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Article Contents

Introduction, telemonitoring of cardiac implantable electronic devices, digital devices, mobile health applications (apps), virtual clinics, artificial intelligence, the future digital aspects for electrophysiology europace.

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The digital journey: 25 years of digital development in electrophysiology from an Europace perspective

Conflict of interest: ES declares lecture fees from Bayer, Bristol-Myers Squibb-Pfizer, Boehringer- Ingelheim, Johnson & Johnson, Merck Sharp & Dohme PS is supported by an Investigator Grant from the National Health and Medical Research Council of Australia. Conflict of Interest Disclosures: PS reports having served on the advisory board of Boston Scientific, CathRx, Medtronic, Abbott Medical and Pacemate. PS reports that the University of Adelaide has received on his behalf lecture and/or consulting fees from Medtronic, Boston-Scientific, and Abbott Medical, Pacemate and CathRx. PS reports that the University of Adelaide has received on his behalf research funding from Medtronic, Abbott Medical, Boston-Scientific, Pacemate, Becton Dickinson and Microport. JH reports advisory board: iRhythm Technologies Consultant: Abbott Medical Lectureship fees: Medtronic & 91 Life/ScottCare EC, LD, FER.

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Emma Svennberg, Enrico G Caiani, Nico Bruining, Lien Desteghe, Janet K Han, Sanjiv M Narayan, Frank E Rademakers, Prashanthan Sanders, David Duncker, The digital journey: 25 years of digital development in electrophysiology from an Europace perspective, EP Europace , Volume 25, Issue 8, August 2023, euad176, https://doi.org/10.1093/europace/euad176

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Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.

In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal.

In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP.

Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.

A comprehensive overview of the past 25 years within the field of digital electrophysiology with a particular focus on publications from the EP Europace journal.

Digital technology has the potential to impact and transform healthcare by providing a platform for patient identification, risk stratification, management, patient interactivity, and education. In the past 25 years there has been a substantial development within the field of digital cardiology, with electrophysiology (EP) in the forefront paralleled by an increase in publications within this field.

This paper seeks to provide an in-depth overview and chronology of the field of digital EP mirroring the substantial influence in this area that EP Europace has had in the past decades. Digital technologies can enable care for arrhythmia patients, and we aim to provide the reader with a brief overview of the digital toolbox, starting with a journey from the early days of telemonitoring, followed by an update on monitoring from a wearable perspective. From there we move forward to mobile applications and virtual clinics. In addition, a chronicle of the development within the field of artificial intelligence (AI), an outlook on the future as well as a regulatory perspective, is provided ( Figure 1 ).

An overview of digital tools available for the electrophysiologist.

The digital journey in EP begins with cardiac implantable electronic devices (CIEDs). A significant evolution has been seen over the last decade, with novel technologies allowing complex programming and wireless remote monitoring (RM) of device function and patient health status. 1–6 Telemonitoring of CIEDs has now evolved to a fully automated system to complement in-office follow-up 7 and has gained even more importance during the COVID-19 pandemic. 8

Previous studies have demonstrated a reduction in time to detection of clinically actionable events, prompting earlier intervention with the implementation of RM compared to standard in-person follow-up care. 9–14 In the multicenter RIONI study, in 619 patients with an implantable cardioverter-defibrillator (ICD) it was shown that home monitoring could provide an accurate evaluation of events by experts. 5 , 15 The PREFER study evaluated 980 pacemaker patients with RM providing earlier and more frequent detection of clinically relevant events. 9 The TRUST study showed a median time to evaluation after an arrhythmic event of less than 2 days, compared to 36 days in the control group. 16 , 17 Access to continuous RM data has resulted in fewer in-person evaluations, a reduction in emergent and unscheduled hospital visits with a decrease in overall healthcare utilization. 6 , 16 , 18–22 However, apart from the COMPAS trial, most of these studies were conducted only in ICD patients. Prompt arrhythmia detection coupled with early recognition of fluid accumulation 23 with an in-built algorithm reduced emergency department and urgent in-office visits by 35% in the remote arm, as demonstrated by the EVOLVO study. 18 Other large-scale studies have shown the potential cost saving associated with RM strategy. 21 , 22 , 24–30

More importantly, a pooled analysis of three randomized controlled trials (TRUST, ECOST, and IN-TIME) involving 2405 patients with ICDs showed a significant reduction of all-cause mortality with RM. 31 Another meta-analysis of nine randomised controlled trials (RCTs) demonstrated non-inferiority of RM and in-office follow-up with RCTs utilizing daily transmission verification, proving significant survival benefit. 32 Large real-world registries have further established the survival benefit of RM also emphasizing the impact of adherence to RM in improving patient outcomes. 29 , 33–35

Despite its various proven clinical benefits, RM implementation and uptake have been modest. 36 Barriers to RM implementation are multifactorial and include patient factors such as health literacy, preference and access, lack of healthcare infrastructure, and inadequate reimbursement. 37 The Altitude Survival Study found that more than 60% of patients with RM-capable devices did not participate in RM. 35 Real-world population studies also revealed poor compliance to RM, with one study reporting 53% of patients without a single RM transmission over the follow-up period, and another with 21% non-compliance rate. 33 , 38

On the other hand, the increasing volume of RM transmissions has reached a staggering proportion and increased the clinic workloads. In a study involving more than 26 000 patients, the number of transmissions and alert burden was quantified, resulting in a total of 205 804 transmissions, 40% of which were alert with only 4.8% requiring urgent clinical response. 39 This data deluge, which includes a high rate of false positives, particularly with the increasing use of implantable loop recorders, leads to an increased burden on clinical staff, and delays in the evaluation of actionable alerts. 40 , 41 Early studies suggested how this may be partially overcome using AI to better identify actionable alerts. 42 However, critical for improved patient outcomes are the clinic-level pathways to manage actionable alerts. Evidence suggests this may pose a significant threat to the success of RM. 43 Ultimately, patient education, streamlined alert settings and clinic workflow, adequate trained staffing, and attractive reimbursement policies must be in place to ensure successful adoption of the RM approach for CIEDs. 44 , 45 Recently, a query has been raised if smartwatches can provide a replacement for CIEDs, which will be addressed in the next section. 46

In concordance with telemonitoring, cardiac rhythm monitoring has also markedly evolved in the past 25 years, progressing from the initial Holter monitors to event recorders, mobile cardiac telemetry, implantable cardiac monitors to increasingly ‘smart’ multipurpose sensing and monitoring instruments. 47 , 48

Wearable devices have become central for cardiac rhythm monitoring. In 1994, the first wrist-worn heart rhythm monitor was introduced. This device was capable of transmitting an analogue transtelephonic signal that was converted into a digitized single-lead ECG tracing. Unfortunately, adoption was limited as patients found the device more difficult to use than traditional Holter monitors. 49 Several other wrist-worn devices and simple textile-based heart rate monitors soon followed. 50 While they provided some insight into a patient’s HR, they were not accurate enough for clinical use and were primarily used by fitness enthusiasts. In the last decade, advances in wearable technology have led to more accurate and reliable devices for cardiac rhythm monitoring. 51 Most of these devices, such as smartwatches, rings, and fitness trackers, use optical sensors to detect the patient’s HR and rhythm using photoplethysmography, providing real-time monitoring and analysis. 52

More modern wearable devices, such as smartwatches, use either photoplethysmography and/or ECG-based HR and rhythm monitoring providing single-lead ECGs or even multiple lead ECGs. These devices can also track other metrics such as physical activity, sleep, and stress levels, providing a more holistic view of the patient’s health.

Digital devices offer new possibilities for continuous or intermittent monitoring and have been increasingly used for screening for atrial fibrillation (AF). 53 , 54 Large-scale studies in different settings and populations have shown that screening for AF using digital devices identifies patients at risk, 55–59 has the potential to reduce relevant outcomes 60 and reduce costs. 61 Screening for AF using new digital devices is recommended in guidelines and consensus documents. 51 , 62 , 63

Clinical usage and acceptance of digital devices have increased in the last few years. The true advantages of digital care were seen during the time of the COVID-19 pandemic when digital devices still allowed specific and dedicated remote patient care for arrhythmia management, as shown in the international TELECHECK-AF project. 64–68 Healthcare providers have since started to recognize the potential benefits of these devices for patient care, and digitally advanced centres are using them as a tool for remote patient monitoring.

While wearable devices have shown promise in cardiac rhythm monitoring, there are still challenges that need to be addressed. In particular, the accuracy and reliability of wearable devices for cardiac rhythm and rate monitoring have been a subject of debate. 69 , 70 Recent studies have shown that although some wearable devices can provide reliable measurements of HR and rhythm, variations due to the manufacturer’s algorithms and the patient population are common. 71 Another challenge is the interpretation of the data generated by these devices, which can be complex and requires specialized knowledge. 72 , 73 As recently shown in an European Heart Rhythm Association (EHRA) survey, digital devices are widely used, but reimbursement for usage and interpretation is a problem to solve in most countries. 74 In the future, we can expect to see further advances in wearable technology for cardiac rhythm monitoring. 75 These devices may become more accurate, reliable, and personalized to the patient's needs, and advances in AI may enable more efficient and accurate interpretation of the generated data. 76 In addition, patient involvement remains an important aspect of digital care. 77 , 78

In conclusion, wearable devices have evolved significantly in the last 25 years and have the potential to revolutionize cardiac rhythm monitoring. While there are still some challenges that need to be addressed, they have shown promise in improving patient outcomes through earlier detection and treatment of cardiac conditions. As technology continues to advance, we can expect to see further improvements in cardiac rhythm monitoring. Many wearables are connected to mobile health applications, which will be discussed in the coming section.

The introduction of novel generations of smartphones using computer-like built-in features and sensors on the market in 2007, allowed for customization of the devices by downloading apps from central stores. This feature, combined with the high-grade adoption of smartphone technology in the population (i.e. 86% penetration rate in Europe in 2021) increases the possible applications in the EP field. 79

In EP, the initial interest with regards to smartphones was focused on safety, in particular by determining the potential interference of smartphones with implantable cardioverter defibrillators. 80

More recently, evaluation studies comparing the accuracy of handheld connected devices and smartphone apps have gained a lot of attention. 81 The use of apps within healthcare has manifold opportunities when implemented in a structured pathway, as described in multiple publications ( Figure 2 ).

Training of healthcare providers and decision support. Providing care that conforms to the guidelines is of pivotal importance to optimize patient outcomes, but adherence to the guidelines is often suboptimal. The availability of interactive clinical practice guidelines through an app, such as the ‘ESC Pocket Guidelines’ app, could facilitate their uptake. On top of this, for AF the CATCH ME Consortium developed the ‘AF Manager’ app as a tool in which healthcare professionals can incorporate patient data to suggest treatment options that conform to the guidelines. 82 , 83

Treatment support. To support patients in their daily treatment and to promote a healthy lifestyle, mHealth apps can enhance adherence to medication, increase adherence to hospital appointments, support patients in rehabilitation and physical activity and assist in tackling of comorbidities. 84–88

Diagnostics and screening. Various apps can be used to screen for arrhythmias making use of different sensors embedded in the smartphone or connected to it. 51 , 71

Longitudinal disease management. mHealth opens a large spectrum for the (remote) follow-up of various clinical parameters to fill in the gaps between the in-person consultation visits, including HR, heart rhythm, symptoms, weight, and blood pressure. 51 , 67 , 68 , 83 , 84 , 87 , 89–91 Apps can also connect with wearables, or with implanted devices to collect valuable clinical information on the patient’s status. 84 , 92–94

Education and awareness. To engage patients in their own care and allow shared decision-making, mHealth apps can assist in delivering validated information and tailored education to patients to increase health literacy. 82 , 87 , 95 , 96

Empowering patients to own their health data and directly contact healthcare providers. Apps can allow patients to get informed about their healthcare data and contact their healthcare providers in case of questions about their management. Moreover, many hospitals have their own applications allowing patients to counsel their health data.

