Aravinda Thiagalingam

Background: Clinical guidelines recommend the early initiation of secondary prevention strategies prior to hospital discharge for patients with myocardial infarction (MI) to reduce morbidity and mortality, but implementation is resource-intensive. Multilingual videos can deliver information in diverse preferred languages and literacy levels, but their impact on MI knowledge among hospitalized patients remains unclear. Objective: This study aims to assess whether the delivery of a multilingual educational video to hospitalized patients with MI can improve patient MI knowledge before hospital discharge. Methods: We conducted a single-arm pre-post study with embedded formative implementation evaluation from December 2023 to October 2024 in a tertiary hospital. The intervention was a video on post-MI management, available in English, Arabic, Hindi, and Mandarin (with Simplified Chinese subtitles). The intervention was delivered via a tablet provided by the research assistant. The primary outcome was the change in patient knowledge of MI, measured by comparing the mean number of correct responses before and after the intervention using a 2-tailed paired t test. We assessed early-stage implementation using 2 prespecified elements from the Proctor implementation outcomes framework: acceptability and fidelity of the video delivery. We performed content analysis on the notes taken from participants’ feedback to improve the video. Results: We recruited 129 participants (mean age of 59.4, SD 12.6 years) for this study. English was the preferred language (n=96, 74.4%) and Hindi was the predominant non-English language (n=17, 13.2%). Of the 129 participants enrolled, 128 completed follow-up immediately postintervention (1 lost interest). The average number of correct responses out of 10 was 5.4 (SD 2.7) at baseline and 7.2 (SD 2.5) postintervention (mean difference=1.9, 95% CI 1.6-2.2; P <.001; Cohen d _rm for paired change=0.72). The educational video was well-accepted, with 83.6% (107/128) of participants finding it easy to understand, 74.2% (95/128) engaging, and 87.5% (112/128) useful. Participants’ feedback for improvement highlighted content complexity and a preference for conversational language and dialects. Fidelity of the intervention was subjectively assessed as reasonably achieved, given that the core components of the intervention (ie, animations and educational content conveyed through the audio and subtitles) were delivered as intended. Fidelity of the implementation strategy was similarly assessed as reasonably achieved because there were no technology issues preventing delivery of the intervention as intended, through video display from a weblink embedded in REDCap, using a tablet with internet connection. Conclusions: A short educational video may improve patient knowledge of MI before discharge. Further scaled research is needed to evaluate the effectiveness and implementation of this intervention in additional languages and diverse populations. This study highlights the need for culturally and linguistically tailored resources in clinical settings, informing future research and policy on inclusive patient education.

Background: The HeartHealth program is a 6-month SMS text messaging–based support program offered to patients with a recent cardiovascular hospitalization or recent cardiovascular clinic visit in Western Sydney, Australia. Its customized content focuses on cardiovascular risk factors, lifestyle, treatments, and general heart health information. Objective: This study aimed to evaluate the implementation of the HeartHealth program. Methods: A mixed methods study was conducted assessing program reach, effectiveness, implementation, and maintenance using program data, participant feedback surveys, and staff focus group discussions. Consecutive adult patients who had attended cardiology clinics or had been discharged from cardiology hospitalization at Westmead Hospital, between April 2020 and April 2024, were included in the analysis. Content analysis was used to interpret the qualitative data. Results: A total of 23,095 patients were invited, 8804 (38.1%) enrolled into the program, and 7964 out of 8804 (90.5%) completed the 6-month duration. Participants enrolled in the HeartHealth program had a mean age of 58.6 years, 60.3% (5302/8788) were male, and 62.4% (5382/8624) were recruited from an outpatient clinic setting. A total of 851,058 SMS text messages were sent, with 99.41% (846,009/851,058) delivered successfully. A total of 3533 out of 7964 (44.4% of program completers) participants completed the postintervention survey, and 4 HeartHealth staff members participated in a focus group discussion. Among the participants who completed the survey, 60.5% (2137/3533) reported that the program improved the healthiness of their diet, 53.6% (1894/3533) reported improved physical activity levels, and 56.1% (1982/3533) reported that it helped remind them to take their medications. Content analysis of participant feedback identified that the program was effective in prompting participants to change their diet, providing emotional support, reminding them of the importance of behavior change, improving their confidence in managing their health, and keeping participants focused. Key barriers included limited personalization, language options, and SMS text messaging scheduling flexibility. Recommended adaptations focused on enhancing personalization, greater engagement by local clinical teams, and expanding program dissemination. Conclusions: The program had a broad reach, translated to improved patient-reported health behaviors, and provided participants with needed support at low cost and low resource requirements. This analysis highlights the successful implementation and scalability of the HeartHealth program and provides key learnings for health systems that are looking to implement similar programs in the future.

