Benefits, challenges, and contributors to success for national eHealth systems implementation: a scoping review

Environmental and contextual factors influencing e-health use among older adults: A rapid review

INTRODUCTION

E-health services offer potential benefits for healthcare delivery, especially for older adults, yet their adoption remains suboptimal due to various barriers. Understanding the environmental and contextual factors influencing e-health use among this demographic is crucial for enhancing their health outcomes.

AIM

This rapid review aims to explore the various environmental and contextual factors affecting the use of e-health among older adults, focusing on identifying strategies to enhance acceptance and usefulness.

METHODS

Adhering to PRISMA guidelines, a mixed-methods rapid review was conducted following the Joanna Briggs Institute (JBI) guidelines. Databases including MEDLINE, EMBASE, Web of Science, Scopus, and Google Scholar were searched. Quantitative data were qualitized for integration with qualitative data, and a thematic analysis was performed on the assembled data.

FINDINGS

A total of 11 studies met the inclusion criteria, encompassing five cross-sectional surveys, four qualitative studies, one longitudinal study, and one Discrete Choice Experiment. The thematic analysis revealed five key themes: social influence and norms, environmental and infrastructure factors, economic factors and cost considerations, family and caregiver support, and organizational support and culture.

CONCLUSION

The review highlights the need for e-health solutions that enhance social support, are adaptable to diverse living environments, address economic barriers with cost-effective solutions, and are culturally sensitive to effectively engage older adults.

The potential and paradoxes of eHealth research for digitally marginalised groups: A qualitative meta-review

Whilst the transformation towards digital healthcare is accelerating, there is still a substantial risk of excluding people with a distance to the online world. Groups like people with a low socioeconomic position, people with a migrant background or the elderly, who are already most at risk of experiencing health inequalities, are simultaneously experiencing increased digital exclusion. Researchers play a role in determining how eHealth access is framed and can thus impact how the barriers to its use are addressed. This qualitative meta-review critically evaluates the way researchers (as authors) discuss eHealth use in digitally marginalised groups. Specifically, it seeks to understand how eHealth is framed to address existing health systems problems; how the barriers to eHealth use are presented and which solutions are provided in response; and who authors suggest should be responsible for making eHealth work. The results of this review found four paradoxes in how current literature views eHealth use. Firstly, that health systems problems are complex and nuanced, yet eHealth is seen as a simple answer. Secondly, that there are many political, social and health systems-based solutions suggested to address eHealth use, however most of the identified barriers are individually framed. This focus on personal deficits results in misallocating responsibility for making these systemic improvements. Thirdly, although eHealth is meant to simplify the tasks of patients and healthcare workers, these are the groups most often burdened with the responsibility of ensuring its success. Lastly, despite tailoring eHealth to the user being the most suggested solution, researchers generally speak about groups as a homogenous entity - thus rendering tailoring difficult. Ultimately, this review finds that a shift to focus research on addressing systemic issues on a systems level is necessary to prevent further exacerbating existing health inequalities.

Needs and Demands for e-Health Symptom Management Interventions in Patients with Post-COVID-19 Condition: A User-Centered Design Approach

Introduction: Post-COVID-19 is an increasing chronic disease for which potential treatment options require further development and examination. A well-established approach to symptom management in post-COVID-19 patients could be e-Health interventions. To enhance the implementation and utilization of e-Health interventions, the needs and demands of patients should be taken into consideration. The aim of this study was to investigate needs and demands of post-COVID-19 patients concerning e-Health symptom management interventions. Methods: A total of 556 patients participated in this cross-sectional online survey study. Recruitment was performed from January 19 to May 24, 2022. Data related to the needs and demands for e-Health interventions were analyzed, along with medical and sociodemographic information. Results: The majority of the patients preferred interventions accessible on smartphones (95.3%). The favored content formats were applications (82.7%), interactive training (69.3%), or audio and video materials (61.1%). Furthermore, the preferred session length was about 10-20 min. The most desired topics included "quality of life," "information about how intensively I may exert myself or do sports," "adjustment to new life situation," and "handling physical changes." Conclusions: This study provides a detailed framework for the content and design of e-Health interventions to support patients managing their post-COVID-19 symptoms. The findings could significantly influence the further development of tailored e-Health interventions to address this pressing global health concern.

