scholarly journals Creating Value In Health Care Through Big Data: Opportunities And Policy Implications

2014 ◽  
Vol 33 (7) ◽  
pp. 1115-1122 ◽  
Author(s):  
Joachim Roski ◽  
George W. Bo-Linn ◽  
Timothy A. Andrews
Keyword(s):  
Health Care ◽  
Big Data ◽  
Policy Implications

EXPRESS: Big Data, Marketing Analytics, and Public Policy: Implications for Health Care

2021 ◽  
pp. 074391562199903
Author(s):  
Praveen K. Kopalle ◽  
Donald R. Lehmann
Keyword(s):  
Health Care ◽  
Public Policy ◽  
Big Data ◽  
Policy Implications ◽  
Marketing Analytics

This paper highlights some benefits to and issues with the application of big data and analytics, with emphasis on its role in health care. It considers both its effectiveness/value (i.e., how it can be used) and concerns about its use related to privacy and acceptance by individuals (i.e., how it should be used)

What Makes a City Bikeable? A Study of Intercity and Intracity Patterns of Bicycle Ridership using Mobike Big Data Records

2020 ◽  
Vol 46 (1) ◽  
pp. 55-75
Author(s):  
Ying Long ◽  
Jianting Zhao
Keyword(s):  
Big Data ◽  
Average Distance ◽  
Policy Implications ◽  
Rating System ◽  
City Centre ◽  
Average Score ◽  
Policy Makers ◽  
Usage Frequency ◽  
Share Data ◽  
The City

This paper examines how mass ridership data can help describe cities from the bikers' perspective. We explore the possibility of using the data to reveal general bikeability patterns in 202 major Chinese cities. This process is conducted by constructing a bikeability rating system, the Mobike Riding Index (MRI), to measure bikeability in terms of usage frequency and the built environment. We first investigated mass ridership data and relevant supporting data; we then established the MRI framework and calculated MRI scores accordingly. This study finds that people tend to ride shared bikes at speeds close to 10 km/h for an average distance of 2 km roughly three times a day. The MRI results show that at the street level, the weekday and weekend MRI distributions are analogous, with an average score of 49.8 (range 0–100). At the township level, high-scoring townships are those close to the city centre; at the city level, the MRI is unevenly distributed, with high-MRI cities along the southern coastline or in the middle inland area. These patterns have policy implications for urban planners and policy-makers. This is the first and largest-scale study to incorporate mobile bike-share data into bikeability measurements, thus laying the groundwork for further research.

Profiling Primary Care Physicians for a New Managed Care Network

1997 ◽  
Vol 10 (3) ◽  
pp. 173-186 ◽  
Author(s):  
R. J. Ozminkowski ◽  
M. Noether ◽  
P. Nathanson ◽  
K. M. Smith ◽  
B. E. Raney ◽  
...  
Keyword(s):  
Health Care ◽  
Primary Care Physicians ◽  
Claims Data ◽  
Severity Of Illness ◽  
Policy Implications ◽  
Case Mix ◽  
Reform Strategies ◽  
Insurance Program ◽  
Insurance Claims Data ◽  
Care Costs

We developed methods for comparing physicians who would be selected to participate in a major employer's self-insurance program. These methods used insurance claims data to identify and profile physicians according to deviations from prevailing practice and outcome patterns, after considering differences in case-mix and severity of illness among the patients treated by those providers. The discussion notes the usefulness and limitations of claims data for this and other purposes. We also comment on policy implications and the relationships between our methods and health care reform strategies designed to influence overall health care costs.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Big Data and the Intelligence Community — Lessons for Health Care

2019 ◽  
Vol 380 (20) ◽  
pp. 1888-1890 ◽  
Author(s):  
Kevin Vigilante ◽  
Steve Escaravage ◽  
Mike McConnell
Keyword(s):  
Health Care ◽  
Big Data ◽  
Intelligence Community

scholarly journals Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

2014 ◽  
Vol 23 (01) ◽  
pp. 21-26 ◽  
Author(s):  
T. Miron-Shatz ◽  
A. Y. S. Lau ◽  
C. Paton ◽  
M. M. Hansen
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Care Providers ◽  
Data Privacy ◽  
Recent Literature ◽  
Unstructured Data ◽  
Small Scale ◽  
Small Data ◽  
Quantified Self ◽  
Care Providers

Summary Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.

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