Uptake of a primary care atrial fibrillation screening program (AF-SMART): a realist evaluation of implementation in metropolitan and rural general practice

Implementing a remote self-screening programme for atrial fibrillation using digital health technology among community-dwellers aged 75 years and older: a qualitative evaluation

OBJECTIVE

To examine the feasibility of implementing remote atrial fibrillation (AF) self-screening among older people supported by a remote central monitoring system.

DESIGN

Process evaluation of the Mass AF randomised clinical trial (ACTRN12621000184875) with one-to-one semistructured interviews using interview guides underpinned by the Critical Realism approach and coded using the UK Medical Research Council Guidance of Process Evaluation Framework.

SETTING AND PARTICIPANTS

Community-dwelling people aged ≥75 years from both genders (ratio 1:1) and urban/rural (ratio 2:1) in Australia.

INTERVENTIONS

Participants were provided handheld single-lead electrocardiogram (ECG) devices and trained to self-record ECGs once daily on weekdays for at least 6 months. A remote central team notified participants and general practitioners (GPs) of AF.

PRIMARY FEASIBILITY OUTCOMES

The strengths, weaknesses, opportunities and threats (SWOT) analysis examined enablers (ie, strengths and opportunities) and barriers (ie, weaknesses and threats).

RESULTS

Overall, 200 participants; 98.5% completed the 6-month programme, 96% reported being satisfied with screening and 48 were interviewed: mean age 79 years, 54% male and 71% urban. 11 GPs were interviewed: 55% female and 64% urban. Programme participants trusted the remote monitoring system that supported the screening programme and provided follow-up pathways where required. GPs saw opportunities to introduce this self-screening programme to at-risk patients and improve patients' risk profiles. Programme participants reported that after being trained to use the device, they felt empowered to do self-screening and found it convenient. GPs saw empowerment could enhance the doctor-patient relationship. Participants and GPs valued screening in diagnosing AF that would otherwise be missed in usual care, but the uncertainty of effective screening duration could be a barrier.

CONCLUSIONS

This screening programme was feasible with the reinforcement of the underpinning enablers. Several implementation strategies were identified using SWOT analysis, including leveraging the opportunity for GPs to introduce this screening programme to at-risk patients.

TRIAL REGISTRATION NUMBER

ACTRN12621000184875.

Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review

BACKGROUND

Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users.

OBJECTIVE

This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation.

METHODS

Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used.

RESULTS

A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC's guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions.

CONCLUSIONS

In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research.

Optimising a clinical decision support tool to improve chronic kidney disease management in general practice

BACKGROUND

Early identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation.

METHODS

Future Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance.

RESULTS

Future Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete.

CONCLUSIONS

This study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform.

Methods in realist evaluation: A mapping review

Realist evaluation is becoming increasingly popular as an evaluation methodology. Its main objective is to uncover the mechanisms that lead to observed outcomes following an intervention and the contextual conditions that enabled this. The focus is on explaining why, for whom and in what circumstances an intervention works. It is a theory-driven approach and is explicitly method neutral, meaning that both quantitative and qualitative data collection methods can be used to unearth the underlying mechanisms that cause the intervention outcomes. In this review, we aim to map the methods used in realist evaluation studies, to draw lessons from the findings and to reflect on ways forward. We found that qualitative methods and interviews specifically are most commonly used in realist evaluations; that theory is often absent behind the methods and sampling techniques used; and that more innovative methods remain underexplored. We conclude the review by proposing four ways forward: (1) developing realist surveys, (2) exploring the relevance of innovative methods, (3) increasing the attention paid to sampling procedures and (4) strengthening the theory-driven nature of method. We believe that these four action points can strengthen the practice of realist evaluation and its outcomes.

Prevalence of atrial fibrillation in a regional Victoria setting, findings from the crossroads studies (2001-2003 and 2016-2018)

OBJECTIVE

To estimate the prevalence of atrial fibrillation (AF) in regional Victoria at two time points (2001-2003 and 2016-2018), and to assess the use of electrocardiogram rhythm strips in a rural, community-based study for AF investigation.

