Ariel Beresniak1, Andreas Schmidt2, Johann Proeve3, Elena Bolanos4, Neelam Patel5, Nadir Ammour6, Mats Sundgren7, Mats Ericson8, Töresin Karakoyun9, Pascal Coorevits10, Dipak Kalra11, Georges De Moor12, Danielle Dupont13. 1. Data Mining International, Route de l'Aéroport, 29-31, CP 221, Geneva CH-1215, Switzerland. 2. F Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel 4070, Switzerland. 3. Bayer Healthcare, Building K9, Leverkusen 51368, Germany. 4. Eli Lilly and Company, Avenida de la Industria, n 30, Alcobendas 28108, Spain. 5. Eli Lilly and Company (Until December 2013), Erl Wood Manor, Windlesham, Surrey, United Kingdom. 6. Sanofi-Aventis R&D, 1 avenue Pierre Brossolette, Chilly-Mazarin F-91380, France. 7. AstraZeneca, Karragatan 1, Mölndal SE 431 83, Sweden. 8. Amgen, 62, Boulevard Victor Hugo, Neuilly-sur-Seine 92523, France. 9. Heinrich-Heine-Universität Düsseldorf, Germany (Until March 2015), Moorenstraße 5, 40225 Düsseldorf, Deutschland. 10. The European Institute for Health Records (EuroRec), De Pintelaan 185, Ghent 9000, Belgium; Ghent University, Department of Public Health, Unit of Medical Informatics and Statistics, De Pintelaan 185, Ghent B9000, Belgium. 11. The European Institute for Health Records (EuroRec), De Pintelaan 185, Ghent 9000, Belgium. 12. Ghent University, Department of Public Health, Unit of Medical Informatics and Statistics, De Pintelaan 185, Ghent B9000, Belgium. 13. Data Mining International, Route de l'Aéroport, 29-31, CP 221, Geneva CH-1215, Switzerland. Electronic address: ddupont@datamining-international.com.
Abstract
INTRODUCTION: The widespread adoption of electronic health records (EHR) provides a new opportunity to improve the efficiency of clinical research. The European EHR4CR (Electronic Health Records for Clinical Research) 4-year project has developed an innovative technological platform to enable the re-use of EHR data for clinical research. The objective of this cost-benefit assessment (CBA) is to assess the value of EHR4CR solutions compared to current practices, from the perspective of sponsors of clinical trials. MATERIALS AND METHODS: A CBA model was developed using an advanced modeling approach. The costs of performing three clinical research scenarios (S) applied to a hypothetical Phase II or III oncology clinical trial workflow (reference case) were estimated under current and EHR4CR conditions, namely protocol feasibility assessment (S1), patient identification for recruitment (S2), and clinical study execution (S3). The potential benefits were calculated considering that the estimated reduction in actual person-time and costs for performing EHR4CR S1, S2, and S3 would accelerate time to market (TTM). Probabilistic sensitivity analyses using Monte Carlo simulations were conducted to manage uncertainty. RESULTS: Should the estimated efficiency gains achieved with the EHR4CR platform translate into faster TTM, the expected benefits for the global pharmaceutical oncology sector were estimated at €161.5m (S1), €45.7m (S2), €204.5m (S1+S2), €1906m (S3), and up to €2121.8m (S1+S2+S3) when the scenarios were used sequentially. CONCLUSIONS: The results suggest that optimizing clinical trial design and execution with the EHR4CR platform would generate substantial added value for pharmaceutical industry, as main sponsors of clinical trials in Europe, and beyond.
INTRODUCTION: The widespread adoption of electronic health records (EHR) provides a new opportunity to improve the efficiency of clinical research. The European EHR4CR (Electronic Health Records for Clinical Research) 4-year project has developed an innovative technological platform to enable the re-use of EHR data for clinical research. The objective of this cost-benefit assessment (CBA) is to assess the value of EHR4CR solutions compared to current practices, from the perspective of sponsors of clinical trials. MATERIALS AND METHODS: A CBA model was developed using an advanced modeling approach. The costs of performing three clinical research scenarios (S) applied to a hypothetical Phase II or III oncology clinical trial workflow (reference case) were estimated under current and EHR4CR conditions, namely protocol feasibility assessment (S1), patient identification for recruitment (S2), and clinical study execution (S3). The potential benefits were calculated considering that the estimated reduction in actual person-time and costs for performing EHR4CR S1, S2, and S3 would accelerate time to market (TTM). Probabilistic sensitivity analyses using Monte Carlo simulations were conducted to manage uncertainty. RESULTS: Should the estimated efficiency gains achieved with the EHR4CR platform translate into faster TTM, the expected benefits for the global pharmaceutical oncology sector were estimated at €161.5m (S1), €45.7m (S2), €204.5m (S1+S2), €1906m (S3), and up to €2121.8m (S1+S2+S3) when the scenarios were used sequentially. CONCLUSIONS: The results suggest that optimizing clinical trial design and execution with the EHR4CR platform would generate substantial added value for pharmaceutical industry, as main sponsors of clinical trials in Europe, and beyond.
Authors: Christian Hoppe; Patrick Obermeier; Susann Muehlhans; Maren Alchikh; Lea Seeber; Franziska Tief; Katharina Karsch; Xi Chen; Sindy Boettcher; Sabine Diedrich; Tim Conrad; Bron Kisler; Barbara Rath Journal: Drug Saf Date: 2016-10 Impact factor: 5.606
Authors: Maryam Y Garza; Michael Rutherford; Sahiti Myneni; Susan Fenton; Anita Walden; Umit Topaloglu; Eric Eisenstein; Karan R Kumar; Kanecia O Zimmerman; Mitra Rocca; Gideon Scott Gordon; Sam Hume; Zhan Wang; Meredith Zozus Journal: AMIA Annu Symp Proc Date: 2021-01-25
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Authors: Philipp Bruland; Mark McGilchrist; Eric Zapletal; Dionisio Acosta; Johann Proeve; Scott Askin; Thomas Ganslandt; Justin Doods; Martin Dugas Journal: BMC Med Res Methodol Date: 2016-11-22 Impact factor: 4.615