Overview of opportunities in mobile phone applications in electrophysiology.

Despite the fact that mHealth apps are widely available, several barriers 93 , 97 , 98 still exist. These include in particular, lack of validation, sparse data on effectiveness and impact on clinically relevant endpoints, poor data integration with electronic health record systems, lack of clear guidance on care pathways to make use of these apps in daily clinical practice, and lack of reimbursement.

Studies formally evaluating the impact of mHealth apps on healthcare professional’s behaviour are scarce and larger-scale studies with representative patient cohorts, appropriate comparators, and longer-term assessment of the impact of mHealth apps are warranted, also in view of the new requirements for conformity assessment introduced by the EU Medical Device Regulation. 87 , 99 As a result, apps are rarely prescribed to patients by healthcare providers in daily clinical practice.

The use of apps for medical purposes could further expand in the future when current barriers in the development, security, validation, cost-effectiveness, interoperability, implementation, and reimbursement of mHealth in daily clinical practice will be solved, and it is an integral part of virtual clinics. 97 , 98

As highlighted in the previous sections the publication of ‘Transtelephone Pacemaker Clinic’ in 1971 and the subsequent rise of RM of CIEDs established cardiac EP as leaders in providing virtual care for patients. 100 The rapid transition to virtual modalities to provide safe, uninterrupted arrhythmia care during the COVID-19 pandemic led to an exponential adoption of digital care by EP. 101–103 This cemented the view of EPs as one of the highest adopters of virtual care (>95% in some systems), and a high rate of virtual care is maintained even after the pandemic has largely subsided. 65 , 66 , 104

EP is particularly conducive to the adoption of virtual clinics as most consultations can be performed entirely virtually. Cardiac rhythm tracings can all be reviewed and analysed online. Discussions, including shared decision-making, can be performed via video or telephone contact. 102 HR and rhythm data from direct-to-consumer digital devices continue to be integrated, helping to enrich arrhythmia patient virtual care. 51 , 65 , 93 , 105

Real-world studies have shown feasibility, safety, and efficacy of virtual arrhythmia clinics, with similar outcomes, quality metrics, and patient satisfaction when compared to in-person visits. 106–108 Virtual AF management has shown particular promise. One of the largest endeavours has been the TELECHECK-AF virtual clinic. 68 , 91 Here, teleconsultation, use of a CE-certified, clinically validated smartphone photoplethysmography HR and rhythm monitoring App (FibriCheck, Flanders, Belgium), and virtual education were combined to support comprehensive AF management. 68 , 109 Patients used the app to check HR/rhythm three times a day for one week, and data was uploaded to the cloud for clinician review before teleconsultation. 68 In 20 days after launch, 9 countries/23 European centers adopted this virtual clinic model; by 6 months nearly 1700 patients were enrolled. 52 , 110 Patients have found the app easy to use (94%), providing them a sense of reassurance (74%); clinicians have given high ratings for on-boarding, cloud access, and reliability. 67 Currently, over 6000 AF patients have received care via this virtual clinic model. The TeleWAS-AF ‘wait-and-see’ programme for patients with recently diagnosed AF uses this same virtual care strategy to help avoid unnecessary cardioversions. 67 A randomized trial, RACE 9 OBSERVE-AF, assessing this virtual care pathway is currently underway (clinicaltrials.gov NCT04612335). In the UK, an ‘AF virtual ward’ has recently been piloted to manage hemodynamically stable AF patients in an ambulatory setting by using digital tools for vital signs monitoring (hand-held daily ECGs, BP monitoring, and O 2 saturations), twice daily ‘virtual’ rounds, and medication adjustments via a clinical pharmacy. This proof-of-concept study recently showed potential for decreasing AF hospital admissions and re-admissions. 111

Virtual clinics for the management of anticoagulation have been well-established. 112 , 113 Virtual clinics for outpatient antiarrhythmic drug loading have been less explored. In an initial feasibility study, three patients with CIEDs requiring sotalol initiation during the COVID-19 pandemic were monitored from home via CIED remote transmissions, mobile cardiac telemetry, a hand-held 6L ECG device Food and Drug Adminstration-cleared for QTc monitoring (KardiaMobile ® 6L, Alivecor, Mountainview, CA), as well as video-telehealth. Successful outpatient initiation of sotalol initiation was performed without any adverse events. 114 A pharmacist-driven virtual clinic for outpatient sotalol loading and monitoring has since been safely piloted using online ECG and lab review, telephone contact, and remote QTc monitoring via the KardiaMobile ® 6L. 115

Virtual clinics for post-AF ablation patients show potential. One study on 46 AF patients from the UK replaced the 3-month post-ablation in-person visit with a video visit coupled with a proprietary vital sign tracking mApp. This virtual clinic showed high overall patient satisfaction (84%) and patient cost and time savings (80%). 116 The Cleveland Clinic ‘Atrial Fibrillation Future Clinic’ randomized 100 post-ablation patients to traditional in-person care vs. virtual care enhanced with a hand-held ECG monitor and follow-up at 6 months. Hospitalization, ER, and clinic visits, as well as anxiety, were similar between groups. In addition, the virtual care group had less use of ambulatory ECGs. 117

As virtual clinics and digital devices are further integrated into EP, patient perspectives—preferences, readiness, digital access, availability, and literacy, as well as cost—must be considered. 51 , 75 Canadian studies have shown that arrhythmia patient virtual care may be well received for quality of life, cost and time savings, and opportunities for participation from caregivers and family members. 118 , 119 However, it may be less preferred for new patients or complex issues requiring nuanced discussions. Fit (or non-fit) of virtual clinics has been found to be dependent on clinician and medical staff’s ability to communicate via these channels effectively and comprehensively. 119 Hybrid models combining in-person with virtual clinics may be an effective middle-ground for both patients and clinicians. Also, AI might have a future role in virtual clinics, by ECG interpretation and prediction of outcomes.

AI and machine learning (ML) are rapidly evolving disciplines within data science that can classify complex data, and thus ‘interpret them’ to predict future patterns or risk of events. 120 Studies published in Europace within the field of AI provide an exciting chronology of our field aimed at better-managing patients with cardiac electrophysiologic disorders.

Europace published its first AI study in 2003, well before its 25th birthday, in which Kappenberger et al . 121 identified ventricular tachycardia (VT)/ventricular fibrillation (VF) with a c-statistic >0.90 by leveraging sensed voltage alterations from sinus rhythm in ICD recipients. This early study incorporated elements that remain foundational to this day, most notably separating the cohorts used for algorithm development from cohorts used for testing to improve the generalizability of results. Studies in 2008 used AI of electrogram shapes to discriminate VT with such high accuracy (c-statistic > 0.95) 122 that an accompanying editorial 123 posed a question that still resonates: ‘[will] automated analysis … replace the electrophysiologist?’.

Of numerous studies using AI to predict VT/VF, Shakibfar et al used random forests to classify daily ICD interrogation summaries in 19 935 patients, providing a c-statistic of 0.80 for imminent electrical storm in an independent test cohort. 124 When explaining their results, the authors found that the most predictive features were percentage of ventricular pacing and level of daytime activity. This use of AI to analyse near continuous ICD data has stimulated much interest and further studies. 125 In an intriguing study by Sammani et al. , deep neural networks were used to develop an autoencoder to represent key features of 1 million ECGs in a latent space; when applied to 695 patients with dilated cardiomyopathy, this interpretable AI predicted long-term VT/VF and found that P wave features, right bundle branch delay and reduced QRS-T voltages were the most predictive. 126 Several studies applied AI to imaging data. Balaban et al. reported that remodelling of LV end-diastolic shape in 156 patients was the strongest multivariate predictor of VT/VF over an extended follow-up of 7.7 years. 127

A remarkable achievement of AI has been to dramatically alter clinical care using simple data. Pioneering work by Attia et al. showed that the ‘AI-enabled 12-lead ECG’ in sinus rhythm can reveal left ventricular dysfunction 128 and patients with paroxysmal AF. 129 This is an exciting field, although further studies are needed since some others suggest that the AI-ECG may not add to traditional risk factors, 130 or may not apply to single ECG leads 76 in ambulatory monitors. AI may effectively ‘learn’ other ECG waveform patterns, for example AI of T-wave morphology was reported to identify gene-positive long QT syndrome patients from controls with a c-statistic of 0.901, better than QTc estimates. 131 Convolutional neural networks applied to the ECG were shown to identify echocardiographic LV hypertrophy better than clinicians in 21 286 patients, with a c-statistic of 0.868 in an external validation set. 132

The ESC-EHRA AF ablation long-term registry recently used AI of multimodal data to predict outcomes after AF ablation in 3128 patients with a c-statistic of 0.72, making the tool available online and outperforming clinical risk scores. 133 AI has been applied to electronic health records to reduce spurious AF alerts, using natural language processing and CHA2DS2-VASc elements, providing 98% accuracy and reducing workload by 84%. 134 AI of clinical data predicted sinus rhythm after electrical cardioversion of AF, 135 and after guideline-directed medical therapy 136 in secondary analyses of the Flec-SL-AFNET 3 and ANTIPAF-AFNET 2 trials, respectively. Neural network classifiers can predict recurrent syncope from patients in the emergency room using the history and ECG with accuracies from 67 to 95%. 137

AI has been used to improve body surface potential mapping, 138 and even to generate 3D maps of ventricular activation from the 12-lead ECG. 139 AI of the ECG can separate typical from atypical atrial flutter mechanisms. 140 A consensus document discussed the use of AI to better understand and map AF. 70 Bhatia et al. applied AI to intracardiac electrograms in AF to identify patterns of organization associated with recurrence after ablation, 141 , 142 and such tools have been incorporated into clinical mapping systems. 143 , 144 Corrado et al recently applied AI to reveal tissue conduction slowing and atrial surface area that may predispose to re-entry during AF. 145 Toprak reported that AI of NT-pro BNP and other circulating biomarkers improved upon traditional clinical variables in predicting incident AF. 146

Europace has also taken the lead in reporting some of the challenges for AI. A notable editorial by Loring and Piccini in 2019 entitled ‘Machine Learning in Big Data: Handle with care’ 147 discussed how AI is not immune to bias in study design. These authors also showed that AI did not improve AF outcome predictions in the large ORBIT-AF and GARFIELD registries over traditional statistical predictors. 148

In summary, AI is an extremely promising discipline to better understand and treat patients with heart rhythm disorders, and future work should focus on defining disease states, patient groups, and algorithmic approaches which will enable the greatest benefit. However, it is vital that the regulatory process is in balance with the development of novel models.