DESIGN

Repeated cross-sectional design involving survey of residents of randomly selected households and a clinic. Predictors of AF were assessed using Firth penalised logistic regression, as appropriate for rare events.

SETTING

Goulburn Valley, Victoria.

PARTICIPANTS

Household residents aged ≥16 years. Non-pregnant participants aged 18+ were eligible for the clinic.

MAIN OUTCOME MEASURES

Atrial fibrillation by 12 lead electrocardiogram (earlier study) or electrocardiogram rhythm strip (AliveCor® device) (recent study).

RESULTS

The age standardised prevalence of AF was similar between the two studies (1.6% in the 2001-2003 study and 1.8% in the 2016-2018 study, 95% confidence interval of difference -0.010, 0.014, p = 0.375). The prevalence in participants aged ≥65 years was 3.4% (1.0% new cases) in the recent study. Predictors of AF in the earlier study were male sex, older age and previous stroke, while in the recent study they were previous stroke and self-reported diabetes. AliveCor® traces were successfully classified by the in-built algorithm (91%) vs physician (100%).

CONCLUSION

The prevalence of AF among community-based participants in regional Victoria was similar to predominantly metropolitan-based studies, and was unchanged over time despite increased rates of risk factors. Electrocardiogram rhythm strip investigation was successfully utilised, and particularly benefited from physician overview.

Increasing screening for atrial fibrillation in general practice: the Atrial Fibrillation Self-Screening, Management And guideline-Recommended Therapy (AF Self-SMART) study

OBJECTIVE

To assess whether atrial fibrillation (AF) self-screening stations in general practice waiting rooms improve AF screening, diagnosis, and stroke risk management.

DESIGN, SETTING

Intervention study (planned duration: twelve weeks) in six New South Wales general practices (two in rural locations, four in greater metropolitan Sydney), undertaken during 28 August 2020 - 5 August 2021.

PARTICIPANTS

People aged 65 years or more who had not previously been diagnosed with AF, and had appointments for face-to-face GP consultations. People with valvular AF were excluded.

INTERVENTION

AF self-screening station and software, integrated with practice electronic medical record programs, that identified and invited participation by eligible patients, and exported single-lead electrocardiograms and automated evaluations to patients' medical records.

MAIN OUTCOME MEASURES

Screening rate; incidence of newly diagnosed AF during intervention and pre-intervention periods; prescribing of guideline-recommended anticoagulant medications.

RESULTS

Across the six participating practices, 2835 of 7849 eligible patients (36.1%) had face-to-face GP appointments during the intervention period, of whom 1127 completed AF self-screening (39.8%; range by practice: 12-74%). AF was diagnosed in 49 screened patients (4.3%), 44 of whom (90%) had CHA₂ DS₂ -VA scores of 2 or more (high stroke risk). The incidence of newly diagnosed AF during the pre-intervention period was 11 cases per 1000 eligible patients; during the intervention period, it was 22 per 1000 eligible patients (screen-detected: 17 per 1000 eligible patients; otherwise detected: 4.6 per 1000 eligible patients). Prescribing of oral anticoagulation therapy for people newly diagnosed with AF and high stroke risk was similar during the pre-intervention (20 of 24, 83%) and intervention periods (46 of 54, 85%).

CONCLUSIONS

AF self-screening in general practice waiting rooms is a feasible approach to increasing AF screening and diagnosis rates by reducing time barriers to screening by GPs. AF self-screening could reduce the number of AF-related strokes.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12620000233921 (prospective).

Regional implementation of atrial fibrillation screening: benefits and pitfalls

Aims

Despite general awareness that screening for atrial fibrillation (AF) could reduce health hazards, large-scale implementation is lagging behind technological developments. As the successful implementation of a screening programme remains challenging, this study aims to identify facilitating and inhibiting factors from healthcare providers' perspectives.

