Managing Variability in Perioperative Services

Managing Variability in Perioperative Services CHRISTINA J. DEMPSEY, RN, MBA, CNOR

V

ariability within perioperative services is so common that perioperative services staff members and managers have come to expect and live with it. After all, patients do not get sick on a schedule. Surgeons have preferences for the days and times when they operate. Perioperative nurse managers can only minimally control staff schedules. The hospital census changes daily, which causes physicians and managers to bump or delay surgeries, board patients in the postanesthesia care unit (PACU) and the emergency department (ED), and place patients anywhere there is an open bed during peak times. Rather than simply responding to variability, however, the goal should be to eliminate variability whenever possible and effectively manage the remainder of variability. The success of this proactive approach rests on several key factors. First, it is important to understand the sources of variability, natural and artificial, and the factors that can be controlled, such as flow management, classifying the urgency of patients who present to the facility, scheduling, ontime starts, staffing and materials management, and purchasing. Then, data collection, analyses, and oversight by a collaborative team of hospital and physician leaders, along with key stakeholders that may include nurses in the OR as well as preoperative and postoperative care units, can eliminate some variability and efficiently manage variability that cannot be eliminated to both improve patient safety and lower overhead costs. The success of this proactive approach to managing variability is exemplified by the sustained success achieved at three facilities.

© AORN, Inc, 2009

SOURCES OF VARIABILITY: NATURAL VERSUS ARTIFICIAL The key to managing variability is to understand its sources. There are two types of variability: the first is natural variability and the other can be considered “artificial variability.”1 Natural variability in health care occurs as the result of patterns of illness or injury; it is random and uncontrollable. In contrast, artificial variability is driven by extrinsic forces (ie, those that are outside the patient’s clinical need), such as physician schedules that can be amended or controlled.1 The arrival of patients to the ED is a good example of natural variability. Patients break bones and have heart attacks in a random fashion; therefore, they present to the ED randomly. Clinical skills are another source of natural variability. Some surgeons operate

ABSTRACT Variability within perioperative services has come to be something physicians, perioperative nurses, and managers expect. Peaks and valleys in schedules; differences in physician preferences for surgical implants, instruments, and supplies; staffing competencies; and inpatient bed availability are just a few examples of day-to-day variability that affects perioperative services personnel. Rather than simply responding to variability, however, the goal should be to eliminate variability in patient flow as much as possible and effectively manage what cannot be eliminated. Combining the hard science of queuing theory and simulation modeling with the soft science of change management and operations improvement expertise is the key to success, and a collaborative team makes it possible. Key words: variability, queuing theory, patient flow management, specialty teams, health care costs. AORN J 90 (November 2009) 677-697. © AORN, Inc, 2009.

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Peaks and valleys in the elective schedule require duplication of staff and materials on peak days, and these human and material resources are underused on low-volume, or valley, days.

more quickly than others, and some staff members are more suited to certain specialties. In neither case is the variability extrinsically driven. Natural variability is random and not caused by any outside influence, and, therefore, it cannot be eliminated. It must be well managed. Although this variability occurs randomly, it is not unpredictable. For example, most ED directors are able to predict admissions from the ED on a daily basis and often can predict the kinds of patients who will be admitted daily, even months in advance; for example, an ED might see an average of 240 patients per day, with a 22% daily admission rate. In contrast, the OR director in most organizations is unable to predict the admissions from the OR, even days in advance, because of the artificial variability that is built into perioperative operations. Artificial variability is nonrandom and driven by extrinsic forces such as physician schedules and preferences in the OR as well as staffing practices. This kind of variability is caused by managers or physician practices that allow scheduling processes, staffing, and OR material and labor resources to be variable, having little or nothing to do with the patients’ clinical needs. This kind of variability lies at the heart of most boarding, staffing, cost, and quality issues. It is controllable and must be eliminated. The elective surgery schedule (with elective meaning that it is beneficial to the patient but not essential for survival2) is a perfect example of controllable variability that affects the whole

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hospital. The elective schedule usually is built around some kind of block scheduling system in which individual surgeons or groups are given blocks of time to perform their inpatient and outpatient surgeries. Often these blocks are built around considerations such as clinic schedules, seniority, or preferences that occur outside the OR or hospital. For example, many surgeons choose to perform complex procedures that require extended lengths of stay early in the week so that patients may be discharged by the weekend. This allows for fewer patients on the weekend and reduces the need for weekend rounds and cross-coverage by other surgeons. It also overloads the hospital early in the week. As a result, when the hospital census is high, patients are placed in beds on units that may not be appropriate for their care, potentially affecting the quality of care and length of stay. In addition, the peaks and valleys in the elective schedule require duplication of staff and materials on peak days, and these human and material resources are underused on lowvolume (ie, valley) days. For example, when all of the total joint procedures are performed on Monday, Tuesday, and Wednesday, either multiple expensive orthopedic trays must be purchased or instruments must be flash sterilized to accommodate the peak volumes. These items are underused, if they are used at all, on days when orthopedic volume is low. Staff expertise is often an issue during peak days as well. Staff members with orthopedic expertise might not be available for all surgeons on peak days and might be reallocated to other services on low-volume days. Quality of care and outcomes may suffer when staff members are required to work outside their area of expertise because surgical specialties are more technologically advanced than ever before. Specialty expertise is increasingly necessary to perform procedures as well as to care for specialty instruments, and it is difficult to be a “jack of all trades” in this environment. The elective block schedule can be redesigned to eliminate much of the artificial variability and the problems it creates. Although some smaller hospitals are not “specialized” to the extent described here, the

