A Multilevel Primary Care Intervention to Improve Follow-Up of Overdue Abnormal Cancer Screening Test Results: A Cluster Randomized Clinical Trial.

Importance: Realizing the benefits of cancer screening requires testing of eligible individuals and processes to ensure follow-up of abnormal results. Objective: To test interventions to improve timely follow-up of overdue abnormal breast, cervical, colorectal, and lung cancer screening results. Design, Setting, and Participants: Pragmatic, cluster randomized clinical trial conducted at 44 primary care practices within 3 health networks in the US enrolling patients with at least 1 abnormal cancer screening test result not yet followed up between August 24, 2020, and December 13, 2021. Intervention: Automated algorithms developed using data from electronic health records (EHRs) recommended follow-up actions and times for abnormal screening results. Primary care practices were randomized in a 1:1:1:1 ratio to (1) usual care, (2) EHR reminders, (3) EHR reminders and outreach (a patient letter was sent at week 2 and a phone call at week 4), or (4) EHR reminders, outreach, and navigation (a patient letter was sent at week 2 and a navigator outreach phone call at week 4). Patients, physicians, and practices were unblinded to treatment assignment. Main Outcomes and Measures: The primary outcome was completion of recommended follow-up within 120 days of study enrollment. The secondary outcomes included completion of recommended follow-up within 240 days of enrollment and completion of recommended follow-up within 120 days and 240 days for specific cancer types and levels of risk. Results: Among 11 980 patients (median age, 60 years [IQR, 52-69 years]; 64.8% were women; 83.3% were White; and 15.4% were insured through Medicaid) with an abnormal cancer screening test result for colorectal cancer (8245 patients [69%]), cervical cancer (2596 patients [22%]), breast cancer (1005 patients [8%]), or lung cancer (134 patients [1%]) and abnormal test results categorized as low risk (6082 patients [51%]), medium risk (3712 patients [31%]), or high risk (2186 patients [18%]), the adjusted proportion who completed recommended follow-up within 120 days was 31.4% in the EHR reminders, outreach, and navigation group (n = 3455), 31.0% in the EHR reminders and outreach group (n = 2569), 22.7% in the EHR reminders group (n = 3254), and 22.9% in the usual care group (n = 2702) (adjusted absolute difference for comparison of EHR reminders, outreach, and navigation group vs usual care, 8.5% [95% CI, 4.8%-12.0%], P < .001). The secondary outcomes showed similar results for completion of recommended follow-up within 240 days and by subgroups for cancer type and level of risk for the abnormal screening result. Conclusions and Relevance: A multilevel primary care intervention that included EHR reminders and patient outreach with or without patient navigation improved timely follow-up of overdue abnormal cancer screening test results for breast, cervical, colorectal, and lung cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT03979495.

Natural Language Processing Can Automate Extraction of Barrett's Esophagus Endoscopy Quality Metrics.

Objectives: To develop an automated natural language processing (NLP) method for extracting high-fidelity Barrett's Esophagus (BE) endoscopic surveillance and treatment data from the electronic health record (EHR). Methods: Patients who underwent BE-related endoscopies between 2016 and 2020 at a single medical center were randomly assigned to a development or validation set. Those not aged 40 to 80 and those without confirmed BE were excluded. For each patient, free text pathology reports and structured procedure data were obtained. Gastroenterologists assigned ground truth labels. An NLP method leveraging MetaMap Lite generated endoscopy-level diagnosis and treatment data. Performance metrics were assessed for this data. The NLP methodology was then adapted to label key endoscopic eradication therapy (EET)-related endoscopy events and thereby facilitate calculation of patient-level pre-EET diagnosis, endotherapy time, and time to CE-IM. Results: 99 patients (377 endoscopies) and 115 patients (399 endoscopies) were included in the development and validation sets respectively. When assigning high-fidelity labels to the validation set, NLP achieved high performance (recall: 0.976, precision: 0.970, accuracy: 0.985, and F1-score: 0.972). 77 patients initiated EET and underwent 554 endoscopies. Key EET-related clinical event labels had high accuracy (EET start: 0.974, CE-D: 1.00, and CE-IM: 1.00), facilitating extraction of pre-treatment diagnosis, endotherapy time, and time to CE-IM. Conclusions: High-fidelity BE endoscopic surveillance and treatment data can be extracted from routine EHR data using our automated, transparent NLP method. This method produces high-level clinical datasets for clinical research and quality metric assessment.

Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up.

Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-text reports, which limit capabilities for automated decision support. As part of the multilevel Follow-up of Cancer Screening (mFOCUS) trial, we developed and implemented a natural language processing (NLP) tool to assist with real-time detection of abnormal cancer screening test results (including mammograms, low-dose chest CT scans, and Pap smears) and identification of gynecological follow-up for higher risk abnormalities (i.e. colposcopy) from free-text reports. We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system. The NLP pipelines have detected scenarios when guideline-recommended care was not delivered, in part because the provider mis-identified the text-based result reports.

Multilevel Follow-up of Cancer Screening (mFOCUS): Protocol for a multilevel intervention to improve the follow-up of abnormal cancer screening test results.

INTRODUCTION: While substantial attention is focused on the delivery of routine preventive cancer screening, less attention has been paid to systematically ensuring that there is timely follow-up of abnormal screening test results. Barriers to completion of timely follow-up occur at the patient, provider, care team and system levels. METHODS: In this pragmatic cluster randomized controlled trial, primary care sites in three networks are randomized to one of four arms: (1) standard care, (2) "visit-based" reminders that appear in a patient's electronic health record (EHR) when it is accessed by either patient or providers (3) visit based reminders with population health outreach, and (4) visit based reminders, population health outreach, and patient navigation with systematic screening and referral to address social barriers to care. Eligible patients in participating practices are those overdue for follow-up of an abnormal results on breast, cervical, colorectal and lung cancer screening tests. RESULTS: The primary outcome is whether an individual receives follow-up, specific to the organ type and screening abnormality, within 120 days of becoming eligible for the trial. Secondary outcomes assess the effect of intervention components on the patient and provider experience of obtaining follow-up care and the delivery of the intervention components. CONCLUSIONS: This trial will provide evidence for the role of a multilevel intervention on improving the follow-up of abnormal cancer screening test results. We will also specifically assess the relative impact of the components of the intervention, compared to standard care. TRIAL REGISTRATION: ClinicalTrials.gov NCT03979495.

Retention strategies for health disparities preventive trials: findings from the Early Childhood Caries Collaborating Centers.

OBJECTIVES: To identify successful strategies for retention of participants in multiyear, community-based randomized controlled trials (RCTs) aiming to reduce early childhood caries in health disparities populations from diverse racial/ethnic backgrounds and across diverse geographic settings. METHODS: Four RCTs conducted by the Early Childhood Caries Collaborating Centers (EC4), an initiative of the National Institute of Dental and Craniofacial Research, systematically collected information on the success of various strategies implemented to promote participant retention in each RCT. The observational findings from this case series of four RCTs were tabulated and the strategies rated by study staff. RESULTS: Participant retention at 12 months of follow-up ranged from 52.8 percent to 91.7 percent, and at 24 months ranged from 53.6 percent to 85.9, across the four RCTs. For the three RCTs that had a 36-month follow-up, retention ranged from 53.6 percent to 85.1 percent. Effectiveness of different participant retention strategies varied widely across the RCTs. CONCLUSIONS: Findings from this case series study may help to guide the design of future RCTs to maximize retention of study participants and yield needed data on effective interventions to reduce oral health disparities.