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Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology.

ABSTRACT: PURPOSE:Matching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials. MATERIALS AND METHODS:We analyzed patients who were enrolled in genome-driven, early-phase trials using PRECISE at Memorial Sloan Kettering Cancer Center between April 2014 and January 2018. Primary end point was the proportion of enrolled patients who were successfully identified using PRECISE before enrollment. Secondary end points included time from sequencing and PRECISE identification to enrollment. Reasons for a failure to identify genomically matched patients were also explored. RESULTS:Data were analyzed from 41 therapeutic trials led by 19 principal investigators. In total, 755 patients were accrued to these studies during the period that PRECISE was used. PRECISE successfully identified 327 patients (43%) before enrollment. Patients were diagnosed with 29 tumor types and harbored alterations in 43 oncogenes, most commonly ERBB2 (21.3%), PIK3CA (14.1%), and BRAF (8.7%). Median time from sequencing to enrollment was 163 days (interquartile range, 66 to 357 days), and from PRECISE identification to enrollment 87 days (interquartile range, 37 to 180 days). Common reasons for failing to identify patients before enrollment included accrual on the basis of molecular alterations that did not match pre-established PRECISE genomic eligibility (140 [33%] of 428) and external sequencing not available for parsing (127 [30%] of 428). CONCLUSION:PRECISE identified 43% of all patients accrued to a diverse cohort of early-phase, genome-matched studies. Purpose-built informatics platforms represent a novel and potentially effective method for matching patients to molecularly selected studies.

SUBMITTER: Tao JJ

PROVIDER: S-EPMC7446398 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology.

Tao Jessica J JJ Eubank Michael H MH Schram Alison M AM Cangemi Nicholas N Pamer Erika E Rosen Ezra Y EY Schultz Nikolaus N Chakravarty Debyani D Philip John J Hechtman Jaclyn F JF Harding James J JJ Smyth Lillian M LM Jhaveri Komal L KL Drilon Alexander A Ladanyi Marc M Solit David B DB Zehir Ahmet A Berger Michael F MF Stetson Peter D PD Gardos Stuart M SM Hyman David M DM

JCO precision oncology 20190724

<h4>Purpose</h4>Matching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials.<h4>Materials and meth ...[more]

PMID: 32914018

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Project description:BackgroundLittle research has focused on implementation of electronic Patient Reported Outcomes (e-PROs) for meaningful use in patient management in 'real-world' oncology practices. Our quality improvement collaborative used multi-faceted implementation strategies including audit and feedback, disease-site champions and practice coaching, core training of clinicians in a person-centered clinical method for use of e-PROs in shared treatment planning and patient activation, ongoing educational outreach and shared collaborative learnings to facilitate integration of e-PROs data in multi-sites in Ontario and Quebec, Canada for personalized management of generic and targeted symptoms of pain, fatigue, and emotional distress (depression, anxiety).Patients and methodsWe used a mixed-methods (qualitative and quantitative data) program evaluation design to assess process/implementation outcomes including e-PROs completion rates, acceptability/use from the perspective of patients/clinicians, and patient experience (surveys, qualitative focus groups). We secondarily explored impact on symptom severity, patient activation and healthcare utilization (Ontario sites only) comparing a pre/post population cohort not exposed/exposed to our implementation intervention using Mann Whitney U tests. We hypothesized that the iPEHOC intervention would result in a reduction in symptom severity, healthcare utilization, and higher patient activation. We also identified key implementation strategies that sites perceived as most valuable to uptake and any barriers.ResultsOver 6000 patients completed e-PROs, with sites reaching 51%-95% population completion rates depending on initial readiness. e-PROs were acceptable to patients for communicating symptoms (76%) and by clinicians for treatment planning (80%). Patient experience was better than the provincial average. Compared to the pre-population, we observed a significant reduction in levels of anxiety (p = 0.008), higher levels of patient activation (p = 0.045), and reduced hospitalization rates (12.3% not exposed vs 10.1% exposed, p = 0.034). A pre/post population trend towards significance for reduced emergency department visit rates (14.8% not exposed vs 12.8% exposed, p = 0.081) was also noted.ConclusionThis large-scale pragmatic quality improvement project demonstrates the impact of implementation strategies and a collaborative improvement approach on acceptability of using PROs in clinical practice and their potential for reducing anxiety and healthcare utilization; and improving patient experience and patient activation when implemented in 'real-world' multi-site oncology practices.

Dataset Information

Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology.

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Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology.

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