How to Harness Data Analytics in Healthcare

For an industry that produces billions upon billions of pieces of data, it’s mind-boggling how little healthcare data gets used. 

With over 2.3 zettabytes of data generated (and growing at a rate of 47% per year) overall, only 57% of healthcare data is used to make intelligent business decisions. And, patient data is stored in fragmented and siloed systems, according to 49% of healthcare leaders, further widening the gap.

That’s why data analytics in healthcare has been a game changer. Healthcare providers are using data analytics to look at tons of patient, operational, and financial info at a glance, helping them see patterns in their data to make decisions that lead to better patient care, smoother operations, and lower costs.

In this guide, we’ll give you the strategies you need to harness the full potential of data analytics in healthcare. Whether your goal is to improve patient outcomes, increase efficiency, or ensure compliance, this guide will help you navigate the path to smarter healthcare through data.

Benefits of Data Analytics in Healthcare

There are many benefits to data analytics in healthcare, but here are several we find to be the most important:

Enhancing Patient Care

By leveraging predictive analytics, healthcare providers can create personalized care plans that lead to improved patient outcomes. This approach involves analyzing medical history, genetic data, and real-time health metrics to deliver tailored, effective care

Predictive analytics plays a key role in the process of identifying patients at risk of complications or hospital readmissions. Hospitals, for example, can use these tools to proactively identify and intervene with high-risk patients, which can reduce readmission rates and improve recovery. This study from 2023 shows how Corwell Health used predictive analytics to prevent 200 patient readmissions, saving their healthcare system $5 million in associated costs.

Optimizing Operational Efficiency

In healthcare, data analytics plays a crucial role in optimizing resources, especially in environments where they are often stretched thin. By analyzing actual demand, organizations can ensure essential resources—such as staff, beds, and medical supplies—are allocated where they’re needed most. Real-time analytics further enhances efficiency by streamlining workflows, improving scheduling, aligning staff shifts with patient care demands, and eliminating bottlenecks in patient care.

Cost Reduction

Data analytics also provides significant financial benefits to healthcare organizations. Leaders can make informed decisions that reduce costs without sacrificing quality of care by using analytics to identify areas of waste, such as over-ordering supplies or redundant processes. 

Predictive tools can also estimate the number of patients who will need care at a certain time, which helps healthcare organizations spend their money and resources wisely. 

Common Applications of Data Analytics in Healthcare

Below are some of the most impactful uses of data analytics in healthcare:

Predictive Analytics

With predictive analytics, healthcare providers can ditch the “wait and see” approach and start taking action before problems arise.

Predictive models can identify individuals who are at risk for chronic diseases or complications by analyzing patient data, including medical history, lifestyle factors, and real-time metrics. This lets clinicians step in early and take action to prevent these health problems, diminishing long-term strain on the healthcare system. For example, analytics can predict if a patient is at risk for diabetes or have a heart attack. If they find the patient early, they can instill preventative measures that keep them from developing that chronic disease.

Population Health Management

Healthcare providers can use data from sources like electronic health records and public health databases to spot health trends and fix inequalities among specific groups of people.

Looking at this data lets providers find trends of chronic diseases in certain areas. Then, they can create special plans, like wellness programs or vaccination campaigns, to help those groups live healthier lifestyles. This practice is called population health management.

Steps to Implement Data Analytics in Healthcare

Here are the steps for integrating data analytics into your healthcare organization.

1. Assess Organizational Readiness

First, you need to know whether your organization is ready for data at all. That starts by assessing the data and infrastructure you currently have.

• Assessment of Data Accuracy and Reliability: Figure out if your data has any mistakes, inconsistencies, or discrepancies. If it does, your data won’t accurately reflect real-world situations.
• Evaluation of Data Completeness: Look for gaps or missing data points. Incomplete data can lead to an improper analysis and flawed decision-making.
• Review of Data Timeliness: Outdated information can stop you from being able to make the right decisions. Make sure your data is being updated and collected quickly enough to fit your healthcare organization’s operational needs.
• Validation of Data Consistency: Inconsistencies can lead to confusion and conflicts in decision-making. See that your definitions, formats, and standards are as consistent as possible.
• Assessment of Data Accessibility: Double-check appropriate access controls are in place to protect sensitive data and relevant compliance regulations.

2. Build a Strong Data Infrastructure

Building a robust data infrastructure involves five key steps—starting with defining your overall data strategy.

1. Define Your Data Strategy 

Start with a thorough data audit to identify your needs. This includes evaluating:

• What data you collect
• Where that data comes from (different systems and databases)
• What form the data currently exists in (structured vs. unstructured, etc.)
• How the data is currently secured
• Who needs access to that data and why

2. Build a Data Model

The structure of your data is defined by the data model. Data models are divided into three categories:

• Conceptual: Gives a high-level overview of the core business structures and processes.
• Logical: Defines the different data categories and their properties, as well as the connections between them.
• Physical: Lays out the schema, the database’s internal tables and columns, and other elements.

3. Choose a Data Repository

To choose a data repository, here are the three types you need to know:

• Data lakes (for raw, unstructured data)
• Data warehouses (for structured and filtered data)
• Hybrid approaches (which combine elements of both) 

You’ll also need to choose between on-premises hosting (which offers more internal control but less flexibility) and cloud hosting (which enables easier and more secure remote access). Cloud storage is a popular choice, with 47% of sensitive healthcare data being stored in the cloud.

4. Clean and Optimize Your Data

Data quality management software is essential whether you choose to build a data warehouse or a data lake. 

• Data warehouses need to be cleaned and optimized during ingestion into the structured database
• Data lakes require the same process to occur during retrieval so the data used is accurate, complete, and up-to-date

The data management process becomes more difficult as data scales. As a result, many organizations utilize automated data monitoring tools that analyze data throughout its entire lifecycle to prevent the introduction of errors and guarantee more usable data.

5. Build an ETL Pipeline

The ETL (extract, transform, and load) process is used to move data from various sources into a data warehouse or data lake. This process standardizes the structure of the data, making it more organized and accessible.

ETL Process

1. Extract: Data is extracted from multiple sources, such as CRM systems, data lakes, and other databases.
2. Transform: The extracted data is transformed and processed to fit the standard data model.
3. Load: The transformed data is loaded into the target database.

The ETL pipeline has to be secure; even a small loss of data during transfer can affect the quality of future analysis.

3. Use Advanced Analytics Tools

Next is inserting advanced analytics tools into your process. This requires investing in predictive models, AI/ML algorithms, and visualization dashboards that analyze data quickly and spit it out into visually digestible formats that are easy to understand.

However, as it always is with healthcare, make sure that these models and tools meet regulatory standards. The data used in these models is highly sensitive, so having HIPAA and HITRUST-compliant policies to keep the data protected is extra important.

How ClearDATA Can Help Integrate Data Analytics in Healthcare

Integrating data analytics in healthcare can optimize patient care, streamline operations, and reduce costs. ClearDATA offers expert support to navigate the complexities of data security and compliance within the healthcare sector.

Our services include:

• Customized Analytics and Reporting: Generate reports tailored to your organization’s usage history and trends to optimize your environment, minimize waste, and lower costs.
• Compliance Monitoring: Access summary and detailed reports that track and trend compliance using key tags (HIPAA, HITRUST, NIST, GDPR, and ISO).
• Data Insights: Utilize our professional services and powerful tools to extract valuable insights from your data, identifying trends, patterns, and exceptions.

If you’re ready to leverage the power of data analytics in healthcare while bolstering security and compliance, contact one of our healthcare cloud experts today.