Using Six Sigma Methodology to Reduce Patient Transfer Times From Floor to Critical-Care Beds

44 Journal for Healthcare Quality

Using Six Sigma Methodology to ReducePatient Transfer Times from Floor toCritical-Care BedsStephan J. Silich, Robert V. Wetz, Nancy Riebling, Christine Coleman, Georges Khoueiry, Nidal Abi Rafeh,Emma Bagon, Anita Szerszen

Abstract: Introduction: In response to concerns regarding delaysin transferring critically ill patients to intensive care units (ICU), aquality improvement project, using the Six Sigma process, wasundertaken to correct issues leading to transfer delay. Objective:To test the efficacy of a Six Sigma intervention to reduce transfertime and establish a patient transfer process that would effec-tively enhance communication between hospital caregivers andimprove the continuum of care for patients. Methods: The projectwas conducted at a 714-bed tertiary care hospital in Staten Island,New York. A Six Sigma multidisciplinary team was assembled toassess areas that needed improvement, manage the intervention,and analyze the results. Results: The Six Sigma process identifiedeight key steps in the transfer of patients from general medicalfloors to critical care areas. Preintervention data and a root-causeanalysis helped to establish the goal transfer-time limits of 3 h forany individual transfer and 90 min for the average of all trans-fers. Conclusions: The Six Sigma approach is a problem-solvingmethodology that resulted in almost a 60% reduction in patient-transfer time from a general medical floor to a critical care area.The Six Sigma process is a feasible method for implementinghealthcare related quality of care projects, especially those thatare complex.

Keywordspatient safety

quality improvementSix Sigma/Lean

IntroductionA delay in transferring newly critically ill pa-tients to an intensive care unit (ICU) may leadto an unfavorable impact due to the subopti-mal environment for delivering the appropri-ate treatment (Beckmann, Gillies, Berenholtz,Wu, & Pronovost, 2004). For example, patientswith septic shock had a significant delay in re-ceiving intravenous fluid boluses and inotropicagents on a medical floor as compared to thosein an ICU (Duke, Green, & Briedis, 2004).A 6-hr transfer delay of critically ill patientsfrom the emergency department to the ICUwasshown to increase hospital and ICU lengths ofstay (LOS) and in-hospital mortality (Chalfin,Treciak, Likourezos, Baumann, & Dellinger,2007). A greater mortality was also observedin patients requiring mechanical ventilationor renal replacement therapy when ICU ad-mission was delayed (De Feo & Barnard,2005).

At our hospital, ICU transfer delays resultedin (1) poor utilization of physician resources(e.g., residents being utilized to observe thecardiac monitor of upgraded patients) and (2)increased nursing demands to provide inten-sive care to these patients without a change innurse:patient ratio. This further affected thephysician and nursing staffs abilities to carefor other patients. Transfer delays beyond 5hr were not uncommon and resulted in de-creased patient, family, and staff satisfaction.In response to these issues, the institution as-sembled a Six Sigma Team.

Six Sigma can be described as a manage-ment philosophy that focuses on developingand delivering near-perfect products and ser-vices (Pyzdek, 2003). It was originally developedby Motorola in 1986 and further enhanced byGeneral Electric (Jiju, 2004). Sigma is a sta-tistical term that measures how far a given pro-cess deviates from the mean (Lundberg et al.,1998). There are six standard deviations invariable performance of a given process. Thecentral idea behind Six Sigma is that if the de-fects of a process can be measured, then so-lutions can be designed to eliminate them. Adefect is anything that could lead to customerdissatisfaction (Fairbanks, 2007). Six Sigma de-fines quality as having less than 3.4 defects permillion opportunities (DPMO). The Six Sigmascore correlates with the number of defects; lessdefects yield a higher score.

Six Sigma Teams consist of people from dif-ferent departments within an institution whoare involved in the process that needs improve-ment. The leadership and technical roles of SixSigma are organized in hierarchical fashion.Master Black Belts are experts in Six Sigmathat assist in data calculations and function asresources to the team. The team is led by BlackBelts whohaveprior experiencewith Six Sigmaand can function as a leader. Green Belts areteam members that have some experience inSix Sigma and have been selected by the in-stitution to become more familiar with the Six

Journal for Healthcare QualityVol. 34, No. 1, pp. 4454

C 2011 National Association forHealthcare Quality

Journal for Healthcare Quality

Vol. 34 No. 1 January/February 2012 45

Sigma process. Yellow andWhite Belts are rel-atively new to Six Sigma (Sonnenfeld, 1985).

