Abstract
Aims
To describe the design of AMI-HOPE, a pharmacist-led, digitally enabled transitional care intervention for patients after acute myocardial infarction (AMI), and its nationwide stepped-wedge cluster-randomized evaluation across Singapore's public healthcare system.
Methods and results
AMI-HOPE is designed as a pragmatic, open-label trial across three health clusters (7 hospitals, 18 polyclinics) in which hospitals crossed over from standard cardiologist-led care to intervention at 3 month intervals. We utilize the Health Discovery+ (HD+) platform for remote vital signs monitoring, rule-based alerts, pharmacist-led teleconsultations, and automated patient education. Target N = 1512 (1:1 ratio); primary outcome is a hierarchical win ratio composite (CV death, MI, stroke, CV readmissions, low-density lipoprotein cholesterol, systolic blood pressure, HbA1c, patient-reported outcome measures). Secondary outcomes include guideline-directed medical therapy titration/adherence, LVEF, and cost-effectiveness. Enrolment completed in September 2025 (N = 1634 randomized; 810 control, 824 intervention). Baseline characteristics (N = 1492 in follow-up) showed a median age of 59 years, 12% female, 61% Chinese ethnicity. Twelve-month follow-up will complete on 30 September 2026.
Conclusion
AMI-HOPE demonstrates a scalable, digitally enabled model shifting early post-AMI care from specialist-led clinics to pharmacists with remote monitoring. This nationwide trial evaluates its clinical efficacy, implementation, and value at population scale.
Keywords: Acute myocardial infarction, Digital health, Remote patient monitoring, Stepped-wedge cluster trial, Pharmacist-led care, Transitional care
Graphical Abstract
Graphical Abstract.
AMI-HOPE: Nationwide pharmacist-led digital transitional care after AMI. Stepped-wedge cluster-randomized trial across three healthcare clusters (7 hospitals, 18 polyclinics). Top: Trial objectives include evaluating clinical efficacy and health-related quality of life outcomes. Bottom: Patient journey from hospital discharge through 1 year recovery, supported by the HD+ platform enabling tele-education (automated content delivery), tele-monitoring (real-time vitals and patient-generated health data), tele-medication (algorithm-guided GDMT titration by pharmacists), and tele-collaboration (integrated care between hospital cardiologists and polyclinic teams).
Introduction
Acute myocardial infarction (AMI) remains a leading cause of mortality and new-onset heart failure globally, with high 30 day mortality (13.7% median 2020–22) and rising AMI incidence being the major challenges faced by Singapore.1–4 Despite strong acute care, gaps in traditional post-discharge transitional care remains under-addressed. Low cardiac rehabilitation participation (<20%), delayed early specialist follow-up, and lags in guideline-directed medical therapy (GDMT) titration limits heart failure prevention (data on file;5,6) Addressing these challenges requires scalable models that can enable rapid GDMT optimization, adherence, and self-management without specialist bottlenecks.
Digital health interventions and allied health task-shifting offer pragmatic solutions. Building on our pilot IMMACULATE trial for allied health remote management,7 AMI-HOPE introduces a pharmacist-led, digitally enabled transitional care pathway integrated with national primary care and electronic medical records (EMR), with the aim to demonstrate the feasibility of shifting early post-AMI care from in-person cardiologist clinics to decentralized, data-driven pharmacist oversight on a national scale.
This manuscript describes the study design of this stepped-wedge randomized trial, including the protocol to AMI-HOPE intervention, digital architecture, statistical analysis plan [including win ratio (WR) primary composite], and planned economic/qualitative evaluations to inform scalable digital cardiovascular care. At the time of submission of this manuscript, enrolment has been completed, and we will report the baseline characteristics of all enrolled participants. Clinical outcomes will be reported separately in a subsequent manuscript.
Methods and design
Study design
AMI-HOPE is an open-label, multi-centred, proof-of-value pragmatic trial. Across Singapore’s three healthcare clusters, 7 hospitals and 18 primary care polyclinics are participating. The target sample size is 1512, with the control and intervention arm in a 1:1 ratio. This study adopts a stepped-wedge cluster-randomized design (Figure 1A). Randomization is overseen by an independent statistical team, in which the three healthcare clusters in Singapore are first randomized, and then the hospitals within each cluster are randomized. All hospitals began in the control condition, and each of the primary care hospitals will cross over to intervention at 3 months intervals over the first 24 months of the study. Once a cluster enters the intervention phase, it will not revert to the control phase. The patients are followed up for 12 months after their recruitment (Figure 1B). Low- and intermediate-risk patients in the intervention group are transitioned to the corresponding polyclinic within their respective cluster after 6 months. Due to the nature of the implementation strategy, neither programme-related healthcare providers nor patients are blinded. We have adopted the Health Discovery+ (HD+) platform, which supports remote vital signs monitoring, rule-based risk alerts, pharmacist teleconsultations triggered by alerts (with options for biweekly review reminders), automated patient education, and GDMT titration. This protocol aligns with the Standard Protocol Items: Recommendations for Interventional Trials 2025 (SPIRIT 2025) (see Supplementary material online, Table S4;8)
Figure 1.
(A) Step-wedge randomization schedule for the AMI-HOPE trial. (B) The period of time each institution’s pharmacist will lead post-AMI care under the step-wedge study design. (A) Cluster randomization and crossover sequence across Singapore's 3 health clusters (7 hospitals). Each step = 3 months; 8 periods total. (B) Patient timeline: 12 month follow-up from index AMI admission; 6 month polyclinic hand-off for stable intervention patients. AMI, acute myocardial infarction.
