Implications of Big Data Analytics on Population Health Management

Big Data ◽  
2013 ◽  
Vol 1 (3) ◽  
pp. 152-159 ◽  
Author(s):  
Paul S. Bradley
Keyword(s):  
Big Data ◽  
Population Health ◽  
Data Analytics ◽  
Health Management ◽  
Big Data Analytics ◽  
Population Health Management

scholarly journals Big Data Analytics

2019 ◽  
Vol 87 (2) ◽  
pp. 24-26
Author(s):  
Shawna Bourne ◽  
Tarun Rihal
Keyword(s):  
Big Data ◽  
Health Outcomes ◽  
Environmental Health ◽  
Population Health ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Factors Affecting ◽  
Water Test ◽  
Safe Food ◽  
The Impact

Utilizing big data to guide decision-making for environmental health outcomes can provide the next level of health outcome improvements on a population basis. Historical shifts in overall health and longevity came with environmental health interventions such as safe food and water supplies, the treatment of waste and the establishment of standards that have reduced acute illnesses in the population. Big data analysis approaches have the potential to have a similar impact on quality and length of life by analyzing the factors leading to chronic illness in the population, and improving outcomes. Through the use of big data and machine learning, we can learn more about the environmental factors affecting population health. This article presents an opportunity to utilize pre-existing data to explore a novel way of assessing the impact of known health hazards. This is demonstrated by using drinking water test results as a case example. We demonstrate how big data analytics can be used in such a scenario to identify environmental public health risk. This approach is beginning to be used to collect new and better organized data with the intent of improving population health outcomes.

Accounting for accountable care: Value-based population health management

2019 ◽  
Vol 49 (4) ◽  
pp. 556-582 ◽  
Author(s):  
Linda F Hogle
Keyword(s):  
Health Outcomes ◽  
Population Health ◽  
Financial Risk ◽  
Health Management ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Accountable Care ◽  
Population Health Management ◽  
Data Intensive ◽  
Account Giving

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients’ health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Modern Health Management With Cognitive Computing and Big Data Analytics

2019 ◽  
pp. 206-224
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Management ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Cognitive Computing ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is an refined advancement for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business. In this way, the primary goal of big data analytics is to help business relationship to have enhanced comprehension of data and, subsequently, settle on proficient and educated decisions.

Population Health Management and the Science of Individuality

2018 ◽  
Vol 7 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Anastasius Moumtzoglou ◽  
Abraham Pouliakis
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Population Health ◽  
Life Science ◽  
Health Management ◽  
Care Delivery ◽  
Management Approach ◽  
Population Health Management ◽  
Health Technologies ◽  
The Individual

This article espouses that population health management (PHM) has been a discipline which studies and facilitates care delivery across a group of individuals or the general population. In the context of population health management, the life science industry has had no motivation to design drugs or devices that are only effective for a distinct segment of the population. The major outgrowth of the science of individuality, as well as the rising ‘wiki medicine', fully recognizes the uniqueness of the individual. Cloud computing, Big Data and M-Health technologies offer the resources to deal with the shortcomings of the population health management approach, as they facilitate the propagation of the science of individuality.

Population Health Management, Emerging Technologies, and the Science of the Individual

2020 ◽  
pp. 21-62
Author(s):  
Anastasius S. Moumtzoglou ◽  
Abraham Pouliakis
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Population Health ◽  
Patient Management ◽  
Health Management ◽  
Care Delivery ◽  
Population Health Management ◽  
Distinct Population Segment ◽  
The Individual ◽  
Population Segment

Population health management (PHM) has been a discipline that studies and facilitates care delivery across a group of individuals or the general population. In the context of PHM, the life science industry has had no motivation to design drugs or devices and even offer treatment of patient management that is only effective for a distinct population segment. The primary outgrowth of the science of individuality, as well as the rising ‘wiki medicine', fully recognizes the uniqueness of the individual. Cloud computing, big data, m-health, and recently, internet of things can offer the resources to deal with numerous shortcomings such as data collection and processing, of the PHM approach, as they facilitate the propagation of the science of individuality.

scholarly journals Leveraging big data in population health management

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Timothy S. Wells ◽  
Ronald J. Ozminkowski ◽  
Kevin Hawkins ◽  
Gandhi R. Bhattarai ◽  
Douglas G. Armstrong
Keyword(s):  
Big Data ◽  
Population Health ◽  
Health Management ◽  
Population Health Management
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