PHLO

Length-of-Stay: An Inadequate Measure of Patient Flow

By: Matt Carroll

Numbers should be the backbone of every business decision, no matter the industry. Unfortunately, using the wrong number or too many numbers creates confusion and bewilderment.

Using only one number on which to base a decision can be misleading, particularly when this indicator is poorly selected or it is not used in conjunction with other key measures. On the other hand, using too many numbers can cloud decisions by creating a lack of focus, lack of timeliness, and, ultimately, a lack of confidence.

For example, if a team manager focuses only on a baseball player's batting average in his selection process, he will miss a lot of other valuable facets in the player's game. If he considered two baseball players with batting averages of .300 and .270, respectively, he may choose the player with the higher average. But we all know that there are other important statistics, including home runs, RBIs, stolen bases, and fielding percentages, that should guide his decision in selecting the best player for his team.

Then again, some information, such as home field batting average during the day, how the player fields balls hit to his left side, how he hits on grass fields, etc., is interesting, but not essential to the selection process. This is simply too much information.

Let's apply this same kind of thinking to the hospital setting, where managers are fixated on length-of-stay (LOS) as the only measure of patient flow. In fact, many hospital scorecards are inundated with LOS trends and ratios (e.g., LOS by DRG or LOS by physician, etc.). But this laser-like focus on LOS can actually harm the patient flow effort because it tends to shift attention to a smaller percentage of patients, instead of the large number of patients that represent the bulk of a hospital's daily activity.

Worse, LOS is a poor indicator of patient flow because it is an imprecise snapshot of conditions taken in the middle of the night. When it comes to patient flow, what's important is the time of discharge; in other words, hours count, not days. If a patient who could have been discharged at 11 a.m. actually left at 6 p.m. and the hospital was on ambulance diversion for three hours between 11 and 6, the LOS measure would not push this issue to the surface. But adding an indicator that tracked percentages of discharges by noon would.

From another perspective, patient flow can be hurt if the hospital staff is perpetually measured, rewarded, and criticized on LOS. Every hospital has a few outliers that dramatically impact LOS, and while these outliers might have economic consequences to the hospital and should certainly get attention, their impact pales in comparison to the total impact of lost patient market share. Outliers have great influence over the numbers: often their LOS is 5 to 10 times longer than the typical patient. But, they often represent less than 5% of the total census. In fact, losing just a few patients due to poor patient flow could easily negate a year of work on the outliers. As the CMO of a client hospital recently stated, "We are focused on the one barge stuck in the mud instead of trying to clear the main channel."

Making LOS a priority leads the care team staff to spend their mornings exhausting all options to minimize length-of-stay for the low number of "barge-stuck-in-the-mud" patients instead of spending their time on the mainstream or "easy" discharges. Thus the majority of patients wait while the minority of patients receives a disproportionate amount of time and attention. If the same care management team were to truly focus on patient flow, they would prioritize their activities to ensure that the non-outliers, the typical patients, are unencumbered through the discharge process. Unfortunately, NUMBERS don't guide this behavior.

When the primary indicator for driving patient flow is length-of-stay (LOS), a predictable pattern emerges: famine and feast. A dearth of available beds in the morning is followed by a mountain of beds late in the afternoon. Unfortunately, the demand for hospital beds peaks about four to six hours before they are available, and then remains constant into the early evening. When demand remains constant and supply is created later in the day, there will be long waits in the emergency room and PACU, physicians who can't find beds for their patients, and ambulance diversion. This results in inexplicable waiting, which leads to loss of revenue, loss of market share, damage to physicians relationships, loss of community support, and so on.

Imagine shifting patient flow to earlier in the day, without considering LOS. Now the care management focus moves from the difficult cases to the easy discharges early in the day. Patients leave at an even pace throughout the day, smoothing out the supply of beds and better meeting demand. There is no extra work added, only a change in the daily routine. Focus for the current day becomes the mainstream discharges in the morning, followed by the more difficult discharges later and then working on the anticipated discharges for the following day.