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peaks and valleys in the surgical schedule combined with the competition for inpatient beds between the ED and OR must still be optimally managed to reduce variability and improve efficiency and quality.

FLOW MANAGEMENT According to Brigham and Women’s Hospital Center for Surgery and Public Health, “the average American has nine operations in their lifetime. More than half of admissions are surgical, and 30 million operations are performed annually in the U.S. alone.”3 Artificial (ie, controllable) variability in perioperative services has a more significant effect on the entire organization than does natural (ie, uncontrollable) variability. The OR volume drives the inpatient census of the hospital, often generating as much or more than 50% of daily hospital admissions.3 An example of daily variability in add-on and elective surgery volumes is shown in Figure 1. The add-on urgent/emergent volume is

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much less variable on a day-to-day basis than the elective volume. The variability in elective volume is completely controllable, because these procedures can be scheduled in advance, although it often is not controlled well because of factors such as physician preferences, block scheduling mismanagement, and staffing issues. On busy days, this variability has the downstream effect of overcrowding the inpatient units and causing patients to be boarded in the PACU and the ED. Most organizations do not have separately staffed ORs for add-on and elective procedures. As a result, bumping or delays in the elective schedule occur when an urgent or emergent patient presents to the OR. Alternatively, the urgent/emergent patient may have to wait for an OR to become available, which could potentially put the patient in danger. Surgeons with a large add-on practice may leave gaps in their schedule blocks to accommodate their urgent/ emergent volumes. Often these gaps will go

Key Add on Add on mean Elective Elective mean 90 80 70 Count

60 50 40 30 20

3/13/07 3/15/07 3/19/07 3/21/07 3/23/07 3/27/07 3/29/07

2/23/07 2/27/07 3/1/07 3/5/07 3/7/07 3/9/07

1/2/07 1/4/07 1/8/07 1/10/07 1/12/07 1/16/07 1/18/07 1/22/07 1/24/07 1/26/07 1/30/07 2/1/07 2/5/07 2/7/07 2/9/07 2/13/07 2/15/07 2/19/07 2/21/07

10 0

Date Figure 1 • Add-on and elective OR procedures by date, WellStar Kennestone Hospital, non-holiday weekdays, January 2, 2007 through March 30, 2007.

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unused and cause downtime during the prime time of the day (ie, the time of day that is set for regularly scheduled hours and blocked for routine, elective scheduling for the organization). Separating these disparate patient populations into designated rooms, elective and urgent/emergent (ie, nonelective), is one way to provide improved access and structure for both populations.

CLASSIFICATION

OF

URGENCY

Before these populations can be separated, a collaborative team of physicians and hospital leaders needs to agree on a definition of what an add-on or nonelective procedure is, and a system to classify these add-on urgent/emergent cases must be developed. The basis of the classification system should be the urgency of the case based on the clinical presentation of the patient. Vital signs, consciousness, blood loss, mechanism of injury, and complications are a few of the clinical parameters that should be used for defining the clinical urgency of the patient who needs a surgical intervention. Providing a framework for the collaborative team to agree on can expedite this process (Table 1). Establishing an urgency classification system allows hospital leaders to analyze the procedural data for procedures that fall into each classification and their arrival times (ie, the times that these procedures are posted for the OR). The procedural data should include booking date and time, the urgency class under which the procedure is posted, procedure duration, and turnover time.

The established waiting times associated with each urgency category should be the acceptable maximum time frames from notification or posting of the surgical procedure until the patient is in the OR. The goal is to accommodate patients based on their clinical need and to prioritize them according to the established classification system. Providing service to the patient before the time limit is preferred. After the classifications have been established, physicians in each subspecialty should determine which procedures in the procedure file fall into each of these categories. When surgeons call to schedule the procedure, they should inform the OR of the urgency category. Data on arrival patterns and volumes of procedures by urgency class can then be analyzed by using a queuing model.

