Learning to use existing capacity more efficiently may be the fastest way for hospitals and health systems to generate revenue.
At a Glance
- A key healthcare reform strategy for hospital executives this year will be determining how capacity management can improve overall performance.
- Real-time data can give a hospital the kind of business intelligence it needs to make the most of what it has, act as an early warning system for problems, and provide the hospital with the tools to make long-term improvements.
- Trueenterprise management systems promise to deliver bottom-line impact that may be greater and more immediate than the conversion to electronic medical records.
With hospitals facing the prospects of tighter payment standards, and with millions of newly insured seeking care, deeper staff cuts and service rollbacks are not a viable means for cost savings. This year, we will instead see more hospital managers embracing a trend long used by industry and commerce-real-time capacity management-to streamline flow, care for more patients, and drive rapid revenue increases.
The real-time enterprise concept is one whose time has come for healthcare systems.
According to Lisa Goldstein, a senior vice president at Moody's Investors Service, deeper cuts in Medicare and Medicaid will endanger the credit ratings of hospitals that don't solve the puzzle of how to do more with less.
In an announcement of the release of a report, Goldstein notes that the inevitable cuts are "critical as Medicare comprises nearly half-43 percent-of hospital gross revenues," and that Medicaid cuts by cash-strapped states will add "significant stress to not-for-profit hospitals for at least the next several years" (Announcement, "Moody's: Not-For-Profit Hospitals Face Revenue Reductions Across the Board," Aug. 9, 2011; the report is titled Hospital Revenues in Critical Condition; Downgrades May Follow). The report predicts that the pressure of reduced revenue growth and reimbursement cuts will increase the number of hospital downgrades in the short-term.
The challenge for hospitals, then, is finding a way to save and make money by using existing hospital capacity more efficiently and by streamlining physical operations quickly.
Real-time capacity management is an important new means for hospitals to meet this challenge. It creates a digital nervous system that allows hospitals to "sense" their organizational functions, using everything from tags that can locate patients, staff, and assets in real-time to data-gathering software that far exceed human ability to gather, process, analyze, and respond.
The use of real-time capacity management means executives and line managers will not have to wait for end-of-the-week, end-of-month, or end-of-quarter reports to act. They can respond as events warrant more quickly, which in turn means money saved and/or earned. The payoff from investing to get to this point includes better use of space and resources, better infection control, faster transfers, increased revenue and savings, and most important, better patient outcomes.
In short, there are five key steps involved in the process of implementing real-time capacity management to improve performance:
- Identify current issues
- Define metrics for managing issues
- Gain access to the data for better understanding.
- Provide the metrics to others for review and education.
- Take action to improve patient flow
What You Need to Get Started
From a technology standpoint, real-time capacity management in a healthcare setting involves the convergence of three technologies-patient flow automation, real-time location, and automated business intelligence. Real-time systems can power live information to the entire hospital in an easy-to-read summary format on such things as patient tracking, staff locating, asset management, and critical workflow process improvement.
With real-time capacity management systems, everything can be presented on graphics-rich "digital dashboards" that provide executives, managers, and placement specialists with a live "motion picture" of their institution. For example, patients can be tracked from the minute they enter the patient flow process-when they walk in the emergency department (ED) door, for example. Once bottlenecks are identified, they can be relieved before they become ingrained in the process.
All employee groups at a hospital can benefit: Executives can see summary information on all aspects of operations; physicians can view incoming and outgoing patients; nursing heads can check patient flow statistics; and department directors can check more in-depth information in their areas-all in real time.
It is important to note, however, that simply putting a patient flow improvement program and automation software in place is only a first step. A successful patient flow program should deliver reductions in ED diversion, ED length of stay (LOS), numbers of patients who leave without being seen, and inpatient LOS, as well as increased admission volumes and faster discharge times.
There are several patient flow automation systems on the market today that streamline core patient flow processes, such as those for patient placement, bed management, transport, and environmental services. Any viable solution should provide tools such as scorecards, dashboards, custom reporting, and services to transform the right data into key indicators and evidence trends that can drive strategic decisions for improving patient flow and accountability.
However, without providing the analytical framework for evaluating and monitoring performance, such a system alone cannot help hospitals make long-term, lasting improvements in operational performance. Implementing robust business analytics solutions allows users not only to track general patient flow statistics, but also to slice those statistics into the useful, actionable intelligence that is needed for transformational change.
For example, by breaking down discharges by time of day as well as overall volume, hospitals can identify bottlenecks in the discharge process and use the information to drive for earlier discharges.
The metrics being captured and analyzed can reveal problems with patient placement, transport, bed turns, room cleans, discharges, mobile asset location, medical device inventory, correct bed identification and assignment, time between procedures, and environmental services and transport exposure to infected rooms and patients. A comprehensive, state-of-the-art, real-time capacity management system should provide the healthcare organization with "instant" snapshots of the following:
- The exact number of patients being admitted from any portal at any time, including via the ED, transfers, and scheduled admissions
- The precise number of staffed beds available and the unique characteristics of those beds (e.g., negative air)
- The expected time of patient discharges based upon a formal, recorded discharge protocol
- The precise location of all mobile medical devices, all patients, all physicians, and other medical personnel within the hospital at any given time
- The location of potential bottlenecks that would slow down the patient flow process
- The disposition of patients awaiting treatment or diagnostic procedures throughout the enterprise
- A 24-hour look ahead at staffing needs based upon real-time patient numbers
- The location of patients with infections and medical equipment exposed to infection
- Admission and discharge analyses
- The root causes of process delays and wait times for staff and, more important, patients
- The precise time of all major patient flow milestones via automatic timestamps, allowing constant measurement of current and long-term performance
- Optimized utilization of equipment and the healthcare enterprise
But how do hospitals access and transform the immense amount of data needed into useful information from which to continuously manage capacity?
