Patient Flow

Staffing the Shift: The Human Impact of Getting it Right

Hospitals, Staffing, Artificial Intelligence, Machine Learning, Patient Flow

“They just don’t care about us,” goes the refrain. When the shift is understaffed, nurses can feel betrayed by the managers and executives responsible for filling shifts.

As an emergency physician, I work at a wide range of Emergency Departments (“ERs”), from large referral centers with four or more providers working around the clock, to tiny ERs with eight beds and a single doc. Regardless of the size or sophistication of the ER, when nurses feel stretched beyond their limits, it’s common to hear them voice distress or to wonder who is looking out for them.

Having worked both at the front lines as a clinician as well as an administrator, I know that often enough leaders do care, and care a great deal, about filling those shifts. But sometimes it can seem impossible to find someone even for a half shift.

At the other extreme, many times I’ve been on an overnight shift at a small ER that has emptied out in the small hours of the morning. Nobody complains about those shifts. And in fact, when there is bitterness about working understaffed shifts, these overstaffed hours can seem like a well-earned respite.

The dream is, obviously, to staff every shift such that staffing precisely meets demand. But there are real-world limits to doing so. Some of these are not amenable to change. There are costs of readiness. Even if a small ER empties out for a few hours overnight, there still needs to be a doctor, a couple of nurses, and enough other staff to run a code.

However, some of these limits are imposed by a lack of information. If we are facing the prospect of staffing some nights with 10 nurses, and others with eight, because there simply aren’t enough available people, wouldn’t it be great to know which nights to staff down?

Of course some of this can be done using historical data. For example, you may know that Monday nights are always the busiest, and thus focus on using more of those staffing hours on Monday nights, leaving others possibly more short staffed.

But leveraging advanced data analytics can extend the value of historical data considerably, by finding patterns not only by day of week, but across seasons, special dates in the calendar and other events. With the right tools, shifts can be tailored to more precisely match staffing to predicted demand, hour by hour.

And still more advanced approaches can go beyond historical patterns, to forecast spikes and dips in arrivals at the ER days ahead of time. For managers, this means that if they can round up some additional hours of nursing coverage, and they know where best to put those hours to use. It also means they are not finding themselves in a position where they are making calls to ask nurses to drop their plans and come in to work that very day. Armed with several days’ advance notice, they can make the necessary staffing arrangements ahead of time, giving nurses more opportunity to make themselves available.

As hospitals adopt these powerful analytical tools, there may be fewer staff working these “slow” hours, but it can also means not getting pummeled on understaffed, “busy” shifts. I believe most of the nurses with whom I work would agree that was a good tradeoff. These cutting edge approaches will give managers the tools to look after the nurses they work with, and show them just how much they care.

This post originally appeared on the Hospital IQ website.

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Surge Actions: The Front End

Not all patient volume surges are alike. With so many differences among patients and illnesses, different levels of acuity, and specialty nursing requirements, when volume picks up, critical bottlenecks will appear in different parts of the system each time. Which part of the system reaches overload first? Which overload is the worst? Which has the greatest impact upon clinical care, hospital operations, and finances? These questions must be asked afresh each time a surge situation develops.

To illustrate this, consider a hospital with “boarders” – admitted patients waiting for a bed – in the Emergency Department (ED), with ambulances diverting to other hospitals, and patients walking out without even being seen by a physician because it’s taking too long. These are all costly scenarios for a hospital from both a financial, as well as a delivery of care perspective. But is it effective to send case management out to all nursing units and spend hours working to get patients home a day or even a few hours earlier in order to free up needed beds? What if, upon deeper examination, it turns out the bottleneck is only with telemetry beds and that there is actually no shortage of ICU or med-surg beds? Imagine how much more effective it would be to focus the efforts of case management on only the type of beds in short supply.

Let’s look at specific sets of actions that can be taken that are associated with the entry points to the hospital, or “front end.” Most hospitals and systems receive incoming patients through several channels: scheduled surgical and procedural cases, scheduled medical encounters (e.g., oncology), direct admissions (from physicians’ offices), transfers from other hospitals (typically for patients requiring a higher level of care), or, finally, ambulance and walk-in patients who arrive via the ED.

