case study 4

T homas Jefferson University Hospitals, an acute-care, 969-bed health- care facility located in Philadelphia, has a large volume of inpatient and outpatient surgical volume flowing through the many operating

rooms (OR). The largest volume of surgeries takes place on the Center City campus.

The OR is a setting abundant with opportunities for improvement. System inefficiencies can lead to suboptimization of OR use, thereby decreasing revenue generation. Delays, from patients arriving to entering the preoperative holding area (HA), have an impact on the ability to start scheduled OR procedures on time. Due to high surgical volumes, delays in first cases have a cascading effect on the entire schedule, bottlenecking pre- and post-procedural areas. These delays have fiscal consequences, too, and are a primary source of patient and employee dissatisfaction.

The OR is one of the most expensive components in an acute care facili- ty, leading to increased focus on efforts to evaluate and improve efficiency.1 By reducing the impact of waste, staff is able to spend more time doing value-added (VA) work (for example, delivering care). The perioperative department is responsible for all surgical procedure activities.

In 2010, the department underwent a strategic plan overview to identify opportunities to streamline and improve operational processes. To facili- tate rapid change, certified lean practitioners were assembled to evaluate and subsequently enhance the operations and patient flow throughout the ORs. Initially, OR leadership tasked the team with improving first case delays. Through data analysis, the team determined that first cases were not starting on time, causing delays throughout the system.

The first improvement event focused on the patient admission to pre- operative (short procedure unit, or SPU) process areas. This scope was chosen first because it marks the beginning of the patient flow process throughout the system on the day of surgery. Subsequent efforts would address OR turnover and post-anesthesia recovery.

Methods

Setting: The project was conducted in the academic medical center’s main OR. The facility runs 57 total ORs, of which 32 are included within the scope of the project. On the day of surgery, the patient flow process begins with checking in at admissions and ends after surgery, where the patient is dis- charged to go home or admitted to an inpatient bed. To effect meaningful change and incremental improvement, the project scope was narrowed to only include patient flow from patient check-in at admissions through arrival at the HA (see Figure 1). The steps from arrival at the HA until discharged home or admitted to a unit were not evaluated in this effort.

Study design: The study design was a pre- and post-intervention assess- ment. The project was managed through the define, measure, analyze, improve and control (DMAIC) framework. During the measure phase,

L E A N

Let It Flow

IMPROVING

PERIOPERATIVE

PATIENT FLOW

USING LEAN

IMPROVEMENT

STRATEGIES

By Dennis R. Delisle

and Kathleen Jaffe,

Thomas Jefferson

University Hospitals,

Philadelphia

10 I F E B R U A R Y 2 0 1 5 I W W W . A S Q . O R G

data were collected from May 2010 to February 2011. The source of data was the OR information system, which provides electronic collection and reporting of patient flow timestamps. The data set captures various process-related times at the patient level. Analysis included 2,129 pre-intervention observations and 1,849 post-intervention observations.

The primary outcome measure was the time from patient check-in to arrival at the HA. The patient flow process in the pre-intervention analysis was 54.073 minutes (Table 1, p. 12). The data are not segment- ed by ORs (for example, by location or specialty). Patients are processed the same way so analysis is on an aggregated level.

Statistical analysis: Data from the OR information sys- tem were analyzed using Minitab 16 statistical software. Analysis was done using two-sample t-test, evaluating the mean of post-intervention data (1,849 observa- tions) to pre-intervention results (2,129 observations). Significance was determined at a α level of 0.05.

Intervention

Prior to a scheduled rapid improvement event, certi- fied lean practitioners conducted voice of customer (VOC) interviews and process analysis observations. These analyze phase deliverables were used to estab- lish the baseline current state. VOC interviews were conducted with more than 20 staff members from all associated areas (registration, SPU, HA, OR, transpor- tation and administration).

The key themes of issues inhibiting process effi- ciency identified through the VOC interviews and observations were:

• Scheduling. • Registration delays due to location of department. • Transportation of patients from SPU to HA.

• Lack of coordination of activities within the SPU (Table 2, p. 12).

The inputs from the interviews and process analysis served as the basis for improvement.

The process analysis showed a large proportion (> 60%) of nonvalue-adding (NVA) activity (Figure 2). Within the current state of the process, 43.4% of the process time involved the patient waiting for something to happen (that is, waiting for a nurse or physician). An additional 8% of the time was commit- ted to traveling from location to location.

In patient flow studies, waiting and transportation are typically the most pervasive forms of waste in the process. These results were expected due to the com- plexity of the process, geography of work areas (that is, admissions and SPU) and organizational structure (admissions, transportation and OR staff having dif- ferent reporting structures and managers).