Methods and results

A mixed-methods approach was used to gather data among practice nurses in primary care in the southern region of the Netherlands to evaluate the implementation of an ongoing single-lead electrocardiogram (ECG)-based AF screening programme. Potential facilitating and inhibiting factors were evaluated using online questionnaires (N = 74/75%) and 14 (of 24) semi-structured in-depth interviews (58.3%). All analyses were performed using SPSS 26.0. In total, 16 682 screenings were performed on an eligible population of 64 000, and 100 new AF cases were detected. Facilitating factors included 'receiving clear instructions' (mean ± SD; 4.12 ± 1.05), 'easy use of the ECG-based device' (4.58 ± 0.68), and 'patient satisfaction' (4.22 ± 0.65). Inhibiting factors were 'time availability' (3.20 ± 1.10), 'insufficient feedback to the practice nurse' (2.15 ± 0.89), 'absence of coordination' (54%), and the 'lack of fitting policy' (32%).

Conclusion

Large-scale regional implementation of an AF screening programme in primary care resulted in a low participation of all eligible patients. Based on the perceived barriers by healthcare providers, future AF screening programmes should create preconditions to fit the intervention into daily routines, appointing an overall project lead and a General Practitioner (GP) as a coordinator within every GP practice.

Staff acceptability and patient usability of a self-screening kiosk for atrial fibrillation in general practice waiting rooms

Background

Current Australian and European guidelines recommend opportunistic screening for atrial fibrillation (AF) among patients ≥65 years, but general practitioners (GPs) report time constraints as a major barrier to achieving this. Patient self-screening stations in GP waiting rooms may increase screening rates and case detection of AF, but the acceptability of patient self-screening from the practice staff perspective, and the usability by patients, is unknown.

Objective

To determine staff perspectives on AF self-screening stations and factors impacting acceptability, usability by patients, and sustainability.

Methods

We performed semi-structured interviews with 20 general practice staff and observations of 22 patients while they were undertaking self-screening. Interviews were coded and data analyzed using an iterative thematic analysis approach.

Results

GPs indicated high levels of acceptance of self-screening, and reported little impact on their workflow. Reception staff recognized the importance of screening for AF, but reported significant impacts on their workflow because some patients were unable to perform screening without assistance. Patient observations corroborated these findings and suggested some potential ways to improve usability.

Conclusion

AF self-screening in GP waiting rooms may be a viable method to increase opportunistic screening by GPs, but the impacts on reception workflow need to be mitigated for the method to be upscaled for more widespread screening. Furthermore, more age-appropriate station design may increase patient usability and thereby also reduce impact on reception workflow.

Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation

BACKGROUND

Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS-exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems.

METHODS

A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach.

RESULTS

Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive.

CONCLUSION

This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research.

TRIAL REGISTRATION

PROSPERO CRD42020203716.

Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study

Background

Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower.

Methods

We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0–1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415.

Findings

Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81–0·89), sensitivity of 84·8% (76·2–91·3), and specificity of 69·5% (66·4–72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81–0·89), sensitivity of 82·7% (72·7–90·2), and specificity of 79·9% (77·0–82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88–0·95), sensitivity of 91·9% (78·1–98·3), and specificity of 80·2% (75·5–84·3).

Interpretation

A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment.

Funding

NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.

2021 Asia Pacific Heart Rhythm Society (APHRS) practice guidance on atrial fibrillation screening

In this paper, the Asia Pacific Heart Rhythm Society (APHRS) sought to provide practice guidance on AF screening based on recent evidence, with specific considerations relevant to the Asia-Pacific region. A key recommendation is opportunistic screening for people aged ≥65 years (all countries), with systematic screening to be considered for people aged ≥75 years or who have additional risk factors (all countries).

