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.

Uncategorized

Managing Surges: Triggers in the Emergency Department

Hospital Operations Management, Patient Flow, Hospital IQ, ED Overcrowding
file_thumbview_approve.php?size=1&id=1905980

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