For example, if three beds can be freed up early in the morning by focusing on the mainstream, an additional 15 people can be moved through the Emergency Room, assuming one in five will be admitted. Small things like this can have a big impact on patient flow.

To implement this type of behavioral change, it is critical to develop multiple key indicators that track the performance of the "main channel" and keep the "barge" numbers at the end of scorecard and out of the spotlight.

When this happens, patients are seen in a timelier manner and receive the correct level of care. Physicians are able to place the right patient in the right bed and nurses have a happier patient who is stabilized more quickly. Administration is happy, because diversion is decreased or eliminated and patient satisfaction increases.

The unintended benefit of this type of behavioral change can be a decrease in length-of-stay without it being the sole indicator and focus of the care management staff. Working on patient flow CAN decrease length-of-stay, but focusing on length-of-stay CANNOT increase patient flow.

If you want to know more e-mail me at matt.carroll@usccg.com

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Queuing Theory Misplaced in Hospitals

A carpenter with a nail gun can build a house. A nail gun in my hands predicts a medical emergency. I am not qualified to use a nail gun and the one time I tried, I nearly nailed my hand to a board.

 

One of my best clients was about to nail his hand to his desk with a queuing theory model. He summoned me to talk about the demand for ICU beds coming from the OR. He wanted help extracting 'controlled variation' from his data so that he could apply a queuing theory (QT) model to determine the number of ICU beds he needed. My first reaction was, "What!" Then, "Why????"

 

It seems my client had just spent a couple of days with IHI and Eugene Litvak, Ph. D., a professor of healthcare and operations management at Boston University and director of its program for the Management of Variability in Healthcare Delivery. He also is an adjunct professor at the Harvard School of Public Health. Dr. Litvak is a great proponent of the use of such quantitive approaches as the queuing theory model to address pressing hospital problems. (Click here to read about Dr. Litvak and Queuing Theory.)

 

As a manufacturing systems engineer, many might think I would be an advocate of such methodology. However, I believe, in most cases, queuing theory is misplaced and even misleading Here's why:

 

1.    Complex tools require practice and expertise. Queuing theory derives its power from rigor and discipline. Today, even operations professionals with 20 years' experience struggle with queuing theory. To be effective, these tools require years of practice. A tool that works for a Ph. D. in operations research is not always a good tool for the typical hospital administrator.

 

2.    QT has limited application. Queuing models are famously restrictive in their content and often are incapable of precisely modeling real-world situations. As Peter Drucker so aptly wrote, "…Hospitals are the most complex human enterprise…" Answers culled from queuing theory may not be particularly useful because they can't model the hospital's natural complexity. I find that, since hospitals have many scenario-specific considerations, simulation modeling is a more appropriate analytical tool.

 

3.    QT has a limited useful life. These are capacity planning/decision tools. When administrators are unable to quantify how many beds they need, Dr. Litvak tells them they need queuing theory to determine the number. How often are you going to need that question answered? Let us say you have a ten-bed ICU and a queuing model suggests that a better number is 12 or 15; what is the administrator to do? Repeatedly asking the question will result in the same answer. This is because QT offers an extremely limited view of the alternatives. Hospital administrators need to look at other options to make the best decision for their hospital.

 

4.    Applying the theory doesn't always work. Here again, the usefulness of the rigor is diminished. Hospitals add capacity in chunks. For example, rarely would it make sense to simply add one ICU bed, they open a whole unit. How do they determine how many units to add? They add as many as they can staff! But that doesn't mean this is an effective approach, even when the queuing theory says otherwise. Because the hospital operation is complex, adaptive, and interdependent on many variables, a broader look is necessary to find the right balance among efficiency, patient safety, quality care, and cost control.

 

5.    Demand on the system is complex and interdependent. Hospital demand is enormously complex. You have to factor into the equation the biological variation, social/economic factors, the overall capacity of the market, the physician preference/patterns, payer mix, quality of nursing care in the units, availability of step-down, skill(ed) nursing units, and much more. This complexity does not lend itself to queuing theory, because the theory can't accommodate all of them, and all of them must be taken into account.