Study population
This study targets adult patients admitted to any of the participating primary care hospital for AMI and have sufficient digital literacy. Enrolled patients must be adults between 21 and 90 years of age, who have been hospitalized for a type I AMI with diagnostic coronary angiography performed with or without percutaneous coronary intervention during the index hospitalization. Participants must own a smartphone with their own data plan running either iOS 12 and above or Android 9 and above.
Individuals planned for palliative care or with a life expectancy of less than 1 year are excluded. Additional exclusion criteria include allergy or intolerance of ACE-I or ARB, already enrolled in Healthier SG with an affiliated general practitioner,9 or foreigners without Singaporean citizenship or permanent residence status. These exclusion criteria were chosen to avoid patients who may not be suitable for an allied health programme.
Potential participants will be identified and recruited prospectively at index admission for AMI on the wards (Figure 2). All eligible participants will provide written informed consent prior to enrolment. Participation is voluntary; individuals may withdraw from the study at any time. Withdrawn participants will not be replaced.
Figure 2.
Patient journey of the AMI-HOPE intervention. Centralized pharmacist oversight via HD+ platform integrates patient-generated data (twice-daily BP/HR), rule-based analytics, teleconsultations, and NG EMR for decentralized delivery across hospitals/polyclinics. Risk-stratified pathways per SMIR score. HD+, Health Discovery+; SMIR, Singapore Myocardial Infarction Registry.
Trial outcomes
The primary objective of this study is to evaluate the clinical efficacy of this pharmacist-led, digital platform assisted model of care based on pre-specified hierarchy of composite outcomes using the WR.10
Secondary outcomes of the study include the following: (i) to assess the effect of the pharmacist-led intervention on prescription, early up-titration, and adherence to ACE inhibitors (ACE-I)/angiotensin receptor blockers (ARB)/angiotensin receptor–neprilysin inhibitors (ACE-I/ARB/ARNI) and beta-blockers, and their subsequent impact on low-density lipoprotein cholesterol (LDL-C), blood pressure (BP), HbA1c control, and left ventricular ejection fraction (LVEF); (ii) to evaluate health-related quality of life through patient-reported outcome measures (PROMs), including the Seattle Angina Questionnaire-7 (SAQ-7), Rose Dyspnoea Questionnaire, the EQ-5D Questionnaire, and Patient Health Questionnaire-9, as recommended by the ICHOM CAD working group;11 and (iii) to measure patient activation via Patient Activation Measure-13 (PAM-13) by assessing patients’ knowledge, skills, and confidence with managing one’s own health.
Trial results will be communicated through peer-reviewed publication, presentation at international cardiovascular conferences, and updates on ClinicalTrials.gov. Plain language summaries will be distributed to participants and posted on the study website, and healthcare professionals will be briefed via hospital grand rounds and national pharmacy networks to facilitate adoption of evidence-based transitional care models.
The AMI-HOPE intervention
Patient-facing mobile application
The HD+ application (Figure 3) is designed to encourage patients to regularly monitor and submit their BP and heart rate (HR) readings. Patients are advised to record and submit these measurements twice daily during the first two months of the programme, followed by weekly submissions for the remainder of the intervention. The app offers optional reminder alarms, which patients may activate based on their preference. All patient-generated health data (PGHD) are automatically transmitted to a secure cloud backend via real-time Bluetooth synchronization, allowing seamless integration with the analytics engine. Key features include (i) automated chatbots that monitor symptoms, check medication adherence, and proactively engage patients. For example, the chatbot may contact patients to address missed readings or abnormal BP values. Responses flagged as concerning are escalated as alerts in the pharmacist's task list for appropriate follow-up action. To reduce the cognitive burden of the pharmacist clinicians, two-way live chats are disabled and patients respond to automated questionnaires and surveys, and depending on their responses, pharmacists are alerted and can reply asynchronously to patient messages. (ii) The app delivers adaptive educational modules based on Bandura’s self-efficacy framework, covering topics such as AMI recovery, diet, exercise, and smoking cessation. These modules are presented at algorithm-determined intervals. For instance, medication education is provided within the first week and rehabilitation guidance in the first month. (iii) Patient-reported outcome measures (PROMs) are integrated into the app, with baseline, 6 month, and 12 month assessments using instruments such as the SAQ-7, EQ-5D, PHQ-9, and PAM-13, administered through in-app surveys. (iv) Usability exceeds ISO 9241-11 thresholds (>80% task success in pilot testing). Automated reminders are triggered if there are no BP readings for 2 consecutive days during the initial 2 months or for 7 consecutive days in subsequent months. If reminders are not addressed, pharmacist outreach is initiated to ensure patient engagement and adherence.
Figure 3.
HD+ mobile application platform features for patients. Screenshots demonstrate HD+ platform features: (Screen 1) adaptive educational modules grounded in Bandura's Social Cognitive Theory; (Screens 2–3) twice-daily BP/HR measurement prompts with Bluetooth-enabled automated cuff synchronization; (Screen 4) historical BP trends with visual analytics and personalized insights; (Screen 5) chatbots for symptom surveillance, medication adherence monitoring, and proactive patient engagement.
