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This study explored health insurance coverage of genetic testing and potential factors associated with precision medicine (PM) reimbursement in Thailand.

The study employed a targeted review method. Thirteen PMs were selected to represent four PM categories: targeted cancer therapy candidate, prediction of adverse drug reactions (ADRs), dose adjustment and cancer risk prediction. Content analysis was performed to compare access to PMs among three health insurance schemes in Thailand. The primary outcome of the study was evaluating PM test reimbursement status. Secondary outcomes included clinical practice guidelines, PMs statement in FDA-approved leaflet and economic evaluation.

Civil Servant Medical Benefits Scheme (CSMBS) provided more generous access to PM than Universal Coverage Scheme (UCS) and Social Security Scheme (SSS). Evidence of economic evaluations likely impacted the reimbursement decisions of SSS and UCS, while the information provided in FDA-approved leaflets seemed to impact the reimbursement decisions of CSMBS. Three health insurance schemes provided adequate access to PM tests for some cancer-targeted therapies, while gaps existed for access to PM tests for serious ADRs prevention, dose adjustment and cancer risk prediction.

This was the first study to explore the situation of access to PMs in Thailand. The evidence alerts public health insurance schemes to reconsider access to PMs. Development of health technology assessment guidelines for PM test reimbursement decisions should be prioritized.

Genetic knowledge is advancing steadily while at the same time DNA sequencing prices are dropping fast, but the diffusion of genetic services (GS) has been slow. The purpose of this paper is to identify GS diffusion drivers in the precision medicine (PM) ecosystem.

After reviewing the literature on innovation diffusion, particularly on GS diffusion, the PM ecosystem actors are interviewed to obtain their perspective. Using content analysis, the interviewees’ visions were interplayed with the literature to achieve driver conceptualization, which posteriorly originated broad themes.

The results indicate that GS diffusion depends on satisfying aspects from three broad themes and respective drivers: technology (evidence strength and credibility, customization, knowledge, data and information, tech evolution speed and cost), human (ethics, privacy and security and user power) and business (prevention, holistic view of the individual, public policy and regulation, business model and management).

The main management implications refer to considering health care in a multidisciplinary way, investing in the propagation of genetic knowledge, standardizing medical records and interpreting data.

This study, to the best of authors’ knowledge, is the first attempt to understand GS diffusion from a broad perspective, taking into account the PM stakeholders’ view. The 13 drivers offer a comprehensive understanding of how GS could spread in health care and they can assist researchers and practitioners to discuss and set strategies based on an initial structured map.

The purpose of this paper is to describe the application of soft systems methodology (SSM) to address the problematic situation of low opt-in rates for Precision Health-Care (PHC).

The design logic is that when trust is enhanced and compliance is better assured, participants such as patients and their doctors would be more likely to share their medical data and diagnosis for the purpose of precision modeling.

The authors present the findings of an empirical study that confronts the design challenge of increasing participant opt-in to a PHC repository of Electronic Medical Records and genetic sequencing. Guided by SSM, the authors formulate design rules for the establishment of a trust-less platform for PHC which incorporates key principles of transparency, traceability and immutability.

The SSM approach has been criticized for its lack of “rigour” and “replicability”. This is a fallacy in understanding its purpose – theory exploration rather than theory confirmation. Moreover, it is unlikely that quantitative modeling yields any clearer an understanding of complex, socio-technical systems.

The application of Blockchain, a platform for distributed ledgers, and associated technologies present a feasible approach for resolving the problematic situation of low opt-in rates.

A consequence of low participation is the weak recall and precision of descriptive, predictive and prescriptive analytic models. Factors such as cyber-crime, data violation and the potential for misuse of genetic and medical records have led to a lack of trust from key stakeholders – accessors, participants, miners and regulators – to varying degrees.

The application of Blockchain as a trust-enabling platform in the domain of an emerging eco-system such as precision health is novel and pioneering.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

The explosive growth in the number of digital tools utilized in everyday learning activities generates data at an unprecedented scale, providing exciting challenges that cross scholarly communities. This paper aims to provide an overview of learning analytics (LA) with the aim of helping members of the information and learning sciences communities understand how educational Big Data is relevant to their research agendas and how they can contribute to this growing new field.

Highlighting shared values and issues illustrates why LA is the perfect meeting ground for information and the learning sciences, and suggests how by working together effective LA tools can be designed to innovate education.

Analytics-driven performance dashboards are offered as a specific example of one research area where information and learning scientists can make a significant contribution to LA research. Recent reviews of existing dashboard studies point to a dearth of evaluation with regard to either theory or outcomes. Here, the relevant expertise from researchers in both the learning sciences and information science is offered as an important opportunity to improve the design and evaluation of student-facing dashboards.

This paper outlines important ties between three scholarly communities to illustrate how their combined research expertise is crucial to advancing how we understand learning and for developing LA-based interventions that meet the values that we all share.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.