 

 

6.    Service time in the system is complex and interdependent. Data on length of stay for most hospitals is largely inaccurate; the variance between actual and recorded times is generally off by several hours. In a typical hospital stay, service time is impacted by ease of diagnosis, the physician's course of action, ease of placement, use of provisional radiology/labs, availability of insurance coverage, availability for care at home, timeliness of a ride home, and even meal times. These interdependent variables make life for queuing theory analysts quite troublesome. I find my own abilities to define service time for QT models just about impossible in these circumstances.

 

 

Look beyond queuing theory, if you want solutions that really work. There are many quantitative approaches that can bolster your performance. For some ideas about what might work best for your hospital, e-mail me at john.dalesandro@gmail.com.

The Unintended Consequences of a Focus on Productivity

 

"Productivity" is one of those concepts hospitals have borrowed from manufacturing because it works so well in that environment. But in hospitals, productivity presents a paradox. Most practitioners in hospitals don't understand the principles behind productivity. As a result, their efforts to be "productive" will likely adversely affect critical elements of the patient experience and potentially the hospital's financial health.

The problem is that productivity is a relative measure that, when used as a sole indicator, can be very misleading. The number of dollars expended per hour for a specific activity, for example, is a productivity measure. These relative measures are designed to give managers and leaders a tool to help guide analysis and exploration. They help navigate and provide perspective. But, allowing productivity to be a primary analysis tool and decision making driver can bring about an undesirable result.

In the interest of full disclosure, I am a manufacturing engineer by training and I think some of my brethren own some of this mess. The patient-to-nurse ratio, the most famous productivity measure, is a perfect example. The med/surg ratio in many hospitals is 6:1. As relative measures go, this makes some sense – for establishing budgets. But it is a poor tactical management tool.

Productivity measurements like this one are insufficient when used to drive daily performance. Armed with the staffing ratio, hospital managers make day-to-day and even shift-to-shift decisions. It happens every day in capacity-constrained hospitals where managers are armed with only experience and a productivity measure. They take steps in the interest of meeting a target, but the following scenario will demonstrate how negative these decisions can be – all in the name of productivity.

At 9 a.m. a nursing manager with a 5:1 ratio has six nurses on shift, but only 25 patients. So, she decides to send one of the nurses home or to another unit. Assuming the fully loaded cost of a nurse is $75/hour, this manager has just "saved" her unit $75 for each hour of the nurse's 10-hour shift, or $750. Since that "productivity decision" is usually not made with the discharge requirements in mind, the decision to reduce staff can result in poor patient flow, highlighted by bed constraints, excessive delays in the ED and PACU (potentially impacting quality), or even ambulance diversions (which translates to lost revenue).

The $750 saved slows the patient flow and eventually costs the hospital somewhere in the low five figures of lost revenue for that day. Worse, the attendant long-term effect of the loss of confidence among doctors and the community could easily translate into losses in the millions over the course of a year. Beyond that, the lack of consistency in schedules is often a quality-of-life issue that leads to chronic problems with staffing and retention.

So the unintended consequence of saving that $750 by managing to productivity measurements alone is multiplied on numerous fronts, all negative and much more costly. Be on guard against managing your hospital's operational effectiveness with only a productivity ratio. No one wants his physician to base a diagnosis and treatment plan solely on blood pressure measurements. The same goes for hospitals and one-dimensional approaches to effectiveness.

Contributing to this work is Kenneth P. Staresinic

 

The worst thing to do in a capacity constrained hospital

Hospitals are complex logistical systems. Beware seemingly easy fixes to pervasive problems.

For example, CEOs at the helms of seriously crowded emergency departments constantly receive internal proposals for adding resources. Whether it's for a new building or more staff, a moment of pause is in order before approving such additions. In fact, it's important to recognize that adding resources could unintentionally aggravate an already troublesome situation.

