use of observations in lean system good

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REFLECTIVE PRACTICE

Lean information management:the use of observational data in

health careAndrew Castle

Applied Angle, Streatham, UK, and

Rachel HarveySustainable Solutions Consulting Ltd, Tadworth, UK

AbstractPurpose The purpose of this paper is to compare and contrast traditional data collectionmethodologies employed in health care with more practical observational methods which areclosely aligned with Lean thinking. When combined with problem solving, observationalapproaches achieve demonstrable improvements in clinical outcomes, productivity and efficiency.The paper aims to describe the changes in mindset and behaviour that are required to adopt theobservational methods.

Design/methodology/approach The approach is to describe and evaluate case study exampleson the use of observational data in the National Health Service in the UK. This is then used to derivegeneric principles about the wider application of observational data in health care.

Findings Traditional data collection methodologies are often insufficient to expose the root causeof a problem and therefore may result in little or no action. The observational methods identify the rootcause and as such offer a much more practical and real-time way of solving process-related problems.

Practical implications The observational methods of collecting data described here offer staff atall levels of the organisation practical approaches to preventing mistakes and errors in health careprocesses.

Originality/value The case studies described here support the reintroduction of observationaltechniques used by the early pioneers of productivity. The originality of the paper is in the use of theseobservational methods in a wide range of clinical settings to provoke changes in working practices.

Keywords Lean production, Problem solving, Quality improvement, Continuous improvement,Operations management, Manufacturing systems

Paper type Viewpoint

1. Introduction

Lean is a term that was first coined Womack, Jones and Roos (Womack et al., 1990;Womack and Jones, 1996) to describe the Toyota Production System (TPS) and thesteps to continuously improve the efficiency and effectiveness of a system by drivingout waste. Womack and Jones identified that in order to meets its customers needs anorganisation must identify what its customers think of as value. Once this is clear, theorganisation can work to eliminate non-value adding process steps or waste, makethe remaining steps in the process flow, implement pull systems where flow is notpossible and work continuously towards perfection.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1741-0401.htm

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Received September 2008Accepted October 2008

International Journal of Productivity

and Performance Management

Vol. 58 No. 3, 2009

pp. 280-299

q Emerald Group Publishing Limited

1741-0401

DOI 10.1108/17410400910938878


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Taicho Ohno (Ohno and Bodek, 1988), an executive at Toyota, led the developmentof the TPS and the concept of the Gemba Walk. The Gemba Walk is an opportunity forstaff to stand back from their day-to-day tasks to walk the floor of their workplace toidentify wasteful activities. Waste might include producing too much too early,

duplication or rework, mistakes and errors, waiting, unnecessary stock, transportationof people or materials over long distances, or unergonomic work environments. Thefirst step is to be able to see the waste and then to eliminate it.

Without data it is difficult to identify the root cause of a problem and to makeaccurate decisions to reach a solution. Ohno demonstrated repeatedly that there is nosubstitute for observing the problems yourself. This idea was repeatedly advocated bythe quality geru W. Edward Deming (2000) who is credited with saying:

. . . in God we trust everyone else must bring data.

In order to make well founded and informed clinical or managerial decisions goodquality data needs to be readily available. For the treatment of individual patients thismight include access to diagnostic results, healthcare records, drug charts and

appropriate consultations with specialists when necessary.Making decisions that impact the way that an organisation functions or meets the

continually changing needs and expectations of patients requires a different kind ofdata. This data needs to be conducive to a detailed analysis of how the current systemworks, the outcomes delivered and the areas that offer an opportunity forimprovement. Accuracy, timeliness, quality and availability are importantstandards, which traditionally within healthcare settings are not met.

The purpose of this paper is to compare and contrast traditional data collectionmethodologies employed in health care with more practical observational methodswhich are closely aligned with Lean thinking. The paper shows that, when combinedwith problem solving, observational approaches achieve demonstrable improvementsin clinical outcomes, productivity and efficiency. It describes how some of the obstaclesto obtaining good quality data can be overcome, as well as some of the limitations ofthis alternative method of collecting data. The paper describes the changes in mindsetand behaviour required to adopt the observational methods.

