Hospital Bed Management Practices: A Review

Flannag

an Noonan

, Jacinta O’Brien

, Eilish Broderick

, Ita Richardson

and Joseph Walsh

School of STEM, Institute of Technology Tralee, South Campus, Tralee, Ireland

Department of Computer Science & Information Systems, University of Limerick, Limerick, Ireland

ita.richardson@lero.ie

Keywords:

Bed Management, Hospital Stay, Hospital Discharge Initiatives.

Abstract:

This paper reviews current literature on the bed management role seeking to highlight developments most

likely to increase efﬁciency. A reduction in the number of in-patient beds due in part to innovative surgical

techniques is causing increased pressure on a very ﬁnite resource. This requires a greater emphasis on the bed

management role and the wider hospital team. A number of studies are presented describing initiatives imple-

mented to support bed management both operationally, procedurally and from a decision support approach.

Finally, literature on people, process technology approaches in healthcare is presented, which could support a

sustainable improvement in the role.

1 INTRODUCTION

The trend in Europe over the last ten years is towards a

reduction in in-patient beds in hospitals. Figure 1 be-

low illustrates the public hospital in-patient bed own-

ership for the larger European states between 2007

and 2015 (Eurostat, 2017).

The reduction is driven by an improvement in sur-

gical techniques leading to a reduction in length

of stay (LOS). In a report produced on the NHS

(Audit-Commission, 2003), it is highlighted that even

though acute beds reduced in England from 123,000

to 107,000, the number of admissions rose due to

reducing length of stay. There is a cost associated

with ‘unoccupied’ beds as reported in (Webster et al.,

2011) which contributes towards bed number reduc-

tions. With an increasing population in Europe, the

management of the decreasing hospital bed resource

becomes more critical.

The aims of this paper are, ﬁrstly to present an

overview of the areas of current bed management

practice that contribute to the management of the

bed resource and patient throughput, secondly, to

highlight the challenges present in bed management,

thirdly, to identify those areas that signiﬁcantly con-

tribute to an improvement and ﬁnally suggest a re-

search area as a focus for providing sustainable gains

in efﬁciency and patient satisfaction.

Section 2 discusses the issues surrounding the bed

management role and its implementation and notes

the contrast of elective and medical patient bed allo-

cation and presents a high level view of the bed man-

agement process. Section 3 reviews trends in address-

ing the issues with a view to increasing the efﬁciency

of patient placement, through focus on the admission

and discharge elements of the process, pooling capac-

ity and modelling and decision support systems. Sec-

tion 4 identiﬁes work on providing a people, process,

technology approach and the challenges therein high-

lighting the success in manufacturing and software in-

dustries using these techniques. Section 5 concludes

the paper.

2 BED MANAGEMENT ISSUES

The bed management role is at the forefront of efﬁ-

cient use of a critical but limited hospital resources

and timely allocation of beds to patients admitted to

hospitals can turn a stressful experience into a com-

forting and positive experience that will have a pos-

itive effect on patients’ well being. The work of

(Boaden et al., 1999) refer to papers from the early

1990s to claim that “the effective management of beds

as a resource has always been an issue with the NHS”.

2.1 Competing Demands

The bed management role seeks to marry the hospi-

tal bed supply with the hospital bed demand (Boaden

326

Noonan, F., O’Brien, J., Broderick, E., Richardson, I. and Walsh, J.

Hospital Bed Management Practices: A Review.

DOI: 10.5220/0007387403260331

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 326-331

ISBN: 978-989-758-353-7

 2019 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

Figure 1: Changes in inpatient bed numbers for a subset of European countries.

et al., 1999). At the top level, two competing demand

streams exist, elective patients and medical emer-

gency patients. The admission of the elective patient

would appear to be more controlled as in most in-

stances the procedures are well deﬁned and in the case

of private patients, is funded by standard insurance

packages with set times deﬁned for individual proce-

dures. Elective day-case patients similar to elective

surgical patients are considered well deﬁned and gen-

erally have dedicated bed resource allocated. How-

ever, (Ortiga et al., 2012) states that numerous case

studies indicate elective admissions cause the greatest

variation as they are more unpredictable and this is

supported by (Allder et al., 2010a). In (Sant et al.,

2015) it is shown that the Day Care Unit in a fo-

cus hospital had 3.9% of patients who were not dis-

charged as planned. In contrast (Allder et al., 2010a)

states that the vast majority of emergency patients re-

quire a very short LOS.

