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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.

Personalized medicine (PM) encompasses a set of procedures, technologies and medications; the term became more prominent from the 2000s onwards and stems from the mapping of the human genome. The purposes of this study were to analyse the development stage of the process of technological innovation for PM and the obstacles that prevent PM from being adopted in the public health system in Brazil.

As a research method, this paper opts for a case study carried out at the Hospital das Clínicas, which belongs to São Paulo Medical School. In total, 22 in-depth interviews were carried out at the hospital to identify current practices in PM, future prospects and barriers imposed to the adoption of PM technologies in public health.

Personalized or precision medicine is already a reality for a small portion of the Brazilian population and is gradually gaining ground in public health care. One finding is that such changes are occurring in a disjointed manner in an incomplete and under development health innovation system. The analysis pointed out that the obstacles identified in Brazil are the same as those faced by high-income countries such as regulation, lack of clinical studies and need to adapt clinical studies to PM. They appear in all stages of the innovation cycle, from research to widespread use.

The research method was a case study, so the findings cannot be extrapolated to other contexts. A limited number of professionals were interviewed, their opinions may not reflect those of their organizations.

There are several studies that discuss how health-care systems in high-income countries could incorporate these new technologies, but only a few focuses on low or middle-income countries such as Brazil.

Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable) risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors). We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

Impairment of intelligence in Duchenne muscular dystrophy (DMD) patients was described by Duchenne de Boulogne himself in 1868. Further studies report intelligence disorders with mayor impairment of memory. The aim of the present study was to assess the presence of affective and personality disorders in a group of children affected by DMD. Twenty six male DMD patients, mean age eleven and four months years old, were assessed for their affective and personality disorder. Only eight subjects had a total IQ below average with major difficulties in verbal and visual-spatial memory, comprehension, arithmetic and vocabulary. All the subjects presented some disorders: tendency to marginalization and isolation, self-depreciation, sense of insecurity, hypochondriac thoughts and marked state of anxiety. These disorders are often a dynamic prolongation of a psychological process which starts when the diagnosis is made and continues, in a slow and latent fashion, throughout the evolution of the disease.

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.

It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.

Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.

In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.

The paper contributes to the discussion on valuation in organization studies and strategic management literature. The nascent literature on valuation practices has examined established markets where producers and consumers are known and rivalry in the market is a given. Furthermore, previous research has operated with a narrow meaning of value as either a financial profit or a subjective consumer preference. Such a narrow view on value is problematic and insufficient for studying the interlacing of innovation and value creation in emerging technoscientific business domains.

The authors present an empirical study about value creation in an emerging technoscience business domain formed around personalized medicine and digital health data.

The results of this analysis show that in a technoscientific domain, valuation of innovations is multiple and malleable, entails pursuing attractiveness in collaboration and partnerships and is performative, and due to emphatic future orientation, values are indefinite and promissory.

As research implications, this study shows that valuation practices in an emerging technoscience business domain focus on defining the potential economic value in the future and attracting partners as probable future beneficiaries. Commercial value upon innovation in an embryonic business milieu is created and situated in valuation practices that constitute the prospective market, the prevalent economic discourse, and rationale. This is in contrast to an established market, where valuation practices are determined at the intersection of customer preferences and competitive arenas where suppliers, producers, service providers and new entrants to the market present value propositions.

The study findings extend discussion on valuation from established business domains to emerging technoscience business domains which are in a “pre-competition” phase where suppliers, customers, producers and their collaborative and competitive relations are not yet established.

As managerial implications, this study provides insights into health innovation stakeholders, including stakeholders in the public, private and academic sectors, about the ecosystem dynamics in a technoscientific innovation. Such insight is useful in strategic decision-making about ecosystem strategy and ecosystem business model for value proposition, value creation and value capture in an emerging innovation domain characterized by collaborative and competitive relations among stakeholders. To business managers, the findings of this study about valuation practices are useful in strategic decision-making about ecosystem strategy and ecosystem business model for value proposition, value creation and value capture in an emerging innovation domain characterized by collaborative and competitive relations among stakeholders. To policy makers, this study provides an in-depth analysis of an overall business ecosystem in an emerging technoscience business that can be propelled to increase the financial investments in the field. As a policy implication, this study provides insights into the various dimensions of valuation in technoscience business to policy makers, who make governance decisions to guide and control the development of medical innovation using digital health data.

This study's results expand previous theorizing on valuation by showing that in technoscientific innovation all types of value created – scientific, clinical, social or economic – are predominantly promissory. This study complements the nascent theorizing on value creation and valuation practices of technoscientific innovation.

The purpose of this paper is to make the case that ethical guardrails in emerging technology businesses are inadequate and to develop solutions to strengthen these guardrails.

Based on literature and first principles reasoning, the paper develops theoretical arguments about the fundamental purpose of ethical guardrails and how they evolve and then uses this along with the characteristics that distinguish emerging technology businesses to identify inadequacies in the ethical guardrails for emerging technology businesses and develop solutions to strengthen the guardrails.

The paper shows that the ethical guardrails for emerging technology businesses are inadequate and that the reasons for this are systematic. The paper also develops actionable recommendations to strengthen these guardrails.

The paper develops the novel argument that reasons for the inadequate ethical guardrails in emerging technology businesses are systematic and stem from the inadequacy of laws and regulations, inadequacy of boards and the focus of business executives.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.

The purpose of this paper is to explore different health care professionals’ discourse about privacy – its definition and importance in health care, and its role in their day-to-day work. Professionals’ discourse about privacy reveals how new technologies and laws challenge existing practices of information control within and between professional groups in health care, with implications not only for patient privacy, but also for the role of information control in professions more generally.

The authors conducted in-depth, semi-structured interviews with n=83 doctors, nurses, and health information professionals in two academic medical centers and one veteran’s administration hospital/clinic in the Northeastern USA. Interview responses were qualitatively coded for themes and patterns across groups were identified.

The health care providers and the authors studied actively sought to uphold the protection (and control) of patient information through professional ethics and practices, as well as through the use of technologies and compliance with legal regulations. They used discourses of professionalism, as well as of law and technology, to sometimes accept and sometimes resist changes to practice required in the changing technological and legal context of health care. The authors found differences across professional groups; for some, protection of patient information is part of core professional ethics, while for others it is simply part of their occupational work, aligned with organizational interests.

This qualitative study of physicians, nurses, and health information professionals revealed some differences in views and practices for protecting patient information in the changing technological and legal context of health care that suggest some professional groups (doctors) may be more likely to resist such changes and others (health information professionals) will actively adopt them.

New technologies and regulations are changing how information is used in health care delivery, challenging professional practices for the control of patient information that may change the value or meaning of medical records for different professional groups.

Qualitative findings suggest that professional groups in health care vary in the extent of information control they have, as well in how they view such control. Some groups may be more likely to (be able to) resist changes in the professional control of information that stem from new technologies or regulatory policies. Some professionals recognize that new IT systems and regulations challenge existing social control of information in health care, with the potential to undermine (or possibly bolster) professional self-control for some but not necessarily all occupational groups.