USING A DIAGNOSTIC TO FOCUS HOSPITAL FLOW IMPROVEMENT STRATEGIES
Roger Resar, MD
Institute for Healthcare Improvement, 20 University Road, 7th Floor, Cambridge, MA 02138 (The work presented here is from the efforts of the IHI Innovation Team.)
Abstract: Current methods to evaluate hospital flow primarily measure micros-system issues, such as numbers of diversions from the ED, or how many patients are boarded in some way somewhere in the hospital, or specific delays in hospital units and annual admissions. The narrow focus on delays, although appropriate, generally leads to attempts at fixing a large system problem from the viewpoint of a micro-system, A newly developed hospital diagnostic that utilizes a broader view of flow can be used to evaluate how a hospital might best respond to delays, but also how to maximize the number of turns each bed generates in a year. The diagnostic utilizes easily obtainable and commonly collected hospital metrics. The diagnostic allows a hospital to categorize itself depending on the number of bed turns and the efficiency of using those beds.
Based on a self-evaluation a hospital can determine which improvement strategies would be most useful, or if ongoing improvement strategies are properly focused to achieve an ultimate goal of reduced delays and increased bed turns (the goal of 90 or more adjusted turns and a bed use efficiency of around 90%). A business case for improvements can be based on increasing turns either by accommodation of more demand or by decreasing unneeded capacity. Improvements in throughput per bed allow leadership to justifiably focus resources on the appropriate change concepts. Serial measurements of the bed turns and bed utilization metric allow the organization to measure the effects of flow improvement strategies over time.
Key words: Bed turns, hospital efficiency, flow streams, delays, flow diagnostic
1. INTRODUCTION
In 2002 the Institute for Healthcare Improvement (IHI) Innovation Team started work on hospital flow and the hospital flow diagnostic. Although the characteristics of the diagnostic and how the diagnostic is used continue to develop, it has become an important focus for the direction of flow improvement work for many hospitals. Initially the diagnostic was designed to direct hospitals towards specific change concepts that would be most appropriate for their particular flow problems. Currently the diagnostic is first being used as a high-level hospital evaluation and then second as an entry point to improvement strategies. The diagnostic can validate a given approach to flow improvement in a hospital and provide a measurement overtime of eventual success of the improvement work. Prior to the use of the diagnostic it was far too easy to tackle hospital delays as isolated unit specific problems. Trying to solve a system problem by working on a single unit has proven to be very wasteful and foolhardy since the best plans of a single unit redesign (since they are connected to the rest of the hospital) can be ruined by changing practices of upstream or downstream units. For example, the best of efforts in the ICU can be totally discounted by changing policies on admissions or transfers to a step down telemetry unit. So rather than making the ICU responsible for delays, the hospital flow system needs to be accountable for delays. It is now clear hospital flow is a system problem and needs to be looked at from a more global point of view.
The chapter will describe the methodology of the hospital diagnostic, demonstrate the degree of variability in the number of bed turns and bed utilization, and lastly suggest strategies for improvement and introduce the use of flow streams to work on delays. Patient flow management is a very complex problem not limited to just patient flow within the hospital. It also includes study of pre-hospital patient flow (e.g., why patients use the ED rather than their primary care physicians) post-hospital patient flow (e.g., availability of rehabilitation or skilled nursing facility beds).
Comprehensive coverage of all these issues would not be possible in this chapter. Those who are interested in those issues are referred to the references at the end of this chapter.
2, HISTORY
Characteristically most hospital flow solutions relate to fixing the
"squeaky wheel", commonly entwined with demands from high producers,
or by looking at isolated unit problems with solutions from a narrow point of
view. Both usually accomplish nothing in terms of overall hospital flow.
Both solutions, however, usually assume demand is greater than capacity by measuring delays. One only has to look at the building boom in hospitals to understand the agreement to the general assumption that demand is far greater than capacity. These approaches might lead an occasional hospital to the right solution, but luck or happenstance not deliberate design would be responsible. Another possible solution to hospital flow has been the approach of the flow communities working with the Institute for Healthcare Improvement. The hospital diagnostic was used by each hospital as a self- evaluation. A description of the hospital flow problem was generated. Next each hospital was encouraged to select appropriate strategies for improving hospital flow. In this scenario new or interesting ideas, such as discharge appointments or extending the flow chain, are tested knowing the appropriateness of the intervention based on the hospital diagnostic and how in many cases how it will be applied specifically to a high volume flow stream.