Lessons Learned from Implementing the SHIELD Intervention: A Peer Education Intervention for People Who Use Drugs

HIV prevention and care peer education interventions have demonstrated effectiveness at changing HIV risk and care behaviors among a variety of at-risk populations in different settings. However, little is known about the implementation of this type of intervention in community-based settings. Further, there is limited information available regarding the facilitators and barriers to implementing peer education interventions in community-based settings. In this study, we explore implementation facilitators, barriers, and strategies to overcome these barriers among 12 organizations that implemented the SHIELD intervention, an evidenced-based peer education intervention for people who use drugs. Guided by the Consolidated Framework for Implementation Research, we identified several facilitators and barriers at the outer, inner individuals, and intervention level of the implementation process. Future evidence-based public health programs should, in addition to addressing effectiveness, be relevant to the needs and lives of clients.

Atrial fibrillation self screening, management and guideline recommended therapy (AF SELF SMART): A protocol for atrial fibrillation self-screening in general practice

BACKGROUND

Opportunistic screening for silent atrial fibrillation (AF) is recommended to reduce stroke, but screening rates are sub-optimal in general practice. We hypothesize that patient self-screening in the waiting room may improve screening and detection of AF.

METHODS AND ANALYSES

This proof-of-concept study tests a purpose-designed AF self-screening station and customised software which seamlessly integrates with general practice electronic medical records and workflow. The self-screening station records a lead-1 ECG. The software automatically (1) identifies eligible patients (aged ≥65 years, no AF diagnosis) from the practice appointment diary; (2) sends eligible patients an automated SMS reminder prior to their appointment; (3) creates individualised QR code to scan at self-screening station; and (4) imports the ECG and result directly into the patients' electronic medical record. Between 5 and 8 general practices in New South Wales, Australia, will participate with an aim of 1500 patients undertaking self-screening. The main outcome measures will be the proportion of eligible patients that undertook self-screening, incidence of newly-diagnosed AF, and patient and staff experience of the self-screening process. De-identified data will be collected using a clinical audit tool, and qualitative interviews will determine patient and staff acceptability.

ETHICS AND DISSEMINATION

Ethics approval was received from the University of Sydney Human Research Ethics Committee in June 2019 (Project no: 2019/382) and the University of Notre Dame Human Research Ethics Committee (Project no: 019145S) in October 2019. Results will be disseminated through various forums, including peer-reviewed publication and conference presentations.Trial registration numberACTRN12620000233921.

Future Health Today: codesign of an electronic chronic disease quality improvement tool for use in general practice using a service design approach

OBJECTIVE

To codesign an electronic chronic disease quality improvement tool for use in general practice.

DESIGN

Service design employing codesign strategies.

SETTING

General practice.

PARTICIPANTS

Seventeen staff (general practitioners, nurses and practice managers) from general practice in metropolitan Melbourne and regional Victoria and five patients from metropolitan Melbourne.

INTERVENTIONS

Codesign sessions with general practice staff, using a service design approach, were conducted to explore key design criteria and functionality of the audit and feedback and clinical decision support tools. Think aloud interviews were conducted in which participants articulated their thoughts of the resulting Future Health Today (FHT) prototype as they used it. One codesign session was held with patients. Using inductive and deductive coding, content and thematic analyses explored the development of a new technological platform and factors influencing implementation of the platform.

RESULTS

Participants identified that the prototype needed to work within their existing workflow to facilitate automated patient recall and track patients with or at-risk of specific conditions. It needed to be simple, provide visual snapshots of information and easy access to relevant guidelines and facilitate quality improvement activities. Successful implementation may be supported by: accuracy of the algorithms in FHT and data held in the practice; the platform supporting planned and spontaneous interactions with patients; the ability to hide tools; links to Medicare Benefits Schedule; and prefilled management plans. Participating patients supported the use of the platform in general practice. They suggested that use of the platform demonstrates a high level of patient care and could increase patient confidence in health practitioners.

CONCLUSION

Study participants worked together to design a platform that is clear, simple, accurate and useful and that sits within any given general practice setting. The resulting FHT platform is currently being piloted in general practices and will continue to be refined based on user feedback.