A major tenet of Six Sigma is that the processmust be organized and data driven. Six Sigmamembers use the five-stepDMAIC (define,mea-sure, analyze, improve, and control) approach,which is an acronym for defining the major is-sues; measuring the system process or practiceprior to any interventions; analyzing the initialdata to develop a root-cause analysis; improv-ing the system process through intervention;and finally the control phase where data arecollected to assess the impact of the interven-tion. We hypothesized that using this approachwould establish a more efficient practice thatwould significantly decrease the average trans-fer time.

MethodsSettingThis study was performed at a 714-bed, tertiarycare, teaching hospital located in New YorkCity.

Scope of the ProjectThe team consisted of a Physician Sponsor,Chief Medical Resident, Director of Bed Man-agement, Patient Care Unit Manager, ChargeNurse, Unit Clerk, Director of Environmen-tal Services, Assistant Director of Health In-formation Management, and Manager of theTransport Department. A hospital administra-tor was the assigned Black Belt responsiblefor team building and project management.Three team members (Director of Bed Man-agement, Assistant Director of Health Informa-tion Management, and a hospital administra-tor from the North-Shore Long Island Jewish(NSLIJ)Healthcare System) underwent GreenBelt training. Severalmembers had prior expe-rience throughother Six Sigmaprojects (YellowBelts) and some members were hearing aboutSix Sigma for the first time (White Belts).

The initial step was the development of theProject Charter to clearly define the problemstatement, business case, and goal and scopeof the project, which was to include all patienttransfers to the ICU and the cardiac care unit(CCU).

Define PhaseA high-level process map (Figure 1) was cre-ated to graphically display and better under-stand the major events that were occurring. At

our hospital, physicians (mostly residents) werebeing utilized to personally observe the heartmonitor in nontelemetry settings. It was alsolearned that some floor nurses had difficultyadministering care (e.g., intravenous pressureagents) that was unfamiliar to them. Familycomplaints also arose when there was a per-ceived, overly long wait to transfer patients tothe ICU/CCU. The objective of this Six Sigmadeployment was to improve the patient trans-fer time, which would in turn have a beneficialimpact on quality by providing critical care inthe appropriatelymonitored setting, improvingthe utilization of the residents, improve patientsafety by not having untrained staff administerintensive drug regimens, and increase patient,family, and staff satisfaction.

The financial impact of this project wasdeemed too difficult to accurately measure.However, it was recognized that quickly freeingup residents allowed them to return to caringfor other patients, thus limiting potential de-lays in LOS. Limiting potential errors, by hav-ing critical-care-trained staff provide the care,could decrease resource wasting and avoid po-tential malpractice suits. Limiting patient com-plaints and improving patient/family satisfac-tion can generate potential income becausecomplaint investigations are costly and highersatisfaction can better ensure that patients willwant to return and refer others to the hospi-tal. Finally, improved staff satisfaction can helplimit staff turnover, which limits recruitmentand training costs.

The most clearly defined, objective measure-ments identified were the time stamps at differ-ent steps generated by the computerized pro-grams used in the transfer process. The clarityof these measurements facilitated the collec-tion of data and easily identified which stepsgenerated the most amount of time. The objec-tiveness of using time stamps ensured that themeasures could not be easily refuted.

Measure PhaseA data-collection plan was created to includeeight identifiable phases for time measure-ment: (1) bed management notified via phoneor transfer order; (2) bed management assignsbed; (3) bed management faxes transfer re-quest to the sending unit; (4) environmentalservices flag the bed clean (ready/availablebed); (5) sending unit informs the receiv-ing unit; (6) sending unit clerk inputs trans-fer order into the computer; (7) transport


Figure 1. High-Level Process Map (Define Phase)

Supplier: The person or organization who provides the inputs to your process. Input: The materials, resources, and data required to execute your process. Process: The series of steps or activities that uses one or more kinds of INPUTS and changes them to an OUTPUT that is of value to the CUSTOMER. Output: The tangible products or services that result from the process. Customer: The person or organization who receives the outputs of the process.