After the current state VOC and process analyses were completed, the lean team entered the improve phase of the project. The lean practitioners facilitated a four-day rapid improvement event with a multi- disciplinary group of staff from registration, SPU, HA and OR (together known as the lean team). The lean team’s goal was to develop and pilot countermeasures to address and eliminate identified waste and issues.

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Figure 1. Current state process map

Patient check-in at admissions

Arrive at SPU

Prep for surgery

Patient arrives at HA

Patient check-in

Registration Check in at desk

Escort patient to bay

RN instructions

Patient changes

Prep for surgery

Arrive at HA

SPU = short procedure unit HA = holding area RN = registered nurse

Figure 2. Value analysis breakdown

8% VA NVA-R Wait Travel Defects

39.9%

0.7%

43.3%

8.1%

VA = Value adding NVA-R = Nonvalue adding but regulatory requirement

During the event, the lean practitioners led a struc- tured problem-solving process. Participating staff brainstormed key barriers and issues that drive inef- ficiency. The salient issues detailed in Table 2 were prioritized through consensus voting. The priority issues were inefficient patient flow logistics and lack of workflow coordination within the SPU.

These issues became improvement targets. From there, the lean team developed solutions through structured brainstorming and additional site visits to the work area. All solutions were prioritized using a tool called the payoff priority matrix (Figure 3), which evaluates a solution on two dimensions: impact (high/ low) and ease of implementation (easy/difficult).

The dark and light green quadrants (high/easy and low/easy) are solutions worth implementing because the gain is greater than the resource requirement. Solutions in the yellow quadrant (high/difficult)

required further consideration. Solutions listed as high/difficult typically are strategic or resource inten- sive in nature so rapid implementation is typically not an option.

Solutions identified in red (low/difficult) should be avoided because they have minimal to no impact on improvement and are difficult to implement. Table 3 shows the results of the solution brainstorming and prioritization. There were three core solutions that

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Table 2. Identified issues and themes

Theme Issues Source of issue

Scheduling

Arrival time Patients are told to come in too early. Admissions and short procedure unit (SPU) staff and patient satisfaction surveys.

Too many patients told to arrive at the same time. SPU and holding area (HA) staff.

Revisions to schedule Schedule not sent to staff until after 5 p.m. the night before, which results in late calls to the patient.

Operating room (OR) scheduling and SPU staff.

No deadline for schedule changes, changes are made by one individual without consent of managers or charge nurses.

OR scheduling and administration staff.

Order of cases is important, revisions are not being reviewed and patients arrive out of order.

OR administration, SPU, HA and OR staff.

Registration delays

Staffing Cannot process patients fast enough if short staffed. Admissions and SPU staff.

Cannot escort patients to the SPU because not enough staff (can result in lost patients/delays).

Admissions staff.

Register patients in order of arrival instead of order of procedure. Admissions and SPU staff.

Facilities/logistics Nearest entry to registration not open at 5 a.m. when earliest patients arrival.

Process observations.

Transportation ORs have their own transporters (schedule is 7 a.m. to 3:30 p.m.), therefore nursing assistants transport first cases.

Process observations and SPU staff.

Lack of coordination

With patients – pre-arrival

Poor/inconsistent communication regarding arrival time. Admissions and SPU staff and patient satisfaction surveys.

Poor/inconsistent communication regarding preparation (eating and drinking).

OR scheduling and SPU staff.

Not clearly setting appropriate expectations for patient wait times, number of family members that should come.

Process observations and SPU staff.

Between staff/ departments

Additional tests patient may need are not noted in schedule (for example, MRI, nuclear).

SPU staff.

A lot of information that SPU staff needs is not in schedule, which causes them to do a lot of investigation.

SPU staff and process observations.

Robotic cases not noted on schedule which causes nurses to have to leave to get equipment.

SPU, HA and OR staff.

Table 1. Descriptive statistics

Variable N Mean StDev Min Q1 Median Q3 Max

Patient flow Processing time from check-in to arrival in holding area

2,129 54.073 18.217 23 41 50 63 130

required detailed action planning and five just- do-it solutions that were quick wins.

After prioritizing solutions, the multi-disciplin- ary team identified three specific countermea- sures to pilot during the improvement event.

1. Patient registration moved from admissions department to SPU at the bedside (Figure 4, p. 14). This process change removes the need for patients to check-in multiple times (previously at admissions, then at SPU). Additionally, patients receive surgery prep instructions prior to registration.

