Information Quality Frameworks for Digital Health Technologies: Systematic Review

Improve the satisfaction of medical staff on the use of home nursing mobile APP by using a hybrid multi-standard decision model

OBJECTIVE

To identify critical satisfaction gaps in a home nursing mobile application (APP) using a systematic decision-making model.

METHODS

Initially, the decision-making trial and evaluation laboratory method was used to analyze the relationship structure and corresponding weights among the indicators. The Importance-Performance Analysis (IPA) method was used to identify the categories of all indicators and their corresponding strategic directions. Twenty-six home nursing specialists currently providing home nursing services were recruited for this study.

RESULTS

The IPA results revealed that "Assurance," "Reliability," and "Personal security protection" are critical satisfaction gaps. From the influence network and weight results, "information quality" and "system quality" were the critical quality factors in the home nursing mobile APP. The influence of the network relationship structure and weight demonstrated a 98.12% significance level, indicating good stability.

CONCLUSION

Continuous improvement in information and system quality is recommended to optimize the overall quality of the home nursing mobile APP. Additionally, user demands should be considered, and personal safety guarantee functions should be developed and integrated into the system to ensure the safety of home nursing workers.

Informing nursing policy: An exploration of digital health research by nurses in England

AIMS

Digital health technologies are designed, implemented, and evaluated to support clinical practice, enable patients to self-manage illness, and further public and global health. Nursing and health policies often emphasise the importance of evidence-based digital health services to deliver better care. However, the contribution nurses make to digital health research in many countries is unknown. Hence, this study aims to examine digital health research conducted by nurses in England.

DESIGN

A bibliometric analysis.

METHODS

The CINAHL, MEDLINE, and Scopus databases were searched between 2000 and 2022, and supplemented with a hand search of nurses' research profiles. Results were screened by title, abstract, and full text against eligibility criteria. Data were extracted and bibliometric analysis used to summarise the findings.

RESULTS

Mental health nurses produced the most digital health research in England, followed by nurses working in community care, with several disciplines underrepresented or missing. Web/online health services or information was the most researched technology, followed by mobile health and telehealth. Nurses based in the south-east and north-west of England produced the most digital health research, with other regions less well represented.

CONCLUSION

Nurse leaders should support nurses to conduct more digital health research by providing dedicated time, funding, and professional development opportunities, particularly in under researched clinical areas, technologies, and geographic regions to further evidence-based practice and patient care. More digital nursing data is needed to support nurse led research in areas like artificial intelligence and data science. The findings supported the national Philips Ives Review by identifying areas of digital nursing research that need more investment in England.

A perspective on digital health platform design and its implementation at national level

Accessible and affordable health services and products including medicines, vaccines, and public health are an important health agenda of all countries. It is well understood that without digital health technologies, countries will face difficulties in tackling the needs and demands of their population. Global agencies including the World Health Organization (WHO), United Nations (UN), International Telecommunication Union (ITU), etc. have been instrumental in providing various tools, and guidance through digital health strategies in improving health and digital health maturity of the countries. The Digital Health Platform Handbook (DHPH) is a toolkit published by WHO and ITU to help countries create and implement a digital health platform (DHP) to serve as the underlying infrastructure for an interoperable and integrated national digital health system. We apply the foundational principles of DHPH and provide a perspective of DHP components in a layered, enterprise architecture of a digital health infrastructure. India has rolled out the blueprint of its National Digital Health Mission (NDHM) to address the emerging needs for digitization of healthcare in the country. In this paper, we also illustrate the design and implementation of WHO-ITU DHP components at the national level by exploring India's digital health mission implementation utilizing various digital public goods to build a digital health ecosystem in the country.

Developing public health surveillance dashboards: a scoping review on the design principles

BACKGROUND

Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards.

METHODOLOGY

This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted.

RESULTS

Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure.

