The changing role of patients, and nursing and medical professionals as a result of digitalization of health and heart failure care.

AIM: The aim of the study is to discuss the changing role of patients, nurses and doctors in an era of digital health and heart failure care. BACKGROUND: With a growing demand for heart failure care and a shortage of health care professionals to meet it, digital technologies offer a potential solution to overcoming these challenges. EVALUATION: In reviewing pertinent research evidence and drawing on our collective clinical and research experiences, including the co-design and development of an autonomous remote system, DoctorME, we offer some reflections and propose some practical suggestions for nurturing truly collaborative heart failure care. KEY ISSUES: Digital health offers real opportunities to deliver heart failure care, but patients and health care professionals will require digital skills training and appropriate health services technological infrastructure. CONCLUSIONS: Heart failure care is being transformed by digital technologies, and innovations such as DoctorME have profound implications for patients, nurses and doctors. These include major cultural change and health service transformation. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should create inclusive and supportive working environments where collaborative working and digital technologies in heart failure care are embraced. Nurse managers need to recognize, value and communicate the importance of digital health in heart failure care, ensuring that staff have appropriate digital skills training.

Integrating avatar technology into a telemedicine application in heart failure patients : A pilot study.

BACKGROUND: Heart failure is a severe condition and telemedicine can improve the care of heart failure. Many patients are unable to use telemedicine applications due to visual impairment and limited health-related literacy. Avatar technology might help to overcome these limitations. METHODS: A telemedicine application was combined with a nurse avatar and offered to heart failure outpatients for 3 months. System usability and patient satisfaction were evaluated monthly by the system usability score (maximum score=100) and the patient satisfaction scale (maximum score=50). RESULTS: In total, 37 heart failure patients were enrolled. The mean system usability score after 1 month was 73 (standard deviation=24) and 72 (standard deviation=10) after 3 months of follow-up, which was not significantly different (p = 0.40). The mean patient satisfaction scale after 1 month was 42 (standard deviation=5) and 39 (standard deviation=8) after 3 months, which was not significantly different (p = 0.10). CONCLUSION: A nurse look-a-like avatar integrated into a telemedicine application was positively assessed by heart failure patients. Future studies are warranted to clarify the role of avatar technology in telemedicine.

Webtool to enhance the accuracy of diagnostic algorithms for HFpEF: a prospective cross-over study.

AIMS: Diagnosis of heart failure with preserved ejection fraction (HFpEF) can be challenging. This study aimed to evaluate the potential of a webtool to enhance the scoring accuracy when applying the complex HFA-PEFF and H2 FPEF algorithms, which are commonly used for diagnosing HFpEF. METHODS AND RESULTS: We developed an online tool, the HFpEF calculator, that enables the automatic calculation of current HFpEF algorithms. We assessed the accuracy of manual vs. automatic scoring, defined as the percentage of correct scores, in a cohort of cardiologists with varying clinical experience. Cardiologists scored eight online clinical cases using a triple cross-over design (i.e. two manual-two automatic-two manual-two automatic). Data were analysed in study completers (n = 55, 29% heart failure specialists, 42% general cardiologists, and 29% cardiology residents). Manually calculated scores were correct in 50% (HFA-PEFF: 50% [50-75]; H2 FPEF: 50% [38-50]). Correct scoring improved to 100% using the HFpEF calculator (HFA-PEFF: 100% [88-100], P < 0.001; H2 FPEF: 100% [75-100], P < 0.001). Time spent on clinical cases was similar between scoring methods (±4 min). When corrections for faulty algorithm scores were displayed, cardiologists changed their diagnostic decision in up to 67% of cases. At least 67% of cardiologists preferred using the online tool for future cases in clinical practice. CONCLUSIONS: Manual calculation of HFpEF diagnostic algorithms is often inaccurate. Using an automated webtool to calculate HFpEF algorithms significantly improved correct scoring. This new approach may impact the eventual diagnostic decision in up to two-thirds of cases, supporting its routine use in clinical practice.

Improving diagnosis and risk stratification across the ejection fraction spectrum: the Maastricht Cardiomyopathy registry.

AIMS: Heart failure (HF) represents a clinical syndrome resulting from different aetiologies and degrees of heart diseases. Among these, a key role is played by primary heart muscle disease (cardiomyopathies), which are the combination of multifactorial environmental insults in the presence or absence of a known genetic predisposition. The aim of the Maastricht Cardiomyopathy registry (mCMP-registry; NCT04976348) is to improve (early) diagnosis, risk stratification, and management of cardiomyopathy phenotypes beyond the limits of left ventricular ejection fraction (LVEF). METHODS AND RESULTS: The mCMP-registry is an investigator-initiated prospective registry including patient characteristics, diagnostic measurements performed as part of routine clinical care, treatment information, sequential biobanking, quality of life and economic impact assessment, and regular follow-up. All subjects aged ≥16 years referred to the cardiology department of the Maastricht University Medical Center (MUMC+) for HF-like symptoms or cardiac screening for cardiomyopathies are eligible for inclusion, irrespective of phenotype or underlying causes. Informed consented subjects will be followed up for 15 years. Two central approaches will be used to answer the research questions related to the aims of this registry: (i) a data-driven approach to predict clinical outcome and response to therapy and to identify clusters of patients who share underlying pathophysiological processes; and (ii) a hypothesis-driven approach in which clinical parameters are tested for their (incremental) diagnostic, prognostic, or therapeutic value. The study allows other centres to easily join this initiative, which will further boost research within this field. CONCLUSIONS: The broad inclusion criteria, systematic routine clinical care data-collection, extensive study-related data-collection, sequential biobanking, and multi-disciplinary approach gives the mCMP-registry a unique opportunity to improve diagnosis, risk stratification, and management of HF and (early) cardiomyopathy phenotypes beyond the LVEF limits.

Future perspective of heart failure care: benefits and bottlenecks of artificial intelligence and eHealth.

Tweetable abstract #eHealth and #ArtificialIntelligence (AI) bring new possibilities for #HeartFailure (HF) care. We elaborate on potential benefits of #AI in #HF and highlight important bottlenecks for its implementation. #Editorial #Cardiology.