Recently, I met with an executive who was struggling with patient throughput. The problem stemmed from an inability to discharge patients in a timely fashion. When the executive dug into the problem, she discovered that the vast majority of discharges where occurring at shift change (a common problem all over the country).

After a great deal of discussion and brainstorming, the idea of hiring a dedicated discharge nurse surfaced. This was considered a winning proposition for everyone on the unit because:

1. Nurses would be freed from a paper-intensive process to maximize the time they spend providing patient care;
2. Bed management could rely on a person who was accountable for ensuring that the beds where cleared as soon as possible;
3. Nurse management had a more consistent process for moving patients out;
4. Case management got a resource to help coordinate and plan their activities; and
5. Administration would solve one of the biggest challenges facing patient throughput, which would logically lead to even greater throughput.

A discharge nurse was identified and placed in the high-volume 35-bed med/surg unit. But, to everyone's consternation, the average discharge time moved to even LATER in the day. Why?

Previously, this unit averaged seven to ten discharges every day. With a 6:1 patient-to-nurse ratio across a 24-hour period, this meant that each nurse would be accountable for one or two discharges per day…and these discharges most often occurred between approximately 10 a.m. and 8 p.m. In this scenario, many people processed discharges, each independent of the others. Each had her own path to discharge, as illustrated in the chart. Thus, if there were a delay or a problem with Patient B, then Nurse B would have to deal with it. None of the other nurses or patients were impacted.

When the discharge nurse arrived, she was saddled with all seven discharges, and, in theory, she had the capacity to discharge all the patients in a timely manner. What wasn't considered was the fact that all the discharges immediately became interdependent. As such, any delay with any patient would automatically impact the rest of the patients.

This is just one example of how a seemingly good solution can actually create a worse problem. Take time to carefully consider the broad ramifications of changes to the hospital environment.

Benefits of shifting hospital focus from admissions to discharge abound

Most hospital operations are organized around admitting, which means that everything is geared toward getting patients moved into a bed. Practically everything in the hospital revolves around admissions: the priorities, the infrastructure, the processes, the measures, the staffing levels, the training, and even the attitude and culture.

    To be sure, this approach is time-tested, but the reality is that, when the hospital is full, this method can strangle patient flow.

    The system starts to break down when a lot of patients pile up waiting for an inpatient bed. Waiting patients is a chronic problem for hospitals. And, when the queue forms, hospital management inevitably mobilizes to "fix the problem." Phones start ringing and managers, directors, or administrators start walking the hospital in search of beds.

    Hospital management usually handles fixing this problem because no one else has access to the information that defines the true state of the hospital. It takes time to manually collect reliable information on patient/bed status, so managers frequently find themselves investigating, expediting, and firefighting. This often pays off in the short term, because the bed crisis is always – and eventually - solved when patient discharge is expedited, a hidden bed is uncovered, or a physician is called to action.

    However, the core problem underlying the bed crunch has not been resolved. So the expediting culture becomes institutionalized, breeding the kind of inefficiency and bureaucracy that has well-intended professionals actually slowing patient flow. This results in a vicious cycle that leads to lower bed capacity and new unintended problems with trust, accountability, and confusion.

 

These problems are more preventable when the focus shifts to discharging patients

 

    The only way to prevent an overload of waiting patients is to have clean beds available. By focusing on discharging patients who meet the criteria for being sent home – regardless of the status of the hospital or non-clinical issues – the hospital will actively prevent the overload condition by systematically moving patients through the hospital.

Changing the focus to discharge puts the census in a new light. Too often in hospitals a patient stay is colored by such non-clinical conditions as socioeconomic issues, familial matters, or physician whims. A hospital passive about discharge tends to leave these decisions unchallenged, until the hospital is full. Then, with expediting, patients who lack the conditions that necessitate hospital care are discharged —sometimes abruptly. This is losing proposition for nearly everyone involved and a situation that is preventable.

With a simple change in focus, the entire hospital becomes organized and geared toward anticipating the action necessary to consistently and safely discharge every patient that meets appropriate criteria. This approach frees up bed capacity before it is needed, smoothing out workflow and optimizing patient placement.