Clinician dashboard and analytics
The clinician dashboard is a web-based platform (Figure 4) that enables real-time visualization of PGHD, including longitudinal trends in BP and HR, anomaly detection, and prioritized task lists for clinical action. The dashboard employs a rule-based analytics engine that processes incoming PGHD through several mechanisms: (i) Threshold-based alerts are generated according to predefined parameters (see Supplementary material online, Table S2). For example, systolic BP (SBP) readings below 90 mmHg or above 180 mmHg or HR below 45 bpm trigger red alerts which prompt clinical review within 4 h. Similarly, significant deviations in weekly BP trends, such as a 20% rise in SBP, are flagged as yellow alerts requiring review within 48 h. (ii) Titration support is provided through pre-specified algorithms that guide weekly up-titration of ACE-I, ARB, and beta-blockers, contingent upon stable BP values (SBP ≥100 mmHg). The dashboard cross-references these recommendations with laboratory results and medical history from the National Electronic Medical Record (NGEMR) system. If a patient is hypotensive or has a LVEF below 40%, the dashboard escalates the case to a cardiologist for further management. (iii) Workflow automation is achieved through automatic assignment of tasks based on the severity of alerts. Pharmacists are able to triage approximately 80% of alerts remotely, while the remaining 20% necessitate laboratory tests or teleconsultations. This integrated approach facilitates seamless hand-off to polyclinic pharmacists at the 6 month mark for patients who are classified as low or intermediate risk and remain clinically stable.
Figure 4.
Health Discovery+ Dashboard and Job-Stack for AMI-HOPE clinicians. Real-time display includes (A) patient list with risk alerts (alert levels as per Supplementary material online, Table S1); (B) longitudinal BP/HR trends; (C) task list with auto-prioritization; (D) titration algorithm support with NG EMR integration. Thresholds: SBP <90/≥180 mmHg, trend deviations >20%. HD+, Health Discovery+.
Onboarding and care phases
The AMI-HOPE intervention consists of the following phases of care: (i) enrolment (index admission), digital ambassadors (DAs) profile patients via the Singapore Myocardial Infarction Registry (SMIR) risk score, obtain consent, install HD+/hiSG apps, train BP use, and schedule the Week 1 teleconsultation; (ii) 0–6 months (early phase), intensive remote monitoring/titration, pharmacists as main point of contact for issues, and decentralized laboratory tests at polyclinics (results are automatically retrieved from NGEMR); and (iii) 6–12 months, stable patients transition to polyclinic pharmacist care with continued BP monitoring and app use; high-risk patients remain co-managed with specialists.
Early post-discharge to 6 months
Clinical monitoring of BP is conducted remotely through the HD+ clinician dashboard by the clinician pharmacist in the AMI-HOPE care team. For each patient, the HD+ dashboard displays longitudinal trends of their daily BP and HR and generates alerts for specific events based on absolute thresholds and trends (see Supplementary material online, Table S2 details thresholds, priorities, and actions). Clinician pharmacists are prompted to review the case within the platform and cross reference with the patient’s medical history in the electronic health record (EHR). The continuous monitoring of BP also allows pharmacists to perform early initiation and up-titration of ACE-I/ARB, MRA (and SGLT2i for diabetic patients), especially for patients whose BP were too low at discharge. All interactions and decisions are documented within the platform and EHR with timestamps to ensure continuity of care and support audit and quality improvement. The clinician pharmacists also serve as the main point of contact for patients to manage minor post-discharge issues and schedule follow-up visits for laboratory testing through the messaging channel on HD+ and phone calls to reduce unnecessary emergency visits.
Consultations are, by default, conducted remotely through telehealth visits, with physical visits only required for laboratory tests. To enhance decentralisation and patient convenience, laboratory tests can be performed at a primary care facility closer to the patient’s home or workplace and both the patient and clinician pharmacists can review their test results remotely through the nationwide EHR.12
6 months and beyond
Stable low- to intermediate-risk patients are transitioned to the cluster specific polyclinic (primary care) at 6 months post-discharge. The monitoring of the patient is handed over from the hospital clinician pharmacist to a polyclinic clinician pharmacist. Patients are to continue their BP monitoring. High-risk patients or those with complex disease may be transitioned to primary care at 8–12 months instead or co-managed long-term with continued shared care and intermittent specialist review.
Standard care
Participants assigned to standard care (i.e. the control group) are monitored and followed up according to existing institutional practice. Usual follow-up care is comprised of inpatient education by the cardiologist, cardiac care nurse, or advanced practice nurse prior to discharge and scheduled face-to-face consultations with the cardiologist at ∼1–3 and 6–9 months post-discharge, during which medication review and adjustment are performed. Patients are only considered for transition to primary care at the polyclinics 12 months post-discharge.
Data collection and monitoring
Patients will be followed up for the entire 12 month period. Table 1 presents the patient-level outcomes that will be collected by clinical research coordinators at fixed intervals throughout the follow up -period. The quality and integrity of collected data will be inspected every 3 months. The study team will conduct interim analysis for administrative reporting to MOH and safety monitoring.
Table 1.
Clinical outcomes collected and analysed
| Outcomes to be assessed | Data collection timepoint | |
|---|---|---|
| hard CVD outcomes | Death | When an event occurs |
| Unplanned CV related (non-fatal MI, non-fatal stroke, others) rehospitalizations | When an event occurs | |
| clinical markers | LDL-C | Each time a lab test is ordered |
| BP | Discharge, 2 week, 1, 3, 6, 9, 12 months | |
| HbA1c | Each time a lab test is ordered | |
| health-related QoL | Patient Oriented Outcome Measures (PROMs) including EQ-5D, SAQ7, RDQ and PAM-13 | 1, 6, and 12 months |
| effect of pharmacist-led intervention | Dose of ACE/ARB/MRA inhibitor | Each time medication is prescribed |
| Smoking status | At each consultation with cardiologist/pharmacist clinician | |
| efficiency | Length of stay | At discharge |
Primary: Hierarchical win ratio composite (CV death→MI→stroke→unplanned CV readmissions→LDL-C→SBP→HbA1c→ICHOM PROMs). Secondary: Individual components, GDMT doses, smoking status, PROMs (SAQ-7, EQ-5D, PHQ-9, PAM-13), length of stay. Data extracted from NGEMR/national registries or collected via HD+ app. PROMs, patient-reported outcome measures; GDMT, guideline-directed medical therapy; ICHOM, International Consortium for Health Outcomes Measurement; NGEMR, Next-Generation Electronic Medical Record.
