Economic evaluations of big data analytics for clinical decision-making: a scoping review.

OBJECTIVE: Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. MATERIALS AND METHODS: We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term. RESULTS: The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes. DISCUSSION: The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data.

Nanoparticles used in biological settings are exposed to proteins that adsorb on the surface forming a protein corona. These adsorbed proteins dictate the subsequent cellular response. A major challenge has been predicting what proteins will adsorb on a given nanoparticle surface. Instead, each new nanoparticle and nanoparticle modification must be tested experimentally to determine what proteins adsorb on the surface. We propose that any future predictive ability will depend on large datasets of protein-nanoparticle interactions. As a first step towards this goal, we have developed an automated workflow using a liquid handling robot to form and isolate protein coronas. As this workflow depends on magnetic separation steps, we test the ability to embed magnetic nanoparticles within a protein nanoparticle. These experiments demonstrate that magnetic separation could be used for any type of nanoparticle in which a magnetic core can be embedded. Higher-throughput corona characterization will also require lower-cost approaches to proteomics. We report a comparison of fast, low-cost, and standard, slower, higher-cost liquid chromatography coupled with mass spectrometry to identify the protein corona. These methods will provide a step forward in the acquisition of the large datasets necessary to predict nanoparticle-protein interactions.

Big Data Governance Needs More Collective Responsibility: The Role of Harm Mitigation in the Governance of Data Use in Medicine and Beyond.

Harms arising from digital data use in the big data context are often systemic and cannot always be captured by linear cause and effect. Individual data subjects and third parties can bear the main downstream costs arising from increasingly complex forms of data uses-without being able to trace the exact data flows. Because current regulatory frameworks do not adequately address this situation, we propose a move towards harm mitigation tools to complement existing legal remedies. In this article, we make a normative and practical case for why individuals should be offered support in such contexts and how harm mitigation tools can achieve this. We put forward the idea of 'Harm Mitigation Bodies' (HMBs), which people could turn to when they feel they were harmed by data use but do not qualify for legal remedies, or where existing legal remedies do not address their specific circumstances. HMBs would help to obtain a better understanding of the nature, severity, and frequency of harms occurring from both lawful and unlawful data use, and they could also provide financial support in some cases. We set out the role and form of these HMBs for the first time in this article.

Real world, big data cost of pharmaceutical treatment for rheumatoid arthritis in Greece.

INTRODUCTION: Rheumatoid Arthritis (RA) is a highly prevalent autoimmune disease associated with joint inflammation and destruction. Treatment for RA, especially with biologic agents (biologics), improves patient functionality and quality of life and averts costly complications or disease progression. Cost of RA pharmaceutical treatment has rarely been reported on the basis of real-world, big data. This study reports on the real-world, big data RA pharmaceutical treatment cost in Greece. METHODS: The Business Intelligence database of the National Organization for Healthcare Services Provision (EOPYY) was used to identify and provide analytics on patients on treatment for RA. EOPYY is responsible for funding healthcare and pharmaceutical care services for approximately 95% of the population in the country. ICD-10 codes were applied to identify patients with RA and at least one reimbursed prescription between 1 June 2014 and 31 May 2015. RESULTS: 35,873 unique patients were recorded as undergoing treatment for RA. Total reimbursed treatment cost for the study period was €81,206,363.70, of which €52,732,142.18 (64.94%) was for treatment with biologics. Of that cost, €39,724,489.71 (48.32%) accounted for treatment with anti-TNFs and/or methotrexate/corticosteroids. CONCLUSION: Real world, big data analysis confirms that the major driver of RA pharmaceutical cost is, as expected, the cost of treatment with biologics. It is critical to be able to match this cost to the treatment outcome it produces to ensure an optimal, no-waste, evidence-based allocation of healthcare resources to need.

A generally applicable lightweight method for calculating a value structure for tools and services in bioinformatics infrastructure projects.

Sustainable noncommercial bioinformatics infrastructures are a prerequisite to use and take advantage of the potential of big data analysis for research and economy. Consequently, funders, universities and institutes as well as users ask for a transparent value model for the tools and services offered. In this article, a generally applicable lightweight method is described by which bioinformatics infrastructure projects can estimate the value of tools and services offered without determining exactly the total costs of ownership. Five representative scenarios for value estimation from a rough estimation to a detailed breakdown of costs are presented. To account for the diversity in bioinformatics applications and services, the notion of service-specific 'service provision units' is introduced together with the factors influencing them and the main underlying assumptions for these 'value influencing factors'. Special attention is given on how to handle personnel costs and indirect costs such as electricity. Four examples are presented for the calculation of the value of tools and services provided by the German Network for Bioinformatics Infrastructure (de.NBI): one for tool usage, one for (Web-based) database analyses, one for consulting services and one for bioinformatics training events. Finally, from the discussed values, the costs of direct funding and the costs of payment of services by funded projects are calculated and compared.

Big data on a big new market: Insights from Washington State's legal cannabis market.

INTRODUCTION: Voters in eight U.S. states have passed initiatives to legalize large-scale commercial production of cannabis for non-medical use. All plan or require some form of "seed-to-sale" tracking systems, which provide a view of cannabis market activity at a heretofore unimagined level of detail. Legal markets also create a range of new matters for policy makers to address. DATA: Publicly available data were obtained on approximately 45 million individually priced items purchased in the 35 million retail transactions that took place during the first two and a half years of Washington State's legal cannabis market. Records include product type (flower, extract, lotion, liquid edible, etc.), product name, price, and potency with respect to multiple cannabinoids, notably THC and CBD. Items sold can be traced back up the supply chain through the store to the processor and producer, to the level of identifying the specific production batch and mother plant, the firm that tested the product, and test results. METHOD: Data visualization methods are employed to describe spatial-temporal patterns of multiple correlated attributes (e.g., price and potency) broken down by product. Text-analytic methods are used to subdivide the broad category of "extracts for inhalation" into more homogeneous sub-categories. To understand the competitiveness of the legal cannabis market in Washington we calculate the Herfindahl-Hirschman index (HHI) for processors and retailers. RESULTS: Cannabis prices fell steadily and proportionally at the processor and retailer levels. Retail and wholesale price maintained a roughly 3:1 ratio for multiple product types after some initial fluctuations. Although a wide range of edibles are sold, they account for a modest share of consumer spending; extracts for inhalation are a larger and heterogeneous market segment. The HHI indicates the cannabis market is highly competitive at the processor level, but less so for retail markets at the county level. CONCLUSIONS: Washington's state-legal cannabis market is diverse and rapidly evolving in terms of pricing, products, and organization. Post-legalization, researchers and policy makers may need to think in terms of a family of cannabis products, akin to how we think of new psychoactive substances and amphetamine-type stimulants, not a single drug "cannabis."