at a glance
From the registry record, last updated by the sponsor on 17 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Leveraging a Penn-based Cancer Trial (EA8191) to Assess the Prospective Performance of Artificial Intelligence Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening and Selection
- ClinicalTrials.gov ID
- NCT06561230
- Acronym / sponsor ID
- 854974
- Conditions
- Prostate Cancer
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- University of PennsylvaniaUniversity, hospital or other
- Collaborators
- Mendel AI
- Study start
- 4 Apr 2025 (actual)
- Primary completion
- Oct 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 19 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this prospective study is to assess the performance of AI (artificial intelligence) augmentation (compared against historical controls) to identify oncology patients who meet inclusion criteria for a clinical trial. The study staff will leverage a natural language processing (NLP)-based AI algorithm that rank-orders patients most likely to meet inclusion criteria for a trial. We hypothesize that this collaborative Human+AI workflow can improve the efficiency, accuracy, and diversity of trial prescreening.
Detailed description
The objective of this prospective study is to assess the accuracy and efficiency of a clinical research coordinator utilizing AI augmentation to identify oncology patients who meet the inclusion criteria for participation in clinical trials. The clinical research coordinator utilizing AI augmentation ("Human+AI") will leverage an autonomous natural language processing (NLP)-based AI algorithm (Mendel AI) developed by artificial intelligence startup company Mendel.ai. The algorithm Mendel AI serves as a supportive tool in the decision-making process by providing the clinical research coordinator a rank-ordered list of patients most likely to meet inclusion criteria for a trial, as well as a list of elements abstracted by the AI algorithm for each patient. The performance of "Human+AI" would be compared against historical control from the selected clinical trial: EA8191/INDICATE (Penn IRB #848795), a national, Phase III, randomized, prostate cancer clinical trial on the use of PET scan findings to direct local and systemic treatment intensification in patients with post-prostatectomy biochemical recurrence.
The electronic health records (EHRs) reviewed as part of this clinical trial screen would draw from randomly-selected Penn patients with upcoming genitourinary radiation oncology and medical oncology appointments at the Perelman Center for Advanced Medicine. Given a randomly selected batch of EHRs from these patients viable for prescreening, the research team aims to determine how well (and also if better, how much better) clinical research staff leveraging Mendel's AI algorithm can identify those patients who met the eligibility criteria for the EA8191 trial (as compared to historical averages for the EA8191).
The study primarily aims to compare (1) the efficiency of the Human+AI collaboration relative to historical efficiency from a Human-alone workflow, (2) the accuracy of the Human+AI collaboration relative to historical accuracy from a Human-alone workflow and (3) the diversity of eligible patients identified by the Human+AI prescreening workflow, compared to historical diversity from a Human-alone workflow.
Our central hypothesis is that workflows that merge traditional CRC-driven prescreening with automated AI - "Human+AI" workflows - can improve the efficiency, accuracy and diversity of trial prescreening.
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Human + AI | – | Other: Chart review |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with a diagnosis code of prostate cancer and with an upcoming genitourinary radiation oncology or medical oncology appointments at the Perelman Center for Advanced Medicine
Key inclusion criteria (30 listed)
- Patient must be male and >= 18 years of age.
- Patient must have had a radical prostatectomy (RP) as definitive therapy for histopathologically-proven prostatic adenocarcinoma
- Patient must have biochemical recurrence (BCR) after RP, defined as follows:
- Patients must have no definite evidence for extrapelvic metastatic disease by conventional imaging modalities (CIM) (CT abdomen/pelvis or MRI abdomen/pelvis AND bone scintigraphy, or equivalent), within 26 weeks prior to Step 0 registration. If a patient only has a study-eligible PET/CT or PET/MR (i.e., PET done without prior CIM): if the PET is negative for extrapelvic lesions, then baseline CIM is NOT required. If the PET positive for extrapelvic lesions, then patient should have a baseline CT/MRI for soft tissue lesions and/or a bone scan for osseous lesions
- Extra-pelvic metastases is defined as any osseous metastases and/or any extrapelvic soft tissue, lymph nodes and organ metastases; extra-pelvic is defined as superior to common iliac bifurcation, outside of standard prostate bed + whole pelvis nodal RT fields. Baseline PET/CT or PET/MR scan (PET1) is eligible for this study if the SOC PET scan is completed with an FDA approved radiotracer for prostate cancer after Step 0 registration and prior to Step 1 randomization OR up to 16 weeks prior to Step 0 registration
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Chart reviewPatients eligible for prescreening will be identified using structured criteria (e.g., anyone with an appointment in the upcoming 2 months with a relevant provider). All individual charts will be de-identified. De-identified EHRs will then be transferred to Mendel's secure data abstraction platform, where trial eligibility criteria will be abstracted. Patients will be rank-ordered by number of eligibility criteria met, with patients meeting the most eligibility criteria at the top of the queue. The study team will then review eligibility criteria and flag eligible patients. | Other | Human + AI |
Primary outcome
Efficiency
Time frame: 1 year
The number of eligible patients per 10 charts prescreened
2 secondary outcomes
Accuracy
Time frame: 6 months
Diversity
Time frame: 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Jun 2026.
- Individual participant dataSharing planned: No. No individual participant data will be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostate Cancer, run by University of Pennsylvania at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 30 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 17 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Efficiency (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Jun 2026.