scholarly journals Public Health, Population Health, Population Health Management, and Describing a Role for Data Analytics: Ideas for Health System Administrators

2020 ◽  
Vol 5 (1) ◽  
pp. 8-10
Author(s):  
Gregory Fant ◽  
Keyword(s):  
Public Health ◽  
Health System ◽  
Population Health ◽  
Data Analytics ◽  
Health Management ◽  
Population Health Management

scholarly journals Setting up a regional health system database for seamless population health management in Singapore

2015 ◽  
Vol 25 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Pradeep Paul George Gunapal ◽  
Palvannan Kannapiran ◽  
Kiok Liang Teow ◽  
Zhecheng Zhu ◽  
Alex Xiaobin You ◽  
...  
Keyword(s):  
Health System ◽  
Population Health ◽  
Health Management ◽  
Regional Health ◽  
Population Health Management ◽  
System Database

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.

Improving the Delivery of Healthcare through Clinical Diagnostic Insights: A Valuation of Laboratory Medicine through “Clinical Lab 2.0”

2018 ◽  
Vol 3 (3) ◽  
pp. 487-497 ◽  
Author(s):  
Kathleen Swanson ◽  
Monique R Dodd ◽  
Richard VanNess ◽  
Michael Crossey
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Population Health ◽  
Clinical Decision Support ◽  
Data Analytics ◽  
Health Management ◽  
Clinical Decision ◽  
Laboratory Medicine ◽  
Population Health Management ◽  
Clinical Diagnostic

Abstract Background As healthcare payment and reimbursement begin to shift from a fee-for-service to a value-based model, ancillary providers including laboratories must incorporate this into their business strategy. Laboratory medicine, while continuing to support a transactional business model, should expand efforts to include translational data analytics, proving its clinical and economic valuation. Current literature in this area is limited. Content This article is a summary of how laboratory medicine can support value-based healthcare. Population health management is emerging as a method to support value-based healthcare by aggregating patient information, providing data analysis, and contributing to clinical decision support. Key issues to consider with a laboratory-developed population health management model are discussed, including changing reimbursement models, the use of multidisciplinary committees, the role of specialists in data analytics and programming, and barriers to implementation. Examples of data considerations and value are given. Summary Laboratory medicine is able to provide meaningful clinical diagnostic insights for population health initiatives that result in improved short- and long-term patient outcomes and drive cost-effective care. Opportunities include data analysis with longitudinal laboratory data, identification of patient-specific targeted interventions, and development of clinical decision support tools. Laboratories will need to leverage the skills and knowledge of their multidisciplinary staff, along with their extensive patient data sets, through innovative analytics to meet these objectives.

Accountable Care: Building Systems for Population Health Management

2014 ◽  
Author(s):  
Sarah Klein Klein ◽  
Douglas McCarthy McCarthy ◽  
Alexander Cohen Cohen
Keyword(s):  
Population Health ◽  
Health Management ◽  
Accountable Care ◽  
Population Health Management ◽  
Building Systems

Using High-Tech and High-Touch Methods for Effective Population Health Management

Iproceedings ◽  
2016 ◽  
Vol 2 (1) ◽  
pp. e17
Author(s):  
Sashi Padarthy ◽  
Cristina Crespo ◽  
Keri Rich ◽  
Nagaraja Srivatsan
Keyword(s):  
Population Health ◽  
Health Management ◽  
High Tech ◽  
Effective Population ◽  
Population Health Management
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