Does point-of-care testing in general practice for leucocyte and differential count change use of antimicrobial medicines? A pilot study

Diagnostic uncertainty when considering prescription of antimicrobials ('antibiotics') in primary care contributes to the major problem of microbial resistance. We conducted a feasibility evaluation of rapid testing for leucocyte and differential count in two urban general practices, surveying the GPs online and interviewing them. GPs reported that the machines were easy to use, the test results influenced their care and they would adopt the system if costs were off-set. Feasibility, acceptability and perceived benefit justify a randomised trial to test the effect on antibiotic prescribing rates and quality of care, with an economic evaluation to inform the cost-benefit.

Opportunistic screening versus usual care for detection of atrial fibrillation in primary care: cluster randomised controlled trial

OBJECTIVE

To investigate whether opportunistic screening in primary care increases the detection of atrial fibrillation compared with usual care.

DESIGN

Cluster randomised controlled trial.

SETTING

47 intention-to-screen and 49 usual care primary care practices in the Netherlands, not blinded for allocation; the study was carried out from September 2015 to August 2018.

PARTICIPANTS

In each practice, a fixed sample of 200 eligible patients, aged 65 or older, with no known history of atrial fibrillation in the electronic medical record system, were randomly selected. In the intention-to-screen group, 9218 patients eligible for screening were included, 55.0% women, mean age 75.2 years. In the usual care group, 9526 patients were eligible for screening, 54.3% women, mean age 75.0 years.

INTERVENTIONS

Opportunistic screening (that is, screening in patients visiting their general practice) consisted of three index tests: pulse palpation, electronic blood pressure measurement with an atrial fibrillation algorithm, and electrocardiography (ECG) with a handheld single lead electrocardiographic device. The reference standard was 12 lead ECG, performed in patients with at least one positive index test and in a sample of patients (10%) with three negative tests. If 12 lead ECG showed no atrial fibrillation, patients were invited for more screening by continuous monitoring with a Holter electrocardiograph for two weeks.

MAIN OUTCOME MEASURES

Difference in the detection rate of newly diagnosed atrial fibrillation over one year in intention-to-screen versus usual care practices.

RESULTS

Follow-up was complete for 8874 patients in the intention-to-screen practices and for 9102 patients in the usual care practices. 144 (1.62%) new diagnoses of atrial fibrillation in the intention-to-screen group versus 139 (1.53%) in the usual care group were found (adjusted odds ratio 1.06 (95% confidence interval 0.84 to 1.35)). Of 9218 eligible patients in the intention-to-screen group, 4106 (44.5%) participated in the screening protocol. In these patients, 12 lead ECG detected newly diagnosed atrial fibrillation in 26 patients (0.63%). In the 266 patients who continued with Holter monitoring, four more diagnoses of atrial fibrillation were found.

CONCLUSIONS

Opportunistic screening for atrial fibrillation in primary care patients, aged 65 and over, did not increase the detection rate of atrial fibrillation, which implies that opportunistic screening for atrial fibrillation is not useful in this setting.

TRIAL REGISTRATION

Netherlands Trial Register No NL4776 (old NTR4914).

Atrial Fibrillation Screen, Management, and Guideline-Recommended Therapy in the Rural Primary Care Setting: A Cross-Sectional Study and Cost-Effectiveness Analysis of eHealth Tools to Support All Stages of Screening

BACKGROUND

Internationally, most atrial fibrillation (AF) management guidelines recommend opportunistic screening for AF in people ≥65 years of age and oral anticoagulant treatment for those at high stroke risk (CHA₂DS₂‐VA≥2). However, gaps remain in screening and treatment.

