Real world evidence: a form of big data, transforming healthcare data into actionable real time

BARICK, Uttam Kumar, Daniel SCHWARZ, Behsad ZOMORODI, Rituraj MOHANTY, Arun GOWDA and Martin KOMENDA. Real world evidence: a form of big data, transforming healthcare data into actionable real time. MEFANET Journal. Brno: Facta Medica, 2015, vol. 3, No 1. ISSN 1805-9163.

Basic information

Original name

Real world evidence: a form of big data, transforming healthcare data into actionable real time

Authors

BARICK, Uttam Kumar, Daniel SCHWARZ, Behsad ZOMORODI, Rituraj MOHANTY, Arun GOWDA and Martin KOMENDA

Edition

MEFANET Journal, Brno, Facta Medica, 2015, 1805-9163

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Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

50300 5.3 Education

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Organization unit

Faculty of Medicine

Keywords in English

RWE, BIG DIP, big data, data mining, real time insights, business decisions, visual analytics

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Abstract

V originále

Data has always played an important role in assisting business decisions and overall improvement of a company’s strategies. The introduction of what has come to be named ‘BIG data’ has changed the industry paradigm altogether for a few domains like media, mobility, retail and social. Data from the real world is also considered as BIG data based on its magnitude, sources and the industry’s capacity to handle the same. Although, the healthcare industry has been using real world data for decades, digitization of health records has demonstrated its value to all the stakeholders with a reaffirmation of interest in it. Over time, companies are looking to adopt new technologies in linking these fragmented data for meaningful and actionable insights to demonstrate their value over competition. It has also been noticed that the consequences of not demonstrating the value of data are sometimes leads regulators and payers to be severe. The real challenge though is not in identifying data sets but transforming these data sets into actionable real time insights and business decisions. Evidence and value development frameworks need to work side by side, harnessing meaningful insights in parallel to product development from early phase to life-cycle management. This should in-turn create evidence and value-based insights for multiple stakeholders across the industry; ultimately supporting the patient as the end user to take informed decisions that impact access to care. This article attempts to review the current state of affairs in the area of BIG data in pharma OR BIG DIP as it is increasingly being referred to.