Introduction: Atrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life (QoL) and increased stroke risk and mortality. Clinical guidelines recommend leveraging digital technologies to support patient education and AF self-management. Conversational artificial intelligence (AI) technologies may support patient engagement with self-management by enabling human-like conversations. This study aims to evaluate the effectiveness of a conversational AI intervention (Conversational HeAlth supporT in Atrial Fibrillation Self-Management - CHAT-AF-S) in improving QoL in patients with AF. Methods and analysis: CHAT-AF-S is a 3-month randomised controlled trial with 1:1 allocation and embedded process evaluation. We will randomise 480 adults (18 years of age and older) with documented AF to the CHAT-AF-S intervention or usual care. Primary outcome is the Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) overall score. We will follow intention-to-treat principles and data analysts will be blinded. Intervention participants will be invited to complete a user experience survey and take part in an interview to explore the feasibility, acceptability, perceived utility, and barriers and enablers to implementing the intervention. Qualitative data will be analysed thematically. Ethics and dissemination: Ethics approval was obtained from the Western Sydney Local Health District Human Ethics Research Committee (2023/ETH00765). Written and informed consent will be obtained from all study participants before commencing any study procedures. Results will be disseminated via peer-reviewed publications and presentations at international conferences. Declaration of Interests: All investigators report nil conflicts of interest. Data Availability: The data that supports this project are available from the corresponding author upon reasonable request. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12623000850673 https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386249

BACKGROUND Multilingual videos deliver information across diverse preferred languages and literacy levels but the feasibility and impact on myocardial infarction (MI) knowledge of this approach during hospitalisation is unclear. OBJECTIVE To evaluate whether a multilingual educational video provided to hospitalised patients post-MI can feasibly improve patient knowledge of MI. METHODS We conducted an effectiveness-implementation hybrid quasi-experimental study from December 2023 to October 2024 in a tertiary hospital in Sydney, Australia. The intervention was a video on post-MI management, available in English, Arabic, Hindi and Mandarin. Primary outcome was change in patient knowledge of MI measured by comparing the mean number of correct responses before and immediately after the intervention using a two-sample t-test. We evaluated implementation as acceptability and fidelity of the video delivery. We performed thematic analysis on the notes taken of participants’ feedback for improving the video. RESULTS We recruited 129 participants (20.2% (26/129) female; mean age 59.4±12.6 years) English was the preferred language (74.4% (96/129)) and Hindi was the predominant non-English language (13.2%, 17/129). Average number of correct responses out of 10 increased from 5.4±2.7 at baseline to 7.2±2.5 post-intervention (mean difference = 1.9; 95% CI 1.6, 2.2, p<.001). The educational video was well-accepted, with 83.6% (107/128) of participants finding it easy to understand, 74.2% (95/128) engaging, and 87.5% (112/128) useful. We assessed fidelity of the intervention as high, as core components (i.e. animations and educational content via audio and subtitles) were delivered as intended. Participants’ feedback for improvement highlighted content complexity and a preference for conversational language and dialects. CONCLUSIONS A MI educational video delivered during inpatient admission, available in multiple languages, may improve patient knowledge in the short-term. Further scaled research is needed to evaluate the effectiveness and implementation of this intervention in additional languages and diverse populations. This study highlights the need for culturally and linguistically tailored resources in clinical settings, informing future research and policy on inclusive patient education. CLINICALTRIAL Not applicable