QUEUING THEORY

Queuing theory is a mathematical tool that has been used in manufacturing and service industries to manage lines (ie, queues). However, it has not been consistently applied to health care. Queuing analysis is a tool that allows management of the true random demand of clinically urgent and emergent patients. Queuing theory is used when • arrivals are random, as with ED patient arrivals; • service time is known, as with the amount of time it takes to perform an emergent OR procedure; and • the number of servers, as with ORs or staff members, are limited. Queuing theory can provide guidance on the optimal numTABLE 1 ber of rooms for randomly arUrgency Classification riving emergency patients, the average waiting time for these Urgency category Maximum wait time patients to access a room, and A = Emergent threat to life, 30 minutes the use rates for the room(s). limb, or eyesight This analysis provides guidance on the number of ORs B = Priority 2 hours that are needed to accommodate the facility’s add-on volC = Urgent 4 hours ume within the established D = Semi-urgent 8 hours wait time limits, as well as the expected use of the add-on E = Nonurgent inpatient 24 hours rooms. Several scenarios that

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reflect the numbers of add-on rooms can be developed and reviewed by a project oversight committee. The output will allow the committee of physicians and hospital leaders to determine the number of add-on rooms that are necessary to accommodate this demand and to make decisions that are based on science instead of anecdote. Output from a queuing analysis provides the organization with the amount of staffed capacity needed throughout the day and week to accommodate urgent/emergent patients. To illustrate, consider a hospital that has set aside one room

for emergent surgical patients. The hospital leaders are concerned because emergency patients are waiting too long to get into the OR, even with a room set aside. The leaders decide to open two more rooms to decrease waiting times to acceptable levels (Table 2). In this illustration, adding one room drops the waiting time for emergency cases (ie, A cases) from almost two hours to less than 18 minutes, which is less than the established maximum wait time. Overall wait time drops from 23 hours to 32.2 minutes. By using queuing theory, the hospital leaders find that only one additional room will be necessary. This

TABLE 2

Queuing Example Scenario 1

Scenario 2

Scenario 3

1

2

3

0.03 0.08 0.08 0.12 0.15 0.46

0.03 0.08 0.08 0.12 0.15 0.46

0.03 0.08 0.08 0.12 0.15 0.46

100 20 120

100 20 120

100 20 120

117.4 150.6 228.3 468.6 3631.6 1380.0

17.9 20.1 24.1 31.1 46.7 32.2

3.0 3.3 3.7 4.3 5.4 4.3

92.0% 8.0%

46.0% 71.0%

30.7% 92.6%

Model inputs Number of nonelective ORs

Case* arrivals per hour A cases B cases C cases D cases E cases Total arrivals per hour Average case length (minutes) Average turnover time (minutes) Total case length including turnover time (minutes)

Model results in minutes Average wait times A cases B cases C cases D cases E cases Overall OR % usage (each OR) OR availability % for an A case

* Case classifications A case—emergent threat to life, limb, or eyesight B case—priority C case—urgent D case—semi-urgent E case—nonurgent inpatient

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is the power of a queuing analysis. By setting these rooms aside, the use of the add-on urgent/emergent rooms is only 46%. However, because these rooms have been designated for urgent/emergent procedures only, the elective rooms may be scheduled back to back and may achieve greater than 90% utilization. By separating the two types of procedures, elective and nonelective, at least a 10% overall increase in OR use may be achieved. This is possible because there are no longer gaps for the rooms where elective cases are scheduled and no more bumping or delays caused by urgent/emergent patient arrivals. Both case types provide greater access to the OR at the appropriate times based on the clinical condition of the patient.

THE BLOCK SCHEDULE After the clinically urgent/emergent volumes have been analyzed by using queuing theory and separate capacity, including ORs and staff members, has been designated for this population, the elective surgery schedule should be analyzed to eliminate as much of the artificial variability as possible. The goal of any block schedule should be to maximize the use of ORs and ensure the placement of patients in preferred destination (ie, nursing) units throughout the week. The blocks should be revised on a quarterly basis to allow for maximum efficiency and optimal use of the elective surgery capacity, while ensuring appropriate patient placement. Based on my experience in hospitals, minimizing the peaks and valleys in the elective schedule and smoothing the volume across the week based on both use and the preferred destination unit for the patient actually opens up more capacity in the OR as well as the downstream inpatient units. Usage rates should be calculated based on the entire time the patient is in the OR plus the additional turnover time (ie, cleaning and setup time from when one patient leaves the OR until the next patient enters the room). Total case duration equates to the time between when a patient enters a room (ie, patient in) to when he or she is out of the room (ie, patient out), plus patient out to patient in for back-to-back procedures. Unfortunately,