A regulatory perspective

Although our journey through digital arrhythmia care over the past decades has shown remarkable progress, there is also a need to be careful when introducing novel technologies. Medical devices are becoming smarter by using software that is increasingly ‘intelligent’, taking advantage of the steep rise in capabilities of AI and ML. As data is the cornerstone of AI learning, testing, and validation, this means that such novel devices need to comply (already or in the near future) with several regulations from the EU: besides the General Data Protection Regulation (GDPR) also the Medical Device Regulation (MDR), Data Governance Act, and the upcoming AI Act, and the European Health Data Space regulation. While this is already a significant challenge for small and even large manufacturing companies, for non-profit hospitals, and academic institutions it has become a major hurdle for the implementation of their innovations. Politicians and regulators across Europe have become aware of this issue, which is pushing innovation to other markets like the USA and China. Finding the proper balance between safety and innovation is still ongoing. 94 , 149 , 150

One must weigh in that zero risk does not exist, and one always must consider the balance between benefit and risk for the individual patient and for society. Presently the balance seems to have swung towards risk aversity, which inadvertently creates risks of with-holding potentially beneficial devices from patients in need of them. 151 , 152 There is also a disconnect between the regulatory requirements from GDPR and MDR and the scientific evidence on which clinicians base their decision to use certain devices in specific circumstances for a given patient. Scientific guidelines and the clinical requirements from GDPR and MDR aim towards the same goals at the highest level, but in their practical implementation, they do not coincide and sometimes only marginally overlap. The regulatory requirements focus on avoiding risk (which is further enhanced by the status of the notified bodies) and are subject to a variable interpretation of the GDPR in the EU Member States and of the MDR by the notified bodies. Scientific guidelines focus more on the benefit-risk balance but are not available for all clinical decisions and are often based on inconclusive or incomplete evidence and only on expert opinion with the inevitable (but mostly not intentional) bias. 153

To bring the two requirement systems closer together and to avoid the high costs of duplicated clinical trials, registries, and studies, one could consider aligning them to their intrinsic purpose, which is allowing the patient/family to make the decisions about diagnostic and treatment options together with their health care provider based on the best available information, for example about benefits, risks, alternatives, and refraining from active therapy. Depending on the clinical situation of the patient, the severity of the pathology, and potential benefit of the intervention a lower or higher risk or uncertainty might be acceptable. It is the core of co-decision making to weigh these factors and come to an informed, balanced conclusion. The information needed to make these choices can vary and that must be reflected in the regulatory framework.

Similarly, the evidence to be provided by a manufacturer before the release of a product into the market should be based on the risk-benefit balance with a larger or smaller emphasis on post-release requirements. Regulators are presently hesitant to allow this because post-release obligations are often more difficult to define and enforce. For medical device software, this might possibly be the only way to address the difficulties with (self-)learning AI software and with the drift in the use of such devices in clinical real life.

In conclusion, the reality of medical device software, with its variable possibility of extensive pre-release clinical testing and potential drift in use and impact, will necessitate a more balanced risk-benefit evaluation and alignment of the regulatory and clinical scientific standards in the future.

As described in the previous sections, EP has a history of utilizing advanced digital solutions and tools. With the current rapid advancement of digital technologies, commonly referred to as digital transformation, including AI using ML and deep learning, RM, wearables, and advanced imaging, we can expect even greater progress in the field. Some potential future aspects for digital in cardiovascular EP follow.

Telemedicine and RM, as discussed in the section on virtual clinics and telemonitoring, have become increasingly important topics in cardiology, 75 especially with the acceleration brought on by the COVID-19 pandemic. We could expect to see improved capabilities and infrastructures for RM, 68 as well as advancements in wearable technologies. With ongoing developments in device miniaturization and wearable tech, monitoring, and treatment options should become more convenient and patient friendly. For instance, implantable sensors or wearable patches may provide continuous monitoring of heart rhythm and other relevant data, enabling early detection and intervention in case of abnormalities. This should lead to better patient outcomes and satisfaction, as well as a reduced workload for healthcare staff and lower healthcare costs.

ML and AI algorithms have the potential to transform EP by enabling automated and precise analysis of vast amounts of data. 63 , 154 , 155 These technologies can aid in predicting, diagnosing, and providing personalized treatment for heart rhythm disorders. The rapid development of digital technologies is expected to lead to significant improvements in imaging and mapping techniques, resulting in better visualization and characterization of the heart’s electrical activity. This could include the widespread adoption of high-resolution imaging and three-dimensional mapping technologies, which would enable more precise diagnosis and treatment planning for patients. In addition, ML-powered algorithms can optimize the outcomes of catheter ablations by providing real-time guidance and feedback.

Advanced imaging using virtual reality and augmented reality has the potential to revolutionize EP training and procedural planning. Virtual reality can provide a safe and controlled environment for simulating ablation procedures, reducing the learning curve and improving safety. Additionally, augmented reality can offer real-time visual guidance during procedures by overlaying relevant information, such as anatomical landmarks or electrode placement, onto the patient’s body. These technologies could ultimately lead to better patient outcomes. Moreover, virtual reality can also be utilized to reduce patient anxiety by aiding in teaching and preparing patients for procedures, as well as assisting in post-procedural rehabilitation. 156

One of the most challenging yet promising areas where AI could have a significant impact is personalized medicine. AI-based algorithms that incorporate individual patient characteristics, such as genetics, lifestyle, and medical history, can provide advanced analytics and computational modelling to predict the optimal treatment approach for each patient. This approach can lead to more targeted and effective treatments with improved outcomes. 157

In conclusion, the future of cardiovascular EP will be shaped by rapid advancements in digital technologies, such as advanced imaging and mapping, AI and ML, telemedicine and RM, virtual and augmented reality, miniaturization and wearable devices, and personalized medicine. These advancements have the potential to significantly improve diagnosis, treatment, and patient outcomes in cardiovascular EP, and represent an exciting time for the field. With this reflection on the past quarter of a century in EP, we can now cast our eyes forward, envisioning that the journey ahead will likely accelerate our digital knowledge. In the coming 25 years in the EP Europace journal, we will continue to provide you with novel digital tools to improve the management of arrhythmia patients and steadfastly aim to increase our coverage of digital topics, using a scientific approach to enable better patient management.

ES is supported by a Grant from Region Stockholm, the Swedish Heart and Lung foundation, CIMED and Swedish Research Council Vetenskaps Rådet DNR (2022-01466) PS is supported by an Investigator Grant from the National Health and Medical Research Council of Australia.

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Saglietto A , Gaita F , Blomstrom-Lundqvist C , Arbelo E , Dagres N , Brugada J et al.  AFA-Recur: an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation . Europace 2023 ; 25 : 92 – 100 .

Rosier A , Mabo P , Temal L , Van Hille P , Dameron O , Deléger L et al.  Personalized and automated remote monitoring of atrial fibrillation . Europace 2016 ; 18 : 347 – 52 .

Oto E , Okutucu S , Katircioglu-Öztürk D , Güvenir HA , Karaagaoglu E , Borggrefe M et al.  Predictors of sinus rhythm after electrical cardioversion of atrial fibrillation: results from a data mining project on the Flec-SL trial data set . Europace 2017 ; 19 : 921 – 8 .

Okutucu S , Katircioglu-Öztürk D , Oto E , Güvenir HA , Karaagaoglu E , Oto A et al.  Data mining experiments on the angiotensin II-antagonist in paroxysmal atrial fibrillation (ANTIPAF-AFNET 2) trial: ‘exposing the invisible’ . Europace 2017 ; 19 : 741 – 6 .

Costantino G , Falavigna G , Solbiati M , Casagranda I , Sun BC , Grossman SA et al.  Neural networks as a tool to predict syncope risk in the emergency department . Europace 2017 ; 19 : 1891 – 5 .

Bacoyannis T , Ly B , Cedilnik N , Cochet H , Sermesant M . Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization . Europace 2021 ; 23 : i55 – 62 .

Vincent KP , Forsch N , Govil S , Joblon JM , Omens JH , Perry JC et al.  Atlas-based methods for efficient characterization of patient-specific ventricular activation patterns . Europace 2021 ; 23 : i88 – 95 .

Luongo G , Vacanti G , Nitzke V , Nairn D , Nagel C , Kabiri D et al.  Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram . Europace 2022 ; 24 : 1186 – 94 .

Bhatia NK , Rogers AJ , Krummen DE , Hossainy S , Sauer W , Miller JM et al.  Termination of persistent atrial fibrillation by ablating sites that control large atrial areas . Europace 2020 ; 22 : 897 – 905 .

Ganesan P , Deb B , Feng R , Rodrigo M , Ruiperez-Campillo S , Rogers AJ et al.  Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms . Europace 2023 ; 25 : euad055 .

Betts TR , Good WW , Melki L , Metzner A , Grace A , Verma A et al.  Treatment of pathophysiologic propagation outside of the pulmonary veins in retreatment of atrial fibrillation patients: RECOVER AF study . Europace 2023 ; 25 : euad097 .

Krummen DE , Baykaner T , Schricker AA , Kowalewski CAB , Swarup V , Miller JM et al.  Multicentre safety of adding focal impulse and rotor modulation (FIRM) to conventional ablation for atrial fibrillation . Europace 2017 ; 19 : 769 – 74 .

Corrado C , Williams S , Roney C , Plank G , O’Neill M , Niederer S . Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation . Europace 2021 ; 23 : i12 – 20 .

Toprak B , Brandt S , Brederecke J , Gianfagna F , Vishram-Nielsen JKK , Ojeda FM et al.  Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods . Europace 2023 ; 25 : 812 – 9 .

Loring Z , Mehrotra S , Piccini JP . Machine learning in ‘big data’: handle with care . Europace 2019 ; 21 : 1284 – 5 .

Loring Z , Mehrotra S , Piccini JP , Camm J , Carlson D , Fonarow GC et al.  Machine learning does not improve upon traditional regression in predicting outcomes in atrial fibrillation: an analysis of the ORBIT-AF and GARFIELD-AF registries . Europace 2020 ; 22 : 1635 – 44 .

Szymanski P , Leggeri I , Kautzner J , Fraser AG . The new European regulatory framework for medical devices: opportunities for engagement by electrophysiologists . Europace 2018 ; 20 : 902 – 5 .

Choby AA , Clark AM . What costs matter? Rethinking social costs of new device technologies . Europace 2013 ; 15 : 1538 – 9 .

Fattore G , Maniadakis N , Mantovani LG , Boriani G . Health technology assessment: what is it? Current status and perspectives in the field of electrophysiology . Europace 2011 ; 13 : ii49 – 53 .

Boriani G , Vitolo M , Svennberg E , Casado-Arroyo R , Merino JL , Leclercq C et al.  Performance-based risk-sharing arrangements for devices and procedures in cardiac electrophysiology: an innovative perspective . Europace 2022 ; 24 : 1541 – 7 .

Prinzen FW , Dagres N , Bollmann A , Arnar DO , Bove S , Camm J et al.  Innovation in cardiovascular disease in Europe with focus on arrhythmias: current status, opportunities, roadblocks, and the role of multiple stakeholders . Europace 2018 ; 20 : 733 – 8 .

Nagarajan VD , Lee SL , Robertus JL , Nienaber CA , Trayanova NA , Ernst S et al.  Artificial intelligence in the diagnosis and management of arrhythmias . Eur Heart J 2021 ; 42 : 3904 – 16 .

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Shiraishi Y , Goto S , Niimi N , Katsumata Y , Goda A , Takei M et al.  Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography . Europace 2023 ; 25 : 922 – 30 .

Author notes

• artificial intelligence
• electrophysiology
• telemonitoring
• mobile applications
• wearable electronic devices

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E&P Digital Transformation: Fundamental Next Step in Creating Value

The adoption of emerging technologies is essential for all businesses, and even more important for well-­established industries because of the perpetual threat of disruption from competitors..

The adoption of emerging technologies is essential for all businesses, and even more important for well-­established industries because of the perpetual threat of disruption from competitors. Industries and companies unable to adopt digital transformation are likely to have a short life span in the current digitally driven global competitive environment. While the E&P industry is more than 100 years old and has seen many ups and downs, the prolonged impact of decreased oil prices since 2014 warrants the need for a transformational modification for it to remain operationally viable.

The upstream sector started the digital journey decades ago, leading to incremental improvements. The first step of this journey is “digitization,” which is the creation of data in digital form. Traditional supervisory control and data acquisition (SCADA) systems, meters, utility line monitors, and other equipment provide digital metrics at different frequencies but there are still many operations that are not fully digital, such as daily drilling reports and log reports.

The second step is “digitalization,” which is leveraging digitized data to generate information with limited value creation such as key performance indicators (KPIs) and dashboards. The industry has created and deployed many of these KPIs and dashboards to assess the health of its business; this “data in motion” is being created with little to no associated provenance, thus creating digitized dark data. In simple terms, dark data are data that exist, but no value is being generated from them. There are many reasons for data to be dark, and for the oil and gas industry the two primary reasons are forgotten data in silos and significant amounts of unused data from past operations.

