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Integrating Power BI with OLAP to Identify Cost Savings in Healthcare

OLAP systems facilitate more efficient data analysis than relational databases like MySQL or DB2. And, effective data analysis can help healthcare facilities identify cost-savings and other factors that contribute to operational efficiency. With the increasing emphasis on data, many companies are integrating Power BI with OLAP to enable better decision-making. Essentially, integrating Power BI with OLAP helps optimize data analysis and enhances your ability to identify trends and possibilities that increase your competitive advantage.

What is OLAP?

Online Analytical Processing (OLAP) refers to a data processing system. It extracts data from relational datasets, organizes it, and enables multi-dimensional data analysis. With such a system, different users can query, report on, and analyze data seamlessly.

In a hospital setting, for example, you can query data that shows medications used in the ER in July. Then, you can compare the data with that from previous months.

OLAP facilitates budgeting, revenue forecasting, and trends analysis, all of which can help hospitals identify cost-saving opportunities.

Understanding How OLAP Systems Work

The analytical processes performed by OLAP systems first require the collection of data from various relational datasets. After the data is collected, it’s stored in data warehouses. Next, the data is sorted and organized into data cubes. This arrangement of data transcends the limitations of relational databases, which can’t display large volumes of data effectively or perform real-time analysis.

OLAP data cubes can contain data categorized by dimensions such as period and revenue. An OLAP cube extends a single table with additional layers in a hierarchical manner. Essentially, OLAP cubes help categorize large volumes of data for quick analysis.

OLAP and Data Mining

Both OLAP and data mining are similar but not the same. OLAP facilitates the aggregating and analyzing of data. It can answer a query such as “How many ER patients were treated with morphine in July?”

On the other hand, data mining uses existing databases to discover new patterns between data sets and generate predictions. It can answer issues such as “What patients are at higher risk of developing heart disease and why?”

Data mining and OLAP are complementary. For instance, an OLAP system can help you discover that there is an increase in surgical fatalities. Then, you can use data mining to identify the reasons behind the increased fatalities.

How OLAP and Data Mining Can Help Identify Cost-Savings in Healthcare

In light of COVID-19, the healthcare industry is facing intense financial pressures. The added costs of outfitting units for COVID-19 patients combined with lost revenues from canceled non-emergency procedures have blindsided the industry. In light of this, efficient data analysis can provide insights to help identify cost-saving opportunities.

For instance, OLAP and data mining systems can help track patient histories and physician track records. Administrators can, therefore, make informed decisions about scheduling — assigning the right doctors to the patients with the most need. As a result, the treatment process is expedited and patient satisfaction improves.

Health insurance companies can also use these data analysis tools to track claims, client histories, and premium payments. Integrating Power BI and OLAP can provide insights that help insurance companies match consumers with the right policies.

In a nutshell, OLAP and data mining can help identify more cost-saving opportunities by categorizing and analyzing data such as:

  • Patient medical history
  • Patient costs
  • Practitioner performance
  • Population health
  • Patient engagement

After analyzing the data, you can reduce costs by:

  • Predicting risks
  • Taking steps to reduce unnecessary ER visits
  • Leveraging robotic process automation to reduce manual errors and avoid expensive malpractice lawsuits
  • Enhancing strategic planning
  • Minimizing opioid abuse levels
  • Improving scheduling and staffing

The Benefits of Integrating Power BI With OLAP

OLAP systems provide a wide range of benefits, especially in the execution of complex data analysis. However, these systems have some drawbacks:

  • While the data warehouses in OLAP systems are continuously updated as data flows in from various sources, the data refreshes at a slow rate.
  • OLAP systems use a snowflake or star schema, which can confuse less tech-savvy hospital administrators or healthcare workers.

Power BI connects various data sources, creating a unified and scalable platform. Its main advantage lies in its interactive data visualization capabilities. Instead of the snowflake or star schema, users can visualize data in graphs, bar charts, and pie charts. Additionally, it connects all your data sources accurately and consistently.

Ultimately, integrating Power BI with OLAP systems helps with analysis and the sharing of insights. And, as a result, this facilitates the making of strategic, actionable decisions. In the healthcare industry, this integration facilitates an efficient response to real-time changes in data.

Ready to leverage the integration of Power BI with OLAP? Entrance is a custom application development expert proficient in Power BI integration. We can help you connect your data analysis tools to Power BI. Contact us now, and you will have stunning data visualizations in as little as 24 hours.

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