Data Analysis and Integration in Healthcare

Data Analysis and Integration in Healthcare

Kara S. Evans (Waynesburg University, USA) and Elizabeth Baoying Wang (Waynesburg University, USA)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/978-1-5225-7071-4.ch010

Abstract

Healthcare providers treat a plethora of conditions associated with the human body for a patient to achieve optimal healthiness. However, aspects of a patients' entire wellbeing can often be overlooked, which leads to issues such as drug interactions, missed diagnoses, and other gaps in care. Healthcare can benefit from implementing better data management and integration to improve data analysis, which could bridge gaps in care. This chapter will explain data analysis and data integration, why they are pertinent in the healthcare system, and their associated rewards and challenges. After analyzing these healthcare facets, this chapter will conclude with a proposal for healthcare providers to leverage technology for patients' general wellbeing and a healthier population.
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Background

Without practices that involve data analysis and integration, healthcare providers fall into possible mishaps with fatal drug interactions, missed diagnoses, and other deadly accidents that could happen without the preemption of information technology and analytics. Also, analytics preempts issues like later cancer stages and heart problems in at risk patients, which would be left out if no data analysis or data integration was used in hospital systems. Additionally, without Electronic Medical Records (EMRs) or other networked sharing of patient data, doctors in the same hospital systems could find inconsistencies or mistakes within a patient’s Personal Health Record (PHR). In traditional databases, only one person or computer can access a patient’s data to change it to avoid race conditions. A race condition happens when two different people or processes are accessing a piece of data to change it and something gets left out by accident. There would be no inclusive way to share patients’ data with all of his or her caregivers, especially in large healthcare systems. Using different analysis systems could allow different caregivers to simultaneously access data and change it, or even allow machines to automatically put data into the patient’s record.

Key Terms in this Chapter

Biometric Wearables: Electronic devices worn/attached to a person in order to track that person’s vitals.

Real-time Analytics: Analysis that takes place in the present, real time, with no need to store data before analyzing it and potentially acting on that information.

Big Data: Consists of large data sets that can be analyzed via computers to reveal trends, patterns, and associations, made up of five key parts: volume, variety, velocity, veracity, and value.

Relational Database: A collection of data organized into columns, rows, and tables, which can be connected to one another via “relations” or columns/rows that the tables have in common.

Data Integration: Implementation of data analysis into various applications.

Information Technology: The study or use of electronic systems, specifically computers, for storing, retrieving, and sending information.

Electronic medical records (EMRs): Electronic records of data with consolidated diagnosis and treatment activities of a patient.

Personal Health Records (PHRs): A person’s own medical record, primarily with only one person’s medical information.

Data Analysis: A systematic use of data to drive fact-based decision making for measurement, planning, learning, and management.

Microsensors: Small sensor technology, able to be inserted or ingested for medical use.

Diagnosis: An assessment given by a medical professional of a medical condition or disease.

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