Talking specifically about the ED: During surges, with profound bed shortages, can demand for beds from patients arriving at the ED be managed? The answer is yes, but only with a coordinated strategy embraced at the very top levels of the organization, with very wide involvement among staff and providers, and most importantly with a set of tools to provide a timely, data-driven, decision-support framework.

There are two main strategies for managing demand for beds from the ED. The first is facilitating treatment discharges to reduce avoidable admissions. The other, more complex approach is directing admissions between hospitals within a multi-hospital system and success depends on these key tenets.

The right case at the right place
Many systems comprise a mix of hospitals, ranging from tertiary- or quaternary-care sites with extensive specialty coverage, through to community hospitals that may offer little more than general medicine, general surgery, and OBGYN coverage. Often the cost per bed-day matches the complexity of services available. Within the same system, the per-bed-day cost at the community sites might be half the cost of the “mothership” that has intensive capital investment in specialized equipment, as well as more intensive and specialized “human capital” on hand.

In purely financial terms, it would make sense for hospitals to direct patients with higher complexity illnesses towards the more resource-intensive hospitals, and those with simpler illnesses towards community hospitals. This would match the clinical capabilities of the facilities to the needs of patients. Further, it would match high costs per bed-day to cases with higher revenue per bed-day, allowing flagship hospitals within systems to provide greater care to the sickest patients.

Managing patient expectations
However, for some simple reasons this is often not the practice. Patients arrive at a particular hospital expecting that if they require admission they will be admitted at the same site. This may be because it is close to where they or their family live. Or it may be because the doctors who are mainstays of their care don’t have admitting privileges at other sites. It may simply be that they are most familiar with the hospital they came to.

Clearly an argument can be made for routinely routing patients according to acuity. And that argument takes on new force at times of high demand for beds. During surges, it is important not only to manage overall demand and supply, but to manage various specific types of demand. In practice, during surges, that will mean directing high- and low-complexity patients to different hospitals within a system.

Readiness of staff and processes
There is much heavy-lifting required to do this. Physicians, nurses, and other staff need to understand why patients are being transported between sites for admission. This is being done not simply for financial reasons, but to provide timely care to people who need it at the site most able to provide that care. Scripting needs to be developed and field tested. Elements of the scripting will generally include reassurances that member hospitals are part of the same system, with the same care processes, and that in a period of high demand they are being admitted to the site that has a bed clean and ready for them with nurses and doctors waiting to provide care.

There are different ways to segment which patients go to which hospitals. One approach is diagnosis-driven. For example non-ICU community acquired pneumonia cases may be admitted to community hospitals, while more complicated pneumonia cases come to the flagship. A different approach is to involve case management in directing the flow of patients between sites. This has the benefit of sparing physicians the effort of remembering and internalizing rules that are essentially administrative rather than clinical.

Leadership support
Awareness and support must extend to the very top leadership as well as governing boards. There will be complaints. If front-line staff and providers are not supported, efforts to route patients between sites will quickly degenerate into inconsistency and then failure.

During times of regular demand, a process for routing patients between sites can improve quality of care and can aid growth and financial strength for a health system. During surge periods, such a strategy goes further and can help weather the storm. Executing on such a strategy can be made far more effective by advanced operation management data platforms that can warn of impending surges, highlight where bottlenecks will occur first or worst, and indicate where opportunities lie for admitting patients across sites within a system.

This article was originally posted on the Hospital IQ website.

Patient Flow

Triggers for Surges: The Role of Advanced Data

Precision matters when hospitals are responding to surges in inpatient demand. Many hospitals learn the hard way just how expensive it can be when there is no effective process for responding to surges as they can result in lost transfers, lost direct admissions, lost admissions from ED walkouts, sometimes even canceled surgical cases, to say nothing of the negative impact to the patient experience, level of clinical care and hospital reputation. Yet hospitals that have woken up to the need for surge planning know getting the response right is just as important. Triggering surge actions when they aren’t necessary or targeting the wrong areas for action can also be expensive and can undermine staff engagement with surge planning. Advanced data systems can take hospitals to the next level of economic and operational performance by identifying triggers for surge response activation that are early, accurate, specific, and targeted.

Even without advanced data, implementing a surge plan with appropriate triggers and actions beats having no plan at all! But as we look at all the classic situations where bottlenecks cause delays and overcrowding, we see just how much can be achieved with advanced data systems that offer accurate and timely insights.