EHealth for Sexual Assault: A Systematic Scoping Review

Providing efficient psychosocial support for survivors of sexual assault is of critical societal importance. Around the globe, technology-based solutions (eHealth) are increasingly being used to accomplish this task, especially following COVID-19. Despite increased importance and reliance on eHealth for sexual assault, minimal efforts have been made to systematically synthesize research in this area. The present study therefore sought to synthesize what is known about eHealth targeting sexual assault survivors' psychosocial needs using a systematic scoping review methodology. To this end, five databases (CINAHL, Embase, PsycINFO, MEDLINE, and Scopus) were systematically searched for studies published from 2010 onwards using terms such as "sexual assault", "eHealth", "digital health", "telehealth", and variations thereof. Of the 6,491 records screened for eligibility, 85 studies were included in the review. We included empirical studies from all countries pertaining to eHealth for sexual assault for survivors 13 years or older. Many innovative eHealth applications for sexual assault exist today, and the included studies suggested that survivors generally experience eHealth positively and seem to benefit from it. Nevertheless, much more clinical and empirical work is needed to ensure accessible and effective solutions for all.

Does Legislation Impede Data Sharing in Australia Across Institutions and Jurisdictions? A Scoping Review

In Australia, regulations governing data, including formal legislation and policies promulgated by private and public agencies, are often seen as a barrier to data sharing. This sharing can include between institutions, as well as across jurisdictional borders in a federated jurisdiction such as Australia. In some cases, these regulations place a barrier to sharing data across borders or between institutions without a prerequisite requirement. In other cases, these regulations may be perceived as a justification not to share data. The objective of this review was to analyse published literature from Australia to see what regulations were used to justify not sharing data, along with any other factors that might discourage data sharing. We searched PubMed, Scopus and Web of Science for empirical and policy articles discussing data sharing in Australia. We then filtered these results via abstract and conducted a full text assessment to include 33 articles for analysis. Although there are a few areas of notable regulatory divergence with respect to legislation governing health data, most regulations in Australia are relatively consistent. Further, the absence of uniform ethics approval between sites in different states was frequently cited as a barrier to data sharing.

Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making

BACKGROUND

Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them.

FINDINGS

The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action.

CONCLUSION

The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.

GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

BACKGROUND

The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs.

OBJECTIVE

We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform.

METHODS

The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities.

RESULTS

Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence-based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors.

CONCLUSIONS

This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space.

Facilitators and Barriers to the Sustainability of eHealth Solutions in Low- and Middle-Income Countries: Descriptive Exploratory Study

BACKGROUND

Despite the widely anticipated benefits of eHealth technologies in enhancing health care service delivery, the sustainable usage of eHealth in transitional countries remains low. There is limited evidence supporting the low sustainable adoption of eHealth in low- and middle-income countries.

OBJECTIVE

The aim of this study was to explore the facilitators and barriers to the sustainable use of eHealth solutions in low- and middle-income nations.

METHODS

A qualitative descriptive exploratory study was conducted in 4 African nations from September to December 2021. A semistructured interview guide was used to collect the data. Data were audio-recorded and transcribed from the local to the English language verbatim, and the audio data were transcribed. On the basis of the information gathered, we assigned codes to the data, searched for conceptual patterns, and created emerging themes. Data were analyzed thematically using OpenCode software.

RESULTS

A total of 49 key informant interviews (10 from Tunisia, 15 from Ethiopia, 13 from Ghana, and 11 from Malawi) were conducted. About 40.8% (20/49) of the study participants were between the ages of 26 and 35 years; 73.5% (36/49) of them were male participants; and 71.4% (35/49) of them had a master's degree or higher in their educational background. Additionally, the study participants' work experience ranged from 2 to 35 years. Based on the data we gathered, we identified 5 themes: organizational, technology and technological infrastructure, human factors, economy or funding, and policy and regulations.

CONCLUSIONS

This study explores potential facilitators and barriers to long-term eHealth solution implementation. Addressing barriers early in the implementation process can aid in the development of eHealth solutions that will better fulfill the demands of end users. Therefore, focusing on potential challenges would enhance the sustainability of eHealth solutions in low- and middle-income countries.

