scholarly journals A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 102 ◽  
Author(s):  
Fernando López-Martínez ◽  
Edward Rolando Núñez-Valdez ◽  
Vicente García-Díaz ◽  
Zoran Bursac
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Information Systems ◽  
Population Health ◽  
Health Management ◽  
Medical Decision ◽  
Population Health Management ◽  
Learning Platform

Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management.

scholarly journals Informatics strategies for risk stratification in population health management

2017 ◽  
Author(s):  
◽  
Lincoln Sheets
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Population Health ◽  
Healthcare Costs ◽  
Health Management ◽  
Area Under The Curve ◽  
Population Health Management ◽  
Interpretable Machine Learning ◽  
Risk Patients ◽  
Improve Health

Risk analysis and population health management can improve health outcomes, but improved risk stratification is needed to manage healthcare costs. Analysis of 157 publications on translational implementations of "risk stratification in population health management of chronic disease" showed a consensus that population health management and risk stratification can improve outcomes, but found uncertainty over best methods for risk prediction and controversy over the cost savings. The consensus of another 85 publications on the methodologies of "data mining for predictive healthcare analytics" was that clinically interpretable machine learning techniques are more appropriate than "black box" techniques for structured big data sources in healthcare, and the "area under the curve" of a prediction model's sensitivity versus one-minus-specificity is a standard and reliable way to measure the model's discrimination. This study used clinically interpretable machine-learning algorithms, combined with simple but powerful data analytic techniques such as cost analysis and data visualization, to evaluate and improve risk stratification for a managed patient population. This study retrospectively observed 10,000 mid-Missouri Medicare and Medicaid patients between 2012 and 2014. Cost and utilization analyses, statistical clustering, contrast mining, and logistic regression were used to identify patients within a managed population at risk for higher healthcare costs, demonstrate longitudinal changes in risk stratification, and characterize detailed differences between high-risk and low-risk patients. The two highest risk stratification tiers comprised only 21% of patients but accounted for 43% of prospective charges. Patients in the most expensive sub-cluster of the most expensive risk tier were nearly twice as costly as high-risk patients on average. Combining contrast mining with logistic regression predicted the most expensive 5% of patients with 84% accuracy, as measured by area under the curve. All the strategies used in this study, from the simplest to the most sophisticated, produced useful insights. By predicting the small number of patients who will incur the majority of healthcare expenses in terms that are clinically interpretable, these methods can support population health managers in focusing preventive and longitudinal care more effectively. These models, and similar models developed by integrating diverse informatics strategies, could improve health outcomes, delivery, and costs.

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

scholarly journals How Population Health Management and Big Data Can Rock Your World

2015 ◽  
Vol 50 (9) ◽  
pp. 840-841
Author(s):  
Bill G. Felkey ◽  
Brent I. Fox
Keyword(s):  
Big Data ◽  
Population Health ◽  
Health Management ◽  
Population Health Management
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