SUPPLIER INPUT PROCESS OUTPUT CUSTOMER

ATTENDING PHYSICIAN

PHYSICIAN ORDER

BED MANAGEMENT NOTIFIED OF

TRANSFER REQUESTTRANSPORT

ORDERRESIDENT PATIENT

UNIT RESIDENTORDER WITH

CORRECT DIAGNOSIS

BED MANAGEMENT ASSIGNS BED ASSIGNED BED

RESIDENT PATIENT

NURSING UNIT NURSING REPORTBED MANAGEMENT

NOTIFIES RECEIVING UNIT & SENDING UNIT

FAXED REPORT RECEIVING UNIT

SENDING UNIT FAXED REPORTSENDING UNIT ORDERS THE

TRANSFER

COMPLETED TRANSPORT

ORDER

TRANSPORT DEPT

PATIENT

TRANSPORT TRANSPORT ORDERPATIENT

TRANSFERREDTO UNIT

COMPLETED PATIENT

TRANSFERPATIENT

High-Level Process Map

Start Point: The moment Bed Management is notified of the transfer request

End Point: The moment Bed Management is notified that the patient is in the Critical Care Bed

department dispatches transporter; and (8) re-ceiving unit clerk inputs electronic transportorder as completed. The process and measure-ment also included a breakdown of the threedifferent work shifts, as well as the numberof beds involved in the transfer (one-, two- orthree-bed transfers). For example, if a patientwas transferred from the floor straight to anawaiting ICU bed, that was considered a one-bed transfer. If there was no available bed inthe ICU and an existing patient in the ICU hadto be moved out so a patient could be movedin, that would be a two-bed transfer and so on.

Preliminary measurements revealed that theaverage time for a patient to be transferredfrom a floor bed to a critical-care bed was214 min, with a maximum delay time of 420min. Additional data showed that the amountof variation in the process (assessed by the stan-dard deviation) was 170 min. Initial capabil-ity analysis revealed 423,728 DPMO. The sigmascore was only 1.6. The performance goals rec-

ommended by the Master Black Belt were toreduce the standard deviation by 50% and raisethe sigma score to approximately 2.2, thus de-creasing the DPMO to 242,000. Subsequently,the goals for this project were set at 90 minfor average transfer time and an upper specifi-cation limit (USL) set at 180 min for a max-imum individual transfer time. The USL of180 min was largely determined by the factthat the measurement phase showed that themaximum number of beds that needed to becleaned in any one unit transfer was three.One hour per each patient transferred in athree-bed transfer would allow for proper clean-ing of the room, transfer orders to be writ-ten, proper communication handoffs betweenhospital personnel, and safe transfer of the pa-tients and their belongings. The USL is an up-per limit above which the process performanceis deemed unacceptable (a defect). The lowerspecification limit (LSL) was set at 0 min,which limited analysis of transfers that went


Figure 2. Root Cause Analysis as a Fishbone Diagram (Analyze Phase)

Utilization

Measurement Materials

Environment

Order sheets

Printers

MDs

Weekend day shift

Night shift

Chart misplaced

Chart not flagged

Unable to reach MD

Process

Transfers

Orders not inputted

Fishbone (Cause and Effect Diagram)

No beds available

NursesClerks

Bed management

Teletracking reports

Chart documentation

Environmental services

People

Faxes

Transporters

Machines

Fax machineDay shift

Discharges

Series computer system

Tele monitorsTransporters not dispatched

RNs do not discharge patient

Bed vs. stretcher

Rounds

Phones

Weekend night shift

Ventilator

1, 2, or 3 bed transfer

Variation in Turn-around

Time for Transferring

Patients

exceptionally well. The customers (represen-tative residents, nurses, bed management per-sonnel, transporters, etc.) of this transfer pro-cess, with the exception of the patients, agreedto these goals/limits.

Analyze PhaseThe data collected in the Measure Phase wereanalyzed to create a list of process steps andidentify sources of variation in the process.Complex processes often have a myriad of de-finable steps. Identifying the few vital steps, orvital Xs as they are often referred to, will helpin avoiding the natural tendency of trying tomanage every process step. By determining thevital Xs, it becomes possible to focus on onlythose that are critical to producing the desiredoutcomes.

The first tool used was a Fishbone Dia-gram, a cause and effect illustration that en-hances identification of potential factors caus-ing an overall outcomein this case, the de-lay in transfer (Figure 2). The statement of theproblem was placed in the box at the headof the diagram. The remainder of the fish-bone consisted of one line drawn across thepage, attached to the problem statement, and

several vertical lines or bones. These verticalbranches, chosen as subcategories of the majorcategories of influence, were labeled with thespecific cause and effect titles. The specifics ofthe fishbone diagram were developed by groupdiscussion. When completed, the diagram pro-vided a visual understanding of the root causesof the problem and allowed the brainstormingfor possible solutions to begin.