The hospital diagnostic was developed by the IHI Innovation Team with a specific set of outcomes around bed turns, For those hospitals with a high number of turns (greater than 90), the goal is to maintain or increase turns, but with minimal delays. For those hospitals with low bed turns, increasing the number of bed turns by capturing deflected demand or reducing unneeded capacity is the goal.
The hospital diagnostic views flow from a viewpoint of the whole system including beyond the walls of the hospital in some cases. One viewpoint that will not be examined is how adequately the hospital services are fulfilling the needs of the community as a societal resource. Hospital throughput will be measured as bed turns and revenue per bed, as well as bed utilization (different than occupancy rate). The strategy will demand minimal delays as an adjunct to any improvement in turns and utilization.
The methodology, tools and other documents related to the topic of hospital flow can be found on the IHI website (www.ihi.org). The following definitions are used in the diagnostic calculation:
• Unadjusted Bed Turns = (Admissions + OIIB)/ by functional beds.
• OIIB Stands for any outpatient in an inpatient bed. These may include the standard observation patients in an inpatient bed, in addition to any other use of an inpatient bed without being an actual admission.
Common uses might be post heart catheterization observation, post
outpatient endoscopy procedure observation, etc. Since the bed is being
used even for a short period of time, it does force admissions to compete
for the bed and must be counted. (A good method to determine this is to
take several weeks and just survey each unit once each 24 hours for any outpatient use of an inpatient bed and average it out for the year.)
• Adjusted Bed Turns = (Admissions x case-mix-index)/functional beds + OIIB/functional beds
• Utilization (efficiency) = unadjusted bed turns divided by potential bed turns. This number is different than occupancy, since occupancy is measured at midnight and usually only once per day.
• Potential Bed Turns = 365 divided by aggregate average length-of-stay (LOS)
• Average Length of Stay (LOS): Average overall length of stay for inpatient admissions. This does not include the standard observation classification and should exclude newborns, which most hospitals do automatically.
• Admissions: The number of inpatient admissions (excluding newborns)
• Observations: Those patients defined by billing purposes as not being an admission, but being cared for in the hospital for 24 hours or less. If they become an admission they should not be counted twice when the OIIB calculation is made.
• Case Mix Index (CMI): All payer case mix as defined by CMS (Center for Medicare and Medicaid Services)
• Bed Turns: Number of times (to the nearest whole number) an inpatient bed is used by a patient whether for admission or for any type of OIIB.
• Functional Beds: The number of beds normally open and staffed in a hospital over a year on average, excluding newborn and delivery beds, but including all other beds. The beds must be staffed around the clock which would exclude admission units etc. For rehab beds they are counted if under the same Medicare number.
The performance metrics from Section 3 are now demonstrated with example calculations.
Unadjusted Bed Turns = (admissions + OIIB) divided by functional beds.
Example:
Admissions: 10,000/yr Observations: 1,000/yr Functional beds: 200
10,000 + 1000 = 11,000/200=55 turns on average per bed per year
Adjusted Bed Turns = ((Admissions x Case Mix Index) + OIIB)/functional
beds. Example:
Admissions Observations Functional beds Case Mix Index
10,000/yr 1,000/yr 200 1.4
10,000x1.4 = 14,000 + 1,000 = 15,000/200=75 turns on average/bed/year Potential Bed Turns = 365/LOS. Example:
LOS 4.0 days
365/4.0 = 91 potential bed turns per year
Utilization = Unadjusted Bed Turns/Potential Bed Turns.
In the above example:
Unadjusted Bed Turns = 55 Potential Bed Turns = 91 Utilization = 55/91 = 60.4%
3. OBSERVATIONS
Figure 12-1 shows the data submitted by hospitals in the IHI Flow Community in 2004. The plot uses adjusted bed turns versus bed utilization percentage. None of the hospitals were unique specialty hospitals, although there was a mixture of academic and non-academic hospitals. The number of actual turns adjusted by the case mix index attempted to level the field by consideration of severity. Even with the adjustment for severity of illness considerable variation exists between these hospitals. The IHI team looked at the plot and selected 90 adjusted bed turns and 90% utilization as desirable goals. The turns and utilization goals were based on reviewing best performers and using the knowledge of queuing theory. Based on these guidelines, the data collected from the hospitals place each hospital into one of four quadrants. Representative samples of hospitals from each quadrant were studied and some visited to learn about the characteristics of the hospitals in each of the quadrants.
One obvious observation needing an explanation is utilization well over 100%. Obviously consistent over 100% utilization is difficult to imagine.