CONCLUSION

Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

A sociotechnical framework to assess patient-facing eHealth tools: results of a modified Delphi process

Among the thousands of eHealth tools available, the vast majority do not get past pilot phases because they cannot prove value, and only a few have been systematically assessed. Although multiple eHealth assessment frameworks have been developed, these efforts face multiple challenges. This study aimed to address some of these challenges by validating and refining an initial list of 55 assessment criteria based on previous frameworks through a two-round modified Delphi process with in-between rounds of interviews. The expert panel (n = 57) included participants from 18 countries and 9 concerned parties. A consensus was reached on 46 criteria that were classified into foundational and contextual criteria. The 36 foundational criteria focus on evaluating the eHealth tool itself and were grouped into nine clusters: technical aspects, clinical utility and safety, usability and human centricity, functionality, content, data management, endorsement, maintenance, and developer. The 10 contextual criteria focus on evaluating the factors that vary depending on the context the tool is being evaluated for and were grouped into seven clusters: data-protection compliance, safety regulatory compliance, interoperability and data integration, cultural requirements, affordability, cost-benefit, and implementability. The classification of criteria into foundational and contextual helps us assess not only the quality of an isolated tool, but also its potential fit in a specific setting. Criteria subscales may be particularly relevant when determining the strengths and weaknesses of the tool being evaluated. This granularity enables different concerned parties to make informed decisions about which tools to consider according to their specific needs and priorities.

Precision Medicine: Disease Subtyping and Tailored Treatment

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

The Association between Electronic Health Literacy and Oral Health Outcomes among Dental Patients in Saudi Arabia: A Cross-Sectional Study

OBJECTIVE

This cross-sectional study aimed to investigate the association between electronic health (eHealth) literacy and oral health outcomes, including the number of teeth and brushing frequency.

METHODS

A total of 478 participants were included in the study and assessed for their eHealth literacy levels. Demographic variables, including age, gender, income, and education, were collected. The participants' number of teeth and brushing frequency were also recorded. Multiple regression analyses were performed to examine the relationship between eHealth literacy and oral health outcomes, adjusting for sociodemographic variables.

RESULTS

The study sample consisted of both males (66.5%) and females (33.5%), with a mean age of 31.95 years. Among the participants, 16.95% were classified as having inadequate eHealth literacy, 24.06% had problematic eHealth literacy, and the majority (59.00%) demonstrated sufficient eHealth literacy. There was a significant association between eHealth literacy and oral health outcomes. Individuals with problematic eHealth literacy had a higher likelihood of having a greater number of teeth (RR = 1.12, 95% CI: 1.05-1.20, p < 0.001) compared to those with inadequate eHealth literacy. Similarly, individuals with sufficient eHealth literacy showed a higher likelihood of having more teeth (RR = 1.14, 95% CI: 1.07-1.21, p < 0.001) compared to the inadequate eHealth literacy group controlling for age, gender, income, and education. Individuals with problematic eHealth literacy exhibited a tendency towards lower odds of irregular brushing (OR = 0.39, 95% CI: 0.15-1.02, p = 0.054), although this result was marginally significant. In contrast, individuals with sufficient eHealth literacy had significantly lower odds of irregular brushing frequency (OR = 0.24, 95% CI: 0.10-0.62, p = 0.003) compared to the inadequate eHealth literacy group.

CONCLUSION

The findings suggest a positive association between eHealth literacy and oral health outcomes. Improving eHealth literacy may have implications for promoting better oral health behaviors and outcomes.

Do psychological distress and digital sports influence the willingness to take the vaccine and precautionary saving? Empirical evidence from Shanghai

Aim

The COVID-19 epidemic has caused risk and uncertainty. This study answers whether and how psychological distress and digital sports influence willingness to take the vaccine and precautionary savings.

Subject and methods

We conducted a cross-sectional study with an online survey sample of 1016 Shanghai residents who live and work there and are aged between 16-60. All of them experienced the COVID-19 lockdown in Shanghai. We used logistic regressions to examine the relationships between the variables of interest.

Results

Three findings were demonstrated. First, psychologically distressed individuals are less inclined to take the vaccine. Second, those engaged in fitness activities via digital media platforms are more willing to get vaccinated. Third, psychologically distressed individuals and digital video-based physical exercisers are more likely to precautionary save.