Next, a Failure Mode and Effects Analysis(FMEA) was done (Figure 3). The FMEA iden-tifies potential and actual points of failure, aswell as corrective actions. In particular, this toolidentifies an effect (outcome) and quantifies itbased on the level of severity (using a scaleof 110). It shows how likely an effect is to oc-cur. The likelihood of effect or the frequencyof occurrence is used to describe how oftenthe outcome is initiated by the root cause. Theprocess of stopping the unwanted outcome isreferred to as detectability. Thus, the resul-tant value is the risk priority number (RPN),which is computed by multiplying the sever-ity by the occurrence by the detectability.Of the eight steps identified, the FMEA yieldedfour critical steps with high RPNs.

Finally, a series of Hypothesis Testing(Figure 4), which uses statistics to determine


Figure 3. Failure Mode and Effect Analysis (Analyze Phase)

Process Step / Input

Potential Failure Mode

Potential Failure Effects

SEV

ER

ITY

Potential Causes

OC

CU

RR

EN

CE

Current Controls

DE

TE

CT

ION

RPN

What is the process step and Input under investigation?

In what ways does the Key Input go wrong?

What is the impact on the Key Output Variables (Customer Requirements)?

What causes the Key Input to go wrong

What are the existing controls & procedures that prevent either the cause or the Failure Mode?

Bed Management Notified

(X-1)

- MD Delays - Notification

Delays Transfer

9

-Lack of Process

Knowledge - MD issues - Communuication

1

No Controls in Place

7

63

Bed Management Assigns Bed

(X-2)

- No Bed Available - No Discharges - No Transfers - Holding Patients

Patient cant be

transferred

10

- 100% Occupancy - NoTransfers

9

Daily Utilization

Rounds

6

540

Bed Management Faxes Sending

Unit (X-3)

- Fax broken

- Bed Manag busy

Sending Unit

unprepared for Transfer

4

Competing Priorities

4

Manager Supervision

2

32

Envion Serv Flags

Bed Clean (X-4)

Technical Breakdown

Bed Manag cant

assign bed

7

- Lack of Personnel - Lack of Technical

Skills

5

Bed Management

Oversight via Teletracking Monitor

1

35

Sending RN Faxes Report to Receiving RN

(X-5)

- Fax Problems

- Too busy

Receiving Unit

cant accept transfer

7

- Timely Notification - Clinical Issues - Shift Change

5

RN Manager &

Supervisor

8

280

Sending Clerk Inputs Order in

Series (X-6)

- No Standardized Process - Clerk Staffing

- Transport not notified - Respiratory not notified (If vented pat)

5

- Lack of Personnel - Shift Change - Employee Oversight

4

Clerk Supervisor Transport Supervisor

5

100

Transport Dispatches Transporter

(X-7)

- No standardized Process - Transporter Staffing

Transport not dispatched but no effect (MD & RN

will transport patient)

4

- Lack of Personnel - Shift Change - Employee Oversight

3

No Controls in Place

(Supervisor Removed)

9

108

Receiving Unit Inputs into Series

(X-8)

Conflicting priority of clerks

Patient may leave Unit with unclear continuum of care

2

- Shift Change - Competing Priorities - Clinical Issues

4

Manager Supervision

2

16

Failure Mode: the manner in which a specific process fails. Cause: a condition that produces a failure mode. Failure Effect: impact on customer requirements if failure mode is not prevented. RPN: Risk Priority Number, which is computed by multiplying the severity by the occurrence by the detectability. Circles: represent the process steps that resulted in the highest RPNs.

the probability that a given hypothesis is true,was undertaken. In brief, a series of various hy-pothesis tests were examined by calculating ap-value, which is also known as the observedsignificance level or the probability value. Thep-value helped delineate the causes that werevital, which focused the determining of thepotential specific causes for the differences.

After careful analysis of these three tools, itwas determined that the increased turn-aroundtime centered on Bed Managements ability to

assign a clean, ready bed. Thus, there neededto be an available bed in order for Bed Man-agement to facilitate this process. The turn-around time greatly increased depending onthe number of bed transfers needed. When theassignment involved a one-, two-, or three-bedtransfer, the average turn-around time was 126,249, or 404 min, respectively.

