Data Analytics Applications in Latin America and Emerging Economies

Eduardo Rodriguez

• Publisher: Auerbach Publications
• Year: 2017
• Print ISBN: 9780367837068
• eBook ISBN: 9781351673167
• Edition: 1
• Topic: Economics
• Subtopic: Economic Theory
• Index: Economics

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions.

The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners.

This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.

II

Analytics Knowledge Applications in Latin America and Emerging Economies

Chapter 3

Analytics Knowledge Application to Healthcare

Esteban Flores and Isabel Rodríguez

Contents

Summary

Introduction to Healthcare Data Analytics

How Healthcare Analytics Could Improve Decision Making?

Components of Healthcare Analytics

Motivating Examples

Fraud Prevention

Workflow

Support for Clinical Decisions

Introduction to Healthcare Data Management

Healthcare Data

About Data Quality

About Data Sources

Electronic Health Records

Biomedical Imaging

Sensor Data

Data Description through Statistical Measures and Indicators

Types of Data

Analyzing Data

Application: Fraud Detection in the Mexican Healthcare System

Fundamentals of Healthcare Fraud

Fraud Identification Using Healthcare Data

Solutions to Prevent Frauds Based on Different Statistics Techniques

References

Summary

Currently health institutions generate large volumes of data inherent both to clinical and administrative activities. The Healthcare Data Analytics methodology provides tools for the collection, storage, analysis, and interpretation of data. It is a valuable tool that transforms data into useful knowledge for those responsible for decision making within these organizations. This chapter is divided into four parts: the first part describes the theme of Healthcare Data Analytics in general; how it has evolved; and the current needs that institutions and decision makers have in this area. In the second, an introduction to the management of healthcare data and the desirable characteristics required for these data before any analysis is given. The third part describes how to study data through statistical measures and indicators. Lastly, the methods for detecting and mitigating fraud in health systems are established, particularly in Mexico.

Introduction to Healthcare Data Analytics

Currently institutions involved in health care are concerned with improving the quality of the service they provide to patients and families. However, despite new operational and administrative approaches few health institutions in the world have achieved it. A tool with great potential for helping these institutions achieve the goal is to adequately use the health information systems, which provide evidence to reduce and prevent medical errors through the collection of information about each meeting with the patient in different medical situations.

Sometimes due to constant use these information collection systems can generate data overload. For example, in a radio frequency identification device for diabetes care data are captured quickly because they are sent to the server every minute, collecting information such as the location and blood sugar levels, blood pressure, age, weight of the patient, etc.

Therefore, institutions must have methodologies to benefit from the data available to them and thus improve clinical and administrative attention. Healthcare Analytics has the specific methodology to collect, explore, analyze, and interpret clinical phenomena of health data and to turn them into sustainable information in order to improve health institutions.

The challenge for these institutions is to take advantage of the advancement of medical devices technology as well as of information systems in order to create and sustain improvements in the quality, performance, security, and efficiency of health administration.

Healthcare Analytics technique can also be used to identify new opportunities and to suggest innovative ways to address old challenges. It even gives managers and administrators of healthcare institutions decision-making criteria to undertake optimal actions at the appropriate time and with the appropriate technology.

It has recently been demonstrated in different hospitals worldwide that the incorporation of these methodologies reduces mortality rates, healthcare costs, and medical complications.

How Healthcare Analytics Could Improve Decision Making?

Decision makers need information about the global functioning of the healthcare institution. Besides the information of accomplished fa...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• About the Editor
• Contributors
• Introduction
• Section I: Evolution and Adoption of the Analytics Process
• Section II: Analytics Knowledge Applications in Latin America and Emerging Economies
• Index