The final step is the transformational step, “digital transformation,” when the business truly encompasses the transformation of talent, business strategy, and resources by leveraging emerging technologies for real-time resource optimization on an ongoing basis.

Is Your Digitalization Creating Value?

The E&P industry claims to have adopted digitization and digitalization in the form of digital oil fields; however this has failed to generate significant economic value during the past 2 decades. The lack of value creation could be attributed to the following:

• Incomplete use or nonuse of data-driven innovation
• Use of inefficient, complex, and fragmented workflows
• Inability to keep pace with the information technology (IT) revolution

As a KPI, nonproductive time (NPT) is related to value generation. The NPT in the industry ranges from 10% to 30% for capital expenditure, which amounts to tens or even hundreds of millions of dollars per well. While a majority of E&P industry experts speak of doing digitalization, it is the author’s view that this does not provide any competitive edge for the E&P industry in keeping pace with the current Industry 4.0 revolution (the name for the current trend of automation and data exchange in manufacturing technologies). The industry instead needs to focus on a “true” digital transformation by leveraging the four pillars of this transformation and keeping in sync with the pace of IT advancements. The four pillars of the E&P digital transformation are

• Real-time resource optimization
• Integrated technology platform
• Big data analytics

Big data analytics forms the foundation of this transformational journey because the industry has created petabytes of dark data in its lifetime, which could have significant economic value and impact. In recent years, Halliburton’s Big Data Center of Excellence has proven that the industry’s dark data can be exploited with minimal risk to generate economic value for the organization using measurable metrics such as net present value, efficiency, accuracy, revenue, and cost savings. For example, a big-data-driven model for using the right mud could increase the production of a mature field by 30,000 BOEPD resulting in additional revenue of $1 million/day.

Digital Transformation Framework

Many challenges to accelerating the pace of digital transformation in E&P remain, including:

• “Old school” thinking that focuses on digitalization rather than transformation
• Data in silos instead of data pipelining through technology platforms
• Availability of the right talent

Many organizations are sitting on caches of dark data in their domain. Creating value from these data is a low- or no-risk activity, which requires less than 1% of the investment a drilling well typically needs, but it requires new-age thinking and action. Recently, a national oil company in Asia worked with Halliburton to exploit the unstructured data for various joint ventures. Using a combination of digital transformation methods like Smart Transform, a 19% NPT was recognized from this data set, and a real-time model to extract such NPT in near real-time is being developed. Several other such opportunities exist in areas of equipment failure, supply chain, design of equipment, operations in the field, exploration, etc.

In previous eras, the linear adoption of technology advancement transformed organizations at a linear pace of growth. The Industry 4.0 revolution focuses on gaining unprecedented levels of efficiencies across operations, enhanced productivity at an accelerated pace, and a paradigm shift in how industries continue to operate in the future. The digital transformation of the E&P industry is of paramount interest; however there remain many challenges due to the legacy nature of the business and lack of proper investment in adopting technology advancements. A proven framework and solutions to accelerate its adoption in upstream oil and gas exist, providing for asset intimacy and real-time resource optimization for maximum economic value generation. This can enable our industry to be equal with the best practices of the current technology revolution and remain competitively viable in the energy landscape of the future.

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Digital customer journeys: from awareness to advocacy.

13 min read Just understanding a customer’s needs and wants is no longer enough. You need to know not only what they think and feel about every online interaction with your product, but also what they might do in the future. This is where digital customer journey mapping comes into play.

Customers expect not only a high-quality digital experience ; they also expect to be treated as individuals. Their online experience must be personalized, relevant, and tailored to their wants, needs and interests. And where customers have high expectations , it follows that they have low tolerance for a below-par experience.

Now, more than ever, customers will abandon a purchase or a brand with a single click if they’re not happy, and move onto another brand that seamlessly delivers what they want. A recent study from the XM Institute asked large organizations to evaluate the quality of the experiences they deliver across different channels. Less than 30% rated any of their digital experiences as “good” or “very good”.

Modern customers are digital kangaroos, able to hop from brand to brand and product to product, on any device. Therefore, it’s essential to ensure that your brand’s path to purchase is as easy as possible, to stop them from hopping off to a competitor.

How do you do this? With digital customer journey mapping.

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What is a digital customer journey?

This is the path to purchase and retention – from first noticing the product to buying and using it. The journey combines all the touchpoints (i.e. points of interaction with your business) a customer has, and collects consumer data, transaction information, cross-device browsing history, and customer service interactions. There are five stages in the digital customer journey:

• Awareness: this is the point at which a customer notices your product . Awareness can come from a multitude of channels: social media and word of mouth from friends, influencers and brand advocates, search engine suggestions, adverts, marketing emails, blogs, SMS, apps, loyalty programs, and affiliate marketing.
• Consideration: A customer likes what they’ve seen, so they start to think about and research the product. They’ll visit your website, engage with a chatbot, sign up for free trials, demos, webinars, look at discounts, and check online reviews and testimonials.
• Purchase: To buy online, customers will create an account (or log into their existing one), fill their shopping cart, may be upsold or cross-sold, apply discounts, choose an electronic payment option, check out, and leave a review about the purchase.
• Experience: This is how well the order is fulfilled, and includes: shipping and delivery, tracking, online help center, support content (FAQs, instructions and assembly guides), chatbots and assisted chat, guarantees, follow-up emails and social media interactions.
• Loyalty: Loyalty programs , personalized rewards, newsletters, and social media interaction are all well and good. But creating an emotional connection with the customer, and ensuring they receive the value they expect from the brand is the new approach to loyalty: is the product good quality? Did the customer receive good support ?

What exactly is a digital customer journey map?

When you map out the digital journey graphically, including all the devices, and touchpoints your customer interacts with, you’ll understand how they make decisions, connect and interact with your brand. You can also identify and rectify any pain points that make the customer experience less than seamless.

What are the benefits of customer journey mapping?

• You’ll walk in your customers’ (virtual) shoes : Employees sometimes find it difficult to empathize or understand the customer’s perspective. They may try to second guess what customers are feeling, rather than experiencing the journey themselves. By collecting feedback at touchpoints along the journey, the customer can express how they are feeling (frustrated? Happy? Disappointed? Cared for?) and employees can jump in to solve issues and make the customer experience smoother and more enjoyable.
• The whole company will work together: All too often, organizations work in silos: not only communication silos (when different teams don’t speak to each other) but also system and data silos that hold customer information that’s specific only to that part of the journey.  It’s the lack of a 360 view of the customer and seamless sharing of insight that creates this fragmented experience. With a customer journey map and centralized customer information, everyone, across all departments, knows where they fit in and what their role is in delivering a seamless experience.
•   You’ll inform your content marketing and content creation: Customers buy more if your content is relevant and targeted to them. Your customer journey map will help you build a full 360° picture of your customers: demographics , behavior , and psychographics , so you can target new and returning buyers.
• You’ll be able to predict customer behavior: Not only will journey mapping give you valuable insight into customers’ wants, needs, feelings, actions and aspirations, you’ll also be able to use the data to predict and influence how customers will behave.
• You’ll be able to identify gaps: when you map out each stage of the journey, and then map out your existing processes, you can not only uncover gaps, but also identify what your highest value journey touchpoints are. Without mapping, you could be focusing on optimizing touchpoints that are not really that influential, while missing a more important point.

Creating your digital customer journey map

The first thing to understand is that you have no control over a customer’s journey. A customer will go where they like, on whichever device or platform they choose, negotiating the touchpoints to achieve their goal of a satisfactory purchase. Your role is to build an omnichannel framework that anticipates where they are going to go and supports their goal.

• Base it on your sales funnel:  You will probably have the basis for your digital customer journey already – your online sales funnel (awareness, interest, decision, action). Use this as a guideline to define how many touchpoints your customers have, and how each interaction funnels into the next.
• Put your customer hat on: Walk through all the stages of your sales funnel as a customer would, noting the touchpoints. What social media would they interact with? Does your website have the right information? How easy is your booking process? How helpful are the after sales people? Is the loyalty scheme attractive? Would you be happy to recommend your own product ?
• Customize your touchpoints: You know which social media platform attracts most customers, how to respond to reviews so your business demonstrates it cares about customers, how your purchase process works, how good your aftersales team is, and how you reward loyal customers. These are the touchpoints that are specific to your company. When you bolt them onto your customer journey map and collect feedback about each of them, you’ll be able to see if they are performing as well as you think they are.
• Create personas: As companies scale, it becomes harder and harder to keep track of individual customers. This is where personas come in: these are fictitious customer types based on real customers, using demographic and psychographic profiles that include age, gender, socioeconomic background, lifestyle, interests, opinions, likes, dislikes, and attitudes. Each persona travels along their customer journey in a slightly different way, enabling a company to recognize the differences and cater to every type of customer.
• Use customer journey mapping software: Customer journey solutions are now so sophisticated that they can give real-time visualizations of your customers moving towards purchase and beyond, capturing their online interactions with your brand. AI-enabled software will flag any touchpoint where customers are struggling and highlight any places where they drop out. Not only can you jump in and fix the problems, you can also measure the impact that improving the customer experience at those points has on the company’s bottom line.

Data you can collect with digital customer journey mapping

These are just some of the types of data you can collect along your digital customer journey. When you feed all these into a single platform for analysis, you’ll be able to see how they relate to each other, and where they have knock-on effects.

• Web-browsing data: Every time someone clicks onto your website, you can track their activity on the site and see how they are interacting with your brand. You can also see what devices they are using to access your site.
• Mobile app data: If a customer is using your mobile app, they already have a degree of loyalty. Mobile apps yield more customer information from profiles, sign-ins, and location.
• Sales data: You can track a customer’s purchase history and shopping habits over time. Do they buy immediately, leave items in their shopping cart, or abandon their cart periodically? Don’t forget sales that didn’t happen – finding out why not is valuable for understanding what needs to improve.
• Advertising data: Who has clicked through to your site from an advertisement? This data will give you information about customers who are just starting out on their journey with you. You can marry advertising data with sales data to test the effectiveness of your ad campaigns.
• Loyalty data: Your best customers are usually those in your loyalty program. By analyzing who they are and how they use your brand, you’ll be able to target people just like them.
• Survey data: Want to know what customers think of your brand? Ask them. Sending surveys at touchpoints along the customer journey can give you quality information about what’s working and what’s not.
• Social media listening: Increasingly, customers interact with brands through social media. Understanding the nature of this interaction can help develop your social, as well as general marketing strategies .
• Aftersales data: Information from your customer services department can reveal a wide range of issues: product quality, delivery reliability, areas that need product support. How customers are treated after they’ve made a purchase is pivotal to whether they become loyal, or not.

What about B2B digital customer journey mapping?

Whether you’re selling B2C or B2B, the main principles of journey mapping are the same. After all, although you are trading with companies, you are still selling to people within those companies – there are just more of them, and your feedback processes will need to be a little different.

When you map B2B journeys , you need to bear the following in mind:

• More types of people are involved in a B2B journey than a B2C one: Therefore, you’ll need to create more customer personas. For example, if you’re supplying an online finance platform, you will have to deal with the CIO, executives, managers, tech personnel and the call center assistants. All these people are your customers, segmented by persona.
• B2B customers are more valuable: Building business relationships can take years of investment, and if you lose a business customer, you might lose a lot of revenue as a result. You’ll need to prioritize and segment your customer personas by business value: the CIO has more purchasing power than a single call center assistant, for example.
• B2B customer feedback is different: Because much B2B is built on personal interaction and recommendation, business people often know each other. It’s more acceptable to pick up the phone and talk through a problem than send out a generic survey. Your feedback techniques will have to be much more personalized to each of your B2B customers, so that they feel heard, and still special.