It was also discovered that there was an in-crease in turn-around time related to how longit took for the sending unit to communicate the


Figure 4. Hypothesis Testing to Determine the Pre-implementation Vital Xs(Analyze Phase)

order (via phone/fax) with the receiving unit,which was due to poor communication and toomany process steps. However, there was no sta-tistical significance in turn-around time in re-lation to the shift time, the day of the week,whether it was a phone or fax order, the specificunit the patient was transferred to and fromand whether or not the transport departmentwas utilized.

Although it was not originally deemed a vi-tal X, it was agreed upon by the team that themedical residents completion of the transferorders was a key step. It was found that there wasno standardized process for a resident-drivencompletion of the transfer orders. Some resi-dents completed their orders immediately, oth-ers completed them later. Also, there appeared(by direct observation) to be poor communi-cation between the physicians and the nursingstaff in the critical-care areas.

Improvement PhaseThe following critical elements were recog-nized: (1) poor process flow; (2) incon-sistent communication; (3) no standardizedorder writing process; (4) overutilization of re-

mote cardiac monitoring; and (5) lack of un-derstanding at the staff level of the importanceof this issue. Next, a specific solution plan wasdeveloped.

One new process was to pilot having a clean,ready bed always available in a large room (ICUAnnex) used for equipment and device storagethat is located directly across the hall from theICU entrance. The environmental services di-rector (ESD) and the ICU charge nurse wouldhave accountability for ensuring that a clean,ready bed was always available. The ICU di-rector personally educated all supervisors onthe new policy. This solution eliminated theneed for the units to call the ESD for beddelivery.

Another improvement was the creation ofan electronic bed assignment notificationvia the installation of Tele-Tracking softwarein the ICU/CCU and Telemetry Unit. Thehead of Bed Management installed the soft-ware, educated all personnel, and ensured thatit was utilized on all shifts. Bed Managementwould notify the receiving and sending unitsvia Tele-Tracking. Additionally, a notificationalert would now be utilized so that when Bed


Figure 5. Summary of Results (Control Phase)

MEAN TIME FROM FLOOR

TO ICU

STANDARD DEVIATION

SIX SIGMA SCORE

YIELD DEFECTS PER MILLION

OPPORTUNITIES PRE-IMPLEMENTATION 214 min 170 min 1.6 54.00% 423,728

TEAM GOAL 90 min 85 min 2.2 75.80% 242,000

SEPT & OCT 2009 92 min 33 min 3.7 98.61% 13,333

NOVEMBER 2009 91 min 45 min 3.4 97.13% 23,255

DECEMBER 2009 91 min 42 min 3.3 96.41% 31,250

JANUARY 2010 85 min 32 min 100% 0

FEBRUARY 2010 81 min 37 min 96.41% 29,411

MARCH 2010 84 min 34 min

6.0

3.3

3.5 97.73% 28,571

APRIL 2010 70 min 29 min 6.0 100% 0

MAY 2010 77 min 31 min 6.0 100% 0

JUNE 2010 84 min 25 min 6.0 100% 0

JULY 2010 81 min 22 min 6.0 100% 0

AUGUST 2010 73 min 30 min 6.0 100% 0

SEPTEMBER 2010 88 min 39 min 6.0 100% 0

OCTOBER 2010 87 min 37 min 6.0 100% 0

Yield: represents the percent of good products or services. Defect Counts: monitor the number of times things go wrong in Defects Per Million Opportunities (DPMO). Defect: any event that does not meet the customers need. Opportunity: any event that can be measured that provides a chance of not meeting a customers requirement. Six Sigma Score: is a commonly used measure of process capability that represents the number of short-term standard deviations between the center of a process and the closest specification limit.

Management assigned a bed, it would flag assuch in both the sending and receiving units,notifying the respective clerks of the bed assign-ment. This eliminated multiple process steps(i.e., the need to fax, phone, and page no-tifications) and resulted in less work for thenurses in the ICU/CCU. Also, the ready tomove function in Tele-Tracking was institutedby the sending unit clerk. This provided realtime notification that patients were ready to bemoved.

The process for writing transfer orders out ofthe ICU/CCU was also changed. The goal wasto ensure that transfer orders were completedimmediately after rounds. All residents wereinstructed to flag the patients charts fordischarge/transfer to alert the unit clerk toplace the transfer order, which notified bedmanagement. This solution would expedite thetransport of patients out of the ICU/CCU tomake beds more quickly available for incomingpatients.