The partial answer is the fact that the calculation for utilization uses bed
turns. The bed turns associated with OIIB do not have a LOS measure since
many of the stays are only for a few hours. Hospitals with very high OIIB fractions commonly will have unusually high utilization rates. If a hospital has a very high OIIB rate with significant delays, this particular flow stream needs to be examined for opportunity.
Figure 12-1 also gives the visual representation of tremendous variability between hospitals in terms of flow and utilization. Yet each of these hospitals have a common problem of significant flow delays otherwise they would not have joined the collaborative. It is hard to imagine how a hospital with only 40 turns and a utilization of 60% should have delays, while it is very understandable why a hospital with 140 turns and 100% utilization should have delays. Those questions and the subsequent quest for the answers will form the basis for the rest of the chapter but should also tempt the reader to "find their dot".
160
140 120 \
100
Flow Diagnostic: Adjusted Turns versus Utilization
"Where's my Dot"
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20% 40% 60% 80% 100% 120% 140% 160%
Figure 12-1. Flow Diagnostic: Adjusted Turns Versus Utilization,
"Where's My Dot"
4. UNDERSTANDING DELAYS FROM THE FLOW DIAGNOSTIC
Figure 12-2 demonstrates the four quadrants and the relationship between capacity, demand and delays in each of the quadrants, in addition to the 90 turn and 90% utilization goals.
Figure 12-3 portrays the expectation of delays. Quadrants 2 and 4 because of the high utilization should expect delays. Quadrants 1 and 3, because of the low utilization, should be surprised by delays. The utilization is given a window, as shown by the dotted lines.
Using Figure 12-4, we see quadrant 1 has a good balance between capacity and demand, but certain hospitals may still have significant inefficiency in utilization of varying degrees; all in our series still have delays. Quadrant 3 is an inefficient hospital in terms of utilization and also has low turns and delays. Both of these quadrants need to question delays from the viewpoint of '*why". With low utilization, one should expect no or minimal delays.
Understanding Delays from the Flow Diagnostic
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Figure 12-2, Flow Diagnostic: Understanding Delays
Understanding Delays from the Flow Diagnostic
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Figure 12-3. Understanding Delays From The Flow Diagnostic
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Significant delays due to high LOS for CMI
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Figure 12-4, Flow diagnostic: understanding delays
Again using Figure 12-4 Quadrant 2 hospitals are highly functional systems, but with delays. Quadrant 4 also high-functioning, in terms of utilization, but in both quadrants we should not be surprised to find delays - in fact they might be expected.
No one quadrant is without it's own set of problems that need to be solved, but from a business case, being in quadrant 1 or 2 would be preferable, with quadrant 1 in the long run best from a queuing theory point of view. Participants in the flow collaborative - because of the nature of the hospitals that joined a collaborative on flow - all experienced significant delays and lost opportunity in one way or another, no matter in which quadrant their dot was placed.
Figure 12-5 shows hospitals in the EHI flow community. Each hospital is represented by a dot and the y axis is unadjusted turns. The x axis is the case mix index divided into four categories. The finding of wide variation within any given case mix grouping is extremely interesting. In category 1, for example, the unadjusted turns range from 40 to 120 turns/bed/year.
Explaining this variation is very difficult. The assumption is that competent managers and executives work in each of these hospitals. In addition to the variation within a grouping, how can we explain a hospital with a case mix index of 1-1.22 and bed turns/year of 60, when another hospital with a case mix of 1.71 and obviously much sicker patients has the same number of bed turns? The answer may well be the proportionality in how specific hospitals handle streams of flow. The defining of the flow streams and the change concepts to improve the flow stream efficiency can be extrapolated in part from understanding the flow diagnostic.
5. STEPS TO USING THE HOSPITAL DIAGNOSTIC This section provides a step-by-step explanation of how to apply the diagnostic.
Stepl
Collect the OIIB (remember these are any outpatients using inpatient beds),
admissions (excluding newborns), case mix index for all payers, functional
beds (see methodology below) and LOS. The data should be collected for a
full year. If multiple years of data are available, you may wish to use this to
establish a trajectory.
Unadjusted Turns Versus Case Mix Index
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Figure 12-5, Unadjusted Turns Versus Case Mix Index
SideBar on methodology to determine functional beds. Most hospitals have licensed beds, staffed beds and even functional beds as different numbers. Usually the actual beds in use on a daily basis are less than the licensed beds. Functional beds can best be described as over a three or four month average how many beds are actually staffed. Difficulty arises when there is a great fluctuation with seasonality. Since seasonality usually is a recurring phenomenon, the three or four month average should include both high and low season. (Many hospitals could just average out the staffed beds for a whole year and get the functional bed number). Absolute accuracy is less important than stability in this measure. If stable over time, the change in bed turns can then be measured to reflect actual change.