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many information systems do not correctly capture procedure duration because they do not calculate this time interval and use instead the surgeon time of incision to dressing to determine use. As a result, the time between when a patient enters the room until incision time, and the time between when the dressing is on to when the patient is out of the room is often not factored into the procedure duration. For procedures with long setup, prepping, or draping times, this can mean up to an hour of time that the patient is in the OR is not factored into the scheduled procedure duration. This translates into scheduling delays for each subsequent procedure and difficulties with staffing. As an example, at the Heartland Regional Medical Center, St Joseph, Missouri, the leadership team identified a 30-minute average discrepancy in scheduled procedure length and actual procedure length during a data analysis performed because of this issue. This is not a unique circumstance, and scrutiny is required to ensure accurate scheduling. By ensuring that the scheduled procedure duration accurately reflects the actual procedure duration, Heartland schedulers and physicians were able to more appropriately schedule into their blocks, providing increased predictability for patients, physicians, and other staff members. Smoothing patient placement on the preferred nursing units also should be a key driver in allocating block time. Consider three orthopedists who each work on Monday and Tuesday and each perform four total joint replacements on each day. The average length of stay for these patients is three days. If the inpatient orthopedic unit has 24 beds, then there should be adequate capacity to accommodate these elective procedures. However, if all of the beds are filled with patients who have had total joint procedures by the end of Tuesday, no bed would be available on the orthopedic unit if a patient presented to the ED on Tuesday with a hip fracture. In this case, one of two things would happen: 1) the patient with the hip fracture would take an orthopedic bed, and the patient who had a total joint procedure would be placed on another unit, or 2) the patient with the hip fracture

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would be placed on another unit. Either way, an orthopedic patient would be placed offservice, where the nurses might not be accustomed to caring for that type of patient and, as a result, the patient might be exposed to adverse events or a longer stay. Alternatively, either patient could be boarded in the ED or the PACU while waiting for any bed if the hospital is at capacity. None of these scenarios provides for optimal patient safety or care. The solution is to use simulation modeling to determine the block schedule that will optimize correct patient placement. This should be done initially and at any time there are significant shifts in patient volume (eg, new service lines are introduced, more inpatient beds are built, an ambulatory surgery center is opened). The result of smoothing across the week based on patient destination unit is more capacity in the OR overall. This provides the capacity for growth of services and accommodation of new physicians. It allows the organization to grow without undertaking costly capital building projects or hiring new personnel.

SIMULATION MODELING Simulation modeling allows an organization to simulate changes in scheduling, nursing unit size, staffing, and other factors in a virtual environment before affecting the “live” environment. The simulation is conducted by using simulation software that incorporates length of stay data, unit capacity, preferred patient placement, and other factors. Simulation helps to identify by day of week and hour of day what nursing areas are under stress from capacity issues, where holds and boarding are occurring, and when flow is a problem. Simulated capacity inputs include such factors as the number of beds in specific nursing units, number of ORs for elective and nonelective volume, and number of ED treatment rooms. Schedule-related inputs include the OR block schedule by service or physician, patient placement preferences by diagnosisrelated group (DRG) or physician preference, and the maximum amount of waiting time before a patient goes to the preferred inpatient bed. Procedure-related inputs include volume by service, physician, or DRG, as well as length

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of stay specific to service, physician, and DRG. The output from the simulation model provides • a census report overall and by unit for specific time of day, • a patient placement report by service or physician, • a bottleneck report by unit and day of week, • a usage report for the OR or ED, and • a block usage report by service and physician. Although a variety of simulation models are commercially available and may provide different output reports, the model for the projects described in this article used proprietary simulation software.4 Output from the simulation model may provide these reports to help guide the perioperative team in ensuring optimal scheduling for both patients and staff members.

RESULTS

OF

FLOW VARIABILITY MANAGEMENT

Facilities have realized significant flow and process improvements when combining the hard science of data analysis with the soft science of change management and operations experience and expertise. For example, three hospitals that achieved significant improvement in flow are St John’s Regional Health Center, an 866-bed level 1 trauma center and community hospital in Springfield, Missouri; WellStar Kennestone Hospital, a 633-bed community hospital in Marietta, Georgia; and Heartland Regional Medical Center, a 353-bed community hospital in largely rural St Joseph, Missouri. ST JOHN’S REGIONAL HEALTH CENTER. St John’s Regional Health Center began its work on variability in 2002.5 The following results were obtained by separating elective and nonelective volume and by smoothing the elective schedule throughout the week. • During the hours of 7:30 AM to 1:30 PM, the number of surgical procedures increased by 5.1% with the separation of an add-on room for unscheduled procedures. • The number of ORs needed for surgical procedures at 3 PM, 5 PM, 7 PM, and 11 PM decreased by 45%. This was a result of the ability to provide a room during the “business” portion of the day for unscheduled procedures rather than waiting until the end of the block to perform these procedures. aornjournal.org