A fourth new procedure called for the accept-ing critical-care physician to determinewhetheror not a remote cardiac monitor was to beplaced on the patient awaiting transfer to theunits. It was realized that while some upgradedpatients (e.g., those ruling in for a myocardialinfarction) required constant cardiac monitor-ing, others did not. Once a monitor was placedon a patient in a nontelemetry ward, a residentphysician had to be assigned to the room toconstantly observe the patient for fatal arrhyth-mias. If the cardiac monitor was safely deemedunnecessary by the attending intensivist, thenthis freed up the resident to facilitate the trans-fer, as well as care for other patients.

Finally, the project itself called attention tothe importance of quickly moving critically illpatients to the critical-care areas. Because alldepartments that shared a role in this processwere part of the Six SigmaTeam, new educationand enhanced teamwork skills developed fromthis project.


Figure 6. Before and After Process Capability

Graph Above: 0-1000: is number of minutes Graph Above: 0-210: is number of minutes LSL: Lower Specification Limit (0 min). USL: Upper Specification Limit (180 min). Target: 90 min.

Transfers: 59 DPMO: 242,000 Mean: 214 min Sigma: 2.2 St Dev: 170 min Yield: 75.80%

Transfers: 462 DPMO: 10,700 Mean: 84 min Sigma: 3.8 St Dev: 35 min Yield: 98.93%

PostimprovementPreimprovement

Control PhaseIn this phase, most of the Six Sigma Team be-comes disbanded. Constant data tracking anddocumentationwere done by the process owner(in this case, the Director of Bed Management)and the Black Belt to measure any improve-ments and ensure that they would be sustained.In addition, the team sponsor and the nursingand physician staffs were updated on a monthlybasis with on-going data.

ResultsAfter implementation of the new processes,data were collected and analyzed on patienttransfers over a period of 1 year for 462 consec-utive patient transfers to the ICU/CCU (Fig-ure 5). The target of decreasing the averagetransfer time to less than 90 min was immedi-ately approached and then finally attained bythe fourth month. In the first 6 months, therewere still rare instances of individual transfertimes exceeding 180 min, which only allowedthe sigma score to reach the mid-three range(but it did break the 2.2 goal). However, bythe eighth month, there were no defects and asigma score of six along with a yield of 100%were reached and maintained for the remain-der of the control phase.

For the entire control phase, the mean timefor the transfer of patients from a floor to acritical-care bed was 84 min as compared to theinitial mean (preimprovement analysis) of 214min; a marked reduction in the transfer timeof 138 min (Figure 6). Additionally, the stan-dard deviation in the transfer time was reducedby 135 min. The standard deviation is one ofthe most common measures of variability in adata set; as it gets smaller, the process capabilitygets better. The postimprovement data showeda standard deviation of only 35min. The overallsigma score was raised from 1.6 to 3.8 and theyield, which represents the percentage of theprocess that is acceptable to the customer, wasraised from 54% to 98.9%.

After seven consecutive months of no de-fects, the project was turned over to the pro-cess owner in December 2010 and the team wasdisbanded. A project summary is depicted inFigure 7.

The improved process alignedwith the hospi-tals strategic business objective, set forth in theProject Charter, which outlined the followinggoals and standards of the project:

Customer satisfaction: Patients, their fami-lies, residents, and staff all experiencedtimely transfers, which led to increased sat-isfaction.


Figure 7. Project Summary

Process Steps Problems Post-Improvement Solution

Resolution

Patients transferred OUT of CCA to accommodate new patients transferred into the CCA

Untimely transfers out of the CCA lead to a lack of beds available to accept transfers INTO the CCA Replacement bed sent by Environmental Services after nursing request placed No standardized process for medical residents to write transfer orders for transferring patients out of the CCA

Bed Management directly notifies Receiving Unit of transfer CCA retrieves clean bed from the ICU Annex Process standardized to begin immediately after rounds with prompt notification of the CCA clerk and nursing staff.

Eliminated delayed notification by nursing No delay in waiting for a clean replacement bed Ensured orders are completed at time or decision to transfer patient. Notification also more timely.

