RESUMEN
BACKGROUND: Atrial fibrillation (AF) is an increasingly common chronic health condition for which integrated care that is multidisciplinary and patient-centric is recommended yet challenging to implement. OBJECTIVE: The aim of Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is to evaluate the feasibility and potential efficacy of a digital intervention (AF-Support) comprising preprogrammed automated telephone calls (artificial intelligence conversational technology), SMS text messages, and emails, as well as an educational website, to support patients with AF in self-managing their condition and coordinate primary and secondary care follow-up. METHODS: Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is a 6-month randomized controlled trial of adult patients with AF (n=385), who will be allocated in a ratio of 4:1 to AF-Support or usual care, with postintervention semistructured interviews. The primary outcome is AF-related quality of life, and the secondary outcomes include cardiovascular risk factors, outcomes, and health care use. The 4:1 allocation design enables a detailed examination of the feasibility, uptake, and process of the implementation of AF-Support. Participants with new or ongoing AF will be recruited from hospitals and specialist-led clinics in Sydney, New South Wales, Australia. AF-Support has been co-designed with clinicians, researchers, information technologists, and patients. Automated telephone calls will occur 7 times, with the first call triggered to commence 24 to 48 hours after enrollment. Calls follow a standard flow but are customized to vary depending on patients' responses. Calls assess AF symptoms, and participants' responses will trigger different system responses based on prespecified protocols, including the identification of red flags requiring escalation. Randomization will be performed electronically, and allocation concealment will be ensured. Because of the nature of this trial, only outcome assessors and data analysts will be blinded. For the primary outcome, groups will be compared using an analysis of covariance adjusted for corresponding baseline values. Randomized trial data analysis will be performed according to the intention-to-treat principle, and qualitative data will be thematically analyzed. RESULTS: Ethics approval was granted by the Western Sydney Local Health District Human Ethics Research Committee, and recruitment started in December 2020. As of December 2021, a total of 103 patients had been recruited. CONCLUSIONS: This study will address the gap in knowledge with respect to the role of postdischarge digital care models for supporting patients with AF. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12621000174886. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34470.
RESUMEN
BACKGROUND: Using electronic data for cardiovascular risk stratification could help in prioritising healthcare access and optimise cardiovascular prevention. AIMS: To determine whether assessment of absolute cardiovascular risk (Australian absolute cardiovascular disease risk (ACVDR)) and short-term ischaemic risk (History, ECG, Age, Risk factors, and Troponin (HEART) score) is possible from available data in Electronic Medical Record (EMR) and My Health Record (MHR) of patients presenting with acute cardiac symptoms to a Rapid Access Cardiology Clinic (RACC). METHODS: Audit of EMR and MHR on 200 randomly selected adults who presented to RACC between 1 March 2017 and 4 February 2020. The main outcomes were the proportion of patients for which ACVDR score and HEART score could be calculated. RESULTS: Mean age was 55.2 ± 17.8 years and 43% were female. Most (85%) were referred from emergency for chest pain (52%). Forty-six percent had hypertension, 35% obesity, 20% diabetes mellitus, 17% ischaemic heart disease and 18% were current smokers. There was no significant difference in MHR accessibility with age, gender and number of comorbidities. An ACVDR score could be estimated for 17.5% (EMR) and 0% (MHR) of patients. None had complete data to estimate HEART score in either EMR or MHR. Most commonly missing variables for ACVDR score were blood pressure (MHR) and high-density lipoprotein cholesterol (EMR), and for HEART score the missing variables were body mass index and comorbidities (MHR and EMR). CONCLUSIONS: Significant gaps are apparent in electronic medical data capture of key variables to perform cardiovascular risk assessment. Medical data capture should prioritise the collection of clinically important data to help address gaps in cardiovascular management.
