5 Essential Data Prep Skills for Healthcare Data Analysts
- Jeffrey Agadumo
- June 13, 2023
This short informative article will explore five crucial skills necessary to succeed as a healthcare data analyst.
Reports project that the global healthcare data analytics market will surpass USD 121.1 billion by 2030.
Why?
Because of:
- The increasing demand for healthcare data analysts and analytics from various government initiatives.
- The increase in data collection and the growing adoption of electronic health records leading to the use of big data in healthcare organizations.
- The need to improve care delivery and clinical outcomes.
The above reasons will fuel the market’s growth and create high demand for skilled healthcare data analysts with essential data preparation skills.
In this age of information and analytics, the importance of data analysis in healthcare is something we cannot overlook, and aspiring data analysts looking to excel in this field can significantly benefit from mastering essential skills.
These skills will adequately equip you to function confidently in healthcare data analytics.
So be sure to read till the end.
Why is Data Analytics Important in Health Care?
Why is Healthcare Data so Important?
Healthcare data is of the utmost importance because it allows us to properly understand and improve our quality of life. It plays vital roles in improving healthcare outcomes, driving advancements, and shaping effective healthcare strategies to benefit individuals and communities.
Some of these roles include;
- Evidence-Based Treatment: Medical professionals can make informed decisions based on evidence by analyzing patient data, including symptoms, medical history, and test results. They can determine the most effective approaches to diagnosis and treatment.
- Health Research and Clinical Trials: By analyzing large medical datasets, researchers gain insights into disease mechanisms, treatment effectiveness, and patient outcomes, ultimately leading to new therapies and medical breakthroughs.
- Health Policy and Regulation: By analyzing healthcare utilization, costs, and outcomes data, policymakers can identify improvement areas, address disparities, and shape policies to enhance the healthcare system.
And this brings us to the point of this article.
5 Data Prep Skills to Maximize for Healthcare Data Analysis
1. Data Cleaning
Healthcare data, often obtained from various sources, can be messy and inconsistent. As a skilled data analyst, you must possess the ability to identify and handle missing values, outliers, and inconsistencies within datasets. This involves performing data cleaning techniques such as removing duplicates, standardizing formats, and resolving discrepancies.
2. Data Integration and Transformation
Healthcare organizations generate vast amounts of data from multiple systems and sources. Therefore, you must integrate and transform diverse datasets into a unified format to derive meaningful insights.
This requires proficiency in data integration tools & techniques, including merging, joining, and blending data from different sources. Additionally, you may need to transform data by applying mathematical operations, aggregating variables, or creating new calculated fields.
3. Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is a critical skill for healthcare data analysts. It involves visually and statistically exploring datasets to understand their underlying structure, identify trends, and detect anomalies.
EDA techniques, such as data visualization, summary statistics, and correlation analysis, enable you to gain valuable insights into the data. By effectively conducting EDA, you can uncover patterns and relationships that may not appear at first glance.
4. Data Versioning and Documentation
One crucial skill that healthcare data analysts need is the ability to manage data versions effectively, document data cleaning and transformation steps, and maintain metadata. This skill ensures reproducibility, promotes collaboration and maintains transparency in data analysis processes.
5. Data Governance and Risk Management
Analysts handle sensitive information that require strict adherence to privacy regulations and ethical guidelines. Therefore, understanding data governance principles and risk management compliance is essential to ensure data security, protect patient confidentiality, and maintain public trust.
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- Collaboration: Enables teamwork, knowledge sharing, and reproducibility of analyses.
- Data Security and Compliance: Ensures data privacy and adheres to healthcare regulations like HIPAA.
- Data Visualization: Creates impactful visual representations for effective communication of insights.
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