Background: Patient education and self-management support are critical for atrial fibrillation (AF) management. Conversational artificial intelligence (AI) has the potential to provide interactive and personalized support, but has not been evaluated in patients with AF. Objective: This study aimed to evaluate the feasibility of a conversational AI intervention to support patients with AF postdischarge. Methods: This was a single-blinded, 4:1-parallel–randomized controlled trial with process evaluation of feasibility and engagement. The primary outcome was the change in Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire total score between groups. Patients with AF (18 y and older) were recruited postdischarge from Westmead Hospital cardiology services and randomized to receive either the intervention or usual care. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that regularly assessed patient health and symptoms and provided self-management support and education. These phone calls were supplemented with an online survey (sent via text message or email) containing replicated call content when participants could not be reached after 3 call attempts. If participant responses were concerning (eg, poor overall health, low medication confidence, and high symptom burden), they would be followed up with an ad hoc phone call and directed to clinical care if required. A semipersonalized education website was also available as part of the intervention, and participants were encouraged weekly (nudges delivered via text messages or emails) to visit it. Results: A total of 103 patients (mean age, 63.7 y, SD 11.2 y; n=72, 70% male) were randomized (82 to the intervention); the target sample size was 385. The difference in the AFEQT total score was nonsignificant (adjusted mean difference 2.08, 95% CI −7.79 to 11.96; P=.46). An exploratory prepost comparison revealed an improvement in total AFEQT score in the intervention group only (baseline: 69.9, 95% CI 64.4 to 75.5; 6 months: 79.9, 95% CI 74.9 to 84.8; P=.01). Participants completed 4 of 7 outreaches on average, and 88.4% (304/344) of completed outreaches were reported as useful. Conclusions: This proof-of-concept study demonstrates the feasibility of conversational AI in supporting patients with chronic conditions postdischarge. Intervention participants had improvement in their atrial fibrillation quality of life, though the forced shortening of the evaluation was unable to demonstrate a significant difference between groups.

Background: Computerized clinical decision support systems (CDSS) are increasingly being used in clinical practice to improve health care delivery. Mobile apps are a type of CDSS that are currently being increasingly used, particularly in lifestyle interventions and disease prevention. However, the use of such apps in acute patient care, diagnosis, and management has not been studied to a great extent. The Pathway for Acute Coronary Syndrome Assessment (PACSA) is a set of guidelines developed to standardize the management of suspected acute coronary syndrome across emergency departments in New South Wales, Australia. These guidelines, which risk stratify patients and provide an appropriate management plan, are currently available as PDF documents or physical paper-based PACSA documents. The routine use of these documents and their acceptability among clinicians is uncertain. Presenting the PACSA guidelines on a mobile app in a sequential format may be a more acceptable alternative to the current paper-based PACSA documents. Objective: This study aimed to assess the utility and acceptability of a clinician-developed app modeling the PACSA guidelines as an alternative to the existing paper-based PACSA documents in assessing chest pain presentations to the emergency department. Methods: An app modeling the PACSA guidelines was created using the Research Electronic Data Capture (REDCap) platform by a cardiologist, with a total development time of <3 hours. The app utilizes a sequential design, requiring participants to input patient data in a step-wise fashion to reach the final patient risk stratification. Emergency department doctors were asked to use the app and apply it to two hypothetical patient scenarios. Participants then completed a survey to assess if the PACSA app offered any advantages over the current paper-based PACSA documents Results: Participants (n=31) ranged from junior doctors to senior physicians. Current clinician adherence to the paper-based PACSA documents was low with 55% (N=17) never using it in their daily practice. Totally, 42% of participants found the PACSA app easier to use compared to the paper-based PACSA documents and 58% reported that the PACSA app was also faster to use. The perceived usefulness of the PACSA app was similar to the perceived usefulness of the paper-based PACSA documents. Conclusions: The PACSA app offers a more efficient and user-friendly alternative to the current paper-based PACSA documents and may promote clinician adherence to evidence-based guidelines. Additional studies with a larger number of participants are required to assess the transferability of the PACSA app to everyday practice. Furthermore, apps are relatively easy to develop using existing online platforms, with the scope for clinicians to develop such apps for other evidence-based guidelines and across different specialties.