Emergency Departments

Surge triggers in the ED will typically involve how many patients are waiting to be seen, how many are currently being treated, and how many are “boarding” – admitted patients waiting for inpatient beds. These measures are good for raising the alarm when there is already a problem. However, many of the actions to address and alleviate surges require significant lead time, such as reassigning nurses or calling them in for extra shifts. Advanced data systems can forecast ED demand days in advance based on historical demand, weather patterns, seasonality and local events to predict the number of patients and mix of acuities arriving hour-by-hour. Combining this with simulation modeling produces predictions days ahead of time to warn of impending high demand situations. Additionally these predictions will be for specific bed types, highlighting whether it is critical care beds, telemetry beds, or med-surg beds that will be most in need.

Inpatient censuses

Inpatient census is another common trigger for surge responses. Once again, hospitals typically have triggers that reflect a state of bed shortage and overcrowding only once the crisis has occurred. Opening additional nursing units and beds takes time, as does calling in staff, working with case managers to prioritize discharges, and other measures to address census crunches. Advanced data approaches combine forecasting with simulation modeling of the entire hospital or health system. This allows hospitals to mobilize early and to focus upon specific levels of care. For instance, there are times when hospitals experience a shortage of critical care beds but not other bed types. Such events require targeted action rather than an overall surge response, and advanced data systems can predict these events well ahead of time.

Advanced data approaches

Advanced data approaches can combine several key elements, such as data from disparate hospital IT systems (e.g., health records, physician order entry systems, bed management and finance). They integrate these disparate data sources into a single data warehouse. They can run thousands of simulations with real data, to quantify not only expected metrics, but variability. They allow “what if” scenario testing to establish whether specific actions will work to improve operations, avoiding putting staff through trial and error initiatives. Complex forecasting can bring in arrivals through all sources, whether it’s emergencies, direct admits, or planned surgeries. Holistic modeling across an entire system can allow decisions to fully realize synergies across sites. Dashboards break down walls between disciplines and departments, allowing everyone to share the same data and the same forecasting in real time. And these systems allow for radical transparency, drilling down to the level of individual encounters behind the metrics to achieve trust and buy-in.

All of these features are important for improving healthcare operations generally. When it comes to census surges specifically, these capabilities will powerfully augment traditional triggers to ensure surge responses are early, accurate, specific, and targeted.

This article originally appeared on the Hospital IQ website

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Surge Planning: When physicians, not beds, are the bottleneck

When healthcare administrators think of hospital bottlenecks and delays, they usually think of three things as the root causes: beds, beds and beds. Of course, staffing and other resources factor in, but usually the common denominator is the ability to provide clean, ready, staffed beds. However, in some hospitals there is a second key constraint in parallel with bed availability: -the availability of providers, or provider “teams,” to care for newly admitted patients.

For many hospitals, this is a non-issue. There are always physicians ready to promptly take a call and accept responsibility for a new patient. Yet for other hospitals, difficulties, shortages, and delays in assigning accepting physicians and teams may have more impact than the availability of beds altogether.

The most prominent examples are teaching hospitals in which resident housestaff provide a significant proportion of care. Once upon a time, residents were expected to work with almost no limit on hours or patient load. Those days are gone. Accreditation Council for Graduate Medical Education (ACGME) rules today sharply limit the number of new patients a resident can accept, the total number of patients they can carry, and the hours they can work.

In other hospitals, attending physicians or advanced practice providers may have similar limits based on contracts or other terms of work. This can be the case in academic settings, but also in the (rare) environments in which providers are unionized, or simply have highly-structured work terms.

Siloing can also create provider shortages and delays. Siloing occurs when provider capacity is broken into many separate teams. In smaller hospitals, it is often hospitalists and primary providers with admitting privileges who admit the vast majority of patients, even when they require specialty care or surgery. In some large tertiary and quaternary referral hospitals, however, there may be highly specialized admitting teams such as cardiology, renal medicine, neurosurgery, or orthopedic surgery. Any siloing can lead to loss of effective capacity and situations where there is a long delay because some teams are “full” even while other teams still have capacity. More significantly, there are often major delays involving three- or even four-way discussions to determine who will accept a new patient.