2. ApproachThe papers approach is to describe and evaluate case study examples on the use ofobservational data in the National Health Service in the UK. These cases are then used toderive generic principles about the wider application of observational data in health care.

3. Traditional data collection methodologies employed in health careTable I shows some of the systems currently in use to collect data in the National

Health Service in the UK.Countless other systems are in use in finance, human resources, occupational health,

estates and other support services.

4. Limitations of traditional data collection methodologiesData tend to be reported in table format which makes visual analysis of trends or thecurrent position difficult. Averages and simple comparisons from one year or month tothe next distort the true picture.

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Data are often reported a month or two after it is collected which makes it difficult toidentify why problems occurred and to take action immediately to prevent problemsfrom happening again. There are numerous committees and meetings where data isdiscussed, the same problems arise and no action is taken.

There is a target-driven culture of reporting data upwards that is requested by theDepartment of Health. Often the front-line staff do not understand why they arecollecting and reporting the information, and receive little or no feedback on theirperformance. This kind of approach discourages front-line staff from collecting and

monitoring the data and taking responsibility for improving their service.The way in which data are reported is often reactive. It is common to hear aboutservices that are overspent or complaints after the event. The authors worked with anophthalmology service which was constantly under scrutiny by the trust board for thecomplaints it received. The result of this was that the manager spent about half of theirworking week formally responding to the complaints. Most complaints were aboutwaiting times in the department. Through observation of the workplace with dedicatedtime, staff within the unit were able to redesign processes to reduce waiting times by 50per cent and reduce the level of complaints.

Wards and departments collate data in different ways and many departmentscollect the same data in different ways. This causes duplication and a lack of clarityabout what data is available, its accuracy and where it can be found. Some specific

examples of the limitations of traditional data collection methods include:. Accuracy of length of stay data 58 patients on a ward with 26 beds because

nurses had not discharged them on the computer.

. Waiting for the results of blood tests not knowing where a particular sample isin the system and when results may become available.

. Unable to track images the diagnostic image results are on a server butclinicians are unable to access them leading to delays in diagnosis.

Type of system Data collected

PAS Patient administration system, being replaced by CRSCRS Care record service

RIS Radiology information systemsPACs Imaging systemsOrdercoms Diagnostic results systems (blood tests)Dr Foster Length of stay and many other metricsPower chart Outcomes of clinical visitsSurginet Data on theatre performance including, knife to skin, anaesthetic time, recovery

time, patient observations, procedures, the consultant, materials requiresE-procurement Procurement and inventory managementNHS logistics Procurement and management of consumable and stock materialsTheatre man Theatre schedulingMDS Minimum data set for reporting on the 18-week target from referral by a GP to

first definitive treatmentJonah A system in the emergency department to manage the four hour wait of patients

from arrival to discharge and identify the number arriving, waiting, beingtreated and discharged by hour of the day

Table I.

UK healthcare datacollection systems

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. No usage data for procurement the only available data is spend, to find out theamount of a specific product purchased the hospital must contact the supplierand ask them.

. Budgets not representing actual costs or spend no uplift on non-pay spend foreight years combined with a 3 per cent year on year cost improvement programme,inflation around 3 per cent for eight years, budget decreased by 3 per cent everyyear at 2000 values whilst costs increased. This led to massive overspends.

. No data on the time the patient entered the theatre, the time the operation startedor finished.

. No good performance metrics within theatres indicating whether organisationsare efficient or productive. The key metric is theatre utilisation which is timeused to operate divided by time available to operate. For example a surgeonoperates for three hours of a possible four providing utilisation of 75 per cent.This does not take in to account any level of productivity as one surgeon mightperform two operations in 2.5 hours giving a utilisation of 62.5 per cent compared

to another surgeon that does one of the same type of operation in three hourswith a utilisation of 75 per cent.

. Poor quality metrics the focus of metrics tends to be on waiting times,complaints and finance rather than infections, outcomes and readmission rates.

. Boards focus on finance not quality quality is often perceived as being costly.

Traditional data collection methodologies are unable to identify that a patient waitedeight hours in a bed because pathology lost their blood sample. Having blood drawnfor a second time, the patient waited a further three hours for the result which indicatedthat they were fine and could go home. The result is that a patient occupied a bed for 12hours and consumed physician, nursing and other staff time for no material benefit.