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began working on flow in 2007.7 They achieved the following results. • Waiting times for all add-on procedures, urgency levels A to E, were reduced by 22%. • The greatest reduction occurred in those procedures that required surgery within two to eight hours, which often were delayed. • B cases (2 hours): 34% reduction in wait times, • C cases (4 hours): 78% reduction in wait times, and • D cases (8 hours): 28% reduction in wait times. • Patient placement in appropriate inpatient beds on appropriate inpatient units increased from 84% to 92% with simulation modeling. • Minutes of elective procedures performed after prime time were reduced by 63%. • Open elective schedulable time increased by more than 20 hours per week. • New smoothed blocks were built on actual use and were adopted with the approval of the Surgical Services Committee. • Conflicts that arose (eg, physicians questioning their block utilization or days of block, questions stemming from potentially inappropriate classification of procedures) were brought to the Surgical Services Committee for resolution. HEARTLAND REGIONAL MEDICAL Key CENTER. Work on flow began in August 2008, and I worked as August 2004 a consultant to implement this August 2005 methodology. Results seen at Heartland Regional Medical 35 Center were as follows. 30 • Separating elective and 25 urgent/emergent volumes and revising the block 20 schedule based on usage 15 created the potential for sav10 ings equivalent to the hours 5 of six full-time employees. • More than 20 hours per 0 week of open schedulable Monday Tuesday Wednesday Thursday Friday time were added to the Day of week schedule in addition to two dedicated add-on rooms for urgent/emergent proceFigure 2 • Elective surgical admissions smoothed across the week at St John’s dure volume. Regional Health Center. Admissions

Staff members on the nursing floors were able to predict their evening and night shift staffing requirements more accurately because the unscheduled patients were returning to the floors during the day shift rather than on the “off” shifts. • Surgeons were not routinely working late into the evening on add-on procedures. • The surgeons involved realized revenue increases of more than 4.6% during this time period. • Fifty-nine percent more medical and/or surgical admissions from the ED were accommodated through the ED, which had been expanded recently, without increasing the inpatient bed capacity, thereby increasing the functional capacity of the hospital to accommodate more inpatient admissions without adding more patient beds or ORs or hiring more staff members. • Staff overtime hours decreased from 6% to 2.9%, which saved the facility $470,000 in overtime costs. How elective admissions were smoothed across the week is shown in Figure 2.6 WELLSTAR KENNESTONE HOSPITAL. Physicians and administrators at WellStar Kennestone Hospital

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cancellations and delays on the day of surgery related to inadequate or inappropriate testing. Algorithms should include laboratory work, imaging, or cardiac workups associated with procedures for each subspecialty. Often, they also include preoperative antibiotics to be given. Use of a standardized method for notifying inpatient units and admission areas when the OR is ready for a patient also is important in reducing delays and confusion.

Fewer elective procedure minutes occurred after prime time (Figure 3).

PREOPERATIVE PATIENT PREPARATION AND VARIABILITY Preoperative patient preparation is another area in which artificial variability often is found. Physician preferences with regard to laboratory work, patient education, and diagnostic testing may be based on history, experience, or preference instead of scientific, evidence-based need. Providing an algorithm for preoperative diagnostic testing and preparation that is approved by both the anesthesia care providers and surgeons who work collaboratively allows for standardization and a reduction of errors before surgery. These algorithms for patient preparation also reduce the number of

TURNOVER TIME Variability in turnover time is driven by procedure mix, staff members and competencies, and equipment needs. After the elective schedule has been smoothed across the week and disparate volumes separated as described, like procedures may be scheduled

1,600 OR overtime & call hours




Prior year mean: 1,055

1,400 1,200 1,000 800 600 400 200

April

March

February

January

December

November

October

September

August

July

0

Month

Figure 3 • OR overtime and call hours, in minutes, from July 1, 2008, through April 30, 2009. Overtime and call hours, listed in minutes, are the number of elective minutes worked after 5:30 PM (ie, when prime time is over). The goal of reducing the number of elective minutes after prime time was met, leading to reduced overtime.

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back to back as much as possible, making turnover of staff members and equipment more efficient. Standardizing preference lists within a specialty is key to reducing turnover time by streamlining what is picked for procedures as well as what needs to be kept in the OR. This reduces cost as well as time. In addition, use of turnover teams allows turnover processes to be standardized just as specialty teams allow for standardization of processes involved in the surgical procedures. Turnover teams, which may include one or two environmental services personnel, are able to focus on cleaning the room and preparing it so that clinical staff members can begin setting up for the next procedure. When clinical staff members are responsible for both care of the patient and room cleaning, cleaning often becomes a lower priority. Establishing turnover teams allows personnel to be dedicated to room teardown and cleanup for multiple ORs. This reduces variability in the way rooms are cleaned and allows for standardization of this very important task. It also allows the clinical staff members to focus solely on their clinical responsibilities.

