Healthcare business intelligence uses specific tools to analyze healthcare data. It helps to identify trends and gain valuable insights. Hospitals, pharmacies, and other healthcare providers collect vast amounts of data to develop their business strategies.
According to Grand View Research, the global market for healthcare business intelligence is expected to grow at a rate of 13.5% annually until 2030.
This article will explore the types of business intelligence tools used in healthcare and real-world examples of how professionals in this field apply healthcare business analytics in their work.
What Is Health Care Business Intelligence?
Business intelligence means using tools to learn from data. When used for healthcare, these tools help hospitals improve patient care and run more smoothly. They include ways to gather data, like data mining, and ways to store, secure, and analyze it.
In healthcare, organizations collect a lot of data from different sources including EHR systems like Mental Health EHR. Managing this data while following privacy rules and other regulations can be tough without good business intelligence tools.
Types of Business Intelligence That Matter In Healthcare
Business intelligence tools designed for healthcare help organizations gather, securely store, and analyze crucial data. Before diving into what these tools can do, it's important to understand where healthcare business intelligence data comes from. In any healthcare setting, data streams from various sources, including:
Health Records: Information from past visits or details you provide when filling out paperwork at the doctor's office.
- Insurance Claims: Information monitored by medical offices for claims submitted to insurance companies.
- Billing: Healthcare facilities keep tabs on billing and processing payments.
- Administrative Information: Governments and regulators often require hospitals to collect specific data.
- Patient Surveys: Feedback from patients about their experiences with healthcare services.
Healthcare business intelligence tools cast a wide net, gathering this diverse data and transforming it into actionable insights for business decisions. This starts with collecting and securely storing the data. Then, tools like data visualization and mining help make sense of it all.
Analysis often includes predicting future trends or assessing the impact of changes. Finally, these tools help create detailed reports that senior leaders can use to make informed decisions.
Examples of How Business Intelligence Improves Healthcare
Here are examples of how business intelligence makes healthcare better:
Managing Costs and Boosting Revenue
Healthcare analytics help determine which processes work best and where improvements can be made. This insight also helps understand the competitive landscape, making it easier to build patient loyalty and offer more value than competitors. As a patient, it is essential that you regularly check your Medicare benefits to stay informed about coverage and avoid unexpected costs.
Improving Patient Health and Safety
Healthcare data analytics give doctors and caregivers more tools to provide better care. They can pinpoint patients at risk, choose the best treatments, and even spot environmental factors that might affect how patients recover.
Enhanced Research
To advance research, scientists utilize data and machine learning to accelerate progress in understanding drug interactions and predicting their effects on patients.
Detect Disease Earlier
These technologies also enhance early disease detection, particularly for conditions like heart and kidney disease. This predictive capability extends to identifying optimal treatments based on health data analysis.
Improve Preventative and Follow-Up Care
Healthcare analytics further improve preventative and follow-up care by predicting individuals at risk of missing appointments. This foresight allows healthcare organizations to intervene proactively.
Adjust Staff Levels
Business healthcare analytics enable hospitals to optimize staffing levels by predicting peak and off-peak periods. This helps maintain appropriate staffing levels, optimise resource allocation, and reduce labor costs.
Challenges of Business Intelligence In Healthcare
But what is holding healthcare practitioners back from setting up a flawless business intelligence strategy? Let’s find out.
High Data Sensitivity
Handling sensitive data is a major concern for healthcare organizations. They not only manage patients' financial information but also handle sensitive patient and clinical data that must comply with strict HIPAA regulations. When it comes to using data for analytics, healthcare providers need a well-thought-out strategy. This involves carefully managing who has access to private information, defining roles clearly, and ensuring data security across all their systems and data storage locations.
Let’s say you are a dermatology practitioner and deal with many conditions that may or may not be medically necessary. A patient comes to you for a cosmetic procedure, which Medicare refuses to pay for. Later, a scammer contacts you, pretending to be Medicare’s representative. You provide them with all the patient information to get reimbursed. Scammers leak that data to cosmetic companies, and you get a penalty for it. Now, you get the point? So, your dermatology billing solution must be HIPAA-compliant.
Difficulties of Data Access
Accessing data in large healthcare organizations, like ACOs, is no small feat. These organizations often run multiple electronic health record (EHR) systems simultaneously.
Some are built in-house, while others are outsourced to EHR vendors. Each EHR system has its own way of storing data, which means extracting information from all these systems requires a deep understanding of each one's technical specifics. It's not just about creating extraction algorithms; it also involves writing custom code to make sure all the data, including critical information on women's pain health, can be integrated and used effectively.
To tackle these challenges, healthcare providers might turn to experts in EHR conversion or specialized business intelligence firms. These professionals have the knowledge and skills to bring together data from different EHR systems and ensure they work smoothly together. This approach helps healthcare organizations make sense of their data without compromising security or compliance with regulatory requirements.
Data Quality
In healthcare, different specialists, like doctors, surgeons, and nurses, input data into systems such as EHRs in various ways. This results in similar types of information being stored in multiple places within each system. When healthcare providers need to access this data, it becomes a challenge.
The ease of entering data creates inconsistencies when it's time to retrieve and report information. Developing a solid business intelligence (BI) strategy is crucial to addressing these issues. It should focus on identifying and eliminating duplicate data while ensuring all parts of a patient's profile are accurately synchronized across different systems. This approach helps healthcare teams maintain data integrity and improve decision-making processes effectively.
To illustrate the complexity and the importance of a robust BI strategy, consider the following comparison infographic:
This infographic compares the typical fragmented data entry process with a streamlined BI-integrated system, highlighting how a well-implemented BI strategy can enhance data accuracy and efficiency in healthcare.
The Bottom Line
This article addressed the impact of business intelligence on healthcare, its benefits, and obstacles. These challenges are not roadblocks but rather growth opportunities. Understanding the various strategies, solutions, and approaches to address these gaps is essential.
Whether an organization opts for an internal BI team or partners with an external business intelligence company, selecting a trustworthy and competent partner is key. This collaboration has the potential to evolve into a long-term relationship, particularly as healthcare undergoes shifts toward pay-for-performance models. It underscores the importance of choosing a partner who can navigate these changes and contribute to sustained improvement in healthcare outcomes.
