No matter your industry, data is a gold mine. Almost no discipline wouldn’t benefit from appropriate data collection, analysis, and management.
Healthcare is not left out of this system. Healthcare data analytics has become more important in recent times. Modern healthcare strives to uphold evidence-based practice while implementing inputs from technologies such as AI, machine learning, and data visualization.
In this article, we look into data analytics in healthcare. You will gain insight into what it is, how it works, and how to use it to the benefit of your healthcare facility and its patients.
What is Healthcare Data Analytics?
Healthcare data analytics is the process of analyzing all healthcare-related datasets (including clinical, administrative, and financial data) in all forms (such as electronic, behavioral, documented, etc.) to reveal insights, patterns, and trends within them.
This data is used to make informed decisions to streamline evidence-based practice, guide processes, and create efficient systems for personalized care.
The process involves complete steps of data collection, data analysis, and data interpretation. With these steps, all data are gathered and then sorted into several groups in which some data may appear in more than one section i.e. data management. The idea is to make sure that every factor that can affect a decision is taken into consideration.
Types of Healthcare Data Analytics
There are four main types of healthcare data analytics each serving a different purpose in terms of approach and decision-making.
1. Descriptive Analytics
Descriptive healthcare analysis is more of an eye-opener, especially for the heads of the organization who can’t be in all places at once. This type of data analysis uses past events and documentation to tell a story of what happened or what caused things.
With descriptive analysis, you can find patterns and reasons for both positive and negative events that have occurred to help you promote beneficial outcomes and prevent the recurrence of negative events
Some examples of descriptive healthcare analysis include:
- Tracking causes of hospital readmission to adjust discharge protocols
- Monitoring ward admission and bed usage rate to decide staff allocation volume
- Monitoring medication adherence to implement reminders using follow-up visits or direct mail
A lot of descriptive data may look administrative but they have clinical implementations that benefit patient care.
2. Diagnostic Analytics
A more medical use of data analytics for healthcare is in diagnosis. Diagnostic analytics involves getting insights from medical information, both past and present, to help medical professionals identify factors that can affect the health outcomes of patients.
This includes practices such as:
- Using the medical history of past patients to predict high-risk new admissions
- Using wearable devices to diagnose conditions remotely
- Processing medical imaging through AI to identify anomalies in the structure
Contrary to popular belief, these tools assist rather than replace medical professionals, making their jobs easier.
3. Predictive Analytics
Predictive analytics is fast becoming the most popular type of healthcare data analytics for both patients and healthcare organizations. It involves using past data and patient data to predict patient needs.
With predictive data, complications are avoided, secondary conditions are prevented, and resources are saved for chronic cases.
A few examples of predictive analytics in healthcare include:
- Tracking patient data to predict disease progression and take early action
- Identifying risk factors present to decide on actionable lifestyle changes
- Analyzing patient behavior and symptoms to decide on the introduction of other disciplines e.g. Inviting a mental health specialist for a patient with life-threatening conditions
Predictive analytics aligns with the aim of modern medicine to keep the community healthy and reserve hospital admissions to critical, chronic, and emergency cases.
4. Prescriptive Analytics
Probably, the best application of evidence-based practice is the use of healthcare analytics to make decisions on the treatments, medication, and services that patients will need based on past data.
Prescriptive analytics uses data such as patient medical history, family and social history, and analyzes it with records. This optimizes resource and staff allocation while aiding decision-making in situations with limited resources.
Benefits of Healthcare Data Analytics
From the types discussed above, it is easy to know that healthcare data analytics presents several benefits to both healthcare organizations and patients. The benefits of healthcare data analytics are numerous and personal.
Here are 5 top benefits of healthcare data analytics:
- Improves patient outcomes
- Supports and speeds up decision-making for medical professionals
- Saves costs for patients and resources for hospitals
- Promotes disease prevention and reduces disease progression
- Improves patient engagement in their treatment plan
Applications of Big Data in Healthcare
Raw data can be incomplete, inconsistent, and difficult to manage. Big data refers to complex datasets that are too large for traditional analytics methods or tools due to the velocity, volume, and variety of healthcare data.
To handle big data effectively, you must leverage high-performance technologies such as distributed computing, cloud-based infrastructure, and specialized software solutions. These systems offer optimized data integration of the large data made available.
Below are some of its very major use cases in healthcare:
- Predicting and identifying disease outbreaks early, allowing for faster public health responses
- Increasing drug development speed
- Assisting with unraveled research such as research into cancer, autoimmune diseases, and idiopathic conditions
- Improve cybersecurity solutions in healthcare
- Enhancing the effectiveness of all types of healthcare data analytics for better decision-making
In healthcare, big data has a large role to play. The data involved includes human behavior, advanced medical research, genomics, and population health studies. This data is vast, but when processed effectively, it provides invaluable insights for medical solutions on both local and global scales.
The Role of a Healthcare Data Analyst
As an organization, you won’t want to burden the clinical or administrative staff with new job roles. Data analysis in itself is a field that is still growing and needs people involved in the business. Many healthcare organizations rely on dedicated healthcare data analysts to handle these tasks.
A healthcare data analyst plays a crucial role in transforming raw healthcare data into meaningful insights that improve patient outcomes, streamline operations, and support decision-making in the healthcare industry. They use data analytics techniques to uncover trends, identify inefficiencies, and ensure compliance with healthcare regulations.
Key Responsibilities of a Healthcare Data Analyst
- Data Collection & Management
The data analyst will be in charge of gathering data from multiple sources, including Electronic Health Records (EHRs), medical devices, insurance claims, and patient surveys. They have knowledge about regulations like HIPAA and GDPR and they follow all these for accurate collection and management.
- Data Cleaning & Standardization
The analyst will sort through the data, identifying and correcting inconsistencies while converting different data formats into a structured and analyzable form. This process in itself already makes the data meaningful to the eye of the layman.
- Data Analysis & Interpretation
A healthcare data analyst uses statistical methods and analytical tools to uncover insights for clinical decision-making and operational efficiency.
- Healthcare Reporting & Visualization
To ensure that the analyzed data can be useful to the healthcare facility, a healthcare data analyst will develop dashboards, charts, and reports using tools to present findings to healthcare administrators, physicians, and policymakers. This data can in turn be used to carry out useful practices such as forecasting patient readmission risks, disease outbreaks, and treatment outcomes, as well as recommend strategies for reducing costs and improving patient care.
For organizations seeking a more scalable approach, data operations workflow automation platforms can further streamline healthcare data management, integration, and transformation, helping to inform healthcare decisions at all levels.