There are no measures in place that allow the organisation to identify this earlier.Traditional methods are unable to identify the level of duplication in information, bywhom and when, such as completing a clinical assessment form in the emergencydepartment and then completing a similar assessment form in the medical assessmentunit containing much of the same information.

Health care is incredibly complex and an enormous quantity of data is collected.Despite this it is still not possible to identify easily how an organisation is performingin terms of quality, cost and delivery.

5. Observational dataA different approach to traditional data collection methods is that pioneered initiallyby Frederick Taylor and W.L. Gantt (Taylor, 2007) and then later by F.B. Gilbreth

(Gilbreth and Carey, 2005) and Taichi Ohno. Rather than collecting the data that arecurrently available, the authors advocate physically going to the location of interest todirectly observe what takes place and to record this in a variety of formats includingvideo, documentation and photographs.

This direct observational approach has been used with success in different areas ofmedicine including the counting of instruments into and out of theatres (Greenberget al., 2007), the insertion of central lines in intensive care units (ICUs) (Pronovost et al.,2006) and the direct observation of activity within an ICU (Donchin et al., 2003).


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The next section details the results of a number of case studies of directobservational data collection.

5.1 The application of video techniques in a sterile services departmentThe authors worked with sterile services in a district general hospital. A sterileservices department is responsible for the cleaning and sterilisation of equipment andinstruments used within the hospital. The process is essentially a closed loop systemwhere a set of instruments is used in theatre as part of an operation. At the conclusionof the operation the instruments are counted out and placed in their own container andtransported to the sterile services department. There they are counted, then decanted into washing racks, washed, dried, checked for damage and cleanliness, packed,sterilised and transported back to the theatre store room.

There was a perception that the sterile services department was responsible fordelaying operations, failing to adequately clean operating equipment and was notresponsive enough to the needs of the organisation. The department in question felt

that they had insufficient staff to meet demand and that these problems weresymptomatic of inadequate levels of staff.The authors worked with the department and theatres over a two month period to

examine processes and improve flow. The process of packing a set of instrumentsrequired close examination. A set of instruments may contain between one and 70 ormore individual instruments as well as consumable items. Within a typical hospitalthere may be as many as 3,500 sets of instruments of 700 varieties plus single specialistinstruments that those working in the packing department need to be familiar with.

The authors videoed the process of packing several different sets of instruments.Figure 1 illustrates the process for one set.

Analysis of the video showed that while the time to pack a set of instruments was285 seconds, only 90 seconds (31.5 per cent) of this process was value adding, i.e. time

that contributes to the preparation of the set so that it may be used for surgery. Theremaining 190 seconds was spent looking for packing lists, moving around thedepartment, obtaining consumables, scanning the sets in to the computer andnumerous other activities.

Analysis of multiple videos revealed that having only one printer and scanner in adepartment with 15 members of staff led to delays in accessing the equipment.Locating all of the consumables in one location as far geographically within thedepartment from where they are needed also contributed to the non value adding time.

A multidisciplinary team worked to eliminate waste in the process. Solutionsincluded: purchasing more scanners and printers and locating them at the point of use,making the packing sheets more accessible and placing consumables at hand. Thepurchase of equipment and redesign of the workspace made the recruitment of a

further 6 members of staff unnecessary, as it was clear that sufficient resources werepresent to meet the current demand.

5.2 The application of gemba walk in an ophthalmology out-patients departmentThe authors worked with an ophthalmology unit to reduce waiting times andcomplaints about waiting times. The department came under constant criticism by thetrust board about the level of complaints received and the manager spent at least halfof their working week responding to formal complaints. Waiting times were not

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Figure 1.Video 1 packing a typical

set in sterile service


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measured but complaints indicated that they could be as much as 2 hours 30 minutesfrom arrival in the department to being seen by a consultant. The cause of theproblems was largely unknown to staff and there was no time to stop and investigate.

The staff were given dedicated time to examine their work processes in detail. This

entailed a week long observation of the department. Staff were assigned to clinics toensure that issues pertinent to specific clinics were picked up. Each member of staffwas introduced to the concept of waste and had a data collection sheet to record theirobservations.