Statistical design and analysis
Primary and secondary outcomes
Primary
The primary outcome will be clinical efficacy assessed using a stratified WR approach. This analysis will be conducted on a hierarchical composite endpoint comprising of the following: (i) cardiovascular death, (ii) non-fatal myocardial infarction, (iii) non-fatal stroke, (iv) unplanned cardiovascular readmissions, (v) LDL-C (mmol/L), (vi) SBP (mmHg), (vii) glycated haemoglobin (HbA1c, %), and (viii) PROMs from the ICHOM Ischemic Heart Disease (IHD) set, ranked by worseness (including SAQ-7, EQ-5D, RDQ, and PHQ-9).
WR prioritizes clinical importance (e.g. death > biomarkers) and can incorporate both continuous and recurrent events, making it well-suited for pragmatic trials with heterogeneous outcomes.10 All pairwise comparisons between intervention and control groups will be evaluated (unstratified initially; stratification by cluster if imbalance occurs). ‘Wins’ are assigned at the first hierarchical level where outcomes differ, with ties carried forward to subsequent levels. The treatment effect will be reported as the WR estimate with 95% confidence interval and P-value, calculated via bootstrap resampling (10 000 resamples).
Secondary
Secondary outcomes will be analysed using linear mixed models (LMMs), which include fixed effects for time, period, and baseline covariates (SMIR risk score, age, sex, ethnicity), as well as random effects for patient and cluster. Adherence, PROMs, and qualitative themes will be presented using descriptive statistics.
Sensitivities
Sensitivity analyses will include a per-protocol population defined as participants with at least 80% adherence, censoring data after withdrawal, and applying inverse-probability weighting to account for missing continuous data. Additional analyses will involve unstratified WR calculations and LMMs for individual components. All statistical tests will use a two-sided alpha of 0.05 without adjustment for multiplicity, employing hierarchical testing. Analyses will be conducted in R using the swCRTdesign and WRestimates packages.
Sample size
We focused our sample size calculation on the difference in objective measures of clinical marker outcomes, specifically SBP, HbA1c, and LDL-C, between intervention and control groups. All calculations are for 90% power with a two-sided alpha = 0.05 and assume a ratio of 1:1 for intervention to control subjects (see Supplementary material online, Table S3). Mean, SD, variance of cluster random intercept (τ), and percentage of missing data are based on the data from the IMMACULATE Trial (see Supplementary material online, Table S3), a pilot programme for AMI-HOPE in which AMI patients from three different hospitals in Singapore received remote intensive management led by AHPs for 6 months post-discharge.7 The sample size for this stepped-wedge cluster-randomized trial (SW-CRT) is calculated with the r package `swCRTdesign`.13
To ensure even distribution across steps, the target recruitment is a total of 1512 participants, with 9 participants at each cluster period across 24 months over 7 sites. A target enrolment of 1512 participants will have >90% power to detect even modest differences in secondary continuous markers (SBP, HbA1c, LDL-C; α = 0.05) and will have >85% power for the WR primary endpoint (simulations: >85% at WR = 1.2, 10% ties; Supplementary material online, Table S3) while handling SW-CRT features of calendar time and cluster randomization.
Regulatory aspects, ethics, and safety
The study protocol has been approved by the NHG Domain Specific Review Board (DSRB), which will oversee the study and data collection through regularly submitted reports. A participant information sheet will explain the study’s aims, participation requirements and duration, possible risks, and benefits. Voluntary participation, confidentiality, and the right to withdraw at any time without impact on clinical care will be emphasized. Written informed consent, in accordance with SGGCP and the Declaration of Helsinki, will be obtained from each participant or their legally accepted representative before any research use of programme data. Participants will be assured that their information will be kept confidential; only authorized investigators and designated personnel will have access to identifiable data, and coded (de-identified) data may be shared with collaborators as permitted by law. All collected and analysed data will be securely stored at the study sites; access will be controlled by the principal investigators. Any protocol amendments or safety-related updates will be submitted promptly to the DSRB, and the board will be notified upon study closure. Trial conduct will be monitored through centralized HD+ platform analytics, quarterly coordinated centre site visits, and ad hoc audits by NUHS and/or MOH (including their Audit Agents) to ensure protocol compliance and data accuracy.
Cost-effectiveness analysis
Cost-effectiveness will be assessed using a semi-Markov model incorporating the health states of first MI, recurrent MI, stroke, and death. Transition probabilities between states are time-dependent and mediated by treatment effects on LDL-C and SBP reduction and HbA1c for patients with DM in subgroup analyses. The economic evaluation will capture intervention delivery costs through measurement and valuation of incremental resource utilization. Health benefits will be expressed using Quality-Adjusted Life Years (QALYs), with utility values for health states derived from both local and international literature. The incremental cost-effectiveness ratio will be calculated as the incremental cost of intervention delivery divided by the incremental health outcome (QALYs gained). We intend to validate the results of the semi-Markov model against trial outcomes where feasible. Our analysis will adhere to international reporting guidelines as stated in the 2022 CHEERS checklist.14
Qualitative interviews
To support sustainable scale-up, a qualitative sub-study will assess patient acceptance of pharmacist leadership, preferences for remote vs. in-person management, and perceived usability of the digital platform, which are key implementation determinants for mainstreaming this model of care.