Step 2
Calculate bed turns with and without adjustment for case severity using the case mix index, and calculate utilization.
SideBar Since the functional beds affects the bed turns fairly dramatically, the leeway within any quadrant allows this to be an average over months and then allows the comparison over time as a trajectory to be helpful.
SideBar: An alternative method to measure utilization, rather than actual
bed turns divided by potential bed turns, is to directly measure the bed
utilization. Each bed in the hospital is numbered. A random number
generator for the total number of beds is created, and each hour for one week
a bed number is generated and a determination is made as to whether the bed
is filled or not. The resulting utilization is then calculated as a simple total filled over the total number of observations. A few rules apply, however:
1. Beds held for a patient in surgery is not a filled bed.
2. If patient is no longer alive, bed is not a filled bed
3. Patient not in room but down for procedure is a filled bed, but patient needed to be in the room prior to the procedure or test
4. Room being cleaned is not a filled room Step 3
Place your hospital into a quadrant based on your calculations. Be careful if you are at the dividing lines since there is some overlap.
Step 4
Evaluate your current flow improvement strategy and see if it is consistent with the quadrant recommendations
Step 5 (Optional)
Evaluate previous years and determine the trajectory of your change based on bed turns and utilization. Is it going in the right direction? In this case, is it moving to the left upper quadrant as improvements are being made in both bed turns and delays?
6. STRATEGIES
Although the aim of the chapter is not to provide a detailed discussion of the specific strategies associated with flow improvement, the appropriate use of the diagnostic still demands an understanding of the approaches to be taken for the specific strategies. In that light, the specific strategies need to be at least understood. The overall aim is to increase throughput (as measured by bed turns) and minimize delays (as measured by time) while assuring that high performance in flow is not at the expense of poor quality.
The specific delay measurements commonly used in the Flow Collaborative are:
• Time from entering the Emergency Department to the floor (Using a door to floor concept)
• Diversions from the Emergency Department (Or alternatively hours on diversion)
• Left without being seen from the Emergency Department
In those hospitals with turns already high, the goal is to maintain or increase turns but now with minimal delays. In those hospitals with low bed turns the goal is to increase turns by capturing deflected demand or reducing capacity. Figure 12-6 presents strategies for quadrants 1 and 3
Quadrants 1 and 3 have poor utilization in common. The best quadrant is 1 but without delays. The worst quadrant situation is 3. Both need to determine whether beds are available and since in all likelihood they are the change concepts need to be related to recapture of wasted capacity. Large volume flow streams are essential starting points. These might include the cardiac service or the orthopedic service as examples. Ultimately those in quadrant 3 will need to increase turns and this might be done by increasing utilization and closing unneeded capacity. Figure 12-7 presents strategies for quadrants 2 and 4
Flow is a property of the entire system and can only be optimized at a system level. Understanding this fact has led to the idea of streams of flow that are then optimized by the specific change strategies.
In order to start the work on flow, a sound administrative system that manifests a bed management process that incorporates planning based on predictions of capacity and demand should be an overarching design in any improvement strategy. The chapter by Linda Kosnik demonstrates an hour by hour evaluation of capacity and demand with explicit processes designed to specific changes in demand or capacity. Some organizations are using bed huddles several times a day in addition to trying to understand larger issues of variation such as seasonality to adjust capacity and demand. A bed administrative system may or may not incorporate an electronic bed board product.
The process of where to go from the diagnostic can best be described in three steps:
1. Determine the major flow stream that will be subjected to the flow change strategies.
2. Rather than working on a given unit look at the project as a system problem crossing multiple silos of interest. A stream of flow should manifest a large volume, exhibit significant delays and flow from at least the admission through the discharge, although the work done by Mark Lindsay would suggest moving this out to the pre-admission and the post-discharge 3. Once a major flow stream has been identified, the specific flow
improvement strategies now need to be applied, particularly
those that are demonstrated as useful for the hospital's
quadrant.
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Figure 12-7, Understanding Delays from the Flow Diagnostic,
Quadrants 2 And 4
6.1 Quadrant 1 (Left upper quadrant)
The quadrant has, in general, adequate turns and reasonable utilization, but still has moderate delays due to high demand. Adjusted bed turns are greater than 90 and utilization is less than 90%. The primary problem is waste of capacity. Any bed use variant could contribute to the wasted capacity. The wasted capacity would be easiest to pinpoint by looking at several large volume flow streams. Examples of a large volume stream might be orthopedic surgery or heart disease requiring medical telemetry.
