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Theunissen LJHJ, Abdalrahim RBEM, Dekker LRC, Thijssen EJM, de Jong SFAMS, Polak PE, van de Voort PH, Smits G, Scheele K, Lucas A, van Veghel DPA, Cremers HP, van de Pol JAA, Kemps HMC. Regional implementation of atrial fibrillation screening: benefits and pitfalls. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:570-577. [PMID: 36710905 PMCID: PMC9779812 DOI: 10.1093/ehjdh/ztac055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/15/2022] [Indexed: 11/06/2022]
Abstract
Aims Despite general awareness that screening for atrial fibrillation (AF) could reduce health hazards, large-scale implementation is lagging behind technological developments. As the successful implementation of a screening programme remains challenging, this study aims to identify facilitating and inhibiting factors from healthcare providers' perspectives. Methods and results A mixed-methods approach was used to gather data among practice nurses in primary care in the southern region of the Netherlands to evaluate the implementation of an ongoing single-lead electrocardiogram (ECG)-based AF screening programme. Potential facilitating and inhibiting factors were evaluated using online questionnaires (N = 74/75%) and 14 (of 24) semi-structured in-depth interviews (58.3%). All analyses were performed using SPSS 26.0. In total, 16 682 screenings were performed on an eligible population of 64 000, and 100 new AF cases were detected. Facilitating factors included 'receiving clear instructions' (mean ± SD; 4.12 ± 1.05), 'easy use of the ECG-based device' (4.58 ± 0.68), and 'patient satisfaction' (4.22 ± 0.65). Inhibiting factors were 'time availability' (3.20 ± 1.10), 'insufficient feedback to the practice nurse' (2.15 ± 0.89), 'absence of coordination' (54%), and the 'lack of fitting policy' (32%). Conclusion Large-scale regional implementation of an AF screening programme in primary care resulted in a low participation of all eligible patients. Based on the perceived barriers by healthcare providers, future AF screening programmes should create preconditions to fit the intervention into daily routines, appointing an overall project lead and a General Practitioner (GP) as a coordinator within every GP practice.
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Affiliation(s)
- Luc J H J Theunissen
- Netherlands Heart Network, De Run 4600, 5504 DB, Veldhoven, The Netherlands,Máxima Medical Centre, De Run 4600, 5504DB, Veldhoven, The Netherlands,Department of Electrical Engineering, Technical University, 5612 AZ, Eindhoven, The Netherlands
| | - Reyan B E M Abdalrahim
- Netherlands Heart Network, De Run 4600, 5504 DB, Veldhoven, The Netherlands,Department of Electrical Engineering, Technical University, 5612 AZ, Eindhoven, The Netherlands
| | - Lukas R C Dekker
- Netherlands Heart Network, De Run 4600, 5504 DB, Veldhoven, The Netherlands,Department of Electrical Engineering, Technical University, 5612 AZ, Eindhoven, The Netherlands,Catharina hospital, Michelangelolaan 2, 5623 EJ, Eindhoven, The Netherlands
| | - Eric J M Thijssen
- Máxima Medical Centre, De Run 4600, 5504DB, Veldhoven, The Netherlands
| | | | - Peter E Polak
- St. Anna hospital, Bogardeind 2, 5664 EH, Geldrop, The Netherlands
| | | | - Geert Smits
- GP Organization PoZoB, Bolwerk 10-14, 5509 MH, Veldhoven, The Netherlands
| | - Karin Scheele
- GP Organization PoZoB, Bolwerk 10-14, 5509 MH, Veldhoven, The Netherlands
| | - Annelies Lucas
- Diagnostics for You, Boschdijk 1119, 5626 AG, Eindhoven, The Netherlands
| | - Dennis P A van Veghel
- Netherlands Heart Network, De Run 4600, 5504 DB, Veldhoven, The Netherlands,Catharina hospital, Michelangelolaan 2, 5623 EJ, Eindhoven, The Netherlands
| | | | | | - Hareld M C Kemps
- Netherlands Heart Network, De Run 4600, 5504 DB, Veldhoven, The Netherlands,Máxima Medical Centre, De Run 4600, 5504DB, Veldhoven, The Netherlands,Department of Industrial Design, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
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Abstract
Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
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