Conclusions

This study contributes to the literature by documenting how people changed their life from the perspective of finance and health during the lockdown and providing practical implications.

Assessing the Quality and Impact of eHealth Tools: Systematic Literature Review and Narrative Synthesis

BACKGROUND

Technological advancements have opened the path for many technology providers to easily develop and introduce eHealth tools to the public. The use of these tools is increasingly recognized as a critical quality driver in health care; however, choosing a quality tool from the myriad of tools available for a specific health need does not come without challenges.

OBJECTIVE

This review aimed to systematically investigate the literature to understand the different approaches and criteria used to assess the quality and impact of eHealth tools by considering sociotechnical factors (from technical, social, and organizational perspectives).

METHODS

A structured search was completed following the participants, intervention, comparators, and outcomes framework. We searched the PubMed, Cochrane, Web of Science, Scopus, and ProQuest databases for studies published between January 2012 and January 2022 in English, which yielded 675 results, of which 40 (5.9%) studies met the inclusion criteria. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions were followed to ensure a systematic process. Extracted data were analyzed using NVivo (QSR International), with a thematic analysis and narrative synthesis of emergent themes.

RESULTS

Similar measures from the different papers, frameworks, and initiatives were aggregated into 36 unique criteria grouped into 13 clusters. Using the sociotechnical approach, we classified the relevant criteria into technical, social, and organizational assessment criteria. Technical assessment criteria were grouped into 5 clusters: technical aspects, functionality, content, data management, and design. Social assessment criteria were grouped into 4 clusters: human centricity, health outcomes, visible popularity metrics, and social aspects. Organizational assessment criteria were grouped into 4 clusters: sustainability and scalability, health care organization, health care context, and developer.

CONCLUSIONS

This review builds on the growing body of research that investigates the criteria used to assess the quality and impact of eHealth tools and highlights the complexity and challenges facing these initiatives. It demonstrates that there is no single framework that is used uniformly to assess the quality and impact of eHealth tools. It also highlights the need for a more comprehensive approach that balances the social, organizational, and technical assessment criteria in a way that reflects the complexity and interdependence of the health care ecosystem and is aligned with the factors affecting users' adoption to ensure uptake and adherence in the long term.

Attitudes of Physicians and Individuals Toward Digital Mental Health Tools: Protocol for a Web-Based Survey Research Project

BACKGROUND

Digital transformation is impacting health care delivery. Great market dynamism is bringing opportunities and concerns alike into public discussion. Digital health apps are a vibrant segment where regulation is emerging, with Germany paving the way with its DiGA (Digitale Gesundheitsanwendungen, in German, meaning digital health apps) program. Simultaneously, mental ill-health constitutes a global health concern, and prevalence is expected to worsen due to the COVID-19 pandemic and its containment measures. Portugal and its National Health System may be a useful testbed for digital health interventions.

OBJECTIVE

The paper outlines the protocol for a research project on the attitudes of physicians and potential users toward digital mental health apps to improve access to care, patient outcomes, and reduce the burden of disease of mental ill-health.

METHODS

Web surveys will be conducted to acquire data from the main stakeholders (physicians and the academic community). Data analysis will replicate the statistical analysis performed in the studies from Dahlhausen and Borghouts to derive conclusions regarding the relative acceptance and likelihood of successful implementation of digital mental health apps in Portugal.

RESULTS

The findings of the proposed studies will elicit important information on how physicians and individuals perceive digital mental health app interventions to improve access to care, patient outcomes, and reduce the burden of disease of mental ill-health. Data collection ran between September 26 and November 6, 2022, for the first study and September 20 and October 20, 2022, for the second study. We obtained 160 responses to the first study's survey and 539 answers to the second study's survey. Data analysis is concluded, and both studies' results are expected to be published in 2023.

CONCLUSIONS

The results of the studies projected in this research protocol will have implications for researchers and academia, industry, and policy makers concerning the adoption and implementation of digital health mental apps and associated interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/41040.

Recent advancements in digital health management using multi-modal signal monitoring

Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.