An example of a customer journey map template

The brands that thrive in this new reality are those that understand what customers want in digital and take action to deliver the experiences they expect. Here’s a customer journey template for you to start mapping out your digital journey.

To learn more about customer journey management take a look at our free online course below:

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Related resources

Customer Journey

B2B Customer Journey 13 min read

Customer interactions 11 min read, consumer decision journey 14 min read, customer journey orchestration 12 min read, customer journey management 14 min read, customer journey stages 12 min read, buyer's journey 16 min read, request demo.

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DIGITAL EP by JOURNEY

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• Peak position 93
• Label COLUMBIA
• Catalogue number CATCO131924419
• First Chart Date 24/05/2008

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1 week - 24/05/2008

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The digital journey: 25 years of digital development in electrophysiology from an Europace perspective

Affiliations.

• 1 Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86 Stockholm, Sweden.
• 2 Politecnico di Milano, Electronic, Information and Biomedical Engineering Department, Milan, Italy.
• 3 Istituto Auxologico Italiano IRCCS, Milan, Italy.
• 4 Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, The Netherlands.
• 5 Research Group Cardiovascular Diseases, University of Antwerp, 2000 Antwerp, Belgium.
• 6 Department of Cardiology, Antwerp University Hospital, 2056 Edegem, Belgium.
• 7 Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium.
• 8 Department of Cardiology, Heart Centre Hasselt, Jessa Hospital, 3500 Hasselt, Belgium.
• 9 Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA.
• 10 Cardiac Arrhythmia Center, University of California Los Angeles, Los Angeles, CA, USA.
• 11 Cardiology Division, Cardiovascular Institute and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
• 12 Department of Cardiology, KU Leuven, 3000 Leuven, Belgium.
• 13 Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 5005 Adelaide, Australia.
• 14 Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany.
• PMID: 37622574
• PMCID: PMC10450797
• DOI: 10.1093/europace/euad176

Aims: Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal.

Results: In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP.

Conclusion: Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.

Keywords: Artificial intelligence; Digital; Telemonitoring.

© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.

Publication types

• Artificial Intelligence
• Cardiac Electrophysiology
• Cardiology*
• Mobile Applications*

Grants and funding

• R01 HL149134/HL/NHLBI NIH HHS/United States
• R01 HL162260/HL/NHLBI NIH HHS/United States

Crafting Successful EPs: Making An EP The Right Way

EPs, ever heard of them? Don’t worry if not – we’ve got you covered. An EP is like a music sweet spot – not too quick like a single, and not as long as an album. EP stands for Extended Play, and in this article, we’re digging deep into the world of crafting successful EPs that really hit the mark.

EPs are like a taste test of an artist’s sound. They’re like those few pages of a book that give you a sneak peek into the whole story. The beauty? You get to enjoy different musical flavors without the full-album commitment.

We’re taking a journey into what makes EPs tick – understanding how they stand out, how to shape the tracks, and even how to ensure the best sonic quality. Whether you’re an emerging artist aiming to leave your mark or an experienced musician seeking to refine your craft, this article is designed to provide valuable insights. So, let’s dive into the art of crafting the perfect EP.”

Understanding EPs

In a world where music flows effortlessly through the digital landscape, EPs have found their own special corner. Let’s explore why these mini collections are making a big impact.

The Goldilocks of Music

While singles offer quick listens and albums spin elaborate tales, EPs stand out by being the Goldilocks of the music world – just right. They’re like those mini-playlists that artists put together to tell a focused story. The thing is, these mini collections pack a punch bigger than their size. If you want more tools to determine whether you should be releasing albums or singles; Read this Article!

A Musical Playground for Artists

Musicians are drawn to EPs like a moth to a flame, and for good reason. Picture it as an artist’s playground, where they get to explore a theme or vibe without committing to a whole album. And listeners? They get a front-row seat to this creative exploration.

A Taste of Artistic Flavor

The charm of EPs is that they’re a taste of an artist’s flavor, a snippet of their sonic world. It’s like having a small plate at a restaurant, trying a bit of everything before you decide what you really love. With EPs, artists dish out a handful of tracks that share a common thread – a vibe, a story, or an emotion. This keeps listeners hooked, curious about what comes next.

Beyond the Blip: Rising Popularity

EPs strike a chord with both musicians and listeners, offering a compact yet rich experience that resonates beyond the moment. So, buckle up as we all come to understand why EPs are the cool kids of the music scene, making a statement without saying too much.

Benefits of Releasing an EP

Releasing an EP isn’t just a casual decision; it’s a strategic move that packs a punch. EPs act like windows into an artist’s evolving sound, offering listeners a glimpse of their creative journey. These bite-sized collections might be small in track count, but they’re big on impact, showcasing an artist’s versatility in a compact format.

Balancing Creativity and Resources

Now, let’s talk practicality. EPs are an artist’s friend when it comes to balancing creativity and resources. Unlike full albums that demand extensive production, EPs offer a streamlined process. They let artists express themselves without the overwhelming pressure of creating a full album, making them a consistent choice for those who want to keep their creative juices flowing.

Navigating the Digital Landscape

In the digital age, EPs stand tall. Their shorter tracks align perfectly with the playlist-driven music scene. This harmony translates into more playtime on streaming platforms, thanks to algorithms that favor concise tracks. As a result, EPs become discoverable gems, reaching wider audiences and finding their way into the hearts of listeners.

A Stepping Stone for Emerging Talents

But what about those rising stars, the fresh talents who aren’t quite ready for the album marathon? EPs offer a stepping stone. They allow emerging artists to introduce themselves, build an identity, and gather a fanbase. Think of it as a safe space to experiment, refine your craft, and prepare for bigger projects down the road.

Unlocking the Potential

In the following sections, we’ll explore why EPs have become a go-to choice for artists of varying levels. Whether you’re a fresh talent ready to embark on a musical journey or a seasoned musician seeking a new avenue, crafting successful EPs is the key to unlocking musical potential.

Defining the Creative Vision

Crafting successful EPs is more than just assembling a handful of tracks; it’s about weaving a cohesive narrative that resonates with your audience. This is where the creative vision comes into play – the guiding star that shapes every note, lyric, and transition.

The Heart of Coherence

Defining a creative vision is like setting the compass for your musical journey. It’s about establishing the core theme or concept that will thread through your EP, creating an emotional journey for your listeners. Think of it as the heart of coherence; it gives your tracks a purposeful connection, elevating them from mere standalone pieces to an immersive experience.

Factors to Consider

Make sure everything that goes into the EP is thematically aligned. It isn’t just about musical similarity; it’s about their emotional resonance. How does everything come together and how does that make your listeners feel? How does everything fit into the overarching story you’re telling? It’s these subtleties that tie everything together.

Navigating Versatility

But hold on, it’s not just about being locked into a single theme. Your creative vision can have shades, allowing for versatility within the confines of the narrative. This is where your artistry truly shines. It’s about curating a collection that reflects your musical identity while surprising your audience with unexpected yet harmonious elements.

Realizing the Vision

Defining a creative vision requires introspection and honesty. What story do you want to tell? What emotions do you want to evoke? By answering these questions, you lay the foundation for an EP that isn’t just a collection of songs, but a testament to your artistry. It’s about guiding your listeners through an experience that leaves an indelible mark.

A Journey of Expression

Remember when crafting successful EPs, your creative vision is your North Star. It’s the pulse that drives your EP, the heartbeat that makes it come alive. In the upcoming sections, we’ll explore how to bring your vision to life through crafting tracks that resonate and ensuring top-notch sound quality that does justice to your artistic journey.

Crafting the Tracks

Crafting successful EPs goes beyond mere assembly; it’s about curating a sonic journey that captivates and resonates. Let’s take a look into the heart of this process – creating tracks that form a compelling narrative.

Harmonious Composition

Each track contributes to the broader canvas of your EP. When selecting or creating tracks, consider their synergy. Are they building an emotional arc or taking listeners on a journey? It’s about the collective impact, not just individual brilliance.

Captivating Intros

View your EP as a storybook – each track a chapter that draws listeners in. The opening moments of each song are akin to a novel’s first lines; they must engage the audience. Craft intros that intrigue, spark curiosity, and set the tone.

Seamless Transitions

Crafting successful EPs is a journey, and journeys flow. Ensure smooth transitions between tracks. Maintain a rhythm that keeps listeners engaged. A well-crafted transition can create a seamless progression, enhancing the overall experience.

Emotional Narrative

Your EP should have an emotional arc, much like a captivating movie. Assemble your track sequence to take listeners through different feelings – from introspection to exhilaration. This emotional narrative keeps listeners invested and eager for what’s next.

Attention to Detail

Crafting tracks for an EP demands meticulous attention. Consider arrangement, instrumentation, and lyrics. Each element contributes to the impact. Think about how each track aligns with your creative vision and the EP’s purpose.

Painting Sonic Strokes

Creating tracks is akin to painting strokes on a canvas. Every second counts, every note resonates. In the following sections, we’ll explore perfecting sound quality, ensuring your vision translates seamlessly to your audience’s ears.

Perfecting Sound Quality

Sound quality isn’t just a bonus; it’s the foundation of crafting successful EPs . As we venture into the realm of perfecting sound, let’s explore the techniques that ensure your tracks are nothing short of exceptional. If you are serious about the quality of your production and want a little bit of free advice, Check out this article filled with production tips.

The Power of Mixing

Mixing is where the magic happens. It’s the art of balancing each element – vocals, instruments, effects – into a harmonious whole. A well-mixed track doesn’t just sound good; it feels good. It evokes emotions, carrying your listeners into the heart of your music. If you’re not a mixing wizard yourself, consider collaborating with professionals who can elevate your tracks to new heights.

The Precision of Mastering

Mastering is like adding the final polish to crafting successful EPs. It’s the process that brings consistency and balance to the entire collection. A skilled mastering engineer enhances the clarity, depth, and dynamics of each track, ensuring they all fit together seamlessly. Don’t underestimate the importance of this step; it’s the difference between tracks that simply sound good and tracks that shine.

The Art of Balance

Balancing is key. Each element – vocals, instruments, percussion – should have its own space, its own moment to shine. It’s like a dance where every partner has its spotlight. Achieving this balance requires attentive listening and, if needed, revisions. Don’t rush; your tracks deserve the time and effort to sound their best.

The Role of Consistency

EPs are like a musical family, and every family member should get along. This is where consistency comes in. Your tracks should feel like they belong together, like pieces of a puzzle that create a beautiful picture when put side by side. Pay attention to tonal consistency, volume levels, and overall sonic character.

Quality Control

Before your tracks reach listeners’ ears, give them a thorough quality check. Listen on different systems – headphones, speakers, car audio – to ensure they translate well across various platforms. Don’t hesitate to make last-minute adjustments if needed.

A Sonic Legacy

Perfecting sound quality isn’t just about technical prowess; it’s about honoring your artistic vision. It’s about leaving a sonic legacy that resonates with your audience. In the next section, we’ll delve into arranging tracks for cohesion, ensuring that your EP is more than just a collection – it’s a symphony of emotions and experiences.

Arranging Tracks for Cohesion

Arranging your EP’s tracks is about creating a captivating journey. Doing so weaves a seamless tapestry of sonic experiences that resonates with your audience.

Flow Like a Story

Each track is a chapter, and the order matters. Consider how tracks flow into one another. Build anticipation, create contrasts, and maintain a consistent mood. Transitions should feel natural, like turning pages. For more about creating a flowing and thoughtful listening experience, we’ve included a link to a great article all about that. Have a read!

Crafting Narratives

Your track order shapes your EP’s emotional journey. Start with an attention-grabbing track, delve deeper into the theme, build to a climax, and wind down. This creates a satisfying narrative arc.