During the observation period it came to light that patients would often queue tobook in at the reception desk, take a seat in the waiting area, be called in by a nurse forvisual acuity tests and dilating if appropriate, and then be directed back to the waitingarea. A nurse would then walk the patients notes to the back of the consultant booths,a distance some 10 metres away. There could be up to five doctors in clinic seeingaround 14 patients per clinic in separate booths. A doctor would take a set of notesfrom the back of the clinic where the nurse had left off and then proceed to call thepatient in from the waiting area. Once the consultation with the doctor was complete,the doctor would hand the patient a slip of paper to hand in to the reception to desk forbooking a follow-up appointment on their way out of the department.

At the end of the observation period dedicated workshops were set up to analyse thedata captured. Key issues identified included:

. Patients seen out of turn as the notes would not always be placed by the nursingstaff at the back of clinic in the order of appointment time. This was a function ofthe doctors operating a pooled appointments system even though each patienthad a scheduled arrival time.

. Nurses carrying out visual acuity tests in a separate treatment room and theirfull skills potential was not maximised.

. Nurses walking patient notes from the treatment room to one of five consultingbooths (1,000 metres per clinic).

. Nurses spending eight hours per day adding a front sheet to every set of patientnotes for coding.

Having reviewed the problems in detail the team:

. Achieved at least a 50 per cent reduction in patient waiting times and enabledclinics to finish on time (illustrated in Figures 2 and 3).

. Almost eliminated nurses walking to deliver notes to consulting booths (1,000metres per clinic).

These improvements were achieved by:

. Nurses basing themselves in a free booth between the consultation boothes or atthe back of clinic to carry out visual acuity tests.

. Closer working relationship between doctors and nursing staff, enabling them tolearn from each other.

. Seeing patients in turn as the doctors now follow their own template of patients.

. A one to one nurse to doctor ratio in most clinics and the nurse coordinates thetemplate.

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. Eliminating eight hours per day of nursing time spent adding a front sheet to

every set of patient notes

These changes would not have been possible without the use of observational data to

gain agreement about what actually happened and why (see Figures 2 and 3).

Figure 2.Waiting times before

(minutes)

Figure 3.

Waiting times after(minutes)


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5.3 Patient tracking in theatresThe largest observational study that the authors have undertaken to date examined theoperating theatres at a district general hospital. This organisation failed to startoperating lists on time. This issue is, in the authors experience, one that organisations

confront on a regular basis. The authors determined that a direct observational studywould be the most effective approach.

The authors identified a team of 16 members of staff to observe includingconsultants and other theatre staff, management and the service improvement team.One observer was allocated to each of the four theatres, one to theatre reception, one tothe surgical admissions unit reception, one to each patient on each list and one to therecovery room.

Each observer documented what happened and when at each stage of the process.The first observer, working at the surgical admissions reception desk, identified all thepatients that entered or left the department and at what time. Once a patient arrived theobserver allocated to that patient reviewed what time they booked in, got changed, sawthe surgeon, the anaesthetist, the nurse, arrived in theatre and finally recovery.

The results of this process along with some notes about issues that arose arepresented below (Figure 4).

Theatre 1 was the only theatre to run in the planned order, no changes were made tothe lists and it started the earliest at 20 minutes late (Figure 5).

Theatre 2 changed orders multiple times between 8 and 9.05am when the firstoperation started 35 minutes late (Figure 6).

In Theatre 3 nobody knew the planned order of the list as a hard copy did notappear until the first patient arrived 55 minutes after the operation was scheduled tostart (Figure 7).

In Theatre 4 the operating list started 1 hour late, the surgeon was still in his suitand outside of theatre 35 minutes after it was due to start (Figure 8). The registrar wasin theatre with the first patient but as it was a major case the registrar was notconfident to start on his own. When the surgeon arrived they were unable to start asnobody had checked to see if there was a power cable that would allow the operatingtable to be raised and lowered as required. Only once the surgeon arrived 60 minutesafter the scheduled start time someone checked to see if the power cable was availableand when it was found to be missing, a further 10 minutes was spent looking for one.

This list was supposed to be an all day list starting at 8.30 am and finishing at5pm. The list eventually finished at 11pm due to the numerous changes and unplannedincidents and over runs.