ON-TIME STARTS

AND

VARIABILITY

On-time starts in the OR are critical to ensure that surgeries proceed as scheduled in terms of staffing, scheduling of patients and physicians, and satisfaction of all concerned. The definition of start time should be well understood by all involved: surgeons, anesthesia care providers, other staff members, and the patient. If the first procedure of the day does not start on time, subsequent procedures will likely also be late in a cumulative manner, causing increased overtime and frustration. All the times associated with the beginning of a procedure should be assessed when defining start time. If start time is defined as when the patient enters the room, then everyone should be available to start the procedure at that time. If anyone is missing, then further parameters must be monitored for compliance (eg, what time the surgeon arrives, when induction takes place), because there may be delays from when the patient enters the room until the incision, which unnecessarily increas-

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es the procedure duration (ie, patient in to patient out). Delays in any of these parameters, regardless of the time the patient enters the room, will result in inaccurate procedure durations and potential scheduling inaccuracies. The time at which all members of the perioperative team must be present is the incision time. However, it is important to monitor the other parameters to ensure that the time from the patient in the room to incision is not prolonged. These time parameters should be monitored: • room ready, • patient in room, • induction, and • incision. Each surgical subspecialty should identify the target time frames for each parameter associated with high-volume procedures and monitor these on an ongoing basis for compliance and opportunities for improvement, no matter which parameter is selected as the start time.

STAFFING MANAGEMENT

AND

VARIABILITY

Staffing can be a daily challenge in the OR when artificial variability is left unchecked. Ensuring the right number of staff members and the specialty expertise required is a guessing game because of the unpredictable nature of the OR schedule when blocks are not efficiently used, schedules are inaccurate, and procedures are delayed or canceled. By eliminating artificial variability, separating elective and nonelective volumes so that they function independently, and smoothing the elective schedule across the week, staffing needs become much more predictable. Staffing the OR, with all of its technologic requirements, may best be accomplished by using a team model. When the block schedule is based on use and destination unit, staffing the block with appropriate specialty expertise is not only possible but also cost-effective. Providing the appropriate specialty expertise may reduce procedure and turnover time and may allow for increased surgical volume capacity, which in turn allows more procedures to be performed during prime hours. This increase during prime hours also may lead to a reduction in overtime hours. The hospital’s business plan should address

Managing Variability

the potential growth in surgical volume or increase in market share for a strategic service line that can result from the increased capacity. The additional volume multiplied by the average revenue per procedure can provide an important key to the viability of the team concept. Several factors must be accounted for when examining whether a team approach is the right choice to manage both the natural clinical variability and the artificial variability caused by the extrinsic factors of preference and scheduling. Some considerations include team composition, compensation, procedure attendance (ie, skill mix for each type of procedure), and ongoing education for team members. Not all staff members should be on a team. A generalist pool is important to support the teams and attend procedures that do not require specialist training and expertise. Team members should be chosen for their specialty expertise, as well as their leadership ability, attendance, and behavior. Physicians within the specialty should have input into deciding which team members will work with them. Additional compensation for team members should also be evaluated based on the market and interest in teams, as well as the cost:benefit implications associated with increased compensation. Ensuring that team members maintain specialty expertise also must be factored into the decision and facilitated by both physician and hospital leaders. St John’s Regional Health Center has seen significant improvement in procedure time, turnover time, and cost by using the specialty team model.8 St John’s took a cautious approach by starting with a heart team and then moving the concept into neurosurgery, orthopedics, and gynecology. These teams demonstrated reduced setup and turnover times, shorter procedure times, and lower costs. With teams, despite the rising costs in health care in general, St John’s was able to keep volumeadjusted cost increases in surgery to 5% per year for two years, compared with cost increases of 17.6% before the orthopedic and neurosurgery teams were implemented. In neurosurgery, enough time was saved in a year to accommodate 200 additional procedures in the same amount of time (Figure 4). Gynecology teams recommended an instru-

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ment change that saved $733 per procedure for laparoscopically assisted vaginal hysterectomy.8

MATERIALS MANAGEMENT

AND

VARIABILITY

Another major source of variability in the OR is the preference list. The preference list contains all of the ingredients (ie, recipe) for a particular surgeon to perform a particular procedure: supplies, instruments, comments, and equipment. The preference list is more than that, however; it drives the billing and, in many cases, the clinical documentation for that procedure as well. All of the things that the preference list drives are shown in Figure 5. In most hospital information systems, the preference list is generated by the scheduled procedure. The information system contains a procedure file from which a scheduler chooses when he or she is scheduling a procedure; therefore, an accurate procedure file is of the utmost importance for ensuring that the correct material and human resources will be identified for the procedure. If the surgeon or his or her scheduler calls to schedule a procedure and the exact procedure to be scheduled is not in the procedure file, then the scheduler must use his or her best judgment to determine what procedure most closely resembles the one to be scheduled. Unfortunately, not every OR has a clinician in the scheduling office, which increases the risk that errors will be made in scheduling the procedure. When the wrong procedure is scheduled, the wrong supplies and equipment will be picked, resulting in rework and increased costs, time wasted, and frustration, as well as increasing the risk that the wrong procedure could be performed. Reducing variability in the procedure file and standardizing the preference lists minimizes the risk for error, reduces cost, and improves quality and efficiency. When the scheduler asks a surgeon to verify the procedure by using the list from the procedure file, the surgeon is able to choose what procedure best fits what he or she will do for a patient, and guesswork is eliminated. After the procedure file and scheduling accuracy is ensured, the next step is to standardize the preference lists. Surgeons who perform similar procedures are often unaware of how their colleagues perform the same procedures or aornjournal.org

