Business intelligence

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Business intelligence (BI) is an umbrella term to describe concepts and methods to improve business decision-making by using fact-based support. (Tutunea) This page discusses BI primarily from a healthcare perspective.

Introduction

Business intelligence, defined in more detail, comprises the strategies, processes, applications, data, products, technologies and technical architectures used to support the collection, analysis, presentation, and dissemination of business information (Nedim). Its origins are attributed to a 1958 article in IBM's Journal of Research and Development (Luhn) where it was called the business intelligence system. (Mettler, Tutunea)

BI is sometimes viewed from two positions: data-centric and process-centric (Mettler). The data-centric position sees BI as transforming operational data into information using analytical tools to help drive business decisions. This approach often divorces the data from its context, however, jeopardizing its interpretability. The process-centric approach addresses this problem by emphasizing the processes from which the data originate to avoid losing context.

Some emphasize the presentation of the data: "the integration of data from disparate sources systems to optimize business usage and understanding through a user-friendly interface" (Madsen). Others contend that the term BI is being replaced by the term 'analytics' (Morr). Analytics "involves the use of data, analysis, and modeling to arrive at a solution to a problem or to identify new opportunities." This is uncannily similar to BI. In general, however, analytics refers to the methods used to processing data and BI refers to the over-arching process of curating information to inform business decisions.

Healthcare BI

While business intelligence has long been used in many other industries, healthcare largely did not adopt these techniques until the 21st century (Gartner). The reasons for this are varied (Metter). Most industries have singular management structures while healthcare has both clinical and administrative ones. Most industries have a well-defined group of customers with a relatively constrained product range, whereas healthcare serves the needs of patients, insurance companies, accrediting agencies, governmental authorities, researchers, and clinicians -- while they all might theoretically serve the patient, their paths often cross in antagonistic ways. Most industries also have discrete, reliable metrics, but healthcare metrics are defined differently by the varying customers, often difficult to capture reliably, and must attend to poorly defined patient factors.

Healthcare organizations have historically struggled to find the elusive link between the investment in information technology and improved organizational performance.* Aside from the aforementioned adoption barriers, this gap has also been driven by the use of information technology (IT) to digitize clinical workflows with inadequate attention paid to using the care delivery information to make business decisions.* The strategic value of IT lies in its power to provide clinicians and leadership with direct visibility into the care delivery process.

Nevertheless, the state of modern healthcare is often characterized by its scarcity, poor quality, and financial pressures (Mettler, Cutler), all of which are ideal targets for BI. The hope of BI in healthcare is to solve these problems by using data to optimizing resource allocation, power quality improvement processes, and make strategic decisions that reduce costs and increase revenue.

Frameworks for Healthcare BI

Mettler et al. provide a framework for BI in healthcare. For a visual aid, see Figure 1 in their paper. The framework is organized in four sections: processes, actors, information, and technology). Processes are defined as sets of partially ordered and coordinated tasks that often cut across organizational units and are the target of data collection and analysis. They separate target processes into three categories: medical (e.g., diagnostics and treatment, research and teaching), business (e.g., finance, risk management), and support (e.g., human resources, logistics and supply). Each process serves an internal and/or external 'actor' (e.g., patient, government, doctor, HR officer), all of whom become stakeholders in the BI system. The information produced by the processes is captured as data categorized as clinical, administrative, or external; when combined, they form a data warehouse. The data is transformed into information using technologies (e.g., reports, expert systems, data mining).

Morr et al. describes the framework of BI differently using four components: a data warehouse, business analytics, business performance management (BPM), and a user interface. The business analytics reflect the types of technologies used to process and analyze the data. The BPM separately describes how data is to be used for decision-making, such as identifying metrics and targets, monitoring frequency, and corrective actions. This framework also emphasizes a user interface as an essential component, pointing out the importance of being able to visualize the data (e.g., interactive dashboard, paper report).

Prerequisites for Effective Healthcare BI

Metter et al. also define certain prerequisites for effective healthcare BI: collaboration, knowledge, trust, institutions, governance. BI systems are only as good as the organizational relationships that surround them.

• Collaboration between different healthcare and social services in an area are important to share BI technology and data to drive decisions for the whole. This is challenging when the services are not all consolidated into one system.
• Knowledge of the system's processes, decisional authorities, and lines of communication are becoming more crucial for making decisions. Modern management strategies often don't fit into formalized organizational charts, which makes finding this information difficult.
• Trust, whether through contracts, competence, or goodwill, is important for sharing data and responsibility for addressing problems revealed by BI.
• Institutional structures formalize rules for BI operations. Clear delineations of processes help make sense of large, complex systems.
• Governance is vital in safeguarding information integrity and ensuring appropriate actions are taken.