Assessment of Clinical Information Quality in Digital Health Technologies: International eDelphi Study

BACKGROUND

Digital health technologies (DHTs), such as electronic health records and prescribing systems, are transforming health care delivery around the world. The quality of information in DHTs is key to the quality and safety of care. We developed a novel clinical information quality (CLIQ) framework to assess the quality of clinical information in DHTs.

OBJECTIVE

This study explored clinicians' perspectives on the relevance, definition, and assessment of information quality dimensions in the CLIQ framework.

METHODS

We used a systematic and iterative eDelphi approach to engage clinicians who had information governance roles or personal interest in information governance; the clinicians were recruited through purposive and snowball sampling techniques. Data were collected using semistructured online questionnaires until consensus was reached on the information quality dimensions in the CLIQ framework. Responses on the relevance of the dimensions were summarized to inform decisions on retention of the dimensions according to prespecified rules. Thematic analysis of the free-text responses was used to revise definitions and the assessment of dimensions.

RESULTS

Thirty-five clinicians from 10 countries participated in the study, which was concluded after the second round. Consensus was reached on all dimensions and categories in the CLIQ framework: informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability). A new dimension, searchability, was introduced in the availability category to account for the ease of finding needed information in the DHTs. Certain dimensions were renamed, and some definitions were rephrased to improve clarity.

CONCLUSIONS

The CLIQ framework reached a high expert consensus and clarity of language relating to the information quality dimensions. The framework can be used by health care managers and institutions as a pragmatic tool for identifying and forestalling information quality problems that could compromise patient safety and quality of care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-057430.

A smart healthcare-based system for classification of dementia using deep learning

Objectives

This study aims to develop a deep learning-based classification model for early detection of dementia using a wearable device that can measure the electrical conductivity of the skin, temperature, and movement as factors related to dementia, interlocking them with an application, and analyzing the collected data.

Methods

This study was conducted on 18 elderly individuals (5 males, 13 females) aged 65 years or older who consented to the study. The Korean Mini-Mental State Examination survey for cognitive function tests was conducted by well-trained researchers. The subjects were first grouped into high- or low-risk group for dementia based on their Korean Mini-Mental State Examination score. Data obtained by wearable devices of each subject were then used for the classification of the high- and low-risk groups of dementia through a smart healthcare-based system implementing a deep neural network with scaled principal component analysis. The correlation coefficients between the Korean Mini-Mental State Examination score and the featured data were also investigated.

Results

Our study showed that the proposed system using a deep neural network with scaled principal component analysis was effective in detecting individuals at high risk for dementia with up to 99% accuracy and which performance was better compared with commonly used classification algorithms. In addition, it was found that the electrical conductivity of skin had the closest correlation with the results of the Korean Mini-Mental State Examination score among data collected through wearable devices in this study.

Conclusions

Our proposed system can contribute to effective early detection of dementia for the elderly, using a non-invasive and easy-to-wear wearable device and classification algorithms with a simple cognitive function test. In the future, we intend to have more subjects participate in the experiment, to include more relevant variables in the wearable device, and to analyze the effectiveness of the smart healthcare-based dementia classification system over the long term.

The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model

The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation-exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson's disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2-7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers.

Quality of information transferred to palliative care

AIMS AND OBJECTIVES

To describe the quality of information coming from previous care units to palliative care.

BACKGROUND

Information quality is an interconnected concept that includes different dimensions and can be viewed from different perspectives. More knowledge is needed from a multi-professional perspective on the information quality coming to palliative care.

DESIGN

Descriptive qualitative study.

METHODS

Altogether 33 registered nurses, practical nurses, social workers and physicians working in palliative care were purposively selected to participate in thematic interviews. The research was carried out in six palliative care units in three hospital districts. The data were analysed by using deductive and inductive content analysis. The COREQ checklist was used.

RESULTS

Three main categories with thirteen categories were identified in connection with the deductive analysis based on the Clinical Information Quality framework: (1). Informativeness of information coming from previous care units to palliative care included accuracy, completeness, interpretability, plausibility, provenance and relevance. (2). Availability of information coming from previous care units to palliative care included accessibility, portability, security and timeliness. (3). Usability of information coming from previous care units to palliative care included conformance, consistency and maintainability. Each category is divided into sub-categories followed by narratives of their content.