In summary, the results of this observational exercise were:

. No theatres started on time.

. Only one (25 per cent) ran in the planned order. All the others changed more than

once.. Missing notes at 7a.m. (37 per cent).. All lists had at least one technical error.

. Admissions staff called a patient at 7.40 a.m. who did not attend. Their wifeexplained that they had been admitted to the hospital the previous week.

. Theatre staff called for a patient to go to the theatre at 11.40 a.m. who had notbeen pre-admitted.

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Figure 4.Analysis of theatres 1 to 4


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Figure 5.Theatre 1

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Figure 6.Theatre 2


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Figure 7.Theatre 3

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Figure 8.Theatre 4


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. Patients were starved from midnight until their operation.

. Single time of admission at 7a.m. rather than scheduled.

. One patient did not attend their preoperative assessment outpatient

appointment. Surgery was cancelled but the patient was still on the list.. Lack of overall co-ordination.

. One theatre ran until 11 p.m.

. The obstetric list arrived with the first patient in theatre.

. A total of 17 patients arrived bringing 14 friends/family and one baby.

. Two patients did not attend (10 per cent). One patient had already been admittedas an emergency and the other did not receive a letter.

. Four operations were cancelled (20 per cent).

. Four patients arrived for a biopsy unexpectedly.

The authors fed back these observations to all of the surgeons and anaesthetists at atheatre audit day. The response was one of disbelief and the immediate reaction ofsome was to suggest that it was: not true, not reflective of how things normally workand the typical observations of someone that could not possibly understand thecomplexities of modern medicine.

It was pointed out during the question and answer sessions that the peopleobserving what happened worked in theatres. The response was then that because itdid not involve a doctor those observing misunderstood what they witnessed. Theauthors clarified that one of the observers was a physician who was in the audienceand confirmed that what was documented was exactly what was observed.

As a result of this observational study a number of clinicians approached themanagement team to tackle the issues raised.

5.4 Patient tracking in the emergency department and medical assessment unitThe flow of patients through a hospital is essential to manage capacity and cope withvariation in the number of admissions and discharges. The authors undertook a studyof patients arriving at an emergency department (ED), through the ED to the medicalassessment unit (MAU) and to discharge or admission to an inpatient ward.

The data are summarised in Table II.The table illustrates the variation in the length of time to complete the various

stages of the process. Aside from diagnostic delays, it was determined that the keystages of the process that could delay a patients journey were: arrival on MAU to nurse

Arrival to nurseassessment

Admission todoctor assessment

Admission tosenior review

Admission todischarge

Admission totransfer

Average 8 mins 2 hrs 6 mins 4 hrs 49 mins 1 hr 25 mins 5 hrs 59 minsMinimum 0 mins 29 mins 3 hrs 15 mins 51 mins 1 hr 53 mins

Maximum 33 mins 6 hrs 9 mins 6 hrs 36 mins 12 hrs 36 mins13 hrs 58

mins

Table II.Summary data onEmergency Departmentand Medical AssessmentUnit

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assessment, admission to doctor assessment (a junior or registrar), admission to asenior review (consultant), admission to discharge or to transfer to a ward.

The data collected from each of these processes is illustrated in the statisticalprocess control (Wheeler and Chambers, 1992) (see Figures 9-11).

Figure 9 plots the length of time from arrival to assessment by a nurse. Zerominutes indicates that the nurse assessment occurred immediately upon arrival. Theprocess control limits represent the routine variation that the process is capable of

Figure 10.Admission to doctor

assessment

Figure 9.Arrival to nurse

assessment


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delivering. In this case it is between zero and 30.7 minutes to assessment. Two specialcauses of variation lie outside the control limits. Data points 2 to 10 all fall below theaverage, but the variation is much wider from points 11 to 23. This data was discussedwith the staff and the causes were attributed to the ability of the staff earlier in the dayto provide an instant assessment of the patient upon arrival in the MAU. However, asthe day progressed, this proved to be impossible leading to the greater variation.

Figure 10 plots the length of time from admission to assessment by a junior doctor.This process is capable of delivering between zero and 315.8 minutes.

Figure 11 plots the length of time from admission to transfer to an inpatient ward.This process is capable of delivering between zero and 1289.6 minutes (21 hours).