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Key 2005 2007

140 13% improvement 118

Time (in minutes)

120

103

100 80 7% improvement 60 19% improvement 40 20

31

45

42

25

0 Turnover time

Setup time

Procedure duration

Measures

Figure 4 • Improvements in OR measures for neurosurgery procedures from 2005 to 2006 (ie, the differences after teams were implemented) resulted in the ability to accommodate more than 200 additional procedures in the same amount of time at St John’s Regional Health Center.

what equipment they use. Allowing a quarterly review of high-volume procedures for all surgeons who perform them is critical to identifying opportunities for standardization and consolidation. Costing out the preference lists also is important so that physicians are able to see how they compare with each other in terms of costs. These reviews should be transparent and include full disclosure of cost, charge, and reimbursement so that physicians understand the full effect of the resources they request for procedures. Reducing the variability in preference lists for surgeons who perform the same procedures also lessens the risk for error because staff members develop expertise when using the same supplies, equipment, and instrumentation no matter which surgeon is performing the procedure. Although this may be considered “cookbook medicine,” it fosters best practices and ensures that the recipe turns out right every time.

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PURCHASING In addition to reducing the variability associated with the procedure file and the preference lists, purchasing is an area in which reducing variability in the process will provide cost savings and enhance quality. By ensuring that physicians play a key role in purchasing surgical equipment and supplies, the hospital can drive standardization and consolidation. Transparency of the cost-to-charge ratios and reimbursement associated with surgery items is important when working with physicians because their buy-in is crucial to the success of any project that involves how they care for their patients. Establishing a team of physicians and hospital leaders along with key nursing stakeholders in a value analysis committee will provide a forum to review the data, discuss clinical efficacy, and make informed decisions

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Preference list General

Surgeon number of cases

Pick list

Items and numbers

Segments

Charting defaults

Comments

Surgeon case specific

Reporting

Daily, weekly, monthly, yearly statistics

Last date used

Open column

Segments used

Prep and equipment

Material management

Last updated by

Hold column

Medication

Positioning devices

Block usage

Procedure average time

Default charges

Specimens

Specimen handling

Case levels and number of attendees for billing

Set up, clean up average

Decrement inventory

Equipment

Suture used

Charting audit reports

Implants

Surgeon and physician assistant glove size and gown preference

Infection control reports

Before/after incision average

Process improvement

Figure 5 • An illustration of how many segments of the hospital that need to be considered for a procedure are driven and affected by the preference list.

regarding purchasing. This team should identify clear and objective criteria for any capital purchase for the OR. In addition, this team should have the decision-making authority to either recommend or deny any purchase within parameters established by the hospital in a charter. By using objective criteria and a transparent approach to information, variability in purchasing can be reduced, thereby allowing a

significant potential to improve purchasing power and reduce costs in terms of rebates or bulk-buy savings.

DATA ANALYSIS Monitoring patient flow and analyzing the effects of changes forms a critical part of managing or eliminating variability. Few, if any, of the results obtained by the sample hospitals aornjournal.org

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would have been possible without rigorous data analysis. Many of the data elements needed to support the efforts described herein exist in most hospitals’ current information technology systems, but a number of important ones may need to be collected manually. In addition, the ability to link across data systems facilitates timely monitoring and reporting for management oversight. Specifically, hospitals need to be able to collect, process, and analyze specific data related to the steps in the care delivery process, points of decision making, and timing of services. This includes key data elements that allow wait times and delays to be identified and measured. For example, the time when a decision is made to admit an ED patient is important to record to establish the extent to which a hospital boards patients. This applies to other areas as well, including the PACU, where the time the patient is ready to leave recovery is important in establishing the extent of holding patients. Other types of data elements that are important for monitoring flow include • time stamps marking when patients arrive and depart from the OR or PACU; • the start and end time of a procedure, and all the parameters associated with the case: in-room time, induction, incision, dressing, and out-of-the-room time; • the time a procedure is booked or rescheduled to take place; • surgery cancellation reason; • time of request for a transport; • block utilization: patient in to patient out plus associated turnover time divided by total allocated block time; • add-on room usage; • waiting time compliance with defined wait time limits; • average waiting time for each category of add-on patient; • start time by surgeon and specialty for all procedures, not just first procedures; • turnover time by physician and service; • average length of stay by DRG; • preferred patient destination unit and percent compliance with placement; and • percentage of procedures performed within prime time as defined by the hospital.