Baseline characteristics
Enrolment has been completed on 30 September 2025. A total of 1634 patients has been enrolled and randomized, with 810 assigned to the control group and 824 to the intervention group. To date, two control participants (0.2%) and 34 intervention participants (4.1%) have withdrawn from the programme, and 106 intervention patients (12.9%) have opted to not have their data disclosed. Accordingly, baseline characteristics are presented for the 1492 participants who remain in follow-up in Table 2.
Table 2.
Baseline characteristics of enrolled participants
| No. (%) | |||
|---|---|---|---|
| Baseline characteristics | Standard care (N = 808) |
AMI-HOPE intervention (N = 684) |
Total (N = 1492) |
| Age, median [IQR], y | 60 [52, 68] | 58 [50, 65] | 59 [51, 67] |
| Female | 100 (12.4%) | 79 (11.5%) | 179 (12%) |
| Ethnicity | |||
| Chinese | 497 (61.5%) | 426 (62.3%) | 923 (61.9%) |
| Malay | 130 (16.1%) | 128 (18.7%) | 258 (17.3%) |
| Indian | 145 (17.9%) | 109 (15.9%) | 254 (17%) |
| Other | 36 (4.5%) | 21 (3.1%) | 57 (3.8%) |
| BMI, median [IQR] | 25.5 [23.0, 28.4] | 26.1 [23.7, 29.3]* | 25.7 [23.3, 28.8] |
| Enrolling clusters | |||
| NHG | 33 (4.1%) | 218 (31.9%) | 251 (16.8%) |
| NUHS | 166 (20.5%) | 178 (26%) | 344 (23.1%) |
| SHS | 609 (75.4%) | 288 (42.1%) | 897 (60.1%) |
| Index event | |||
| STEMI | 483 (59.8%) | 433 (63.3%) | 916 (61.4%) |
| Elevated first troponin | 636 (78.7%) | 594 (86.8%)* | 1230 (82.4%) |
| Length of stay, median [IQR], d | 4 [4, 5] | 4 [3, 5] | 4 [3, 5] |
| Medical history | |||
| Myocardial infarction | 122 (15.1%) | 68 (9.9%)* | 190 (12.7%) |
| CABG | 32 (4.0%) | 12 (1.8%)* | 44 (2.9%) |
| PCI | 143 (17.7%) | 76 (11.1%)* | 219 (14.7%) |
| Diabetes | 320 (39.6%) | 235 (34.4%)* | 555 (37.2%) |
| Hypertension | 481 (59.5%) | 331 (48.4%) | 812 (54.4%) |
| Current smoker | 319 (39.5%) | 250 (36.5%) | 569 (38.1%) |
| Baseline medication | (N = 804) | (N = 638) | (N = 1442) |
| Dual antiplatelets | 801 (99.6%) | 634 (99.4%) | 1435 (99.5%) |
| β-blocker | 694 (86.3%) | 522 (81.8%)* | 1216 (84.3%) |
| Statin | 796 (99%) | 623 (97.6%) | 1419 (98.4%) |
| Ezetimibe | 344 (42.8%) | 270 (42.3%) | 614 (42.6%) |
| PCSK9-based agents | 13 (1.6%) | 10 (1.6%) | 23 (1.6%) |
| Calcium channel blocker | 88 (10.9%) | 39 (6.1%)* | 127 (8.8%) |
| ACE-I/ARB/ARNi | 668 (83.1%) | 477 (74.8%)* | 1145 (79.4%) |
| Diuretics | 41 (5.1%) | 23 (3.6%) | 64 (4.4%) |
| MRA | 149 (18.5%) | 77 (12.1%)* | 226 (15.7%) |
| SGLT2 inhibitors | 302 (37.6%) | 193 (30.3%)* | 495 (34.3%) |
| Metformin | 262 (32.6%) | 212 (33.2%) | 474 (32.9%) |
| Insulin | 52 (6.5%) | 29 (4.5%) | 81 (5.6%) |
| Oral anticoagulants | 65 (8.1%) | 49 (7.7%) | 114 (7.9%) |
| LVEF, median [IQR], % | 49.5 [40.0, 58.0] | 50.0 [41.0, 58.0] | 50.0 [40.0, 58.0] |
As the continuous variables were non-normally distributed, they are presented as median [interquartile range (IQR)]. Continuous variables were compared using the Mann–Whitney U test, and categorical variables were compared using the chi-square test. Values in the intervention group marked with an asterisk (*) are significantly different from the standard-care group at P < 0.05.
Discussion
AMI-HOPE operationalises pharmacist-led, digitally enabled transitional care at a national scale, addressing gaps in traditional specialist clinic post-AMI care. Building on the IMMACULATE pilot's safety in remote GDMT titration,7 AMI-HOPE scales this via the HD+ platform's rule-based analytics, real-time PGHD, and NGEMR integration across three clusters (7 hospitals, 18 polyclinics). Unlike tele-rehab trials focused on exercise,15,16 pharmacist studies without digital backbone,17 or remote BP monitoring trials that does not close the loop with allied health practitioners,18–21 AMI-HOPE combines intensive remote monitoring, algorithm-guided titration, and automated education to deliver care at specialist-equivalent intensity through task-shifting and automation.
