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The Need for Greater Healthcare Business Intelligence

By December 11, 2012Article

One of the many challenges faced by modern healthcare providers is the increasing demand that they demonstrate a deep understanding of the efficiency and quality of care they deliver. Increasingly, payments are as tied to the provision of service as they are to the ability to measure, interpret and report on it. Frequently this challenge is compounded by the lack of measured information or information systems that are in disarray. In short, healthcare organizations must be able to make decisions based upon data generated during operations. Utilizing business intelligence (BI) technology to drive business and clinical decisions is the only way to affordably meet today’s data analysis requirements. A good BI tool increases efficiency, reduces operational expenses, contributes to producing superior clinical outcomes and improves the ability to make large strategic decisions.
Implementing BI solutions is not as simple as buying software and having IT manage it. This article addresses the four primary challenges to successfully implementing business intelligence capabilities: (1) adopting an information-driven culture, (2) defining key performance metrics (KPM) to track, (3) guaranteeing data quality and (4) finding and implementing BI tools that meet an organization’s needs.
Given the challenges healthcare organizations face today and will face tomorrow, the ability of an organization to transform data into information and information into intelligence will be vital to its survival.  As the economics of healthcare evolve, modern healthcare organizations need a robust set of methodologies, processes, architectures and technologies to capture and transform raw   into meaningful and useful information. Effectively implementing and utilizing BI is increasingly important for the healthcare industry, empowering an organization to leverage clinical information to enhance decision making, improve outcomes and, ultimately, remain competitive.
Essential components of a meaningful BI implementation
Before a healthcare organization adopts a BI solution, it should account for, plan and take action based on the following four key components of successful implementations.
1. Adopt an information-driven culture
Adopting an information-driven culture is essential to BI success. Even the best technical solution will fail if the business culture is not information driven. With senior leadership evaluating and managing business based on key performance metrics, an organizational culture is created whereby data collection and analysis is valued. Without senior management’s leadership in establishing BI, it is impossible to create the information-driven culture necessary for success. This is especially true in an organization that requires substantial investment to implement a BI initiative.
For a BI implementation to succeed, the business must have a clear vision of the key performance metrics upon which the business will be evaluated and must seriously pursue improvement on the chosen metrics. Without rewards and consequences tied to performance metrics, BI will be largely ignored.
2. Define and build a BI organization
An information-driven organization operates most effectively when BI resides between the business and IT as BI requires close coordination between IT and the business to be successful. IT provides the infrastructure/environment for BI including the hardware, software and tools and must have the capability to support the BI suite in terms of time/resources and expertise; IT also has responsibility for all tasks related to data acquisition and integration. The BI organization is responsible for the design and use of the end-user business analytical environment and meeting the organization’s overall information needs.
3. Establish a data management program: 
Successful BI implementations must include a comprehensive data management program to assure accurate, high-quality and consistent data collection and resulting information and intelligence. The data management structure and process should be in place prior to implementation of any BI applications to ensure the integrity of data collection and analysis to the satisfaction of business users — earning their trust and commitment to the BI solution. 
Select and acquire BI tools: As with any information solution, a BI implementation requires the installation of systems, processes and tools to enable a breadth of required capabilities. These tools are essential to data collection and the resulting quality of useful and actionable information. Each organization has unique requirements and must properly define them and understand its overall objectives to effectively select or create the tools to maximize BI’s benefit.
Taking these four key components of a meaningful BI implementation into consideration, an organization should evaluate its BI goals and capabilities, thoughtfully evaluate its current business and the steps required to reach their desired outcome and formulate its business objectives and overall requirements.
Managing technology aspects of a BI implementation
When an organization has defined the business objectives of its BI implementation, a variety of technical questions need to be addressed. An organization will have to evaluate their existing technology, assess the scope of their evolving needs, decide how best to improve or extend the life of an existing solution, and decide whether or not to retire and replace existing solutions in whole or part.
The requirements-gathering stage is critical to determining the needs of the business, and poor decision making can be costly. If the business is not confident in its own ability to perform requirements gathering, utilizing an external consultant is preferable to leaving this to internal resources alone.
The requirements gathering process may suggest that an organization invest in new technology. If a BI solution must be implemented from the ground up, an organization must first establish high-level requirements, taking care to review and evaluate possible future needs.
Many organizations already possess some level of BI technology. If the current technology is determined to be sufficient, then an evaluation of its lack of progress or success is important. But keep in mind that planning for the future is imperative. A recent Gartner study forecast that by 2014, 85 percent of currently deployed BI solutions will not scale to future volume and complexity demands.
The importance of BI maturity models
An organization’s BI goals and capabilities need to be compared and measured against a BI maturity model to better understand the current capabilities and the steps necessary to achieve an implementation that is as mature as possible. BI maturity models describe the stages organizations advance through while evolving their BI infrastructure to its greatest strategic level of implementation.
A number of generic BI maturity models already exist, from which an organization can specifically design a model unique to its needs. The model an organization adopts will provide more value if the model utilizes concrete benchmarks to benchmark the organization’s BI maturity. An added benefit of a quantifiable model is the provision of a clear pathway for progressing to the next stage of maturity. Such a maturity model must address the scope, environment and technologies employed in complex BI implementations. At the least, the model should detail implementation of tools to transform and load data from disparate sources into the BI target data warehouse, historical and current business data storage, and front-end BI applications used to provide querying, reporting and analytic functions to an organization’s knowledge workers.
The articulation of a BI maturity model lays the strategic blueprint for developing a BI tool that meets the business’ current and future needs. It guides the timing of and investment in BI development. Additionally, using a maturity model improves the probability of a successful BI implementation by selecting the most effective approach to implementation, establishing a method to identify technology to support data collection and analysis, and selecting a BI solution based on business goals.
The benefits of healthcare BI are clear
A successful BI implementation should generate an excellent return on investment. BI can give an organization direct line of sight to total cost of care, reduce the length of the revenue cycle, streamline reporting, provide clinical quality measurements, reduce exposure to malpractice litigation and increase operational efficiency.
BI can help healthcare organizations track organizational performance against strategic goals; define, plan, and align business goals and metrics; proactively discover business trends and opportunities; respond rapidly to new customer demands; and prioritize business activities.
Furthermore, BI Implementations can reduce the effects of information asymmetry and offer access to cost-saving plans, information exchanges and other incentivized programs offered through the government and third parties.
All healthcare organizations benefit from strategically aligned decision making. Organizations utilizing effective BI can leverage the intelligence it produces to set targets, realize results and understand replicable drivers to their business success. If your healthcare organization has not yet created a business intelligence Initiative, now is the time!
Khrystyna Kosyk is consulting services director at SoftServe, Inc., a leading global provider of software development, testing and consulting services. Kris has been in the industry for nine years and specializes in IT consulting services, leading a group of talented individuals. She has vast experience in IT outsourcing consulting, quality assurance, project management and process improvement. 
Ross Arnold, senior manager at Jimenez Consulting, has over 20 years of IT leadership experience, 15 of those years in the healthcare industry across the provider, payer, medical manufacturing, and medical publishing verticals. His focus has been on the planning and execution of large-scale data management, data integration and business intelligence initiatives. 

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