There are three primary approaches to developing a business intelligence solution suitable for analyzing complex operational data: developing a homegrown system from commercially available tools, enlisting a consultant to develop the system, or deploying a purpose-built system tailored for operational analysis. Each approach has pros and cons to consider.
Developing a Homegrown System
Homegrown business intelligence solutions are used by hospital developers, system analysts, and IT staff working with operational leaders to understand the workflows and analyses they wish to perform.
Pros. The group working with the hospital to develop homegrown business intelligence solutions will have an intimate understanding of infrastructure and needs. The system will be tailored to the hospital's specific needs.
Cons. The development process for homegrown business intelligence solutions is highly resource-intensive. Internal staff are diverted from daily tasks to identify and develop all of the systems and tools needed. The process requires close coordination with health IT vendors to secure the right data without having an impact on production systems.
The domain knowledge of these organizations tends to focus on revenue cycle management or clinical data, as opposed to operational intelligence. The combined costs, along with the need for permanent staff to manage the system, can be staggering, resulting in a large gap of time before costs can be recouped and true value reaped from the system.
Use of Consultants
Hospitals may choose to work with consultants in developing a business intelligence solution suitable for analyzing complex operational data.
Pros. These organizations can leverage years of experience in deploying business intelligence solutions and can tailor the project specifically to a hospital's needs.
Cons. The downside includes associated cost, lack of knowledge about the operational data, and a lengthy time to show value. As with the homegrown system, costs can run in the millions and the domain knowledge of these organizations tends to focus on revenue cycle management or clinical data. This approach often requires a long-term relationship with the consulting group, along with vendor coordination. Consultants may be handcuffed unless vendors provide them with data extractions to efficiently use in their analyses.
All-in-One System from a Provider
These systems are tailored specifically toward the goals of hospitals.
Pros. Purpose-built business intelligence systems drive value quickly. A good vendor-backed system will include professionals skilled in addressing hospitals' technical and clinical needs simultaneously. The system provider also should have expertise in designing business intelligence and an extensive domain knowledge deep-rooted in patient flow processes.
Because these systems are tailored toward the hospital's goals, the heavy lifting of systems analysis has already been done; no in-depth data planning is needed from the hospital. Such systems can make use of existing IT infrastructure, greatly driving down costs in both planning and execution. This allows hospitals of all sizes to use effective business intelligence.
Cons. This approach involves overhead costs for maintenance and ongoing analysis. A focused system may require retraining for employees previously trained on more generic analytics systems. In addition, the scope is limited to data on capacity management and patient flow. Hospitals also need to ensure that the analytics solution is updated and maintained and that the vendor is able to integrate data from third-party systems.
What kind of benefits can access to real-time business intelligence data deliver? Consider the examples of two organizations that are seeing quick results.
Methodist Healthcare. Since this eight-hospital, 1,800-bed system in San Antonio made capacity management its top priority for FY10, its transfer center volume has tripled. The transfer acceptance rate is now 99 percent, monthly diversions have dropped from 700 hours to fewer than eight hours, lost bed time has decreased from 76 minutes to 35 minutes, bed assignment time has been reduced by 78 percent, and the organization exceeded annual budget expectations by 8 percent in FY10.
Methodist managers are sharing flow statistics with bedside nurses through a real-time dashboard that projects unit level metrics and discharge processes by each floor. Health system managers believe this real-time monitoring will smooth day-to-day performance more effectively than waiting for end-of-month reports to rectify problems.
Methodist also is using capacity management tools to focus on "dead bed time," where clean beds sit readily available for long periods while patients are waiting for beds. Many providers focus on the time that it takes to clean a bed or admit a patient, but Methodist hopes to drive down dead bed time by holding patient placement accountable for effectively using the beds.
Scripps Health. This San Diego-based not-for-profit health system with four acute care hospitals on five campuses and 2,600 affiliated physicians is going through a transition in analyzing capacity information. Scripps no longer focuses solely on daily/weekly/monthly performance reports. Instead, the health system is targeting areas that show inefficiency, identifying the reasons for those outliers, and adopting change to improve performance. Scripps has seen improvements in many of its primary areas since adapting customizable reporting tools.
This ability to dig into the data paid off for Scripps within days of retooling its reporting system. Like many providers, Scripps was having problems with its environmental services department falling behind in its efforts due to limited staffing or improper alignment of staffing. Most providers focus on the discharge process with nursing, physicians, and others, but environmental services can become a bottleneck area for an organization if the department's processes are not streamlined. Capacity management tools showed the trends and bottlenecks within the first two days of installation.
Like these two organizations, other hospitals can place themselves ahead of the game in their approach to business intelligence. By developing ways to obtain and use business intelligence data in real time, they can increase patient flow and revenue while maintaining high-quality care and service at lower costs, preparing them for the standards of this new era of health care.
Jason Baim is vice president and managing director, business analytics division, TeleTracking Technologies, Inc., Pittsburgh (Jason-Baim@teletracking.com).
Publication Date: Wednesday, February 01, 2012