Precision medicine is poised to have an impact on patients, health care delivery systems and research participants in ways that were only imagined 15 years ago when the human genome was first sequenced. While discovery using genome-based technologies has accelerated, these have only begun to be adopted into clinical medicine. Here we define precision medicine and the stakeholder ecosystem required to enable its integration into research and health care. We explore the intersection of data science, analytics and precision medicine in creating a learning health system that carries out research in the context of clinical care and at the same time optimizes the tools and information used to delivery improved patient outcomes. We provide examples of real world impact, and conclude with a policy and economic agenda that will be necessary for the adoption of this new paradigm of health care both in the United States and globally

Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.

The domain of healthcare acquired its influence by the impact of big data since the data sources involved in the healthcare organizations are well-known for their volume, heterogeneous complexity and high dynamism. Though the role of big data analytical techniques, platforms, tools are realized among various domains, their impact on healthcare organization for implementing and delivering novel use-cases for potential healthcare applications shows promising research directions. In the context of big data, the success of healthcare applications solely depends on the underlying architecture and utilization of appropriate tools as evidenced in pioneering research attempts. Novel research works have been carried out for deriving application specific healthcare frameworks that offer diversified data analytical capabilities for handling sources of data ranging from electronic health records to medical images. In this paper, we have presented various analytical avenues that exist in the patient-centric healthcare system from the perspective of various stakeholders. We have also reviewed various big data frameworks with respect to underlying data sources, analytical capability and application areas. In addition, the implication of big data tools in developing healthcare eco system is also presented.

The shortage of data scientists has restricted the implementation of big data analytics in healthcare facilities. This survey study explores big data tool and technology usage, examines the gap between the supply and the demand for data scientists through Diffusion of Innovations theory, proposes engaging academics to accelerate knowledge diffusion, and recommends adoption of curriculum-building models. For this study, data were collected through a national survey of healthcare managers. Results provide practical data on big data tool and technology skills utilized in the workplace. This information is valuable for healthcare organizations, academics, and industry leaders who collaborate to implement the necessary infrastructure for content delivery and for experiential learning. It informs academics working to reengineer their curriculum to focus on big data analytics. The paper presents numerous resources that provide guidance for building knowledge. Future research directions are discussed

Technological innovations in psychiatry are revolutionizing health care and are an important strategy for managing mental health care needs in the United States, particularly, in rural and underserved areas. To improve access to care, the University of Rochester (UR) has developed a digital behavioral health model of care. This article describes that program and reviews the evolution of digital health care in psychiatry and the pros and cons of this form of delivery of services.

Background: The prevalence of smartphones today, paired with the increasing precision and therapeutic potential of digital capabilities, offers unprecedented opportunities in the field of digital medicine. Smartphones offer novel accessibility, unique insights into physical and cognitive behavior, and diverse resources designed to aid health. Many of these digital resources, however, are developed and shared at a faster rate than they can be assessed for efficacy, safety, and security—presenting patients and clinicians with the challenge of distinguishing helpful tools from harmful ones.

Main text: Leading regulators, such as the FDA in the USA and the NHS in the UK, are working to evaluate the influx of mobile health applications entering the market. Efforts to regulate, however, are challenged by the need for more transparency. They require real-world data on the actual use, effects, benefits, and harms of these digital health tools. Given rapid product cycles and frequent updates, even the most thorough evaluation is only as accurate as the data it is based on.

Conclusions: In this debate piece, we propose a complementary approach to ongoing efforts via a dynamic self-certification checklist. We outline how simple self-certification, validated or challenged by app users, would enhance transparency, engage diverse stakeholders in meaningful education and learning, and incentivize the design of safe and secure medical apps

Background: Early Health Technology Assessment (EHTA) is an evolving field in health policy which aims to provide decision support and mitigate risk during early medical device innovation. The clinician is a key stakeholder in this process and their role has traditionally been confined to assessing device efficacy and safety alone. There is however, no data exploring their role in this process and how they can contribute towards it. This motivated us to carry out a systematic review to delineate the role of the clinician in EHTA as per the PRISMA guidelines.

Methods: A systematic search of peer reviewed literature was undertaken across PUBMED, OVID Medline and Web of science up till June 2018. Studies that were suitable for inclusion focused on clinician input in health technology assessment or early medical device innovation. A qualitative approach was utilised to generate themes on how clinicians could contribute in general and specific areas of EHTA. Data was manually extracted by the authors and themes were agreed in consensus using a grounded theory framework. The specific stages included: All stages of EHTA, Basic research on mechanisms, Targeting for specific product, Proof of principle and Prototype and product development. Bias was assessed utilising the NICE Qualitative checklist.

One prominent barrier faced by healthcare consumers when accessing health services is a common requirement to complete repetitive, inefficient paper-based documentation at multiple registration sites. Digital innovation has a potential role to reduce the burden in this area, through the collection and sharing of data between healthcare providers. While there is growing evidence for digital innovations to potentially improve the effectiveness and efficiency of health systems, there is less information on the willingness of healthcare consumers to embrace and utilise technology to provide data.

Over the past 10 years, the healthcare system has undergone a significant shift in the structure of health care delivery. Many hospitals and physician groups have organized themselves to address the most pressing national priorities in health care: controlling increases in the cost of care and improving the quality of care that US residents receive. Payers, both public and private, have supported this shift by implementing new alternative models of payment that incent the delivery of cost-efficient,high-quality care. The most promising alternative payment model to fee-for-service payment is the accountable care organization (ACO)model. Some available evidence suggests that ACOs, both for Medicare beneficiaries and for commercially insured patients, reduce total cost of care and improve quality.1-3Although there are different ways to construct ACO models, at its core, an ACO is a contract between clinicians and the payer to meet rigorous clinical quality and experience goals and lower spending

Digital data are anticipated to transform medicine. However, most of today’s medical data lack interoperability: hidden in isolated databases, incompatible systems and proprietary software, the data are difficult to exchange, analyze, and interpret. This slows down medical progress, as technologies that rely on these data – artificial intelligence, big data or mobile applications – cannot be used to their full potential. In this article, we argue that interoperability is a prerequisite for the digital innovations envisioned for future medicine. We focus on four areas where interoperable data and IT systems are particularly important: (1) artificial intelligence and big data; (2) medical communication; (3) research; and (4) international cooperation. We discuss how interoperability can facilitate digital transformation in these areas to improve the health and well-being of patients worldwide