Obviously, the large volume stream work should focus on reducing wasted capacity by identifying and remove bottlenecks in the flow stream. The bottlenecks are commonly admissions, transfers or discharges and specific change concepts for each of these interfaces. For example, the use of discharge appointments could be linked to either transfers or admissions.
Detailed information on this and other concepts can be found at wv\^v^,ihi.org.
6.2 Quadrant 2 (Right upper quadrant)
The quadrant has adequate turns, but the utilization is so high that staff burnout and safety issues need to be considered. These are super high functioning hospitals. Adjusted turns are greater than 90% and utilization is greater than 90%. Significant delays are seen due to mismatch of capacity with a high demand, more than wasted capacity. Hospitals in this quadrant may truly have a need to add more capacity, but only after significant efforts have been made to correct the mismatch between capacity and demand. The mismatch is significant enough that use of electronic bed tracking systems may have tremendous utility for organizations in quadrant 2. Poor habits need to be purged from the system such as holding beds, lack of good discharge planning and lack of pre-admission planning for elective surgery.
Again, the emphasis should be to start with larger flow streams and apply these change concepts.
6.3 Quadrant 3
Hospitals in quadrant 3 present a very unique opportunity. They
experience moderate delays due to inefficient use of capacity. In most cases
they have more beds than necessary, but due to the inefficient use of the
capacity staff keep too many beds open. The result is adjusted turns less
than 90 and utilization less than 90%. Again, the concept of isolating several
major flow streams and applying some of the change principles would be the
strategy. Those change concepts include eliminate bed holds, developing
bed cleaning and turnaround strategies, reducing internal transfers, decreasing use of inpatient beds by outpatients or decreasing capacity. Most hospitals have an initial response of shock when a suggestion is made to reduce capacity, but the reduction in capacity reduces the staffed beds and immediately increases turns per bed and utilization of the beds remaining.
6 A Quadrant 4
Quadrant 4 has significant delays due to a high LOS for the case mix index. Adjusted turns are less than 90 and utilization is greater than 90.
Almost all the hospitals studied have a comparatively long length of stay for multiple high volume diagnoses. The utilization of flow streams would necessarily need to focus on one of those high volume diagnoses. The focus should be on conditions that have a long LOS compared to other hospitals.
The strategy needs again to look at the bottlenecks. A commonly found bottleneck is the inability to work outside the hospital for certain chronic disease states, such as chronic ventilator use. An easy measurement is to look at the LOS for all patients admitted to a nursing home. Hospitals average five to seven days around the country. Longer than seven days suggests that flow efforts need to extend outside the walls of the hospital.
The business case for increasing turns can be illustrated in the following example. The data are from a real hospital.
• Admissions: 16,704 with 4,246 observations
• Case mix index: L49
• LOS: 4.7
• Average functional staffed beds 432
• Average revenue (actual collections) from an admission = $7,525
• Unadjusted Turns = (Admissions + Observations)/Average Functional Beds = 48 Turns/year
• Adjusted Turns = (Admissions x CMI + Observations)/Average Functional Beds = 67 Turns
• Potential Bed Turns = 365/LOS = 77 Turns
• Hospital efficiency = Adjusted Turns/Potential Turns = 74%
High performing hospitals have 90 or more adjusted turns and an efficiency of 90%. Adding 10 turns per year per bed produces $32,000,000 in revenue per year, assuming same average revenue per admission.
Increasing turns to the level of high performing hospitals (89 turns) produces
$60,000,000 in revenue per year.
Figure 12-8 is an example of using the diagnostic retrospectively to determine whether the appropriate flow improvement strategies were selected and the impact on turns. Initially the hospital was near the intersection of quadrant 2 and 4. Turns were reasonably high and LOS was actually low for the case mix index. Since the demand was high existing capacity needed to be optimized. An effective administrative system was added with an hour-by-hour response system. Discharge appointments were synchronized. Extensive collaboration with nursing homes was highly developed to provide continuum of care. Over the four years of measurement turns increased by 20 per bed, while at the same time the case mix index edged up slightly. At the same time the utilization decreased into a more reasonable sustainable pace from a little over 100% to 86%. The financial benefit was approximately $11 million to the organization.
Flow Diagnostic: Adjusted Turns versus Utilization
"Where's my Dot"
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