The healthcare information highway moves at the speed of light. The amount of data healthcare facilities receive daily is overwhelming. Extrapolating this data and understanding the impact different components have on each other is crucial to keep your organization moving forward. Implementing business intelligence in healthcare is becoming not only a popular solution but a necessary one. It enables practices to transform raw data into powerful insights that drive smarter clinical, financial, and operational decisions.
What is Business Intelligence?
Business intelligence, commonly referred to as BI, is the technology that brings all of your data together in a visual and organized fashion. So why does your practice need BI?
Think of it this way – you and your management team are already the superheroes of your practice, juggling a multitude of tasks – from managing referrals to scheduling appointments, responding to reviews, following up on accounts receivable, and providing patient care. Just as Batman is more effective with his utility belt and James Bond with his gadgets, business intelligence is the power tool that elevates your capabilities. BI doesn’t just increase your superhero status. It amplifies your collective impact, driving your practice forward with greater efficiency.
No longer will you have to spend valuable time generating multiple reports to analyze KPIs, assess performance measures, and monitor your revenue cycle. BI enables you to quickly visualize and navigate data to access the health of your organization and make informed business decisions, improve operational efficiency, and optimize the revenue cycle.
Implementing Business Intelligence in Healthcare
Developing and implementing business intelligence in healthcare is far from straightforward. It is a full-scale project with a beginning and end, that includes actions and milestones along the way. Without careful planning and execution, projects can fall short of organizational objectives and fail to deliver a return on investment (ROI). To successfully introduce BI into your practice and ensure it is readily adopted by your staff, a systematic six-step approach is required.
Discovery – Understanding User Needs
The first step in implementing business intelligence in healthcare is the discovery phase. It involves pinpointing precisely what challenges your practice is facing. What departments within your practice face the biggest challenges? What are the priorities within those challenges and which areas would benefit most from enhanced analytics? Once your organization’s top challenges have been identified, develop an outline to work through them. Common pain points include improving patient access, optimizing clinical documentation, decreasing denials, and monitoring KPIs like revenue cycle metrics.
In addition to practice management and stakeholders, organizational subject matter experts (SMEs) must be part of the discovery process. This should include executive-level SMEs and operational SMEs from different departments. IT professionals should also be involved to ensure the technical setup matches these operational insights. An inclusive discovery process lays the groundwork for a strategic and effective BI deployment.
Collect Data – Identifying Data Sources
The next step in implementing business intelligence in healthcare is data collection. It is an intricate operation that sets the stage for all subsequent analytics. It begins by cataloging data sources within the organization, which can range from electronic medical records (EMRs) and practice management systems to payroll and call center logs. This step involves not just identifying where the data resides, but also the systems and technologies required to compile it, such as the need for a data warehouse or the utilization of APIs (Application Program Interface) for direct BI tool integration.
Collecting and unifying various data types—clinical, operational, financial, and retail (in cases where healthcare intersects with direct consumer sales such as physical therapy equipment or dermatology products)—is central to the BI process. This often requires ETL tools like Azure Data Factory for centralization. The ETL (extract, transform, load) process is fundamental to BI, extracting data from disparate sources and transforming it for in-depth analysis. Ensuring that all pertinent data is captured in the ETL phase is critical for an effective BI strategy that delivers comprehensive insights into organizational performance.
Data Modeling – Building a Scalable Foundation
Data modeling in healthcare business intelligence is a complex yet essential process that involves more than just linking data points together. It’s about creating a structured framework that accurately represents the multifaceted nature of healthcare data, encompassing clinical, operational, and financial domains.
This step starts with the construction of fact tables, which are the core components of the data model, containing transactional details such as patient appointments, payments, and procedural data. These tables are the factual backbone of the model, documenting the actual events and transactions that occur within a healthcare setting.
To connect and give context to the information in the fact tables, common identifiers like patient IDs are used. These identifiers link to dimension tables, which contain descriptive attributes related to the transactions—patient demographics, provider information, service details, and more. For example, a patient table would include the patient ID as well as names, addresses, and other pertinent details.
A well-constructed data model facilitates the seamless integration of different types of healthcare data, enabling a unified view that is necessary for thorough analysis and reporting. When these elements are systematically aligned, the model not only improves data retrieval performance but also becomes scalable. This scalability is critical for the development of new reports and allows for quick adaptation to changing analytical needs.