METHODS AND RESULTS

General practitioners/nurses at practices in rural Australia (n=8) screened eligible patients (≥65 years of age without AF) using a smartphone ECG during practice visits. eHealth tools included electronic prompts, guideline‐based electronic decision support, and regular data reports. Clinical audit tools extracted de‐identified data. Results were compared with an earlier study in metropolitan practices (n=8) and nonrandomized control practices (n=69). Cost‐effectiveness analysis compared population‐based screening with no screening and included screening, treatment, and hospitalization costs for stroke and serious bleeding events. Patients (n=3103, 34%) were screened (mean age, 75.1±6.8 years; 47% men) and 36 (1.2%) new AF cases were confirmed (mean age, 77.0 years; 64% men; mean CHA₂DS₂‐VA, 3.2). Oral anticoagulant treatment rates for patients with CHA₂DS₂‐VA≥2 were 82% (screen detected) versus 74% (preexisting AF)(P=NS), similar to metropolitan and nonrandomized control practices. The incremental cost‐effectiveness ratio for population‐based screening was AU$16 578 per quality‐adjusted life year gained and AU$84 383 per stroke prevented compared with no screening. National implementation would prevent 147 strokes per year. Increasing the proportion screened to 75% would prevent 177 additional strokes per year.

CONCLUSIONS

An AF screening program in rural practices, supported by eHealth tools, screened 34% of eligible patients and was cost‐effective. Oral anticoagulant treatment rates were relatively high at baseline, trending upward during the study. Increasing the proportion screened would prevent many more strokes with minimal incremental cost‐effectiveness ratio change. eHealth tools, including data reports, may be a valuable addition to future programs.

REGISTRATION

URL: https://www.anzctr.org.au. Unique identifier: ACTRN12618000004268.

Screening for atrial fibrillation and other arrhythmias in primary care

Background

Atrial fibrillation (AF) and other arrhythmias are prevalent and often encountered by general practitioners (GPs). In response to the growing prevalence and to assist practitioners in the diagnosis and management of AF, the Cardiac Society of Australia & New Zealand and Heart Foundation of Australia published the first Australian AF Guidelines in 2018. We aimed to examine (a) the proportion of GPs who performed any form of AF screening and identify the methods they applied, (b) GPs’ awareness of the AF Guidelines and approaches to arrhythmia screening, (c) the roles of conventional 12-lead ECG and mobile health devices, and (d) GPs’ confidence in ECG interpretation and need for training.

Methods

A cross-sectional online survey titled “GPs Screen their patients for Atrial Fibrillation and othEr aRrhythmia (GPSAFER)” was conducted from October 2018 to March 2019. The participants were recruited via various GP networks across Australia. Ethics approval was granted by The University of Sydney.

Results

A total of 463 surveys were completed. Many GPs (394/463, 85.1%, 95% CI 81.5–88.2%) performed some forms of AF screening and applied at least one AF screening method, most frequently pulse palpation (389/463, 84.0%). Some (299/463, 64.6%) GPs considered assessing their patients for other arrhythmias (237/299, 79.3% for complete heart block and 236/299, 78.9% for long-QT). Most GPs (424/463, 91.6%) were not using mobile ECG devices in their practice but some (147/463, 31.7%) were contemplating it. One third (175/463, 37.8%) of GPs were aware of the Australian AF Guidelines; those aware were more likely to perform AF screening (98.9% vs 76.7%, p <  0.001). Factors significantly and positively associated with AF screening were “awareness of the AF Guidelines” (p <  0.001), “number of years working in general practice” (p <  0.001), and “confidence in ECG interpretation of AF” (p = 0.003). Most GPs reported that they were very or extremely confident in interpreting AF (381/463, 82.3%) and complete heart block (266/463, 57.5%). Many GPs (349/463, 75.4%) would like to receive online ECG interpretation training.

Conclusions

Assessment of arrhythmias is common in general practice and GPs are open to further training in ECG interpretation and using mobile ECG devices to aid their clinical practice. Increasing awareness of AF Guidelines and improving confidence in ECG interpretation may increase AF screening.

Health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis

BACKGROUND

Mobile health (mHealth), refers to healthcare practices supported by mobile devices, such as mobile phones and tablets. Within primary care, health workers often use mobile devices to register clients, track their health, and make decisions about care, as well as to communicate with clients and other health workers. An understanding of how health workers relate to, and experience mHealth, can help in its implementation.

OBJECTIVES

To synthesise qualitative research evidence on health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services, and to develop hypotheses about why some technologies are more effective than others.