Balance and Dynamics

Alternate energies and emotions for balance. High-energy tracks followed by introspective ones create a dynamic ebb and flow, keeping listeners engaged.

Transitions that Engage

Transitions aren’t just gaps; they’re opportunities. Well-crafted transitions create anticipation, surprise, or continuation. Experiment with crossfades, thematic links, or brief interludes.

The Holistic Experience

Listen to your EP as a whole. Does it feel like a journey? Does each track contribute to the narrative? The goal is an experience greater than its parts.

Orchestrating Emotions

Arranging tracks orchestrates a symphony of emotions. It guides listeners through a sonic odyssey. In the next section, we’ll explore visual identity and branding’s role in enhancing the holistic EP experience.

Visual Identity and Branding

An EP isn’t just about the music; it’s a sensory experience that extends to the eyes. Visual identity and branding create the first impression, a glimpse into the world you’ve crafted. Let’s delve into how these elements can elevate your EP from a collection of tracks to a holistic artistic statement.

The Art of Visual Storytelling

Your EP cover isn’t merely an image; it’s a visual story. It’s the first thing listeners see, and it should encapsulate the essence of your music. Think about the emotions you want to evoke – is it mysterious, vibrant, melancholic? The cover sets the tone and offers a sneak peek into the sonic journey within. For help with creating an album cover, We recently posted an article that includes some great advice. Bookmark the resource!

Creating a Consistent Aesthetic

From cover art to social media graphics, consistency matters. A unified aesthetic helps listeners recognize your EP across various platforms. This cohesive branding builds a stronger connection and reinforces your EP’s identity.

Reflecting Your Sound

Visuals should mirror your music’s mood and theme. If your EP exudes nostalgia, your visuals can have a vintage touch. If it’s cutting-edge, opt for a modern aesthetic. The goal is alignment – visuals that enhance the emotional impact of your tracks.

The Role of Branding

Branding isn’t just about logos; it’s about encapsulating your artistic essence. It’s how you present yourself to the world. Consistent fonts, color schemes, and imagery across all touchpoints convey professionalism and dedication to your craft.

Inviting the Listener In

Your EP’s visual identity is like an open door, inviting listeners to step inside your world. It’s an opportunity to spark curiosity, prompt exploration, and deepen engagement. A well-crafted visual identity extends the lifespan of your EP, making it memorable and shareable.

Aesthetic Harmonies

Visual identity and branding harmonize with the music, enriching the overall experience. As we move forward, we’ll explore the pitfalls to avoid, ensuring that every aspect of your EP creation is thoughtfully executed. From sound to visuals, it’s all part of the symphony you’re composing for your audience. If you need to hire some help to put this together, you may start by looking on Fiverr for help.

Avoiding Common Pitfalls

Creating an EP is a meticulous process, and steering clear of common pitfalls is essential to crafting a standout musical experience. Let’s explore some pitfalls to sidestep on your journey to EP excellence.

Cohesion is Key

One of the most significant pitfalls is lack of vision. Without a clear theme or concept, your EP risks becoming a disjointed collection. Avoid choosing tracks solely based on personal preferences; consider how they resonate as a whole. Neglecting thematic alignment undermines the very essence of an EP’s narrative power.

Quality Trumps Speed

Rushing production is another trap. Skimming through recording and production leads to compromised sound quality and mixing issues. Don’t underestimate the impact of professional mixing and mastering; it’s the coat of polish that makes your tracks shine.

Flow and Harmony

Track sequence matters more than you might think. A chaotic order disrupts the EP’s narrative, while stacking similar tracks leads to monotony. Crafting a dynamic flow, where tracks ebb and flow seamlessly, keeps listeners engaged.

Visuals Tell a Tale

Visual identity isn’t to be neglected. Generic artwork dilutes your EP’s impact. Ensure your visuals mirror your music’s soul and resonate with your audience. The right visuals set the stage for an immersive experience.

The Echo of Silence

Skipping promotion is a grave misstep. Releasing without proper marketing leaves your EP in obscurity. Leverage social media, streaming platforms, and promotional channels to amplify its reach.

Complete Before You Compete

Releasing prematurely is perilous. Without proper polishing and marketing materials, you risk diluting your impact. Thoroughly plan your release to maximize its potential.

Originality Elevates

Don’t mimic trends blindly. Sacrificing originality for trends dampens the longevity of your EP’s impact. Your unique perspective is what makes it resonate.

Listen and Adapt

Avoid disregarding feedback. Constructive criticism is a compass for improvement. Engage with your audience and foster a sense of community around your EP.

Sustaining Momentum

After the release, capitalize on the buzz. Plan follow-up activities, from live performances to additional content, to maintain momentum and growth.

The Path to Mastery

By steering clear of these pitfalls, you’re steering your EP towards excellence. In the upcoming conclusion, we’ll summarize the key takeaways, empowering you to embark on your EP journey with confidence.

As we conclude, remember this: crafting successful EPs is a testament to your passion, dedication, and creativity. You’re not just weaving tracks; you’re crafting an experience that resonates with others.

You, as a serious musician, want to create something substantial, and that’s why we want to remind you of the essential intricacies – from thematic cohesion to sound quality. You hold the reins to these variables that define your work. Challenges may emerge, but your sophistication will guide you through.

Trust your instincts, continue to hone your craft, and strive for excellence. You possess the necessary qualities; you only need to navigate the journey. You’re on the path to creating something remarkable. Your musical journey is your canvas; remember, the process is as enriching as the result.

Chad Joshua

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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The digital journey: 25 years of digital development in electrophysiology from an Europace perspective

Emma svennberg.

Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86 Stockholm, Sweden

Enrico G Caiani

Politecnico di Milano, Electronic, Information and Biomedical Engineering Department, Milan, Italy

Istituto Auxologico Italiano IRCCS, Milan, Italy

Nico Bruining

Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, The Netherlands

Lien Desteghe

Research Group Cardiovascular Diseases, University of Antwerp, 2000 Antwerp, Belgium

Department of Cardiology, Antwerp University Hospital, 2056 Edegem, Belgium

Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium

Department of Cardiology, Heart Centre Hasselt, Jessa Hospital, 3500 Hasselt, Belgium

Janet K Han

Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA

Cardiac Arrhythmia Center, University of California Los Angeles, Los Angeles, CA, USA

Sanjiv M Narayan

Cardiology Division, Cardiovascular Institute and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA

Frank E Rademakers

Department of Cardiology, KU Leuven, 3000 Leuven, Belgium

Prashanthan Sanders

Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 5005 Adelaide, Australia

David Duncker

Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany

Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.

In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal.

In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP.

Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.

What’s New?

• A comprehensive overview of the past 25 years within the field of digital electrophysiology with a particular focus on publications from the EP Europace journal.

Introduction

Digital technology has the potential to impact and transform healthcare by providing a platform for patient identification, risk stratification, management, patient interactivity, and education. In the past 25 years there has been a substantial development within the field of digital cardiology, with electrophysiology (EP) in the forefront paralleled by an increase in publications within this field.

This paper seeks to provide an in-depth overview and chronology of the field of digital EP mirroring the substantial influence in this area that EP Europace has had in the past decades. Digital technologies can enable care for arrhythmia patients, and we aim to provide the reader with a brief overview of the digital toolbox, starting with a journey from the early days of telemonitoring, followed by an update on monitoring from a wearable perspective. From there we move forward to mobile applications and virtual clinics. In addition, a chronicle of the development within the field of artificial intelligence (AI), an outlook on the future as well as a regulatory perspective, is provided ( Figure ​ Figure1 1 ).

An overview of digital tools available for the electrophysiologist.

Telemonitoring of cardiac implantable electronic devices

The digital journey in EP begins with cardiac implantable electronic devices (CIEDs). A significant evolution has been seen over the last decade, with novel technologies allowing complex programming and wireless remote monitoring (RM) of device function and patient health status. 1–6 Telemonitoring of CIEDs has now evolved to a fully automated system to complement in-office follow-up 7 and has gained even more importance during the COVID-19 pandemic. 8

Previous studies have demonstrated a reduction in time to detection of clinically actionable events, prompting earlier intervention with the implementation of RM compared to standard in-person follow-up care. 9–14 In the multicenter RIONI study, in 619 patients with an implantable cardioverter-defibrillator (ICD) it was shown that home monitoring could provide an accurate evaluation of events by experts. 5 , 15 The PREFER study evaluated 980 pacemaker patients with RM providing earlier and more frequent detection of clinically relevant events. 9 The TRUST study showed a median time to evaluation after an arrhythmic event of less than 2 days, compared to 36 days in the control group. 16 , 17 Access to continuous RM data has resulted in fewer in-person evaluations, a reduction in emergent and unscheduled hospital visits with a decrease in overall healthcare utilization. 6 , 16 , 18–22 However, apart from the COMPAS trial, most of these studies were conducted only in ICD patients. Prompt arrhythmia detection coupled with early recognition of fluid accumulation 23 with an in-built algorithm reduced emergency department and urgent in-office visits by 35% in the remote arm, as demonstrated by the EVOLVO study. 18 Other large-scale studies have shown the potential cost saving associated with RM strategy. 21 , 22 , 24–30

More importantly, a pooled analysis of three randomized controlled trials (TRUST, ECOST, and IN-TIME) involving 2405 patients with ICDs showed a significant reduction of all-cause mortality with RM. 31 Another meta-analysis of nine randomised controlled trials (RCTs) demonstrated non-inferiority of RM and in-office follow-up with RCTs utilizing daily transmission verification, proving significant survival benefit. 32 Large real-world registries have further established the survival benefit of RM also emphasizing the impact of adherence to RM in improving patient outcomes. 29 , 33–35

Despite its various proven clinical benefits, RM implementation and uptake have been modest. 36 Barriers to RM implementation are multifactorial and include patient factors such as health literacy, preference and access, lack of healthcare infrastructure, and inadequate reimbursement. 37 The Altitude Survival Study found that more than 60% of patients with RM-capable devices did not participate in RM. 35 Real-world population studies also revealed poor compliance to RM, with one study reporting 53% of patients without a single RM transmission over the follow-up period, and another with 21% non-compliance rate. 33 , 38

On the other hand, the increasing volume of RM transmissions has reached a staggering proportion and increased the clinic workloads. In a study involving more than 26 000 patients, the number of transmissions and alert burden was quantified, resulting in a total of 205 804 transmissions, 40% of which were alert with only 4.8% requiring urgent clinical response. 39 This data deluge, which includes a high rate of false positives, particularly with the increasing use of implantable loop recorders, leads to an increased burden on clinical staff, and delays in the evaluation of actionable alerts. 40 , 41 Early studies suggested how this may be partially overcome using AI to better identify actionable alerts. 42 However, critical for improved patient outcomes are the clinic-level pathways to manage actionable alerts. Evidence suggests this may pose a significant threat to the success of RM. 43 Ultimately, patient education, streamlined alert settings and clinic workflow, adequate trained staffing, and attractive reimbursement policies must be in place to ensure successful adoption of the RM approach for CIEDs. 44 , 45 Recently, a query has been raised if smartwatches can provide a replacement for CIEDs, which will be addressed in the next section. 46

Digital devices

In concordance with telemonitoring, cardiac rhythm monitoring has also markedly evolved in the past 25 years, progressing from the initial Holter monitors to event recorders, mobile cardiac telemetry, implantable cardiac monitors to increasingly ‘smart’ multipurpose sensing and monitoring instruments. 47 , 48

Wearable devices have become central for cardiac rhythm monitoring. In 1994, the first wrist-worn heart rhythm monitor was introduced. This device was capable of transmitting an analogue transtelephonic signal that was converted into a digitized single-lead ECG tracing. Unfortunately, adoption was limited as patients found the device more difficult to use than traditional Holter monitors. 49 Several other wrist-worn devices and simple textile-based heart rate monitors soon followed. 50 While they provided some insight into a patient’s HR, they were not accurate enough for clinical use and were primarily used by fitness enthusiasts. In the last decade, advances in wearable technology have led to more accurate and reliable devices for cardiac rhythm monitoring. 51 Most of these devices, such as smartwatches, rings, and fitness trackers, use optical sensors to detect the patient’s HR and rhythm using photoplethysmography, providing real-time monitoring and analysis. 52

More modern wearable devices, such as smartwatches, use either photoplethysmography and/or ECG-based HR and rhythm monitoring providing single-lead ECGs or even multiple lead ECGs. These devices can also track other metrics such as physical activity, sleep, and stress levels, providing a more holistic view of the patient’s health.