    Equally important, the hospital permanently resolves the expediting problem. If the hospital sticks to its creed of discharging every patient that meets the criteria for being sent home, then, by definition, when the hospital is full, it really is full.

In addition, because patients have been moved proactively through the hospital, the common "bed crunch" expediting becomes needless. Eliminating the constant expediting will result in a dramatic improvement in staff satisfaction by relieving the stress of constantly being in crises. Patient/bed information will be truly trustworthy and the entire staff can become more effective, improving organization confidence and accountability.

It might seem like magic, but a simple change in philosophy can have a huge impact on hospital operations.

      

    

Why length of stay is a poor measure of hospital performance

Nearly every hospital measures and manages its performance based on inpatient length of stay (LOS). But LOS as an absolute measure really doesn't provide much useful information for managing a hospital. Here is why:

 

Lack of clear-cut root causes undermines accountability - Who is ultimately accountable for patient length of stay? The nurse? The doctor? The lab? The hospital administrator?

 

There are many reasons patients are in the hospital and, in an ideal world, objective clinical criteria would govern census. However, in the real world, there are statutory, socio-economic, familial, emotional, and even political drivers that affect a patient's status. It is nearly impossible to objectively determine root causes for excessive LOS because each patient's circumstances are so different, making root causes tough to classify and hard to analyze for better management.

 

In the absence of clear-cut root causes, no one member of the hospital staff can be held accountable for controlling LOS. As a result, lack of individual accountability makes everyone accountable; and, when everyone is accountable, no one is accountable.

 

Imprecise Measurement - Hospitals measure length of stay by the day. It doesn't matter to them whether a patient is admitted at 10 AM or 10 PM; any admission before midnight counts as a day. Since when does two hours constitute a day? Measuring length of stay this way is imprecise and dilutes the power of the metric. Wouldn't it make more sense to measure length of stay, with its attendant costs, on a rolling 24-hour basis?

 

That practice is already at work with observation patients. As an observation patient approaches the 24-hour target, doctors, nurses, and case managers are all actively managing the patient to either admit or discharge regardless the time of day. In this case, the target is met because there is a clear, unambiguous goal and patients on this 24-hour clock tend to be (decisioned? or have their status resolved?) discharged in 24 hours. LOS would be far more meaningful if it were less ambiguous and matched to specific conditions for better measurement, just like the observational patients.

 

Hospital performance can improve with a simple change in how LOS is calculated, which would also make it a better measurement on which to base management changes.

 

Conflicting goals - There are many conflicting goals in every hospital. (The conflict between hospital reimbursement and physician reimbursement is the stuff of legend.) While the process is certainly imperfect, the simple fact is that, hospitals are paid for utilization of their assets. So, up to a certain point, inpatient LOS is a good measurement for the hospital.

 

But there is an optimum length of stay that is as unique to each hospital as its geography, service model, patient mix, charge master, and contracts. Using one standard LOS to measure hospitals across a disparate universe just makes no sense (See example below).

 

Not Normalized - In benchmarking LOS across the nation, there is shocking disparity between the different regions of the USA. Using CMS data, we compared two hospitals that are nearly identical, one in Cleveland, OH, the other in Queens, NY. Both are short-term acute-care facilities, with 250 beds, proprietary corporation control, and a case mix index of 1.25.

 

When we compared the average LOS for DRG 127, Heart Failure & Shock (CHF), a perennial high volume diagnosis, here is what we found: The Ohio hospital saw 237 cases with an average LOS of 5.16 days. The New York hospital saw 150 cases with an average LOS of 7.86 days. What explains this difference? Who cares? CHF is CHF. There is no clinical reason for treatment requiring 2.5 more days in New York than in Ohio, but there may be numerous other reasons that cannot be determined by single-mindedly looking at length of stay.

 

Good measures are relevant measures - Good measures are clear, objective, assignable and relevant. They are not open to interpretation. (An automobile is going 55 MPH or it is not.) Good measures are actionable, which means targets can be set, actions assigned, and outcomes evaluated. Good measures can be used to initiate and sustain intervention or process improvement.