Upgraded floor patient placed on cardiac monitor

Mandated resident to observe monitor, which delayed care to other patients

Intensivist makes the determination if monitoring is medically necessary prior to transfer to CCA

Enhanced resident utilization and time management

Multiple communication steps between the sending and receiving units

Overly complex communication process

Installed Tele-tracking in all CCA and transport department Conducted software training/education

Streamlined communication and patient throughput

Multiple process steps involving clerks, nurses and transporters of the sending unit to transfer patient

Large delay created in the notification process that the patient was ready to be moved

Implemented use of Ready to Move function in Teletracking

Real Time Electronic Notification that patient is ready to be moved out of CCA

CCA: critical care areas (e.g., Intensive Care Unit or Cardiac Care Unit)

Operational excellence: Improved utilizationof residents and nurses enhanced opera-tional excellence.

Quality: Better communication proceduresled to a decrease in the risk of adverseevents for a patient transferred to a moni-tored bed.

Economic profit: Though not directly mea-sured, immediate and delayed financialbenefits (see Section Define Phase)were likely realized, as well as unneces-sary costs were avoided (e.g., complaintinvestigations).

DiscussionSix Sigma provided a comprehensive analysis ofthe patient transfer process prior to implement-ing new solutions. Six Sigma utilizes data, thevoice of the customer(s), and statistical analysisto determine the factors that aremost critical toquality improvement. It also requires account-

ability and constant evaluation after implemen-tation of new solutions (a control phase), whichfosters sustainability. Furthermore, the use ofthe Six Sigma jargon provides for a universallanguage that can compare and contrast theeffectiveness of different projects.

A very key step was to set realistic improve-ment goals that were measureable. This can-not be overstated. The analyze phase helped tounderstand what would be a realistic goal forindividual and average patient transfer times.

Interviews with staff to find out their con-cerns and insights were very helpful. Assem-bling a team of individuals who performed in-tegral roles of the patient transfer process wasimportant. This ensured buy-in prior to theimplementation phase and served as the basisfor creating the fishbone (cause and effect)diagram and conducting the FMEA.

One limitation of our study is that im-provements could have been secondary tothe Hawthorne effect, which postulates that


processes being watched improve because theyare being watched (Tennant, 2001). Nonethe-less, we believe the changes that were made tothe overall process lead to the significant re-sults. Another limitation was that we could notpilot the new solutions in one area of the hospi-tal while continuing the old process, to serve asa real-time control in another. However, a truehistorical control was used, whichwasmeasuredin the months just before the implementationphase. Lastly, the financial impacts of the newprocesses were not directly measured.

Although the NSLIJ system has employedand trained Six Sigma experts, other organi-zations can still benefit from using the varioustools often implemented in a Six Sigma project,even without the specific Six Sigma resourcesand experts. For example, an organization canassemble a team of various disciplines to define,measure, analyze, improve, and control a frag-mented process. They can create a cause andeffect diagram, run an FMEA, identify key steps(the vital Xs), brainstorm, and formulate prac-tical solutions and measure the outcome of theimplemented strategy. For this specific problem(ICU transfer delays), implementing a comput-erized bed-tracking software program, having aclean, ready bed near the ICU, improving theefficiency and communication of transfers intoand out of the ICU and determining the needfor cardiac monitoring prior to transfer led toalmost immediate, major reductions in transfertimes, which were sustained over 1 year.

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Authors BiographiesStephan J. Silich, JD, is the Director of Business Develop-ment and Six Sigma Certified Blackbelt for Staten IslandUniversity Hospital in Staten Island, New York.

Robert V. Wetz, MD, is the Associate Chairman of Medicine& Residency Program Director for the Department ofMedicine at Staten Island University Hospital in StatenIsland, New York.

Nancy Riebling, MS, MT, is the Director of OperationalPerformance Solutions and Six Sigma Certified Mas-ter Blackbelt for North Shore-LIJ HealthSystem in LongIsland, New York.

Christine Coleman is the Director of Bed Managementfor Staten Island University Hospital in Staten Island,New York.

Georges Khoueiry, MD, is the Associate Director of StudentEducation for the Department of Medicine at Staten IslandUniversity Hospital in Staten Island, New York.

Nidal Abi Rafeh, MD, is a cardiology fellow atStaten Island University Hospital in Staten Island,New York.

Emma Bagon, RN, is the Patient Care Unit Manager in theIntensive Care Unit at Staten Island University Hospitalin Staten Island, New York.

Anita Szerszen, DO, is the Director of Geriatric Researchand co-chair of the Research Division for the Departmentof Medicine at Staten Island University Hospital in StatenIsland, New York.