CONCLUSIONS

This study provides new knowledge on the quality of information coming to palliative care from a multi-professional perspective. Professionals working in palliative care units highlight issues describing good information quality, but also point out quality issues and areas for improvement.

RELEVANCE TO CLINICAL PRACTICE

The results can guide the development of documentation practices and Health Information System development as well as be used in the generation of a new audit instrument of information quality.

Database quality assessment in research in paramedicine: a scoping review protocol

INTRODUCTION

The paramedic practice environment presents unique challenges to data documentation and access, as well as linkage to other parts of the healthcare system. Variable or unknown data quality can influence the validity of research in paramedicine. A number of database quality assessment (DQA) frameworks have been developed and used to evaluate data quality in other areas of healthcare. The extent these or other DQA practices have been applied to paramedic research is not known. Accordingly, this scoping review aims to describe the range, extent and nature of DQA practices within research in paramedicine.

METHODS AND ANALYSIS

This scoping review will follow established methods for the conduct (Johanna Briggs Institute; Arksey and O'Malley) and reporting (Preferred Reporting Items in Systematic Reviews and Meta-Analyses extension for scoping reviews) of scoping reviews. In consultation with a professional librarian, a search strategy was developed representing the applicable population, concept and context. This strategy will be applied to MEDLINE (National Library of Medicine), Embase (Elsevier), Scopus (Elsevier) and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies will be included if they report quantitative results of DQA using data that relate primarily to the paramedic practice environment. Protocols, commentaries, case studies, interviews, simulations and experimental data-processing techniques will be excluded. No restrictions will be placed on language. Study selection will be performed by two reviewers, with a third available to resolve conflicts. Data will be extracted from included studies using a data-charting form piloted and iteratively revised based on studies known to be relevant. Results will be summarised in a chart of study characteristics, DQA-specific outcomes and key findings.

ETHICS AND DISSEMINATION

Ethical approval is not required. Results will be submitted to relevant conferences and peer-reviewed journals.

TRIAL REGISTRATION

10.17605/OSF.IO/Z287T.

Posyandu Application for Monitoring Children Under-Five: A 3-Year Data Quality Map in Indonesia

Posyandu is an Indonesian mother-child health, community-based healthcare. The provision of the Posyandu data quality map is crucial for analyzing results but is limited. This research aimed to (a) demonstrate data quality analysis on its completeness, accuracy, and consistency and (b) map the data quality in Indonesia for evaluation and improvement. An observational study was conducted using the Posyandu application. We observed data in Indonesia from 2019 to 2021. Data completeness was identified using children’s visits/year. Data accuracy was analyzed using WHO anthropometry z-score and implausible z-score values analyzing the outliers. Cronbach’s α of variables was used to know data consistency. STATA 15.1 SE and QGIS 3.10 was used to analyze and map the quality. Data completeness and accuracy in three years show a good start for the pilot project area, continued with declines in pandemic time, while some other areas demonstrated a small start, then slightly increased. The overall consistency decreased through the study period. A good report on data completeness can occur initially in a pilot project area, followed by others. Data accuracy and consistency can decrease during the pandemic. The app can be promising when synchronized with the government health information system.

Digital Inequalities in Cancer Care Delivery in India: An Overview of the Current Landscape and Recommendations for Large-Scale Adoption

Introduction

COVID-19 pandemic has caused major disruptions to delivery of various cancer care services as efforts were put to control the outbreak of the pandemic. Although the pandemic has highlighted the inadequacies of the system but has also led to emergence of a new cancer care delivery model which relies heavily on digital mediums. Digital health is not only restricted to virtual dissemination of information and consultation but has provided additional benefits ranging from support to cancer screening, early and more accurate diagnosis to increasing access to specialized care. This paper evaluates the challenges in the adoption of digital technologies to deliver cancer care services and provides recommendation for large-scale adoption in the Indian healthcare context.