Finally, there is the question of incentives. In environments such as academic hospitals, but also some others, there may be no incentive to providers to take on more new patients, or carry a larger panel. In such environments, delays and long discussions can be more common.
What does this mean for those of us working to improve patient flow? In particular, what does it imply when planning to handle surges in patient volume? There are several effective strategies to consider.

First, wherever possible, reduce silos. It makes no sense to have some teams half full while patients wait hours because other teams are out of capacity. This can be done by merging teams, maximizing flexibility regarding types of patients that can be accepted, and allowing overflow between teams.

Second, assigning teams must be policy driven, and not a recurrent, case by case, discussion. Empowering emergency physicians and the admissions office to make “automatic,” single-step assignments will dramatically reduce delays.

Third, even in highly specialized academic centers, there can be robust hospitalists or general internist teams designed in such a way that they can “flex up” rapidly to accommodate spikes in patient volume.

Fourth, in any hospital for which providers can be a bottleneck, providers need to be part of a surge plan. This means having triggers and actions. Actions can include temporarily enlarging team panels. This may be something that is worked out with Graduate Medical Education (GME) leadership, or it may involve supplementing resident teams with advanced practice providers. New teams may be created on a short-term basis to handle a surge, diverting physicians from educational, research or other tasks. Overflow rules between teams can be relaxed.

Finally, all operational planning, and in particular surge planning, becomes more effective with accurate demand and supply modeling, and forecasting. Discrete event simulation, what-if scenario testing, and advanced forecasting methodologies giving three or more days’ warning of volume spikes, are technologies and methodologies available to hospitals and health systems today.

These tactics and strategies are just as effective at maximizing the care we can provide with available physicians as they are at maximizing care we provide with available beds. Finding ways to resolve these problems in the hospital ensures that healthcare professionals can continue toward the ultimate goal – improving the access to and quality of patient care.

Originally appeared on the Hospital IQ blog:  https://www.hospiq.com/blog/surge-planning-when-physicians-not-beds-are-the-bottleneck/

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Managing Surges: Triggers in the Emergency Department

Hospital Operations Management, Patient Flow, Hospital IQ, ED Overcrowding
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Curing capacity issues in a hospital involves actions ranging from long-term, strategic improvements in matching supply and demand for beds, through to immediate actions to address problems in the here-and-now. However, there are short-term preparedness measures, specifically defining triggers to action, that can mitigate or bypass the disagreements and loss of time in activating responses to census surges. The Emergency Department (ED) is a good place to start.

The ED is often the “canary in the coal mine” for hospital census surges. When the hospital becomes critically full, admitted patients wait hours or even days to be assigned beds, all the while “boarding” in ED stretchers. With stretchers in the ED full, new emergencies cannot be brought in. Meanwhile, the waiting room is full, and patients begin to walk out rather than wait to see the doctor. Ambulance dispatches may officially ask to take new patients to other hospitals in the area (“diversion”), or unofficially do the same thing as they witness the crowding and delays. Research has shown that ED overcrowding is associated with worse clinical outcomes and higher mortality rates for ED patients. ED overcrowding is a failure on all fronts: a failure in the mission to provide care, and failure in quality of care, and a serious financial failure from loss of case volume.

When is the right time to call for help? Doctors and nurses in the ED often say they know overcrowding when they see it, but struggle to have an agreed-upon measure. Hospital and emergency leaders want a measure that is backed by research, and validated so they can understand when overcrowding will lead to walkouts, diversion and delays in care.

But how can one scale represent an inner-city ED with 70 beds seeing 100,000 patients a year, and just as faithfully represent a rural ED with 12 beds and 20,000 patients? Researchers have developed standardized scales used internationally to provide standardized apples-to-apples scales and triggers to action, across EDs of different types and sizes.

NEDOCS  is the most widely used scale. The inputs include 1) Demand: total number of patients in the ED (and waiting room), the number of critical patients (1:1 nursing, on ventilator) 2) Supply: number of ED beds, number of hospital beds 3) Delay measures: boarders, length of stay in the ED, waiting room time.

Other scales include READI (more factoring of acuity, plus provider staffing), EDWINEDCS (includes hospital occupancy) and SONET.