For more information on this article, contact Stephan J.Silich at [email protected].

Journal for Healthcare Quality is pleased to offerthe opportunity to earn continuing education(CE) credit to those who read this article andtake the online posttest at http://www.nahq.org/education/content/jhq-ce.html. This co-ntinuing education offering, JHQ 233, willprovide 1 contact hour to those who completeit appropriately.

Core CPHQ Examination Content AreaIII. Performance Measurement & Improve-ment

CE 233-Objectives1. Describe the different phases of a Six Sigma

Project using the DMAIC approach.


2. Identify the different roles of the members of aSix Sigma Team.

3. Describe possible solutions to achieving fastertransfer times for patients being moved from ageneral medical floor to a critical care area.

Six Sigma Questions1. Of the following, which process wouldmore likely

ensure staff buy-in for a complex, Six Sigmaproject?

A. Allow the involved staff to obtain their own data forthemeasure phase and compare it to the data foundby the Six Sigma team.

B. If the Six Sigma project is done correctly, the staffwill automatically buy-in to the changes.

C. Invite key personnel that are part of the scope ofthe project to be Six Sigma teammembers from thebeginning.

D. Show the appropriate department heads the datafrom the measure phase and have them relay thedata to their staff.

E. Show the staff the completed project charter after 6months of the analyze phase to allow for appropri-ate adjustments prior to presentation to the staff.

2. The upper specification limit (USL) of the per-formance goals for a Six Sigma project is set by:

A. allowing the six sigma team to determine a realisticgoal that would be pleasing to the customers.

B. determining which limit would consistently yield asigma score of six.

C. random determination using Six Sigma statisticalanalyses (e.g., FEMA).

D. the Master Black Belt.E. waiting until the control phase is complete.

3. According to the article, one way to improve thetransfer time for patients being upgraded to thecritical care unit would be:

A. to assign security personnel to oversee and expeditethe transfer of all critically ill patients.

B. to have the nurses and residents transport the pa-tient from the floor to the critical care area them-selves.

C. to install an electronic, tele-tracking, bed manage-ment software program.

D. to place all upgraded patients on a cardiac monitorwhile outside of the critical care area.

E. to write all transfer orders out of the critical careareas by the end of the work day.

4. What are the 5 phases in a Six Sigma project?

A. Define, Measure, Analyze, Improve and ControlB. Define, Measure, Assess, Implement and ControlC. Design, Measure, Analyze, Improve and ControlD. Design, Measure, Assess, Implement and Control

5. A Fishbone Diagram is used to help identify therelationship between:

A. A Good Process and Bad Process.B. Cause and Effect.

C. A.DPMO (Defects per million opportunities) andSix Sigma.

D. A.The pre-implementation processes and the post-implementation processes.

6. A FMEA (Failure Mode Effects and Analysis) isused to:

A. Estimate the potential financial gains of a Six Sigmaproject.

B. Estimate the risk of failure.C. Identify defects within a series of linked events.D. Identify redundancies within complex processes.

7. Which of the following is a direct financial benefitof a Six Sigma project?

A. Cost avoidanceB. Customer SatisfactionC. Incremental RevenueD. Task Elimination

8. Which of the following is a benefit of processmap-ping?

A. It can identify indirect cause and effect relation-ships.

B. It can reveal unnecessary, complex and redundantsteps.

C. It determines the likelihood of obtaining a highsigma score for each individual process step.

D. It provides the sequence of events for the imple-mentation phase.

9. According to the article, which of the followingwas found to directly lead to increased (worse)patient transfer times?

A. Not using the transport department lead to worsepatient transfer times.

B. Particular floors had worse transfer times.C. The more patients that had to be transferred to

allow one patient to enter the critical care area (i.e.,multi-bed transfers) had increased transfer times.

D. Transfers that occurred during the night shifts hadworse transfer times from the floor to the criticalcare area.

E. Weekend transfers during the winter months hadworse transfer times.

10. Which of the following best defines the role of agreen belt in a Six Sigma project?

A. An employee that is completely new to the Six Sigmaprocess.

B. Personnel that are being trained to eventually runtheir own Six Sigma projects.

C. The data collector and biostatistician for theproject.

D. The financial supporter and manager of the SixSigma project.

E. A.The team member responsible for compiling thevarious figures and graphs.

Documents

Using Six Sigma Methodology to Reduce Patient Transfer Times From Floor to Critical-Care Beds