The Implementation Playbook: study protocol for the development and feasibility evaluation of a digital tool for effective implementation of evidence-based innovations

BACKGROUND

Evidence-based innovations can improve health outcomes, but only if successfully implemented. Implementation can be complex, highly susceptible to failure, costly and resource intensive. Internationally, there is an urgent need to improve the implementation of effective innovations. Successful implementation is best guided by implementation science, but organizations lack implementation know-how and have difficulty applying it. Implementation support is typically shared in static, non-interactive, overly academic guides and is rarely evaluated. In-person implementation facilitation is often soft-funded, costly, and scarce. This study seeks to improve effective implementation by (1) developing a first-in-kind digital tool to guide pragmatic, empirically based and self-directed implementation planning in real-time; and (2) exploring the tool's feasibility in six health organizations implementing different innovations.

METHODS

Ideation emerged from a paper-based resource, The Implementation Game©, and a revision called The Implementation Roadmap©; both integrate core implementation components from evidence, models and frameworks to guide structured, explicit, and pragmatic planning. Prior funding also generated user personas and high-level product requirements. This study will design, develop, and evaluate the feasibility of a digital tool called The Implementation Playbook©. In Phase 1, user-centred design and usability testing will inform tool content, visual interface, and functions to produce a minimum viable product. Phase 2 will explore the Playbook's feasibility in six purposefully selected health organizations sampled for maximum variation. Organizations will use the Playbook for up to 24 months to implement an innovation of their choosing. Mixed methods will gather: (i) field notes from implementation team check-in meetings; (ii) interviews with implementation teams about their experience using the tool; (iii) user free-form content entered into the tool as teams work through implementation planning; (iv) Organizational Readiness for Implementing Change questionnaire; (v) System Usability Scale; and (vi) tool metrics on how users progressed through activities and the time required to do so.

DISCUSSION

Effective implementation of evidence-based innovations is essential for optimal health. We seek to develop a prototype digital tool and demonstrate its feasibility and usefulness across organizations implementing different innovations. This technology could fill a significant need globally, be highly scalable, and potentially valid for diverse organizations implementing various innovations.

Applications of Machine Learning in Palliative Care: A Systematic Review

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.

Effectiveness of eHealth Self-management Interventions in Patients With Heart Failure: Systematic Review and Meta-analysis

Background

Heart failure (HF) is a common clinical syndrome associated with substantial morbidity, a heavy economic burden, and high risk of readmission. eHealth self-management interventions may be an effective way to improve HF clinical outcomes.

Objective

The aim of this study was to systematically review the evidence for the effectiveness of eHealth self-management in patients with HF.

Methods

This study included only randomized controlled trials (RCTs) that compared the effects of eHealth interventions with usual care in adult patients with HF using searches of the EMBASE, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and CINAHL databases from January 1, 2011, to July 12, 2022. The Cochrane Risk of Bias tool (RoB 2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence for each outcome of interest. Meta-analyses were performed using Review Manager (RevMan v.5.4) and R (v.4.1.0 x64) software.

Results

In total, 24 RCTs with 9634 participants met the inclusion criteria. Compared with the usual-care group, eHealth self-management interventions could significantly reduce all-cause mortality (odds ratio [OR] 0.83, 95% CI 0.71-0.98, P=.03; GRADE: low quality) and cardiovascular mortality (OR 0.74, 95% CI 0.59-0.92, P=.008; GRADE: moderate quality), as well as all-cause readmissions (OR 0.82, 95% CI 0.73-0.93, P=.002; GRADE: low quality) and HF-related readmissions (OR 0.77, 95% CI 0.66-0.90, P<.001; GRADE: moderate quality). The meta-analyses also showed that eHealth interventions could increase patients’ knowledge of HF and improve their quality of life, but there were no statistically significant effects. However, eHealth interventions could significantly increase medication adherence (OR 1.82, 95% CI 1.42-2.34, P<.001; GRADE: low quality) and improve self-care behaviors (standardized mean difference –1.34, 95% CI –2.46 to –0.22, P=.02; GRADE: very low quality). A subgroup analysis of primary outcomes regarding the enrolled population setting found that eHealth interventions were more effective in patients with HF after discharge compared with those in the ambulatory clinic setting.

Conclusions

eHealth self-management interventions could benefit the health of patients with HF in various ways. However, the clinical effects of eHealth interventions in patients with HF are affected by multiple aspects, and more high-quality studies are needed to demonstrate effectiveness.