5.5 Other data affecting length of stay in the medical assessment unitA detailed analysis of data collected within pharmacy revealed that 31 per cent of takehome drugs contain a medication error and 48 per cent are missing drug intoleranceinformation. Between 40 and 60 per cent of urgent prescriptions are not picked upwithin 30 minutes

When pharmacy receives prescriptions late, patients are delayed in going home.The data indicated that only 11 per cent of prescriptions change in the last 24 hours of apatients stay. However, pharmacy receives less than 10 per cent of prescriptions morethan 24 hours in advance. One argument that is made against providing more

prescriptions in advance is that while only 11 per cent may change it is not possible toknow which 11 per cent. Consequently, the pharmacy department prepare allprescriptions on the day of discharge in a matter of hours.

An alternative approach would be to provide all prescriptions 24 hours in advanceso that pharmacy has more time to prepare them and make minor alterations on theday of discharge.

The data that were collected about the patient journey from admission to dischargewas presented back to the clinicians. This resulted in some fundamental changes in

Figure 11.Admission to transfer

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behaviour and practice. The duplication of documentation was eliminated and themedical consultants relocated from the medical assessment unit to the emergencydepartment. This reduces delays in assessing patients and prevents inappropriateadmissions. It is unlikely that these changes would have resulted from any amount of

analysis of the data available on the organisations IT systems.

6. ResultsEach of the case studies presented in the previous section resulted in fundamentalchanges in processes from observing the current process in a way that staff can relateto.

Rather than management, analysts or management consultants reviewing theprocess and making recommendations back to the organisation, the data was collectedby shop floor staff. As a result the need for change and urgency was more apparent.

7. Limitations of methodologyThere are limitations to the use of observational data and the methodology outlined inthis paper including:

. It is resource-intensive it can require a significant number of people to collectsufficient quantities of data for it to be of use.

. There may be a Hawthorne effect (Draper, 2008) simply by observing peopletheir performance may improve.

. There may be a limited time period of analysis, i.e. 1 day, 1 shift this might be amisrepresentation of what happens day-to-day as it is quite possible that the dayupon which the data is collected is an anomaly.

. People may feel as though they are being spied on so behave differently this is a

problem that arises in any type of work where a person, department or group issubjected to scrutiny in order to be able to improve.

The authors have demonstrated through the case studies in this paper that themethodology is valid in a range of clinical settings for examining a process to identifyopportunities for improvement.

8. Accuracy and usefulnessThe authors believe that if the limitations of the methodology are minimised themethodology can be extremely beneficial.

The benefits of using observational data methodology are that:

. front line staff see what actually happens with their own eyes;

. there is an opportunity to stand back from daily routines to see thingsdifferently;

. if 15 people observe the same thing the evidence is irrefutable;

. it creates a burning platform and sense of urgency one of Kotters (Kotter,1996) 8 steps to change; and

. it helps people to move away from the acceptance of mediocre to the need forcontinuous improvement.


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Womack, J.P., Jones, D.T. and Roos, D. (1990), The Machine that Changed the World: The Story ofLean Production, 1st ed., HarperCollins Publishers, Philadelphia, PA.

About the authors

Andrew Castle is a Consultant at Applied Angle. Andrew started his career in packagingmanufacturing working in the USA and returned to the UK were he completed the Fellowship inManufacturing Management at Cranfield University. Following a further two years in anindustrial engineering environment he joined the National Health Service (NHS). Andrew hasworked in both primary and secondary care applying Lean and working with organisations toimprove patient and process pathways. He has lectured on Lean at Surrey University and at theUniversity of Westminster.

Rachel Harvey is the owner of Sustainable Solutions Consulting Ltd, a managementconsultancy that specialises in improving quality, productivity and efficiency in the NHS. Shestarted her career in information in the NHS and has spent the last 7 years applying Lean tohealthcare in acute and primary care NHS trusts. Rachel was the Head of Improvement atKingston Hospital NHS Trust where she made Lean the organisations approach toimprovement. She lectures in Lean on the MSc Integrated Governance in Health and Social

Care at the University of Westminster and the MSc in International Health Management atImperial College London. She is the corresponding author and can be contacted at: [email protected]


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