 

Hospital performance measured solely or primarily by LOS is meaningless. Want a simple, easy, and very effective way to measure hospital performance? Email me. <click here>

 

 

Note: Data used is from the Medicare Hospital Market Service Area File which is updated annually by CMS. The file includes Medicare discharges, patient days, and gross charges by ZIP code for each hospital. Data are based on 100% of all Medicare fee-for-service claims during a calendar year. Go to CMS.gov for more info.

Standard Presupposition: Why Manufacturing Improvement Methods Fail Hospitals

For the last quarter century our country's best minds have invested time, money, and talent in search of a way to cure what ails hospitals. Despite aggressive pursuit of know-how from other industries, (particularly manufacturing which has produced dazzling performances for the likes of Toyota and GE) the application of best practices has not worked in the hospital setting.

Shouldn't we be in a better place by now?

After all, the health care industry is extremely collegial; successes are not only shared, they spread rapidly from hospital to hospital. If the 'answer' was readily available, shouldn't it have permeated the industry? If it were simply a matter of applying Lean or implementing electronic medical records, for example, U.S. hospitals as an industry would be the embodiment of excellence.

Sadly, they are not; few look to a hospital as a case study in efficiency. In fact, as many as 90% of today's non-rural hospitals self-report some form of 'operational dysfunction.'

Why are these seemingly common sense, manufacturing solutions failing in hospitals?

One problem is that, conceptually, hospitals look deceptively like factories. That is a trap, because, fundamentally, they don't behave like factories, and they certainly are not managed like factories. As a result, industrially oriented remedies have provided some relief, they tend to result in localized and transient improvements.

What makes a hospital different?

One of the biggest differences between a factory and a hospital is standardization. Standardization is one of those things often taken for granted in today's world, but it was a very big deal 200 years ago.

All assembly line (and now supply chain) techniques are founded on standard and interchangeable parts. Standardization elevates the importance of the process and makes labor a commodity. Every manufacturing management technique depends on a foundation of standardization. It drives out process variation, inventory, and cost. Without standardization, there would be no assembly line, nor there an industrial revolution. For quick read on the history of American Industrialization <click here>

Before standardization and the resulting assembly line, only master craftsmen could build an automobile. But, Henry Ford made automobiles affordable by deconstructing the 'art' of automotive construction. Through the use of standardization, the time to build a car was compressed by over 90% and craftsmanship became irrelevant.

Unfortunately for most hospitals, there is little hope for this kind of standardization. It is hard to find a person who would advocate standardized personal healthcare. It is far too emotional an issue. The baby boom generation will need personalized healthcare.

And, since clinicians are expected to provide personalized care, standardization, no matter the driving force (even the Federal government), is unlikely to work.

Bottom line: Hospitals have to start looking beyond the application of basic manufacturing theories to achieve the kinds of improvements needed to meet patient demands and minimize costs, and toward solutions based on skilled clinicians providing highly individualized treatment to diverse patient populations, in a complex and dynamic environment. There is a better path.

ICU Physician Staffing: What’s the Big Deal?

 

The big deal is, according to The Leapfrog Group, an estimated 50,000+ lives could be saved every year by implementing ICU Physician Staffing (IPS) programs in all urban hospitals.  But, says the same organization, only 26% of reporting hospitals have adopted the program.

Let's look at just a few of the common pitfalls that ICUs encounter in the absence of IPS, every one a deterrent to patient safety, quality care, and hospital profitability:   

• Beds are less available or are not used appropriately, which means fewer patients receive the right level of care at the right time;
• Lack of a "go to" physician leads to a breakdown in patient ownership and treatment;
• Physician access is limited when he is obligated outside the ICU;
• Patients receive fragmented care (organ-focused consulting model vs. comprehensive patient management);
• Services are inadvertently duplicated because of poor management and communication;
• Patient safety is compromised;
• Variable night coverage, e.g., in-house vs. pager, fellow vs. resident, resident vs. intern, lowers quality care;
• Inconsistent use of evidence-based protocols and guidelines delays treatment; and
• Delays in initiating appropriate interventions increase.