2. The SPU charge nurse (responsible for patient flow monitoring) was relocated and tasks were standardized. The charge nurse role is integral in managing flow and assign- ing nurses to patients based on priority and need. The role rotates among three individu- als, which necessitates a standard approach to documentation and responsibilities. The added structure enables staff to work effi- ciently while the charge nurse monitors the flow of patients, nurses and registrars.

3. SPU patient interview processes were streamlined to accommodate the integra- tion of registration staff. The patient interview process performed by the nurse was lengthy. The need-to-know information was identified and rescripted. This standardized the approach and freed time for the registrars to complete the admission prior to the patient’s surgery prepara- tion.

Before this lean event, patient registration occurred in the admissions department. The admissions depart- ment is located on the first floor of the main OR building, while the SPU is on the ninth floor. The cumbersome amount of patient travel—from parking garage to main entrance to admissions to SPU—cre-

ated a lot of NVA activity. This resulted in increased patient wait times and a decrease in on-time first case starts.

The lean team walked the entire process to see it through the eyes of the customer (a core tenet of lean). After evaluating patient travel, the team identi- fied an opportunity to move registration to the bed- side in SPU. The team determined the appropriate registration staff members, as well as hours of opera- tion for the improvement pilot.

Integration of a new discipline (registration) within the existing work flow process in the SPU was chal- lenging. The admissions and SPU staff needed to collaborate and adjust to the new process steps and the addition of staff and equipment in the SPU. These changes posed opportunities to improve communica- tion and streamline flow, but not without patience and process refinement along the way.

The lean team piloted bedside registration, using the lean practitioners as mock patients. Piloting the idea helped troubleshoot issues and identify preventa- tive strategies to reduce likelihood of occurrence. For example, the team found that there were not enough computers on wheels in the SPU for the registrars. During the pilot, the team borrowed additional com- puters on wheels to demonstrate effectiveness prior to making a recommendation to purchase them.

Collaboration was promoted through staff education,

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Figure 3. Payoff priority matrix

Note: For more information on the matrix, visit www.mindtools.com/ CXCCorporateTour/ActionPriorityMatrix.php (case sensitive).

High

Dif�cult/Easy High/Dif�cult

Im p a ct

o n p

ri m

a ry

m

e tr

ic /p

ro b le

m

Low/Easy Low/Dif�cult

Low

Easy Dif�cult Ease of implementation

Table 3. Payoff priority matrix results

Im p a c t

High • Redefine short procedure unit (SPU) charge nurse role to coordinate workflow.*

• Redesign SPU patient and workflow pro- cesses to integrate registration staff.*

• Place patient way-finding signs along pathway from garage to SPU.**

• Develop scripts for operating room (OR) schedulers regarding patient preparation day before surgery.**

• Develop process for OR schedulers to ensure preprocedure studies are com- pleted and on file.**

• Patient regis- tration at the bedside in SPU.*

• Redesign patient trans- portation process from SPU to hold- ing area.

Low • Education registration and SPU staff on process.**

• Educate registration staff on electronic medical record.**

• Develop automatic co-pay collection pro- cess in SPU.

Easy Difficult

Ease of implementation

* Core countermeasures chosen for implementation. ** Just-do-it solutions that were completed secondary to core counter-

measures.

including clarity in role expectations related to the preoperative patient preparation process. Care provid- ers on the preoperative team must be aware of their individual roles within the team, as well as the contribu- tion of each role to the common goals of preparing patients and patient information for surgery.2 This dynamic environment requires consistent and reliable communication and cooperation across disciplines.

Central to this collaborative effort is the SPU charge nurse. The charge nurse’s role is primarily focused on managing patient and clinician workflow in the SPU. To streamline processes and enhance communica- tion, the team changed the charge nurse’s physical location, as well as redistributed and balanced work- loads. This solution addressed the lack of coordina- tion and limited communication across departments and among clinicians.

Staff education was critical in the process redesign. Education focused on two main areas: the patient

experience and process changes. Staff developed scripts for communicating expectations related to the OR preparation process and potential delays and waiting along the way. The scripts served to manage patient expectations and ease the anxiety of waiting prior to transportation to the HA.

