Healthcare is a very complex knowledge-based industry that generates vast amounts of clinical and financial data. Rapid data accumulation exceeds the ability of most healthcare organizations to use data to improve operational efficiency, clinical quality, and financial efficiency.
Healthcare managers and clinicians have to sift through vast amounts of information from many different levels to answer complex questions. Data comes from various sources, different formats, and points in time, making it more difficult for end users to interpret the information effectively.
Organizations can improve their information management efficiency and achieve higher business goals by adapting business Intelligence maturity models for healthcare. Through the careful and prudent use of business intelligence (BI), healthcare organizations can transform vast amounts of data into insights that help to improve patient outcomes, increase operational efficiency, and support public health efforts.
What is business intelligence in healthcare?
Business intelligence is an extensive group of technologies, applications, and processes used to collect, access, and analyze data to help users make better decisions.
The main goal of a BI system is to improve the timeliness and quality of input required for decision-making processes. That means delivering actionable information in the right form, at the right place, and at the right time.
Careful and practical use of business intelligence in healthcare can turn data into knowledge that can improve patient outcomes and operational efficiency. A thoughtful approach that allows managers and vendors to understand their readiness for business intelligence and the critical steps towards a mature BI process can help develop an overall BI strategy.
One method that organizations can use to assess their BI readiness is to use a healthcare business intelligence maturity model. BI maturity models identify the strengths and weaknesses of an organization’s information maturity level.
A robust maturity model is essential to guide and provide stakeholders with a maturity level of their organization and how ready it is to make the best of BI. Due to the healthcare industry’s unique challenges, the processes included in most existing BI maturity models do not always address the complex requirements of hospitals and care providers.
Read more about where to start with BI maturity model in our previous article.
What are the challenges for Business Intelligence Maturity Models in healthcare?
Three key areas that make healthcare BI particularly challenging are:
the need to integrate clinical and financial data
the different types of data formats necessary for advanced analytics
the needs and expectations of external information necessary for clinical, operational, and financial decision-making
Complex decision-making processes
Healthcare decisions are often complicated by the need to integrate poorly structured, uncertain, and potentially conflicting information from multiple sources. Patients do not respond to treatments the same way, and decisions may depend on the task and the decision maker’s expertise. Embedding clinical decision support tools into clinicians’ workflow can radically transform everyday decision-making for the better.
Mixed payment mechanisms complicate medical reimbursement, processing, and analysis of data. Different payment and reimbursement methods need to be integrated for effective use of BI.
Diverse payment models
Various payment models are used to reduce overall healthcare costs. Changes in payment methods require integrating information from multiple organizations. By combining data from the entire care process, including hospitals, clinics, nursing homes, ambulatory health facilities, and other settings, predictive analytics can be used to make better patient care decisions.
There has been a recent shift to involve patients in health decision-making. This includes sharing health information and providing tools such as telehealth and personal health records (PHRs) to help communicate and manage care. As the technology matures, patients will require their PHR information to be shared with providers and integrated into electronic health records.
How to adapt a business intelligence maturity model for healthcare?
Due to the unique challenges of the healthcare industry, traditional BI maturity models should be adapted to serve care providers better and provide an accurate assessment of their maturity level. The following steps are a starting point for healthcare organizations looking to transform and improve their BI strategy.
Create a conceptual structure
A maturity model for healthcare BI should provide a framework for a consistent approach to healthcare business intelligence development. Appropriate process maturity frameworks for healthcare complexity can help assess maturity levels accurately. For example, suppose it is a process focused on external data exchange and interoperability. In that case, maturity levels can be assigned, ranging from inconsistent data definitions and missing data standards to full integration with internal data systems.
Integrate different systems
Healthcare processes often transcend departmental boundaries. Operational, financial, and clinical data must be integrated to take full advantage of business intelligence. Healthcare organizations focusing on integrating data can increase profit margins, improve patient satisfaction, and provide better care.
Capture important healthcare processes
A healthcare business intelligence maturity model should capture key process areas and critical success factors in business and clinical intelligence development. The integration of operational, financial, and clinical information, as well as the exchange and interoperability of external data, are vital components in realizing the full benefits of BI in healthcare maturity models.
Integrate people, technology, and organizational processes
When including technology, people, and organizational processes, we can consider assessing the level of maturity in areas such as vision and BI strategy, management enablement, change management, employee skill levels, knowledge management, data quality, and technology infrastructure. Combined with healthcare processes, including integration and interoperability, these areas should create a comprehensive healthcare BI maturity model.
Evaluate data quality
Data quality is becoming increasingly important for many companies. This is especially true in healthcare, where cost pressures are high, and there is a desire to improve patient care. If the system, information, and service qualities are not credible, it will affect their actual use.
Business intelligence maturity models explicitly adapted for healthcare use can significantly benefit the industry. The right maturity model can provide insight into the key steps and processes required to achieve a desired BI maturity level, providing readiness assessment and planning for a BI strategy.
At Itirra, we provide companies with custom healthcare solutions and opportunities to explore potential improvements to support their business goals. For a personalized recommendation based on your unique business model, don’t hesitate to get in touch or schedule a meeting with me.