Methods

We performed a search of PubMed and Google Scholar for numerous terms related to adoption of digital health technologies for cancer care during pandemic. We also analyze various socio-ecological challenges—from individual to community, provider and systematic level—for digital adoption of cancer care service which have existed prior to pandemic and lead to digital inequalities.

Results

Despite encouraging benefits accruing from the adoption of digital health key challenges remain for large scale adoption. With respect to user the socio-economic characteristics such as age, literacy and socio-cultural norms are the major barriers. The key challenges faced by providers include regulatory issues, data security and the inconvenience associated with transition to a new system.

Policy Summary

For equitable digital healthcare, the need is to have a participatory approach of all stakeholders and urgently addressing the digital divide adequately. Sharing of health data of public and private hospitals, within the framework of the Indian regulations and Data Protection Act, is critical to the development of digital health in India and it can go a long way in better forecasting and managing cancer burden.

Using COMPASS (Context Optimisation Model for Person-Centred Analysis and Systematic Solutions) Theory to Augment Implementation of Digital Health Solutions

Digital health research is an emerging discipline that requires easy-to-understand theoretical frameworks and implementation models for digital health providers in health and social care settings. The COVID-19 pandemic has heightened the demand for digital health discipline-specific instruction on how to manage evidence-based digital health transformation. Access to the use of these models guarantees that digital health providers can investigate phenomena using safe and suitable approaches and methods to conduct research and identify answers to challenges and problems that arise in health and social care settings. The COMPASS theory is designed to aid transformation of health and social care environments. A navigational rose of primary quadrants is divided by four main compass points, with person-centred care being central to the philosophy. Two axes produce Cartesian planes that intersect to form a box plot, which can be used to discover human and physical resource weightings to augment digital health research design and implementation. A third continuum highlights stakeholders’ capabilities, which are critical for any multidisciplinary study. The COMPASS mnemonic guides end users through the process of design, development, implementation, evaluation, and communication of digital health transformations. The theory’s foundations are presented and explained in context of the ‘new normal’ of health and social care delivery.

Database Supported Long-term Management of Chronic Diseases - Data from the German Disease Management Programmes as a Source for Continuing Medical Education

Disease Management Programmes (DMPs) have been introduced by German Federal Government in 2002 to improve long-term care for patients with specific chronic diseases. Digitisation has been a requirement to reliably document patient data in DMPs. This report presents data from six DMPs in the German federal state of North Rhine-Westphalia. It demonstrates that high level long-term quality of care can be achieved and maintained. But beyond clinical purposes DMP data are also an invaluable source to supply content in CME.

Acceptance of Digital Sports: A Study Showing the Rising Acceptance of Digital Health Activities Due to the SARS-CoV-19 Pandemic

In pandemic times, the possibilities for conventional sports activities are severely limited; many sports facilities are closed or can only be used with restrictions. To counteract this lack of health activities and social exchange, people are increasingly adopting new digital sports solutions—a behavior change that had already started with the trend towards fitness apps and activity trackers. Existing research suggests that digital solutions increase the motivation to move and stay active. This work further investigates the potentials of digital sports incorporating the dimensions gender and preference for team sports versus individual sports. The study focuses on potential users, who were mostly younger professionals and academics. The results show that the SARS-CoV-19 pandemic had a significant negative impact on sports activity, particularly on persons preferring team sports. To compensate, most participants use more digital sports than before, and there is a positive correlation between the time spent physically active during the pandemic and the increase in motivation through digital sports. Nevertheless, there is still considerable skepticism regarding the potential of digital sports solutions to increase the motivation to do sports, increase performance, or raise a sense of team spirit when done in groups.

Using data science to improve outcomes for persons with opioid use disorder

Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.

Preparing for a New World: Making Friends with Digital Health

While digital health solutions have shown good outcomes in numerous studies, the adoption of digital health solutions in clinical practice faces numerous challenges. To prepare for widespread adoption of digital health, stakeholders in digital health will need to establish an objective evaluation process, consider uncertainty through critical evaluation, be aware of inequity, and consider patient engagement. By "making friends" with digital health, health care can be improved for patients.