It is important to note that each of these prerequisites contribute to the other. For example, collaboration builds knowledge and trust, which in turn are necessary to create institution and governance. Having an institutional structure and governance can in turn promote trust and collaboration while clarifying knowledge.

Laura Madsen proposes an alternate set of five 'tenets' of BI, adapted into the following more modern terms by El Morr et al.: data quality, technology, value, change management, and leadership.

Regardless of the conceptual framework used, it is clear that deploying effective healthcare BI involves addressing a wide range of organizational factors. A BI system cannot stand on its own.

Components of a comprehensive business intelligence and data warehousing program

• A structured program to capture and manage meta data - "data about data"
• Defined business and clinical rules to cleanse data contained in transactional systems
• Tools to extract, translate, and load data from source systems into a format that enables analysis, reporting, and decision support
• A database management system that integrates and stores information from many source systems into a single solution
• Several different business views of data including population or disease specific data marts to support specific business or clinical questions
• An operational data store to manage information for real time and near real time monitoring applications

Analytics

Analytics support the function of BI. These are the techniques that collect, process, analyze, and visualize the data to provide insights. Trevor Strome identifies five basic layers to analytics: business context, data, analytics, quality and performance management, and presentation.

• The business context defines the objective and measurable goal of the analytics. Data should not be collected for its own sake for no particular reason. It should be intentional.
• The data is defined by identifying the context, the data source and its quality, and the method for collection and storage.
• The type of analytics is defined by choosing the appropriate software and algorithms to process the data.
• Quality and performance measurement refer to the defined metrics, their targets, and strategies for evaluation and improvement.
  • Metrics with defined targets are called indicators. They provide simplified conclusions about whether goals are being met.
• Presentation refers to the data visualization techniques used to communicate the information in the data meaningfully to various stakeholders.

Business intelligence was defined by David Losehin as the processes, technologies, and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business applications. As such, business intelligence is a key discpline that needs to be adopted by healthcare organizations to transform the vast quantities of data contained in their transactional EHR systems into a format that enables improved strategic, tactical, and operational decision making.

Examples of business intelligence technologies

• Decision Support Systems
• Executive Information Systems
• Online Analytical Processing (OLAP)
• Query and Online Reporting
• Business Process Monitoring
• Performance Scorecards and Dashboards
• Data Mining
• Predictive Analytics

A healthcare organization must also develop a core competency in data warehousing to enable business intelligence applications. Wiliam Inmon defined data warehousing as a "subject-oriented, integrated, non-volatile, time vriant, collection of data organized to support management needs." Data warehousing includes both the process and the technologies required to achieve value from the data assets of an organization.

References

1. Gartner, Hype Cycle for Business Intelligence and Data Warehousing, 2005
2. Gartner, Hype Cycle for Healthcare Provider Applications, 2005
3. TDWI Business Intelligence Fundamentals, The Data Warehousing Institute, January 2005.
4. Mettler T, Vimarlund V. Understanding business intelligence in the context of healthcare. Health Informatics Journal. 2009;15(3):254-264. https://doi.org/10.1177/1460458209337446
5. Nedim Dedić, Clare Stanier. Measuring the Success of Changes to Existing Business Intelligence Solutions to Improve Business Intelligence Reporting. 10th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS), Dec 2016, Vienna, Austria. pp.225-236, https://doi.org/10.1007/978-3-319-49944-4_17
6. Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180. https://doi.org/10.1177/0165551506070706
7. Cutler, D. M., & Morton, F. S. (2013). Hospitals, market share, and consolidation. JAMA, 310(18), 1964-1970. doi:10.1001/jama.2013.281675
8. Madsen, L. (2012). Healthcare business intelligence: a guide to empowering successful data reporting and analytics. John Wiley & Sons.
9. Luhn, H. P. (1958). A business intelligence system. IBM Journal of research and development, 2(4), 314-319.
10. Tutunea, M. F., & Rus, R. V. (2012). Business intelligence solutions for SME's. Procedia economics and finance, 3, 865-870.
11. El Morr, C., & Ali-Hassan, H. (2019). Analytics in healthcare: a practical introduction. Springer.

Submitted by Amrish T. Pipalia