Digital devices offer new possibilities for continuous or intermittent monitoring and have been increasingly used for screening for atrial fibrillation (AF). 53 , 54 Large-scale studies in different settings and populations have shown that screening for AF using digital devices identifies patients at risk, 55–59 has the potential to reduce relevant outcomes 60 and reduce costs. 61 Screening for AF using new digital devices is recommended in guidelines and consensus documents. 51 , 62 , 63

Clinical usage and acceptance of digital devices have increased in the last few years. The true advantages of digital care were seen during the time of the COVID-19 pandemic when digital devices still allowed specific and dedicated remote patient care for arrhythmia management, as shown in the international TELECHECK-AF project. 64–68 Healthcare providers have since started to recognize the potential benefits of these devices for patient care, and digitally advanced centres are using them as a tool for remote patient monitoring.

While wearable devices have shown promise in cardiac rhythm monitoring, there are still challenges that need to be addressed. In particular, the accuracy and reliability of wearable devices for cardiac rhythm and rate monitoring have been a subject of debate. 69 , 70 Recent studies have shown that although some wearable devices can provide reliable measurements of HR and rhythm, variations due to the manufacturer’s algorithms and the patient population are common. 71 Another challenge is the interpretation of the data generated by these devices, which can be complex and requires specialized knowledge. 72 , 73 As recently shown in an European Heart Rhythm Association (EHRA) survey, digital devices are widely used, but reimbursement for usage and interpretation is a problem to solve in most countries. 74 In the future, we can expect to see further advances in wearable technology for cardiac rhythm monitoring. 75 These devices may become more accurate, reliable, and personalized to the patient's needs, and advances in AI may enable more efficient and accurate interpretation of the generated data. 76 In addition, patient involvement remains an important aspect of digital care. 77 , 78

In conclusion, wearable devices have evolved significantly in the last 25 years and have the potential to revolutionize cardiac rhythm monitoring. While there are still some challenges that need to be addressed, they have shown promise in improving patient outcomes through earlier detection and treatment of cardiac conditions. As technology continues to advance, we can expect to see further improvements in cardiac rhythm monitoring. Many wearables are connected to mobile health applications, which will be discussed in the coming section.

Mobile health applications (apps)

The introduction of novel generations of smartphones using computer-like built-in features and sensors on the market in 2007, allowed for customization of the devices by downloading apps from central stores. This feature, combined with the high-grade adoption of smartphone technology in the population (i.e. 86% penetration rate in Europe in 2021) increases the possible applications in the EP field. 79

In EP, the initial interest with regards to smartphones was focused on safety, in particular by determining the potential interference of smartphones with implantable cardioverter defibrillators. 80

More recently, evaluation studies comparing the accuracy of handheld connected devices and smartphone apps have gained a lot of attention. 81 The use of apps within healthcare has manifold opportunities when implemented in a structured pathway, as described in multiple publications ( Figure ​ Figure2 2 ).

Overview of opportunities in mobile phone applications in electrophysiology.

• Training of healthcare providers and decision support. Providing care that conforms to the guidelines is of pivotal importance to optimize patient outcomes, but adherence to the guidelines is often suboptimal. The availability of interactive clinical practice guidelines through an app, such as the ‘ESC Pocket Guidelines’ app, could facilitate their uptake. On top of this, for AF the CATCH ME Consortium developed the ‘AF Manager’ app as a tool in which healthcare professionals can incorporate patient data to suggest treatment options that conform to the guidelines. 82 , 83
• Treatment support. To support patients in their daily treatment and to promote a healthy lifestyle, mHealth apps can enhance adherence to medication, increase adherence to hospital appointments, support patients in rehabilitation and physical activity and assist in tackling of comorbidities. 84–88
• Diagnostics and screening. Various apps can be used to screen for arrhythmias making use of different sensors embedded in the smartphone or connected to it. 51 , 71
• Longitudinal disease management. mHealth opens a large spectrum for the (remote) follow-up of various clinical parameters to fill in the gaps between the in-person consultation visits, including HR, heart rhythm, symptoms, weight, and blood pressure. 51 , 67 , 68 , 83 , 84 , 87 , 89–91 Apps can also connect with wearables, or with implanted devices to collect valuable clinical information on the patient’s status. 84 , 92–94
• Education and awareness. To engage patients in their own care and allow shared decision-making, mHealth apps can assist in delivering validated information and tailored education to patients to increase health literacy. 82 , 87 , 95 , 96
• Empowering patients to own their health data and directly contact healthcare providers. Apps can allow patients to get informed about their healthcare data and contact their healthcare providers in case of questions about their management. Moreover, many hospitals have their own applications allowing patients to counsel their health data.

Despite the fact that mHealth apps are widely available, several barriers 93 , 97 , 98 still exist. These include in particular, lack of validation, sparse data on effectiveness and impact on clinically relevant endpoints, poor data integration with electronic health record systems, lack of clear guidance on care pathways to make use of these apps in daily clinical practice, and lack of reimbursement.

Studies formally evaluating the impact of mHealth apps on healthcare professional’s behaviour are scarce and larger-scale studies with representative patient cohorts, appropriate comparators, and longer-term assessment of the impact of mHealth apps are warranted, also in view of the new requirements for conformity assessment introduced by the EU Medical Device Regulation. 87 , 99 As a result, apps are rarely prescribed to patients by healthcare providers in daily clinical practice.

The use of apps for medical purposes could further expand in the future when current barriers in the development, security, validation, cost-effectiveness, interoperability, implementation, and reimbursement of mHealth in daily clinical practice will be solved, and it is an integral part of virtual clinics. 97 , 98

Virtual clinics

As highlighted in the previous sections the publication of ‘Transtelephone Pacemaker Clinic’ in 1971 and the subsequent rise of RM of CIEDs established cardiac EP as leaders in providing virtual care for patients. 100 The rapid transition to virtual modalities to provide safe, uninterrupted arrhythmia care during the COVID-19 pandemic led to an exponential adoption of digital care by EP. 101–103 This cemented the view of EPs as one of the highest adopters of virtual care (>95% in some systems), and a high rate of virtual care is maintained even after the pandemic has largely subsided. 65 , 66 , 104

EP is particularly conducive to the adoption of virtual clinics as most consultations can be performed entirely virtually. Cardiac rhythm tracings can all be reviewed and analysed online. Discussions, including shared decision-making, can be performed via video or telephone contact. 102 HR and rhythm data from direct-to-consumer digital devices continue to be integrated, helping to enrich arrhythmia patient virtual care. 51 , 65 , 93 , 105

Real-world studies have shown feasibility, safety, and efficacy of virtual arrhythmia clinics, with similar outcomes, quality metrics, and patient satisfaction when compared to in-person visits. 106–108 Virtual AF management has shown particular promise. One of the largest endeavours has been the TELECHECK-AF virtual clinic. 68 , 91 Here, teleconsultation, use of a CE-certified, clinically validated smartphone photoplethysmography HR and rhythm monitoring App (FibriCheck, Flanders, Belgium), and virtual education were combined to support comprehensive AF management. 68 , 109 Patients used the app to check HR/rhythm three times a day for one week, and data was uploaded to the cloud for clinician review before teleconsultation. 68 In 20 days after launch, 9 countries/23 European centers adopted this virtual clinic model; by 6 months nearly 1700 patients were enrolled. 52 , 110 Patients have found the app easy to use (94%), providing them a sense of reassurance (74%); clinicians have given high ratings for on-boarding, cloud access, and reliability. 67 Currently, over 6000 AF patients have received care via this virtual clinic model. The TeleWAS-AF ‘wait-and-see’ programme for patients with recently diagnosed AF uses this same virtual care strategy to help avoid unnecessary cardioversions. 67 A randomized trial, RACE 9 OBSERVE-AF, assessing this virtual care pathway is currently underway (clinicaltrials.gov {"type":"clinical-trial","attrs":{"text":"NCT04612335","term_id":"NCT04612335"}} NCT04612335 ). In the UK, an ‘AF virtual ward’ has recently been piloted to manage hemodynamically stable AF patients in an ambulatory setting by using digital tools for vital signs monitoring (hand-held daily ECGs, BP monitoring, and O 2 saturations), twice daily ‘virtual’ rounds, and medication adjustments via a clinical pharmacy. This proof-of-concept study recently showed potential for decreasing AF hospital admissions and re-admissions. 111

Virtual clinics for the management of anticoagulation have been well-established. 112 , 113 Virtual clinics for outpatient antiarrhythmic drug loading have been less explored. In an initial feasibility study, three patients with CIEDs requiring sotalol initiation during the COVID-19 pandemic were monitored from home via CIED remote transmissions, mobile cardiac telemetry, a hand-held 6L ECG device Food and Drug Adminstration-cleared for QTc monitoring (KardiaMobile ® 6L, Alivecor, Mountainview, CA), as well as video-telehealth. Successful outpatient initiation of sotalol initiation was performed without any adverse events. 114 A pharmacist-driven virtual clinic for outpatient sotalol loading and monitoring has since been safely piloted using online ECG and lab review, telephone contact, and remote QTc monitoring via the KardiaMobile ® 6L. 115

Virtual clinics for post-AF ablation patients show potential. One study on 46 AF patients from the UK replaced the 3-month post-ablation in-person visit with a video visit coupled with a proprietary vital sign tracking mApp. This virtual clinic showed high overall patient satisfaction (84%) and patient cost and time savings (80%). 116 The Cleveland Clinic ‘Atrial Fibrillation Future Clinic’ randomized 100 post-ablation patients to traditional in-person care vs. virtual care enhanced with a hand-held ECG monitor and follow-up at 6 months. Hospitalization, ER, and clinic visits, as well as anxiety, were similar between groups. In addition, the virtual care group had less use of ambulatory ECGs. 117

As virtual clinics and digital devices are further integrated into EP, patient perspectives—preferences, readiness, digital access, availability, and literacy, as well as cost—must be considered. 51 , 75 Canadian studies have shown that arrhythmia patient virtual care may be well received for quality of life, cost and time savings, and opportunities for participation from caregivers and family members. 118 , 119 However, it may be less preferred for new patients or complex issues requiring nuanced discussions. Fit (or non-fit) of virtual clinics has been found to be dependent on clinician and medical staff’s ability to communicate via these channels effectively and comprehensively. 119 Hybrid models combining in-person with virtual clinics may be an effective middle-ground for both patients and clinicians. Also, AI might have a future role in virtual clinics, by ECG interpretation and prediction of outcomes.

Artificial intelligence

AI and machine learning (ML) are rapidly evolving disciplines within data science that can classify complex data, and thus ‘interpret them’ to predict future patterns or risk of events. 120 Studies published in Europace within the field of AI provide an exciting chronology of our field aimed at better-managing patients with cardiac electrophysiologic disorders.