The stepped-wedge design suits this proof-of-value rollout, capturing real-world implementation while controlling secular trends.22 WR analysis prioritizes clinical hierarchy over time-to-first-event, accommodating the pragmatic reality of repeated biomarker measurements and PROMs in digital trials.10 Powering for continuous markers (SBP/LDL-C/HbA1c) ensures sensitivity to intervention effects, with adequate margin for the primary composite (see Supplementary material online, Table S3). Cost-effectiveness and qualitative sub-study will quantify scalability beyond efficacy.14
Implementation advantages
Table 3 illustrates AMI-HOPE's efficiency: weekly remote titration vs. sporadic clinics; bidirectional EMR vs. read-only; and 6 month primary care hand-off vs. 12 month. Our early enrolment success (N = 1634; 4% withdrawal) validates digital onboarding and polyclinic integration. Notably, platform usability exceeds ISO standards, with <24 h pharmacist response sustaining engagement in this smartphone-proficient cohort (median age 59 years).
Table 3.
AMI-HOPE intervention vs. standard care
| Feature | AMI-HOPE (intervention) | Standard care |
|---|---|---|
| Lead provider | Clinician pharmacist (cardiologist backup) | Cardiologist |
| Monitoring | Twice-daily remote BP/HR via HD+ app/cuff | None structured |
| GDMT titration | Weekly remote, algorithm-guided | Opportunistic at 1–3 and 6–9 month clinics |
| Consultations | Telehealth default (<24 h response) | In-person clinics |
| Education/PROMs | Automated app modules + serial surveys | Inpatient counselling only |
| Primary care transition | 6 months (stable low/intermediate risk) | 12 months |
| Documentation/audit | Platform timestamps | Manual clinic notes |
Lead provider, monitoring modality, titration frequency, consultation type, education delivery, primary care transition timing, EMR integration, and documentation differ systematically. HD+, Health Discovery+ platform; GDMT, guideline-directed medical therapy; PROMs, patient-reported outcome measures; EMR, electronic medical record; Protocol-defined differences; no patient-level comparison implied.
Limitations
The AMI-HOPE trial has several limitations that require contextualization. Open-label delivery causes the study to be vulnerable to Hawthorne effects,23 though control clusters may also benefit from AMI-HOPE awareness during rollout. The open-label nature may also introduce response bias and placebo effect to the evaluation of PROMs;24 however recent systematic reviews on oncology trials demonstrate no statistically significant differences in PROM results between blinded and open-label designs.25,26 Missing data (30–46% for biomarkers) will be handled using inverse-probability weighting, but attrition bias remains possible. Smartphone dependency excludes ∼15% AMI patients (digital divide) and generalizability beyond Singapore's integrated public system requires adaptation. The multi-component intervention complicates the isolation of digital vs. human effects; qualitative methods may be required to elucidate intervention mechanisms.
Conclusion
If efficacious, AMI-HOPE offers a generalizable digital infrastructure for high-needs CV transitional care—reallocating specialist time, standardizing titration via algorithms, and enabling population-scale monitoring. The HD+ framework supports extension to heart failure or multi-morbidity, with predictive analytics upgrades possible. This model aligns with ESC priorities for integrated, digitally enabled CV care in resource-constrained systems, with potential to inform digital therapeutics policy worldwide. Clinical and economic results will clarify the value of this approach at scale.
Supplementary Material
Contributor Information
Melody Wang, Yong Loo-Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Zhi Zhen Lim, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Siew-Pang Chan, Yong Loo-Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Zhen-Long Teo, National University Heart Centre, National University Health System, Singapore, Singapore.
Sock-Hwee Tan, CArdiovascular DiseasE National Collaborative Enterprise, Consortium for Research and Innovation, Singapore, Singapore.
Vinay Panday, National University Heart Centre, National University Health System, Singapore, Singapore.
Ho Jien Sze, National Heart Centre, Singapore, Singapore.
Yee-May Wong, Department of Cardiovascular Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Grace Chang, Khoo Teck Puat Hospital, Singapore, Singapore.
Faclin Ng, National University Heart Centre, National University Health System, Singapore, Singapore.
Imran Syed, Khoo Teck Puat Hospital, Singapore, Singapore.
Patrick Lim, Khoo Teck Puat Hospital, Singapore, Singapore.
Zijuan Huang, National Heart Centre, Singapore, Singapore; Sengkang General Hospital, Singapore, Singapore.
Ziliang Lim, National Healthcare Group Polyclinics, Singapore, Singapore.
Valerie Teo, National Healthcare Group Polyclinics, Singapore, Singapore.
Ian Phoon, SingHealth Polyclinics, Singapore, Singapore.
Galih Kunarso, SingHealth Polyclinics, Singapore, Singapore.
Siang-Chew Chai, Changi General Hospital, Singapore, Singapore.
Kai-Rong Siau, National University Polyclinics, Singapore, Singapore.
Yew-Seng Kwan, National University Polyclinics, Singapore, Singapore.
Samuel Ho, Khoo Teck Puat Hospital, Singapore, Singapore.
Laurane Lim, SingHealth Polyclinics, Singapore, Singapore.
Marvin Sim, National University Heart Centre, National University Health System, Singapore, Singapore.
Hui-Rei Yap, Department of Pharmacy, National Healthcare Group, Singapore, Singapore.
Wan-Lin Oh, SingHealth Polyclinics, Singapore, Singapore.
Hui-Ping Chong, Department of Pharmacy, National University Health System, Singapore, Singapore.
Angela Yeo, Ministry of Health Office for Healthcare Transformation, Singapore, Singapore.
Praveen Deorani, Ministry of Health Office for Healthcare Transformation, Singapore, Singapore.
Robert Morris, Ministry of Health Office for Healthcare Transformation, Singapore, Singapore.
