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The accuracy and efficiency of electronic screening for recruitment into a clinical trial on COPD.

ABSTRACT: Participant recruitment is an important process in successful conduct of randomized controlled trials. To facilitate enrollment into a National Institutes of Health-sponsored clinical trial involving patients with chronic obstructive pulmonary disease (COPD), we developed and prospectively validated an automated electronic screening tool based on boolean free-text search of admission notes in electronic medical records. During a 2-week validation period, all patients admitted to prespecified general medical services were screened for eligibility by both the electronic screening tool and a COPD nurse. Group discussion was the gold standard for confirmation of true-positive results. Compared with the gold standard, electronic screening yielded 100% sensitivity, 92% specificity, 100% negative predictive value, and 72% positive predictive value. Compared with traditional manual screening, electronic screening demonstrated time-saving potential of 76%. Thus, the electronic screening tool accurately identifies potential study subjects and improves efficiency of patient accrual for a clinical trial on COPD. This method may be expanded into other institutional and clinical settings.

SUBMITTER: Schmickl CN

PROVIDER: S-EPMC3156898 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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The accuracy and efficiency of electronic screening for recruitment into a clinical trial on COPD.

Schmickl Christopher N CN Li Man M Li Guangxi G Wetzstein Marnie M MM Herasevich Vitaly V Gajic Ognjen O Benzo Roberto P RP

Respiratory medicine 20110514 10

Participant recruitment is an important process in successful conduct of randomized controlled trials. To facilitate enrollment into a National Institutes of Health-sponsored clinical trial involving patients with chronic obstructive pulmonary disease (COPD), we developed and prospectively validated an automated electronic screening tool based on boolean free-text search of admission notes in electronic medical records. During a 2-week validation period, all patients admitted to prespecified gen ...[more]

PMID: 21576012

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Project description:ObjectiveEfficiently identifying eligible patients is a crucial first step for a successful clinical trial. The objective of this study was to test whether an approach using electronic health record (EHR) data and an ensemble machine learning algorithm incorporating billing codes and data from clinical notes processed by natural language processing (NLP) can improve the efficiency of eligibility screening.MethodsWe studied patients screened for a clinical trial of rheumatoid arthritis (RA) with one or more International Classification of Diseases (ICD) code for RA and age greater than 35 years, from a tertiary care center and a community hospital. The following three groups of EHR features were considered for the algorithm: 1) structured features, 2) the counts of NLP concepts from notes, 3) health care utilization. All features were linked to dates. We applied random forest and logistic regression with least absolute shrinkage and selection operator penalty against the following two standard approaches: 1) one or more RA ICD code and no ICD codes related to exclusion criteria (ScreenRAICD1 +EX ) and 2) two or more RA ICD codes (ScreenRAICD2 ). To test the portability, we trained the algorithm at one institution and tested it at the other.ResultsIn total, 3359 patients at Brigham and Women's Hospital (BWH) and 642 patients at Faulkner Hospital (FH) were studied, with 461 (13.7%) eligible patients at BWH and 84 (13.4%) at FH. The application of the algorithm reduced ineligible patients from chart review by 40.5% at the tertiary care center and by 57.0% at the community hospital. In contrast, ScreenRAICD2 reduced patients for chart review by 2.7% to 11.3%; ScreenRAICD1+EX reduced patients for chart review by 63% to 65% but excluded 22% to 27% of eligible patients.ConclusionThe ensemble machine learning algorithm incorporating billing codes and NLP data increased the efficiency of eligibility screening by reducing the number of patients requiring chart review while not excluding eligible patients. Moreover, this approach can be trained at one institution and applied at another for multicenter clinical trials.

Project description:Background/aimsElectronic-based recruitment methods are increasingly utilized in clinical trials to recruit and enroll research participants. The cost-effectiveness of electronic-based methods and impact on sample generalizability is unknown. We compared recruitment yields, cost-effectiveness, and demographic characteristics across several electronic and traditional recruitment methods.MethodsWe analyzed data from the diet gout trial recruitment campaign. The diet gout trial was a randomized, controlled, cross-over trial that examined the effects of a dietary approaches to stop hypertension (DASH)-like diet on uric acid levels in adults with gout. We used four electronic medical record and four non-electronic medical record-based recruitment methods to identify and recruit potentially eligible participants. We calculated the response rate, screening visit completion rate, and randomization rate for each method. We also determined cost per response, the screening, and randomization for each method. Finally, we compared the demographic characteristics among individuals who completed the screening visit by recruitment method.ResultsOf the 294 adults who responded to the recruitment campaign, 51% were identified from electronic medical record-based methods. Patient portal messaging, an electronic medical record-based method, resulted in the highest response rate (4%), screening visit completion rate (37%), and randomization rate (21%) among these eight methods. Electronic medical record-based methods ($60) were more cost-effective per response than non-electronic medical record-based methods ($107). Electronic-based methods, including patient portal messaging and Facebook, had the highest proportion of White individuals screened (52% and 60%). Direct mail to non-active patient portal increased enrollment of traditionally under-represented groups, including both women and African Americans.ConclusionAn electronic medical record-based recruitment strategy that utilized the electronic medical record for participant identification and postal mailing for participant outreach was cost-effective and increased participation of under-represented groups. This hybrid strategy represents a promising approach to improve the timely execution and broad generalizability of future clinical trials.

Project description:BackgroundManual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial.MethodsWe collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated.ResultsWithout automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%).ConclusionBy leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.

Project description:Intimate partner violence (IPV) is a significant public health issue, with a 25% lifetime prevalence. Screening for IPV in primary care is a recommended practice whose effectiveness is debated. To assess the effect of an electronic health record (EHR)-based multifactorial intervention screening on the detection of IPV risk in primary care practice. This cluster randomized clinical trial used a stepped-wedge design to assign 15 family medicine primary care clinics in the Medical University of South Carolina Health System in the Charleston region to 3 matched blocks from October 6, 2020, to March 31, 2023. All women aged 18 to 49 years who were seen in these clinics participated in this study. A noninterruptive EHR alert combined with confidential screening by computer questionnaire using the EHR platform followed by risk assessment and a decision support template. The main outcomes were the rate at which patients were screened for IPV across the clinics and the rate at which patients at risk for IPV were detected by screening procedures. The study clinics cared for 8895 unique patients (mean [SD] age, 34.6 [8.7] years; 1270 [14.3%] with Medicaid or Medicare and 7625 [85.7%] with private, military, or other insurance) over the study period eligible for the screening intervention. The intervention had significant effects on the overall rate of screening for IPV, increasing the rate of screening from 45.2% (10 268 of 22 730 patient visits) to 65.3% (22 303 of 34 157 patient visits) when the noninterruptive alert was active (relative risk, 1.46 [95% CI, 1.44-1.49]; P < .001). The confidential screening process was more effective than baseline nurse-led oral screening at identifying patients reporting past-year IPV (130 of 8895 patients [1.5%] vs 9 of 17 433 patients [0.1%]). The intervention was largely effective in increasing screening adherence and the positive detection rate of IPV in primary care. A highly private approach to screening for IPV in primary care may be necessary to achieve adequate detection rates while addressing potential safety issues of patients experiencing IPV. ClinicalTrials.gov Identifier: NCT06284148.

Dataset Information

The accuracy and efficiency of electronic screening for recruitment into a clinical trial on COPD.

Publications

The accuracy and efficiency of electronic screening for recruitment into a clinical trial on COPD.

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