There is a lot of debate to be had over which scale is best for a particular ED. However, the most important thing is to start measuring. It is relatively straightforward to measure all five of these scales four times a day. At the same time, get input from the charge nurse and from physicians on whether crowding is impacting patient care, patient experience and the sense of pressure in the work environment. Put these together with walkouts and ambulance diversion and after a month you will have a good idea of which scales can be a trigger for action.

Of course, all these scales have one thing in common – they tell you when the storm has hit. They do not offer a weather forecast. New computer-intensive approaches involving discrete event simulation and machine learning to predict incoming patients, plus a holistic model of patients already in the hospital, can offer several days of advance notice, providing crucial time to fill staffing gaps and address hospital census among other measures.

Finally, hospital crowding is not only about the ED, and there are triggers for action that should also be considered that have nothing to do with the ED. More on that soon.

This article originally appeared on the Hospital IQ Blog site.

Patient Flow

Only a Holistic Approach Can Fix Patient Flow

Patient Flow, Hospital Administration, Operations Management, Overcrowding, Discrete Event Simulation
Modeling the Entire Hospital

When working to improve patient flow, it is vital to recognize that the different parts of a hospital are deeply interconnected. The nursing units where overcrowding is most evident may not be the units where the mismatch between demand and capacity is causing the problem. Further, patients have complex journeys through their hospital stays, and do not simply move “downstream” from ED to ICU to the floor. In order to improve patient flow in hospitals, sophisticated data analytics are needed to guide decisions on beds and staffing, and to target efforts on length of stay. And to deliver meaningful, actionable information, such analytics must be “holistic”. By holistic, I mean that analytics must encompass the entire interconnected picture of a hospital or even a system, as well as the full complexity of patient movements.

Those of us who work in patient flow have long realized that we cannot address any one part of the hospital in isolation. When tasked with “fixing the ED” to reduce crowding, walkouts, and ambulance diversion, and to improve door-to-doctor time, we know that process changes in the emergency department alone won’t get us to where we need to be. In most cases, emergency departments with significant crowding and delays are that way because beds in the emergency department during peak hours are filled with patients who are admitted and who are waiting to be moved to inpatient beds. Thus, freeing up inpatient capacity is key to unblocking the emergency department.

The same things hold true for other parts of the hospital. We cannot address problems in intensive care units without addressing the floor. We cannot address the PACU without addressing the intensive care units.

We also know that to fix problems in one part of the hospital we have to look both “upstream” and “downstream” to ensure we are actually addressing the real problem and not a symptom. The ICUs may be full because there aren’t enough telemetry beds for patients to move out to, not because there are too few ICU beds.

However, the picture is far more complicated than words like “upstream” and “downstream” can convey. Patients may move from the floor up to the ICU or go from the ICU to the OR, and sometimes they are directly discharged home from the ICU.

The true complexity of all the different patient journeys between different parts of the hospital has to be adequately represented if we are to target our true goals: namely, how often do we want to be able to place patients in the right level of care and the right nursing unit and how much of a delay are we willing to tolerate.

Nineteenth century approaches cannot yield the quantitative answers needed to fix flow. Walking around and looking at units that are full or at beds that are empty does not adequately explain what actions are required. Midnight and noon census, basic arithmetic and spreadsheets all fall short, as does queuing theory alone, which simplifies patients’ journeys into flow between compartments based on formulas and probability distributions.

We need computationally intensive advanced data modeling, such as discrete event simulation and what-if scenario testing, if we are to make the best use of our beds. Truly holistic models represent all the different parts of the hospital, including the ED, the OR, PACU, ICUs, floor; they take into account multiple sites across health systems; they factor in real-life constraints from hospital policies; and they represent the full complexity of patient journeys which crisscross throughout institutions. Only such holistic approaches can accurately represent the complexity we wish to manage. And only such approaches can provide us the operational clarity we need to improve patient access, and to better align staffing structure for overall financial performance.

In the broader culture, when the word holistic comes up, it can come across as a throw away adjective, used to sell everything from skin care products to dog food. But in hospital administration, a 21st century data analytics platform that offers a holistic view for operational planning and management is far from being a consumer luxury. It is an essential requirement for delivering efficient, effective patient care.

Paris Lovett, MD, MBA, FACEP, FACHE
Chief Medical Officer, Hospital IQ
Post originally appeared on the Hospital IQ Website