Quality Audit – Ensuring Data Integrity
While implementing business intelligence in healthcare, the quality audit step is crucial, ensuring the data’s accuracy and reliability. A single discrepancy can have significant decision-making implications. Without complete trust, user adoption of the information generated is unlikely.
This step in the process begins with validating data against host system reports like the EMR. Initially, the data is verified for high-level consistencies, such as total payments, to establish a foundation of trust in the BI system. However, the quality audit needs to delve much deeper than just aggregate values. It must extend to the granular level, matching data at the account, patient, visit, and CPT (current procedural terminology) code levels, ensuring that all figures align perfectly without any excess or duplications. It’s not uncommon to have issues and challenges that cause a quality audit to fail. There are almost always a few outliers. Some of the most common ones include:
- Posting date prior to service date
- Missing or “NULL” data
- Patients or providers with the last name “Test”
Queries can be set up to help identify these common outliers and remove them, but the last one can be extremely difficult, sometimes impossible. It is important that your organization have a policy for training that “Test” is used as the first and last name of the fictitious patient or provider and that the same one is used every time training takes place. That way it can be excluded from the query language and removed from all the data.
Visualization & Development – Designing for Adoption
This step is where the end-user experience resides and the data’s narrative comes to life. It’s critical because it determines how intuitively end users can interact with the BI tools and garner insights from the data presented. The success of a BI system is heavily reliant on its user interface—its ability to be understood with minimal training is ideal. Effective visualization requires a thoughtful design that tells the data’s story clearly and logically.
To create compelling visualizations, one must consider the journey through the data. For instance, if the visualization starts with a line of charges, it should naturally lead the user to related details, such as breakdowns by provider, procedure, or date. This narrative approach guides the user through the data, enhancing understanding and driving deeper engagement.
BI visualizations typically serve one of three purposes: to inform, to enable research, or to drive workflow. Informative visualizations offer at-a-glance KPIs to quickly assess performance. Research-oriented visualizations allow users to explore data further, such as identifying the most and least common procedures to evaluate service offerings. Workflow visualizations can be particularly transformative, turning AR and denial data into actionable worklists for staff to address, thereby streamlining operations.
Training & Adoption – Optimizing User Experience
When implementing business intelligence in healthcare, training and adoption are essential for realizing ROI. If no one is using the BI tool or they aren’t using it correctly, then that’s money wasted. The key to successful training is to emphasize how the BI tool can be integrated into everyday workflows, focusing on the practical application of insights rather than the mechanics of operation.
For example, instead of merely instructing users on how to set up a subscription, explain why a subscription is valuable—such as how a weekly point-of-service collection report can inform staff meetings and drive improvements in copay collection rates.
It’s about showing users the direct impact of BI tools on their responsibilities and how these tools can enhance their performance and accountability. Demonstrating how to use reports to pinpoint missed copays, identify patterns in insurance plans, and improve collection practices, provides users with a deeper understanding and appreciation for the BI system.
Monitoring the adoption rate post-training is as important as the training itself. It’s vital to track how frequently and effectively the dashboards and reports are being used. Low usage should prompt immediate action to understand the barriers—be it further training needs, improvements in visualization, or addressing data quality issues. Swiftly adapting to these needs and making the necessary modifications can enhance end-user buy-in and ensure that the BI dashboards are not just implemented but are fully leveraged to drive the organization’s success.
Ready to Implement Business Intelligence?
As we previously mentioned, using business intelligence in the healthcare sector is fast becoming a necessity. However, it is a complex process that not every practice has the in-house resources to handle. Working with business intelligence professionals is often the most effective and efficient way to complete the implementation process.
But why should you choose Parable Associates? Our team combines their expertise in healthcare with advanced analytical skills, utilizing cutting-edge technology such as Power BI and Tableau to address complex healthcare challenges, identify opportunities for operational improvements, optimize your revenue cycle, and support the success of your healthcare practice. Our extensive experience in healthcare technology includes working with some of the top names in the industry – Experity, ModMed, and WebPT to name a few.
From business intelligence implementation to consulting and training, Parable Associates is the perfect solution. Contact us and book a discovery call today!