Europace published its first AI study in 2003, well before its 25th birthday, in which Kappenberger et al . 121 identified ventricular tachycardia (VT)/ventricular fibrillation (VF) with a c-statistic >0.90 by leveraging sensed voltage alterations from sinus rhythm in ICD recipients. This early study incorporated elements that remain foundational to this day, most notably separating the cohorts used for algorithm development from cohorts used for testing to improve the generalizability of results. Studies in 2008 used AI of electrogram shapes to discriminate VT with such high accuracy (c-statistic > 0.95) 122 that an accompanying editorial 123 posed a question that still resonates: ‘[will] automated analysis … replace the electrophysiologist?’.

Of numerous studies using AI to predict VT/VF, Shakibfar et al used random forests to classify daily ICD interrogation summaries in 19 935 patients, providing a c-statistic of 0.80 for imminent electrical storm in an independent test cohort. 124 When explaining their results, the authors found that the most predictive features were percentage of ventricular pacing and level of daytime activity. This use of AI to analyse near continuous ICD data has stimulated much interest and further studies. 125 In an intriguing study by Sammani et al. , deep neural networks were used to develop an autoencoder to represent key features of 1 million ECGs in a latent space; when applied to 695 patients with dilated cardiomyopathy, this interpretable AI predicted long-term VT/VF and found that P wave features, right bundle branch delay and reduced QRS-T voltages were the most predictive. 126 Several studies applied AI to imaging data. Balaban et al. reported that remodelling of LV end-diastolic shape in 156 patients was the strongest multivariate predictor of VT/VF over an extended follow-up of 7.7 years. 127

A remarkable achievement of AI has been to dramatically alter clinical care using simple data. Pioneering work by Attia et al. showed that the ‘AI-enabled 12-lead ECG’ in sinus rhythm can reveal left ventricular dysfunction 128 and patients with paroxysmal AF. 129 This is an exciting field, although further studies are needed since some others suggest that the AI-ECG may not add to traditional risk factors, 130 or may not apply to single ECG leads 76 in ambulatory monitors. AI may effectively ‘learn’ other ECG waveform patterns, for example AI of T-wave morphology was reported to identify gene-positive long QT syndrome patients from controls with a c-statistic of 0.901, better than QTc estimates. 131 Convolutional neural networks applied to the ECG were shown to identify echocardiographic LV hypertrophy better than clinicians in 21 286 patients, with a c-statistic of 0.868 in an external validation set. 132

The ESC-EHRA AF ablation long-term registry recently used AI of multimodal data to predict outcomes after AF ablation in 3128 patients with a c-statistic of 0.72, making the tool available online and outperforming clinical risk scores. 133 AI has been applied to electronic health records to reduce spurious AF alerts, using natural language processing and CHA2DS2-VASc elements, providing 98% accuracy and reducing workload by 84%. 134 AI of clinical data predicted sinus rhythm after electrical cardioversion of AF, 135 and after guideline-directed medical therapy 136 in secondary analyses of the Flec-SL-AFNET 3 and ANTIPAF-AFNET 2 trials, respectively. Neural network classifiers can predict recurrent syncope from patients in the emergency room using the history and ECG with accuracies from 67 to 95%. 137

AI has been used to improve body surface potential mapping, 138 and even to generate 3D maps of ventricular activation from the 12-lead ECG. 139 AI of the ECG can separate typical from atypical atrial flutter mechanisms. 140 A consensus document discussed the use of AI to better understand and map AF. 70 Bhatia et al. applied AI to intracardiac electrograms in AF to identify patterns of organization associated with recurrence after ablation, 141 , 142 and such tools have been incorporated into clinical mapping systems. 143 , 144 Corrado et al recently applied AI to reveal tissue conduction slowing and atrial surface area that may predispose to re-entry during AF. 145 Toprak reported that AI of NT-pro BNP and other circulating biomarkers improved upon traditional clinical variables in predicting incident AF. 146

Europace has also taken the lead in reporting some of the challenges for AI. A notable editorial by Loring and Piccini in 2019 entitled ‘Machine Learning in Big Data: Handle with care’ 147 discussed how AI is not immune to bias in study design. These authors also showed that AI did not improve AF outcome predictions in the large ORBIT-AF and GARFIELD registries over traditional statistical predictors. 148

In summary, AI is an extremely promising discipline to better understand and treat patients with heart rhythm disorders, and future work should focus on defining disease states, patient groups, and algorithmic approaches which will enable the greatest benefit. However, it is vital that the regulatory process is in balance with the development of novel models.

A regulatory perspective

Although our journey through digital arrhythmia care over the past decades has shown remarkable progress, there is also a need to be careful when introducing novel technologies. Medical devices are becoming smarter by using software that is increasingly ‘intelligent’, taking advantage of the steep rise in capabilities of AI and ML. As data is the cornerstone of AI learning, testing, and validation, this means that such novel devices need to comply (already or in the near future) with several regulations from the EU: besides the General Data Protection Regulation (GDPR) also the Medical Device Regulation (MDR), Data Governance Act, and the upcoming AI Act, and the European Health Data Space regulation. While this is already a significant challenge for small and even large manufacturing companies, for non-profit hospitals, and academic institutions it has become a major hurdle for the implementation of their innovations. Politicians and regulators across Europe have become aware of this issue, which is pushing innovation to other markets like the USA and China. Finding the proper balance between safety and innovation is still ongoing. 94 , 149 , 150

One must weigh in that zero risk does not exist, and one always must consider the balance between benefit and risk for the individual patient and for society. Presently the balance seems to have swung towards risk aversity, which inadvertently creates risks of with-holding potentially beneficial devices from patients in need of them. 151 , 152 There is also a disconnect between the regulatory requirements from GDPR and MDR and the scientific evidence on which clinicians base their decision to use certain devices in specific circumstances for a given patient. Scientific guidelines and the clinical requirements from GDPR and MDR aim towards the same goals at the highest level, but in their practical implementation, they do not coincide and sometimes only marginally overlap. The regulatory requirements focus on avoiding risk (which is further enhanced by the status of the notified bodies) and are subject to a variable interpretation of the GDPR in the EU Member States and of the MDR by the notified bodies. Scientific guidelines focus more on the benefit-risk balance but are not available for all clinical decisions and are often based on inconclusive or incomplete evidence and only on expert opinion with the inevitable (but mostly not intentional) bias. 153

To bring the two requirement systems closer together and to avoid the high costs of duplicated clinical trials, registries, and studies, one could consider aligning them to their intrinsic purpose, which is allowing the patient/family to make the decisions about diagnostic and treatment options together with their health care provider based on the best available information, for example about benefits, risks, alternatives, and refraining from active therapy. Depending on the clinical situation of the patient, the severity of the pathology, and potential benefit of the intervention a lower or higher risk or uncertainty might be acceptable. It is the core of co-decision making to weigh these factors and come to an informed, balanced conclusion. The information needed to make these choices can vary and that must be reflected in the regulatory framework.

Similarly, the evidence to be provided by a manufacturer before the release of a product into the market should be based on the risk-benefit balance with a larger or smaller emphasis on post-release requirements. Regulators are presently hesitant to allow this because post-release obligations are often more difficult to define and enforce. For medical device software, this might possibly be the only way to address the difficulties with (self-)learning AI software and with the drift in the use of such devices in clinical real life.

In conclusion, the reality of medical device software, with its variable possibility of extensive pre-release clinical testing and potential drift in use and impact, will necessitate a more balanced risk-benefit evaluation and alignment of the regulatory and clinical scientific standards in the future.

The future digital aspects for electrophysiology Europace

As described in the previous sections, EP has a history of utilizing advanced digital solutions and tools. With the current rapid advancement of digital technologies, commonly referred to as digital transformation, including AI using ML and deep learning, RM, wearables, and advanced imaging, we can expect even greater progress in the field. Some potential future aspects for digital in cardiovascular EP follow.

Telemedicine and RM, as discussed in the section on virtual clinics and telemonitoring, have become increasingly important topics in cardiology, 75 especially with the acceleration brought on by the COVID-19 pandemic. We could expect to see improved capabilities and infrastructures for RM, 68 as well as advancements in wearable technologies. With ongoing developments in device miniaturization and wearable tech, monitoring, and treatment options should become more convenient and patient friendly. For instance, implantable sensors or wearable patches may provide continuous monitoring of heart rhythm and other relevant data, enabling early detection and intervention in case of abnormalities. This should lead to better patient outcomes and satisfaction, as well as a reduced workload for healthcare staff and lower healthcare costs.

ML and AI algorithms have the potential to transform EP by enabling automated and precise analysis of vast amounts of data. 63 , 154 , 155 These technologies can aid in predicting, diagnosing, and providing personalized treatment for heart rhythm disorders. The rapid development of digital technologies is expected to lead to significant improvements in imaging and mapping techniques, resulting in better visualization and characterization of the heart’s electrical activity. This could include the widespread adoption of high-resolution imaging and three-dimensional mapping technologies, which would enable more precise diagnosis and treatment planning for patients. In addition, ML-powered algorithms can optimize the outcomes of catheter ablations by providing real-time guidance and feedback.

Advanced imaging using virtual reality and augmented reality has the potential to revolutionize EP training and procedural planning. Virtual reality can provide a safe and controlled environment for simulating ablation procedures, reducing the learning curve and improving safety. Additionally, augmented reality can offer real-time visual guidance during procedures by overlaying relevant information, such as anatomical landmarks or electrode placement, onto the patient’s body. These technologies could ultimately lead to better patient outcomes. Moreover, virtual reality can also be utilized to reduce patient anxiety by aiding in teaching and preparing patients for procedures, as well as assisting in post-procedural rehabilitation. 156

One of the most challenging yet promising areas where AI could have a significant impact is personalized medicine. AI-based algorithms that incorporate individual patient characteristics, such as genetics, lifestyle, and medical history, can provide advanced analytics and computational modelling to predict the optimal treatment approach for each patient. This approach can lead to more targeted and effective treatments with improved outcomes. 157

In conclusion, the future of cardiovascular EP will be shaped by rapid advancements in digital technologies, such as advanced imaging and mapping, AI and ML, telemedicine and RM, virtual and augmented reality, miniaturization and wearable devices, and personalized medicine. These advancements have the potential to significantly improve diagnosis, treatment, and patient outcomes in cardiovascular EP, and represent an exciting time for the field. With this reflection on the past quarter of a century in EP, we can now cast our eyes forward, envisioning that the journey ahead will likely accelerate our digital knowledge. In the coming 25 years in the EP Europace journal, we will continue to provide you with novel digital tools to improve the management of arrhythmia patients and steadfastly aim to increase our coverage of digital topics, using a scientific approach to enable better patient management.

Contributor Information

Emma Svennberg, Department of Medicine Huddinge, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86 Stockholm, Sweden.

Enrico G Caiani, Politecnico di Milano, Electronic, Information and Biomedical Engineering Department, Milan, Italy. Istituto Auxologico Italiano IRCCS, Milan, Italy.

Nico Bruining, Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, The Netherlands.

Lien Desteghe, Research Group Cardiovascular Diseases, University of Antwerp, 2000 Antwerp, Belgium. Department of Cardiology, Antwerp University Hospital, 2056 Edegem, Belgium. Faculty of Medicine and Life Sciences, Hasselt University, 3500 Hasselt, Belgium. Department of Cardiology, Heart Centre Hasselt, Jessa Hospital, 3500 Hasselt, Belgium.

Janet K Han, Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA. Cardiac Arrhythmia Center, University of California Los Angeles, Los Angeles, CA, USA.

Sanjiv M Narayan, Cardiology Division, Cardiovascular Institute and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.

Frank E Rademakers, Department of Cardiology, KU Leuven, 3000 Leuven, Belgium.

Prashanthan Sanders, Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 5005 Adelaide, Australia.

David Duncker, Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany.

ES is supported by a Grant from Region Stockholm, the Swedish Heart and Lung foundation, CIMED and Swedish Research Council Vetenskaps Rådet DNR (2022-01466) PS is supported by an Investigator Grant from the National Health and Medical Research Council of Australia.

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