Introduction: Cardiac implantable electronic devices (CIED) can transfer data to the healthcare team, remotely. National and international cardiac organisations recommend all patients use this technology, however patient engagement is suboptimal. Previously, in cardiovascular patients, SMS messaging services have shown improvements in patient engagement and subsequent health outcomes. This paper describes the protocol and intervention of a randomised controlled trial (RCT) to assess the feasibility of a novel CIED remote monitoring SMS patient support programme on self-efficacy in managing CIED and cardiovascular health following CIED implantation. Methods/analysis: A two-arm RCT will be conducted of 100 participants with 1:1 allocation to intervention or control. Participants awaiting-CIED or immediately post-CIED implantation from sites throughout Australia will be invited to partake. The intervention group will receive regular SMS communication with a range of educational and self-efficacy resources, in conjunction with engagement initiatives following CIED clinical issue detection. The control group will receive CIED remote monitoring education and clinical issue management as per standard practice at their respective sites. The primary outcome will assess the patient’s capacity to manage their CIED as measured by the ‘Self-Efficacy Expectations After Implantable Cardioverter Defibrillator Implantation Scale’. Secondary outcomes will assess participant’s ability to manage their cardiovascular condition, CIED remote monitoring, quality of life, impact on health service utilisation, cardiovascular behavioural risk factor change and motivation to improve cardiovascular health. A sample size of 100 will have a 90% power to detect a minimum difference of 1.07 in the ‘Self-Efficacy Expectations After Implantable Cardioverter Defibrillator Implantation Scale’ between the intervention and control group with an alpha value of 0.05. Ethics and dissemination: Ethics approval for this study has been obtained from the Western Sydney Local Health District Human Research Ethics Committee. The project results will be published in peer-reviewed journals, at scientific meetings and in the media. Trial registration number: ACTRN12623000702617

BACKGROUND The HeartHealth program is a six-month SMS message-based support program offered to patients with a recent cardiovascular hospitalisation or recent cardiovascular clinic visit in Western Sydney, Australia. Its customised content focuses on cardiovascular risk factors, with a focus on lifestyle, treatments and general heart health information. OBJECTIVE This study aimed to evaluate the implementation of the HeartHealth program. METHODS A mixed-methods study was conducted assessing program reach, effectiveness, implementation and maintenance using program data, participant feed-back surveys and staff focus-group discussions. Consecutive adult patients who had attended cardiology clinics or had been discharged from cardiology hospitalisation at Westmead Hospital, between April 2020 and April 2024 were included in the analysis. Content analysis was utilised to interpret the qualitative data. RESULTS A total of 23095 patients were invited, 8804 (38.1%; 8804/23095) enrolled into the program and 7964 (90.5%; 7964/8804) completed the six-month duration. Participant mean age was 60.5 years, 60.5% were male, and 59.7% were recruited from an out-patient clinic setting. A total of 851058 SMS messages were sent with 99.41% being successfully delivered. 3533 participants completed the post-program survey and four HeartHealth staff members participated in a focus group discussion. 60.5% of participants reported the programming improving their regular vegetable intake and 53.6% reported improved physical activity levels. Content analysis of participant feedback identified that the program was effective in prompting participants to change diet, provided emotional support, reminded them of importance of behaviour change, improved their confidence in managing their health and kept participants focused. HeartHealth staff recommended that future program adjustments should expand program dissemination and improve program personalisation. CONCLUSIONS The program had a broad reach, translated to improved health behaviours, provided participants with needed support at low cost and low resource requirements. Key program barriers and future adaptions to long-term maintenance were identified, such as increased program personalisation, increased site staff involvement and wider program dissemination. This analysis highlights the successful implementation and scalability of the HeartHealth program and provides key learnings for health systems who are looking to implement similar programs in the future.

Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and periprocedural planning. Traditional risk models have demonstrated modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. This study was reported according to PRISMA, CHARMS and TRIPOD guidelines. PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1^st November, 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding and the composite outcome major adverse cardiovascular events (MACE). Thirty-four models were included across 13 observational studies (4105916 patients). For all-cause mortality, the pooled C-statistic for top-performing ML models was 0.89 (95% CI, 0.84-0.91), compared to 0.86 (95% CI, 0.80-0.93) for traditional methods (p=0.54). For major bleeding, the pooled C-statistic for ML models was 0.80 (95% CI, 0.77-0.84), versus 0.78 (95% CI, 0.77-0.79) for traditional methods (p=0.02). For MACE, the C-statistic for ML models was 0.83 (95% CI, 0.75-0.91), compared to 0.71 (95% CI, 0.69-0.74) for traditional methods (p=0.007). Out of all included models, only one model was externally validated. Calibration was inconsistently reported across all models. Prediction Model Risk of Bias Assessment Tool (PROBAST) tool demonstrated high risk of bias across all studies. ML models marginally outperformed traditional risk scores in the discrimination of all-cause mortality, major bleeding or MACE following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesised to improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations​.