 

Research supports the argument that by implementing IPS two important things would occur, both of which are critical to hospital health:

Both hospital and ICU mortality would be drastically reduced and length of stay in overcrowded hospitals could be reduced to make healthcare more accessible.

The criterion for ICU Physician Staffing is straightforward. It requires:

• the presence of a physician board-certified in critical care medicine or other appropriate training (e.g., emergency medicine certification with a critical care fellowship) during daylight hours;
• that physician time be dedicated exclusively to ICU patients; and
• when the physician is not present, maximum response time to a pager call is 5-minutes 95% of the time, or a telemedicine presence with similar response time.

Given that critical care medicine has been in existence for more than 40 years and that intensivists (physicians who specialize in ICU care) have been recognized as board-certified specialists since 1987, the problem isn't a lack of qualified personnel.  So what's the big deal?

    50,000 lives.

 

This week's Blog was written by Christopher Saunders, RN.  He is a senior healthcare consultant with USC Consulting Group, LLC (USCCG). He has been providing clinical leadership for the firm's healthcare practice since 1997.   His email is chris.saunders@usccg.com

 

 

Focused Improvement Provides Greater Success

Most of the hospitals I visit are buried in improvement projects. In many cases, there is value in simply cutting the project list and focusing the hospital on fewer concurrent initiatives. Stopping or postponing a few projects has all sorts of positive consequences:

• Reduce the time spent in meetings, project management, training, status reports, etc.;
• Defragment the capital budget and strengthen funds allocated to remaining projects;
• Eliminate pet projects and the political or emotional consequences; and
• Strengthen the probability of success: in most cases, groups focused on a few things are more successful than those working on many things. 

 

The hard part is figuring out which projects to support and which ones to kill. This is not always a logical exercise. Here is a straightforward process to that end.

 

1. Consider the leverage, or the extent that the project's benefits will influence the hospital's overall performance. This is a version of what the military calls a force multiplier. It refers to a factor that dramatically increases (hence "multiplies") the effectiveness of a force. High leverage projects should get priority.

When looking for leverage in a hospital, consider the following:

 

Centralized services have greater leverage. Benefits to centralized services are multiplied throughout the operation. For example, lab turnaround can impact every single department in a hospital. Faster labs mean faster ED, faster ICU, faster OR, faster discharge, etc. Prioritize initiatives that touch a large percentage of patients. A 10% reduction in cycle time affecting all patients in a 300-bed hospital is better than a 90% cycle time reduction in an 18-bed ICU.

 

Interdepartmental tasks have greater leverage. There is always non-value-add time in the handoff between departments. It can be spotted by the way it's measured: turnaround time. Patient transfers, testing, admissions, etc, all have inherit delays that can be reduced to the betterment of both departments; hence the leverage. Click here to read more about this in a prior posting on this blog.

 

Transportation is high leverage. Processes with some kind of transportation are often forgotten, but have high leverage. Transportation is a non-value-added resource, and under close scrutiny by finance people. There are never enough transporters, so there is always a queue and move time in these processes. Significant leverage can be harvested by focusing on how to reduce these time buckets.

 

2. Consider the real improvement potential. Just the nature of hospitals requires them to look at improvement in absolute terms. "If we could save just one life…then it would be worth it." While true, there is merit in looking at benchmarks and evaluating just how far away the hospital's existing performance is from the best demonstrated performance prior to the launch of an improvement effort. Projects offering the greatest chance for improvement should get priority.

 

Here are some steps at objectively evaluating improvement potential:

 

Work to quantify the benefits operationally. Make sure the project sponsors can articulate a logical cause/effect and make sure that this cause/effect can be objectively measured. For example: "Adding point-of-care testing will reduce our cycle in ED diagnosis from 90 minutes to 15 minutes."

