It seems as if virtually every player in the healthcare field is demanding information on performance, quality, safety, efficiency, effectiveness and of course, costs.
The difference between being crushed, hounded or in the driver seat by these requests depends on a number of management and clinical decisions regarding people, processes, approaches, clinical methods, policies, technologies, etc. One tool that can help with all of these decisions is business intelligence.
Business intelligence (BI) is the commercial equivalent of evidence-based clinical decision making. The difference it makes is in the systematic consolidation of data from various sources, and the organization of that data for use in making business decisions.
As you can imagine, this data is comprised of millions of bits of information spread across a number of systems (e.g., encounters, labs, claims, billing, etc.). BI extracts data from these systems and brings it into a centralized, secure, historical repository, which is organized for business users to slice, dice, sort and sum it efficiently in order to support business decisions.
With that kind of organized business evidence, you can confront the tidal wave of demands for information. The first step is to determine what BI applications are most appropriate for your organization and particular situation.
The Top 10
Patient registry — Far and away, the best BI application for healthcare is the explosion of potential uses for a registry. Processes such as chronic care management, disease management programs, case management, quality measurement and patient volume analysis are but a few potential uses.
Priority conditions scoreboard — An executive scoreboard on the organization’s patients, processes, staffing, activities and results around the Institute of Medicine’s publicly stated 15 priority conditions is essential to success.
Recognition/pay for performance contracts — Data is needed on services performed, timeliness, interventions provided, clinical outcomes and their effect on improved patient functionality. This information is then analyzed by payers for its effect on the worker productivity, lack of absenteeism, among other measures.
Quality accreditation reporting support — Either directly or indirectly, your performance is likely to be judged using measures by quality accreditation organizations such as the NCQA. The qualification requirements are fairly complex and require extraction, reconciliation and auditing of data from a number of internal systems, such as claims, encounters, labs, etc.
Care team data support — Care teams need to measure their own performance, outcomes and efficiency, as well as compare those measures with other teams in the same organization. In addition, new clinical and operational methods from other organizations can be evaluated and “imported” using evidence gleaned from medical literature and other sources.
Research hypothesis discovery — Data from clinical experience, especially the diagnosis-treatment-outcome relationship can help target research studies based on what is actually happening in the real world. This can make the process of developing abstracts, manuscripts and funding requests faster and easier.
Access and outreach planning support — A relatively new area of analysis of historical data for trends and patterns in patient volumes, patient demographics, conditions treated and surveys of access problems can be used in decisions on how to make clinical care more reachable, more convenient, and potentially more affordable.
Waste reduction and cost effectiveness analysis — Using historical data from your clinic management and accounting systems on cycle times, labor utilization, supplies utilization, procedures employed and medications prescribed, as well as their costs, you can find and correct wasteful activities, and potentially even market this as an advantage.
Staffing and scheduling analysis — Having the business evidence to support staffing and scheduling decisions, such as patient census data, patient flow patterns, etc. is essential information to cut labor costs. Getting this right is essential to your bottom line, as well as to making sure care is safe, effective, efficient and patient-centered.
Patient Education Statistics — Patients are embracing the simple idea of correlating their patient care (diagnoses, treatments, care plans) against their patient outcomes (lab results, functionality measures, etc.), as a motivator to take greater control of their illness and ownership of their own care, leading to better outcomes.
The demand for quality information is only going to intensify. Taking a proactive, offensive approach to gathering, organizing and accessing your business intelligence, your operational and financial evidence, is one of the best ways to meet this rising demand. Policy, capital, organizational and cultural transformations are also necessary.
Hopefully, the descriptions above will give you some ideas of the potential of healthcare quality-focused business intelligence applications that are relevant to your organization and situation.
Scott Wanless is a member of Greenbrier & Russel’s Business Intelligence practice, a business and technology consulting and training firm. He has over 20 years of experience in business intelligence across numerous industries including healthcare, laboratory research, insurance, lending, manufacturing, retail and state government. Wanless can be reached at [email protected].