SEARCH METHODS

We searched MEDLINE, Embase, CINAHL, Science Citation Index and Social Sciences Citation Index in January 2018. We searched Global Health in December 2015. We screened the reference lists of included studies and key references and searched seven sources for grey literature (16 February to 5 March 2018). We re-ran the search strategies in February 2020. We screened these records and any studies that we identified as potentially relevant are awaiting classification.

SELECTION CRITERIA

We included studies that used qualitative data collection and analysis methods. We included studies of mHealth programmes that were part of primary healthcare services. These services could be implemented in public or private primary healthcare facilities, community and workplace, or the homes of clients. We included all categories of health workers, as well as those persons who supported the delivery and management of the mHealth programmes. We excluded participants identified as technical staff who developed and maintained the mHealth technology, without otherwise being involved in the programme delivery. We included studies conducted in any country.

DATA COLLECTION AND ANALYSIS

We assessed abstracts, titles and full-text papers according to the inclusion criteria. We found 53 studies that met the inclusion criteria and sampled 43 of these for our analysis. For the 43 sampled studies, we extracted information, such as country, health worker category, and the mHealth technology. We used a thematic analysis process. We used GRADE-CERQual to assess our confidence in the findings.

MAIN RESULTS

Most of the 43 included sample studies were from low- or middle-income countries. In many of the studies, the mobile devices had decision support software loaded onto them, which showed the steps the health workers had to follow when they provided health care. Other uses included in-person and/or text message communication, and recording clients' health information. Almost half of the studies looked at health workers' use of mobile devices for mother, child, and newborn health. We have moderate or high confidence in the following findings. mHealth changed how health workers worked with each other: health workers appreciated being more connected to colleagues, and thought that this improved co-ordination and quality of care. However, some described problems when senior colleagues did not respond or responded in anger. Some preferred face-to-face connection with colleagues. Some believed that mHealth improved their reporting, while others compared it to "big brother watching". mHealth changed how health workers delivered care: health workers appreciated how mHealth let them take on new tasks, work flexibly, and reach clients in difficult-to-reach areas. They appreciated mHealth when it improved feedback, speed and workflow, but not when it was slow or time consuming. Some health workers found decision support software useful; others thought it threatened their clinical skills. Most health workers saw mHealth as better than paper, but some preferred paper. Some health workers saw mHealth as creating more work. mHealth led to new forms of engagement and relationships with clients and communities: health workers felt that communicating with clients by mobile phone improved care and their relationships with clients, but felt that some clients needed face-to-face contact. Health workers were aware of the importance of protecting confidential client information when using mobile devices. Some health workers did not mind being contacted by clients outside working hours, while others wanted boundaries. Health workers described how some community members trusted health workers that used mHealth while others were sceptical. Health workers pointed to problems when clients needed to own their own phones. Health workers' use and perceptions of mHealth could be influenced by factors tied to costs, the health worker, the technology, the health system and society, poor network access, and poor access to electricity: some health workers did not mind covering extra costs. Others complained that phone credit was not delivered on time. Health workers who were accustomed to using mobile phones were sometimes more positive towards mHealth. Others with less experience, were sometimes embarrassed about making mistakes in front of clients or worried about job security. Health workers wanted training, technical support, user-friendly devices, and systems that were integrated into existing electronic health systems. The main challenges health workers experienced were poor network connections, access to electricity, and the cost of recharging phones. Other problems included damaged phones. Factors outside the health system also influenced how health workers experienced mHealth, including language, gender, and poverty issues. Health workers felt that their commitment to clients helped them cope with these challenges.

AUTHORS' CONCLUSIONS

Our findings propose a nuanced view about mHealth programmes. The complexities of healthcare delivery and human interactions defy simplistic conclusions on how health workers will perceive and experience their use of mHealth. Perceptions reflect the interplay between the technology, contexts, and human attributes. Detailed descriptions of the programme, implementation processes and contexts, alongside effectiveness studies, will help to unravel this interplay to formulate hypotheses regarding the effectiveness of mHealth.