 

Evaluate the existing performance. Using the point-of-care example from above, the ED with an 8-hour length of stay has more potential than one with a 2-hour length of stay. When you juxtapose projects with an eye toward killing some of them, it is best to prioritize those that can make the greatest impact.

 

Double check the root cause and the commitment to change. A complete analysis links a project's promises to the overall performance. Also, it is vital that required changes in behaviors be explicitly specified. Again, using the point-of-care testing, is the test turnaround time a frequent cause for the long cycle time in the ED? How often do tests sit in charts waiting for docs? Are the docs committed to changing their practices with the addition of point-of-care testing? Sometimes smart people fix the wrong problem.

 

 

Armed with this analysis, projects can be placed in a simple grid to help make the decisions easy. Simply plot the relative improvement potential vertically and the relative leverage horizontally.

 

Those projects in box 3 are the best investments, providing the best ROI. Box 4 should be reserved for very strategic investments. Projects in box 1 require only small investments or should be relegated to quick, 'just do it' type projects. Box 2 should be eliminated without further consideration.  

How to Make a Dramatic, Quick, and Easy Cycle Time Reduction

By definition, a clinician with a patient ratio is multi-tasking and hospitals are full of multi-taskers. Because multi-taskers move between tasks without necessarily completing any one within a specific time frame, an extra time burden is placed on steps in any given process. For example, when samples are to be delivered to a lab for analysis (coordination time), that process is often interrupted when the clinician, is sidetracked by another important task (e.g., another more critical patient in the ED) before the samples can finally make their way to the lab. The same thing happens on the other end of the spectrum (handoff time), when work needs to be documented or otherwise passed on to another person. In chart form, it looks like this:

• Coordination Time: This is the time that someone spends becoming aware of a task and making the decision to act it. For example, a nurse must become aware of a new order, and has to understand the order, before she can complete it.

• Process Time: This is the time it takes someone to complete a task.

• Handoff Time: This is the time that someone spends completing a task so that the next person in line can do his part. For example, a nurse may write a note in the chart for a doctor.

In most industries, and especially in hospitals, the focus tends to be on reducing the process time. While that might be a natural focal point, you can gain more benefit by reducing or eliminating non-value-added time during coordination time, and handoff time. In fact, most analyses of process activities reveal that as much as 80% of total cycle time is lost in these essentially non-value-added activities. Worse, the source of many of the quality failures in healthcare can be traced to problems with the coordination or handoff of activities. One of the drivers behind the billions about to be spent on electronic health records and Computerized Physician Order Entry deals with the handoff between doctor and nurse/pharmacist. Click here to read a Jan 2007 Time Magazine article on deaths linked to sloppy handwriting.

When reducing cycle time by focusing on coordination time and handoff time, you necessarily set into motion a new approach to squeezing out all sorts of waste from your process. Here are some of the benefits of this approach:

Improve Quality.

Clean coordination and handoffs eliminate errors while speeding up the process. Patients' clinical quality climbs, while the overall patient experience is enhanced

Increase Productivity.

Most hospitals lack the information support needed to measure and manage non-value-add time; in fact, this is usually a bone of contention between departments. (Note the common squabble between ED and Lab/Radiology on turnaround time.) Reducing non-value-add time provides better results while actually reducing the effort to achieve the results. Productivity goes up along with quality.

Improve Morale.

When people can spend more of their day on value-added work, they can better see the results of their work, there is a better sense of accomplishment, and better alignment with the reasons they entered healthcare. I have yet to meet anyone who needs to spend more time in meetings or waiting for a fax!

Implement Easily.

Hospitals are notorious for resistance to change. Too often resistance to change is a function of forced efforts to reduce process time. While there is always room for improvement in that area, especially in hospitals, it is usually much easier to reduce coordination time and handoff time. In fact, people tend to participate more enthusiastically in anything that takes non-value-add time off the table.

The next time you are working on a process improvement project, try studying the complete task cycle time to see if you can find ways to eliminate the handoff or take seconds off the coordination time and handoff time.