Hee-Hwa Ho, Department of Cardiovascular Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Derek J Hausenloy, Yong Loo-Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Hwee-Lin Wee, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Doreen Tan, School of Pharmacy, National University of Singapore, Singapore, Singapore.
Mark Y Chan, Yong Loo-Lin School of Medicine, National University of Singapore, Singapore, Singapore; National University Heart Centre, National University Health System, Singapore, Singapore.
Author contributions
Tsz Lam Melody Wang (Formal analysis, Investigation, Methodology, Writing—original draft [equal]), Zhi-Zhen Lim (Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing [equal]), Siew-Pang Chan (Data curation, Formal analysis, Investigation, Methodology [lead]), Zhen-Long Teo (Data curation, Investigation [equal], Project administration [lead]), Sock-Hwee Tan (Conceptualization, Investigation, Project administration [equal]), Vinay Panday (Investigation, Supervision, Writing—review & editing [equal]), Ho Jien Sze (Investigation, Writing—review & editing [supporting]), Yee-May Wong (Conceptualization, Investigation, Writing—review & editing [supporting]), Grace Chang (Conceptualization, Writing—review & editing [supporting], Investigation [equal]), Faclin Ng (Investigation, Methodology, Project administration [supporting], Supervision [lead]), Imran Syed (Investigation, Writing—review & editing [supporting]), Patrick Lim (Investigation, Writing—review & editing [supporting]), Zijuan Huang (Investigation, Writing—review & editing [supporting]), Ziliang Lim (Supervision, Writing—review & editing [equal]), Valerie Teo (Investigation, Project administration, Writing—review & editing [supporting]), Ian Phoon (Conceptualization, Project administration, Writing—review & editing [supporting]), Galih Kunarso (Investigation, Writing—review & editing [supporting]), Siang-Chew Chai (Investigation, Writing—review & editing [supporting]), Kai-Rong Siau (Investigation, Supervision, Writing—review & editing [supporting]), Yew-Seng Kwan (Investigation, Supervision [supporting]), Samuel Ho (Investigation [supporting]), Laurane Lim (Investigation, Project administration, Supervision [supporting]), Marvin Sim (Investigation [equal], Project administration, Supervision [supporting]), Hui-Rei Yap (Investigation, Project administration, Supervision [supporting]), Wan-Lin Oh (Investigation, Project administration, Supervision [supporting]), Hui-Ping Chong (Investigation [supporting]), Angela Yeo (Investigation, Methodology, Project administration, Supervision [equal]), Praveen Deorani (Conceptualization, Investigation, Methodology, Writing—review & editing [equal]), Robert Morris (Conceptualization [equal], Methodology, Software [lead]), Hee-Hwa Ho (Investigation, Project administration, Supervision [equal]), Derek J. Hausenloy (Conceptualization, Funding acquisition, Project administration [lead], Writing—review & editing [supporting]), Hwee-Lin Wee (Conceptualization, Investigation, Writing—review & editing [equal], Formal analysis, Methodology [lead]), Doreen Tan (Conceptualization, Writing—review & editing [equal], Supervision [lead]), and Mark Y Chan (Conceptualization, Data curation, Methodology, Writing—original draft, Writing—review & editing [equal], Funding acquisition, Investigation, Project administration, Resources, Software, Supervision [lead])
Lead author biography
Melody Wang is a Research Associate at the Yong Loo Lin School of Medicine, National University of Singapore. She holds a Bachelor's degree from the University of Hong Kong and a Master's degree in Statistics from the National University of Singapore. Her work centres on translational research in cardiovascular disease, with a focus on the design and evaluation of clinical trials. Her research interests also include AI on health equity and clinical risk prediction models.
Zhi Zhen Lim is a Research Associate at the Saw Swee Hock School of Public Health, National University of Singapore. She holds a Master of Public Health and a Bachelor of Science in Pharmacy from the National University of Singapore. Zhi Zhen is a registered pharmacist with the Singapore Pharmacy Council. Her research interests lie in health economics and outcomes research and health equity. Her work centres on economic and programme evaluation of health interventions and policies, applying quantitative methods to inform evidence-based decision-making and advance equitable access to healthcare.
Supplementary material
Supplementary material is available at European Heart Journal – Digital Health.
Funding
This work was supported by the Ministry of HealthHealth Services Development Programme (HSDP) (grant number MH70:70/1-2017), the National Medical Research Council (NMRC) (grant number MOH-001425-00), and the Ministry of Health Office for Healthcare Transformation (MOHT).
Data availability
Data cannot be shared for ethical/privacy reasons. The data underlying this article cannot be shared publicly as this study is pragmatic trial in which consent was given only for retrospective analysis of patient data for purposes of health services research by local healthcare providers. The data will be shared on reasonable request to the corresponding author.