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Some of the common problems that should be watched for in data systems include the following: • Data elements needed for monitoring flow may not be consistently collected. • Data elements may have to be collected manually because they are not available electronically. • Information technology systems may not allow captured data to be accessed without special reporting requests. • Data systems from different service areas may not be integrated, making it difficult to link patient records and trace patient movement through the hospital. • Effective systems to monitor patient flow activities on an ongoing real-time basis may not currently exist. • Definitions for important data elements may not be consistent. The hospital should systematically review the types of data and information needed to assess patient flow and operational efficiency throughout its major service areas, with particular emphasis on the OR, and assess the extent to which these elements are or can be captured electronically or manually. As a hospital moves forward with patient flow improvements, the ability to monitor and react to patient flow changes in real time will become a higher priority. Knowing what resources are being used or are available in real time allows for better management of patient flow and resource allocation.

OVERSIGHT A collaborative environment between physicians and hospital leaders is a critical factor for success in reducing variability. A project oversight team, comprising strong physician and hospital leaders, should meet regularly. The team must be commissioned by the hospital and medical staff leaders to make decisions within the hospital bylaws and policies. Their tasks should include reviewing data, making data-driven decisions that work in the interest of the hospital and the physicians, monitoring the effect of flow improvements, and enforcing rules and policies with their peers. The physicians on the team may not be the chairs of their departments but should be progressive

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change agents who have credibility with their peers. Hospital leaders at the table should be able to make hospital decisions or “write the check” without needing multiple further layers of approval. In some cases, nurse representatives from perioperative areas should be included for their bedside clinician perspective. Hospital leaders must ensure that clinical nursing staff members are kept abreast of and offered input opportunities in key decisions. The group should be small enough to make decisions and stick to an agenda but large enough to represent the majority of both physician and hospital interests. Every member of the committee should have an equal vote and role on the committee. This group should not only be the decision makers but should also be the enforcers of the decisions and policies they implement. That means that the physicians monitor their peers and that the group, not the hospital administrators, makes decisions and then accepts the consequences, both good and bad. One example of a “bad” decision may be bowing to a surgeon’s preference that is not supported by data and would cause scheduling problems if the committee moves forward with the decision. This decision may cause • other physicians to question how the committee makes decisions and, therefore, cause the committee to lose credibility; • the physician who inappropriately gained time or preference to prohibit another surgeon whose preferences are supported by the data from receiving more time; and • bed availability to be compromised, as well as conflicts related to instrument and equipment and expert staff member availability. The committee must own these consequences along with the benefits when it works well. Ideally, the players in this group will not change often. The more the group works together, the more progress can be made quickly. Frequent turnover of the members requires the group to bring new people up to speed rather than moving forward on the project at a rapid pace. Setting target dates and ensuring compliance is critical to building trust within the

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group. In addition, this consistency builds the hospital and medical staff members’ confidence and trust in the group.

CONCLUSION There are many sources of variability within perioperative services. By using a consistent and collaborative approach to identify the variability associated with the OR and the causes of the variability, natural (ie, uncontrollable) variability may be managed and artificial (ie, controllable) variability reduced or, better yet, eliminated. The result will be to improve patient care and predictability for staff members and physicians, as well as to reduce costs while improving revenue for the organization. Ultimately, addressing variability improves and sustains quality, safety, and satisfaction for patients, physicians, and other staff members.

REFERENCES 1. McManus ML, Long MC, Cooper A, et al. Variability in surgical caseload and access to intensive care services. Anesthesiology. 2003;98(6):1491-1496. 2. Elective. Merriam-Webster OnLine. http://www .merriam-webster.com/dictionary/elective. Accessed August 14, 2009. 3. Who we are. Brigham and Women’s Hospital. http://www.brighamandwomens.org/Centerfor SurgeryandPublicHealth/About.aspx. Accessed August 14, 2009. 4. Press Ganey Simulation [software]. Version 1.6. South Bend, IN, Richard Siegrist, chief executive officer, Press Ganey Associates, Inc; 2009. 5. Henderson D, Dempsey C, Appleby D. A case study of successful patient flow methods: St. John’s Hospital. Front Health Serv Manage. 2004;20(4):25-30. 6. Dempsey C. Smooth the elective OR schedule? A large hospital makes it happen. OR Manager. 2006; 22(4):1, 17-21. 7. Saunders C, Dempsey C. Improving patient flow. Physician involvement drives success at Georgia hospital. Healthc Exec. 2008;23(6):46-48. 8. Dempsey C, Larson K, Shockley T. Teamwork: it’s good business. OR Manager. 2009;25(2):11-13. Christina J. Dempsey, RN, MBA, CNOR, is the senior vice president for clinical operations at Patient Flow Press Ganey Associates, Inc, South Bend, IN.

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