Process changes were taught through daily huddles between admissions and SPU staff. Staff also was provided process maps that depicted their roles and responsibilities (Figure 4). Because the preoperative patient moves through a series of activities in a short period of time, encountering multiple staff, interde- pendent teamwork is essential to keep the patient the center of focus.3

Results

After the improve phase, the pilot solutions were hardwired into daily operations. The control phase

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SPU = short procedure unit

Figure 5. Monthly average of SPU processing time

65

60

55

50

1 2 3 4 5 6 7 8 9 10

45

40

35

M in

u te

s

Average pre-duration Average post-duration

Figure 6. Time value analysis before and after intervention

VA

49.13%

2.79%

48.08%

39.94%

0.67%

59.38%

NVA-R NVA

VA = Value adding NVA = Nonvalue adding NVA-R = Nonvalue adding but regulatory requirement

Figure 4. Post-intervention process

Arrive at SPU

Patient registered

Prep for surgery

Patient arrives at HA

Patient check-in

Patient to cubicle

RegisterInstructions Patient

changes clothes

Prep for surgery

Arrive at HA

SPU = short procedure unit HA = holding area

included weekly monitoring of action plan progress, as well as data updates and reporting. Figure 5 depicts the pre- and post-intervention monthly processing time trends, including the pre-intervention with the added registration time.

Post-intervention data demonstrate a statisti- cally significant improvement in mean processing time from pre-intervention of 54.073 minutes (95% confidence interval 53.299-54.847 minutes) to post- intervention 46.095 minutes (95% confidence interval 45.272-46.918 minutes, p = 0.000) (Table 4).

The time value analysis graph depicts the dis- tribution of VA to NVA activities (Figure 6). Post- intervention, the percentage of VA activities increased from 39.9% to 49.1%, which was a direct result of reducing and eliminating NVA activities such as trans- portation and waiting.

As described earlier, delays in the front end of the patient flow process have a negative impact on the efficiency of the OR. Delays in first cases have cascad- ing effects on the day’s schedule and therefore merit targeted improvement efforts.

An additional analysis was done to assess the down- stream impact of the improvements on this metric. The pre-intervention time period was August 2010 to October 2010, and post-intervention was November 2010 to February 2011. Prior to the intervention, the on-time first case start percentage was 71.9%. After the changes, the percentage increased to a statistically significant 75.9% (p = 0.00063) (Figure 7).

Looking at more lean projects

Using lean thinking and its associated tools, a multi- disciplinary team was able to facilitate improvements

within the perioperative department of a high-volume academic medical center. One of the most common causes of OR delays is getting the patient into the OR.4 Improvements on the front end of the process position the department to positively affect on-time first case starts, as well as the overall flow of the OR schedule. Considerations for future lean projects as an outcome of this initial effort include:

1. Evaluation of patient transport process from SPU to holding area.

2. Evaluation of OR scheduling process. 3. Enhancement of communication throughout all

perioperative areas.

ACKNOWLEDGEMENTS

The authors thank Bonnie Grady, Beth-Ann Piotrowski, Keena Johnson, Mary Eddis, Gina Burton, Bonnie Gray, Carrie Lamina, Mike Perino and Megan Illg for their help in preparing this article.

REFERENCES

1. James G. Wright, Ann Roche and Antoine E. Khoury, “Improving On- time Surgical Starts in an Operating Room,” Canada Journal of Surgery, Vol. 53, No. 3, June 2010, pp. 167-170.

2. Kara Schultz, Pascale Carayon, Ann Schoofs Hundt and Scott R. Spring- man, “Care Transitions in the Outpatient Surgery Preoperative Process: Facilitator and Obstacles to Information Flow and Their Consequences,” Cognition: Technology & Work, Vol. 9, Issue 2, 2007, pp. 219-231.

3. Jacqueline Ross, “Distractions and Interruptions in the Perianesthesia Environment: A Real Threat to Patient Safety,” Journal of Perianesthesia Nursing, Vol. 28, No. 1, February 2013, pp. 38-39.

4. C. Janice Wong, Kathleen J. Khu, Zul Kaderali and Mark Bernstein, “Delays in the Operating Room: Signs of an Imperfect System,” Canada Journal of Surgery, Vol. 53, No. 3, June 2010, pp. 189-195.

BIBLIOGRAPHY

Fairbanks, C.B., “Using Six Sigma and Lean Methodologies to Improve OR Throughput,” Association of Perioperative Registered Nurses Journal, Vol. 86, No. 1, July 2007, pp. 73-82.

Table 4. Descriptive statistics

Variable N Mean StDev Min Q1 Median Q3 Max

Pre-patient flow Processing time from check in to arrival in holding area

2,129 54.073 18.217 23 41 50 63 130

Post-patient flow Processing time from check in to arrival in holding area

1,849 46.095 18.040 13 33 42 55 120

Figure 7. On-time first case starts

79%

75%

77%

73%

71%

Feb. ’11Jan. ’11Dec. ’10Nov. ’10Oct. ’10Sep. ’10Aug. ’10

69%

67%

65%

P e rc

e n ta

g e o

f c a se

s o n t

im e

Pre Post

Month

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