References
- 1. Flather MD, Yusuf S, Køber L, Pfeffer M, Hall A, Murray G, et al. Long-term ACE-inhibitor therapy in patients with heart failure or left-ventricular dysfunction: a systematic overview of data from individual patients. Lancet 2000;355:1575–1581. [DOI] [PubMed] [Google Scholar]
- 2. Jenča D, Melenovský V, Stehlik J, Staněk V, Kettner J, Kautzner J, et al. Heart failure after myocardial infarction: incidence and predictors. ESC Heart Fail 2021;8:222–237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol 2020;76:2982–3021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Zhang S, Lin Y. Advancements, challenges, and innovative strategies in cardiac rehabilitation for patients with acute myocardial infarction: a systematic review. Curr Probl Cardiol 2025;50:102934. [DOI] [PubMed] [Google Scholar]
- 5. Ministry of Health (Singapore) . Myocardial Infarction Registry 2015. Singapore: National Registry of Diseases Office (NRDO); 2015. [Google Scholar]
- 6. Arah OA, Westert GP, Hurst J, Klazinga NS. A conceptual framework for the OECD health care quality indicators project. Int J Qual Health Care 2006;18:5–13. [DOI] [PubMed] [Google Scholar]
- 7. Chan MY, Koh KW, Poh S-C, Marchesseau S, Singh D, Han Y, et al. Remote postdischarge treatment of patients with acute myocardial infarction by allied health care practitioners vs standard care: the IMMACULATE randomized clinical trial. JAMA Cardiol 2021;6:830–835. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Chan A-W, Boutron I, Hopewell S, Moher D, Schulz KF, Collins GS, et al. SPIRIT 2025 statement: updated guideline for protocols of randomised trials. Lancet 2025;405:e19–e27. [DOI] [PubMed] [Google Scholar]
- 9.Healthier SG: Ministry of Health Singapore. 2026. https://www.healthiersg.gov.sg/ [Accessed 21 April 2026]
- 10. Pocock SJ, Ariti CA, Collier TJ, Wang D. The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. Eur Heart J 2012;33:176–182. [DOI] [PubMed] [Google Scholar]
- 11. McNamara RL, Spatz ES, Kelley TA, Stowell CJ, Beltrame J, Heidenreich P, et al. Standardized outcome measurement for patients with coronary artery disease: consensus from the International Consortium for Health Outcomes Measurement (ICHOM). J Am Heart Assoc 2015;4:e001767. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Synapxe . Next Generation Electronic Medical Record (NGEMR) 2024. https://www.synapxe.sg/healthtech/national-programmes/next-generation-electronic-medical-record-ngemr [Accessed 21 April 2026]
- 13. Voldal EC, Hakhu NR, Xia F, Heagerty PJ, Hughes JP. swCRTdesign: an RPackage for stepped wedge trial design and analysis. Comput Methods Programs Biomed 2020;196:105514. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. MDM Policy Pract 2022;7:23814683211061097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Homem F, Reveles A, Amaral A, Coutinho V, Gonçalves L. Improving transitional care after acute myocardial infarction: a scoping review. Health Care Sci 2024;3:312–328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Ramachandran HJ, Yeo TJ, Seah CWA, Yeo TM, Chua MCH, Syed Gani Q, et al. Feasibility and effectiveness of an integrative cardiac rehabilitation employing smartphone technology (I-CREST): a pilot randomized controlled trial. Eur J Cardiovasc Nurs 2025;24:959–970. [DOI] [PubMed] [Google Scholar]
- 17. Ho PM, Lambert-Kerzner A, Carey EP, Fahdi IE, Bryson CL, Melnyk SD, et al. Multifaceted intervention to improve medication adherence and secondary prevention measures after acute coronary syndrome hospital discharge: a randomized clinical trial. JAMA Intern Med 2014;174:186–193. [DOI] [PubMed] [Google Scholar]
- 18. Eze CE, Dorsch MP, Coe AB, Lester CA, Buis LR, Farris KB. Ehealth literacy and participation in remote blood pressure monitoring among patients with hypertension: cross-sectional study. J Med Internet Res 2025;27:e71926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Marozzi MS, Desantis V, Corvasce F, Falcone GS, Santovito M, Colleoni G, et al. Impact of remote monitoring on well-being, therapeutic adherence, and organ damage evaluation in hypertensive patients: the PROSIT study. Eur Heart J Digit Health 2026;7:ztag001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Mehta SJ, Volpp KG, Troxel AB, Teel J, Reitz CR, Purcell A, et al. Remote blood pressure monitoring with social support for patients with hypertension: a randomized clinical trial. JAMA Netw Open 2024;7:e2413515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Teng T-Q, Sun G-X, Yu Z-Y, Liu Z-S, Wang T, Wu Q, et al. Efficiency of remote monitoring and guidance in blood pressure management: a randomized controlled trial: the role of remote monitoring in improving hypertension management. BMC Med 2025;23:459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Rodrigues EJ, Eisenberg MJ, Pilote L. Effects of early and late administration of angiotensin-converting enzyme inhibitors on mortality after myocardial infarction. Am J Med 2003;115:473–479. [DOI] [PubMed] [Google Scholar]
- 23. Fernald DH, Coombs L, DeAlleaume L, West D, Parnes B. An assessment of the Hawthorne effect in practice-based research. J Am Board Fam Med 2012;25:83–86. [DOI] [PubMed] [Google Scholar]
- 24. Hróbjartsson A, Emanuelsson F, Skou Thomsen AS, Hilden J, Brorson S. Bias due to lack of patient blinding in clinical trials. A systematic review of trials randomizing patients to blind and nonblind sub-studies. Int J Epidemiol 2014;43:1272–1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Lord-Bessen J, Signorovitch J, Yang M, Georgieva M, Roydhouse J. Assessing the impact of open-label designs in patient-reported outcomes: investigation in oncology clinical trials. JNCI Cancer Spectr 2023;7:pkad002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Mouillet G, Efficace F, Thiery-Vuillemin A, Charton E, Van Hemelrijck M, Sparano F, et al. Investigating the impact of open label design on patient-reported outcome results in prostate cancer randomized controlled trials. Cancer Med 2020;9:7363–7374. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data cannot be shared for ethical/privacy reasons. The data underlying this article cannot be shared publicly as this study is pragmatic trial in which consent was given only for retrospective analysis of patient data for purposes of health services research by local healthcare providers. The data will be shared on reasonable request to the corresponding author.