Consumers’ Willingness to Pay for eHealth and Its Influencing Factors: Systematic Review and Meta-analysis

Background

Despite the great potential of eHealth, substantial costs are involved in its implementation, and it is essential to know whether these costs can be justified by its benefits. Such needs have led to an increased interest in measuring the benefits of eHealth, especially using the willingness to pay (WTP) metric as an accurate proxy for consumers’ perceived benefits of eHealth. This offered us an opportunity to systematically review and synthesize evidence from the literature to better understand WTP for eHealth and its influencing factors.

Objective

This study aimed to provide a systematic review of WTP for eHealth and its influencing factors.

Methods

This study was performed and reported as per the Cochrane Collaboration and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, CINAHL Plus, Cochrane Library, EconLit, and PsycINFO databases were searched from their inception to April 19, 2022. We conducted random-effects meta-analyses to calculate WTP values for eHealth (at 2021 US dollar rates) and meta-regression analyses to examine the factors affecting WTP.

Results

A total of 30 articles representing 35 studies were included in the review. We found that WTP for eHealth varied across studies; when expressed as a 1-time payment, it ranged from US $0.88 to US $191.84, and when expressed as a monthly payment, it ranged from US $5.25 to US $45.64. Meta-regression analyses showed that WTP for eHealth was negatively associated with the percentages of women (β=−.76; P<.001) and positively associated with the percentages of college-educated respondents (β=.63; P<.001) and a country’s gross domestic product per capita (multiples of US $1000; β=.03; P<.001). Compared with eHealth provided through websites, people reported a lower WTP for eHealth provided through asynchronous communication (β=−1.43; P<.001) and a higher WTP for eHealth provided through medical devices (β=.66; P<.001), health apps (β=.25; P=.01), and synchronous communication (β=.58; P<.001). As for the methods used to measure WTP, single-bounded dichotomous choice (β=2.13; P<.001), double-bounded dichotomous choice (β=2.20; P<.001), and payment scale (β=1.11; P<.001) were shown to obtain higher WTP values than the open-ended format. Compared with ex ante evaluations, ex post evaluations were shown to obtain lower WTP values (β=−.37; P<.001).

Conclusions

WTP for eHealth varied significantly depending on the study population, modality used to provide eHealth, and methods used to measure it. WTP for eHealth was lower among certain population segments, suggesting that these segments may be at a disadvantage in terms of accessing and benefiting from eHealth. We also identified the modalities of eHealth that were highly valued by consumers and offered suggestions for the design of eHealth interventions. In addition, we found that different methods of measuring WTP led to significantly different WTP estimates, highlighting the need to undertake further methodological explorations of approaches to elicit WTP values.

Improvement of the Japanese healthcare data system for the effective management of patients with COVID-19: A national survey

OBJECTIVE

The burden of data entry in public platforms used for reporting patients with novel coronavirus disease 2019 (COVID-19) is a challenge in the healthcare setting. The key to mitigating the burden of data entry is system integration and elimination of double data entry. In addition, the linkage between public platforms and electronic medical records (EMRs) involves external networks, which are an important target for security management. The purpose of this study was to elucidate the status and challenges of infrastructure for continuous data reporting from hospitals in Japan.

MATERIALS AND METHODS

An online survey of Japanese care delivery institutions was conducted from January 25 to February 22, 2021, to obtain data on the admission of patients with COVID-19, use of information infrastructures, and status of network connections with external organizations. The survey request was distributed to each care delivery institution by Japanese health authorities.

RESULTS

Of the care delivery institutions that responded to the survey, 53.9% treated patients with COVID-19. Of these institutions, 73.3% used EMRs. 57.8% of the EMRs were connected to an external network. The purpose of connecting to the external network was to contribute to regional health information-sharing with other hospitals (22.0%), report online medical insurance claims (27.5%), and conduct intrahospital system maintenance (61.5%). A frequent concern about connecting an EMR to an external network was data leakage.

DISCUSSION

In cases where the frequency of reporting patients with COVID-19 is high, health authorities should provide information regarding anti-data-leakage measures and coordinate frameworks for efficient, sustainable data collection.

CONCLUSIONS

We obtained information on existing infrastructures for patient data sharing among care delivery institutions and public health authorities. Our findings may be referenced by the government to make informed decisions about investments.