2.2 Other Considerations

The report (Audit-Commission, 2003) covers a range

of issues that affect the placing of patients in beds in

a hospital, such as, speed of admission, admitting a

patient to an appropriate ward, providing single sex

wards, isolation requirements, avoiding cancellation

of elective surgical procedures due to restrictions on

beds. This is not an exhaustive list but servers to il-

lustrate the breath of issues faced by the bed manage-

ment role. Fundamentally the bed management role

is to make the best use of existing beds.

2.3 Process

Figure 2 below, provided in (Boaden et al., 1999), but

reproduced from an earlier Audit Commission report,

illustrates a high level view of the bed management

process.

Figure 2: The bed management process (Boaden et al.,

1999).

The bed resource is constrained until a patient exits

the process through discharge and the bed they oc-

cupied is made available for the next patient. When

emergency admissions are high, such as in the win-

ter period there is a motivation to reduce the num-

ber of elective procedures as suggested in (Audit-

Commission, 2003; Proudlove et al., 2003; Ortiga

et al., 2012). This is particularly true in public hospi-

tals where public perception of long emergency room

waiting times can create pressure to address this is-

sue. This also has impact in terms of the waiting list

times being extended. An exploration of this issue

(Proudlove et al., 2003) highlights the bed manage-

ment role in maintaining a stock of beds but raises

Hospital Bed Management Practices: A Review

327

the point that due to the routine hours of the hospi-

tal in general and the bed management presence in

particular, capacity can decrease due to out of hours

admissions.

Many papers (Allder et al., 2010a; Allder et al.,

2010b; Zhu, 2011; Wertheimer et al., 2014; Patel

et al., 2017) raise the issue of patients being admitted

at a time prior to the normal patient discharge time

occurring. This gives rise to an apparent shortage of

beds. This further impacts the patient experience, in

that they are provided with temporary accommoda-

tion only to be moved again to their ﬁnal destination.

This creates an air of uncertainty for them which can

cause distress or angst and impacts their recovery time

and hence LOS. This can exacerbate bed shortages.

3 BED MANAGEMENT TRENDS

Figure 2 provides a simple linear view of the pa-

tient journey in terms of the bed management process.

Within the bed management role there is most scope

to affect change in the admission or discharge areas.

3.1 Addressing Discharge

Discharge is a common process element to target.

Some of the studies mentioned above (Wertheimer

et al., 2014; Patel et al., 2017; Cho et al., 2017)

and others (Mustafa and Mahgoub, 2016; Zhu, 2011;

Webber-Maybank and Luton, 2009) suggest tackling

this problem by focussing on early discharge. Other

sources (Petitgout, 2015) suggest a dedicated dis-

charge planning function as as a solution. However in

their study (Mabire et al., 2018) on nursing discharge

planning, it is shown that for older patients, being dis-

charged to home, there is an increase in length of stay

without additional beneﬁts to the patient. They also

added that nursing discharge planning is a complex

intervention and difﬁcult to evaluate. In (NHS, 2004),

it is highlighted that 80% of discharges are ‘simple’

discharges and that the focus should be on completing

these in a timely fashion for best efﬁciency. The re-

port also suggests that the focus on discharge should

begin immediately with a discharge date set within

twenty-four hours of the patients’ arrival in hospital.

Another area of improvement it suggests is extend-

ing discharges as a seven-day activity. This is also

echoed in other reports (Audit-Commission, 2003;

Allder et al., 2010a) where its impact is illustrated by

the LOS of a patient being correlated to the day of

admission.

3.2 Discharge Initiatives

Three very similar studies are discussed (Wertheimer

et al., 2014; Patel et al., 2017; Mustafa and Mahgoub,

2016) that apply interventions to the discharge pro-

cess to bring about earlier discharge. All signify the

importance of teamwork and the role of the multi-

disciplinary team in discharge. Figure 3 illustrates the

broad roles that are involved in treating a patient, for

both elective and emergency patients (NAO, 2000).

Figure 3: The roles involved in patient treatment (NAO,

2000).

A study (Wertheimer et al., 2014) states that all the

stakeholders and staff involved in the launch of an ini-

tiative to increase the discharges before noon (DBN)

were invited to a kick-off meeting. This was imple-

mented to educate everyone involved in the impor-

tance of the issue and the plan for improvement. The

focus was on it being a multi-disciplinary team re-

sponsibility to achieve discharges. Two separate ar-

eas were involved in the initiative and competition

was fostered between the two areas towards achieving

the goal. The main tools were a checklist and a web-

site using automated emails to provide updates. Daily

meetings were used to focus on the current day’s dis-

charges, plan the following day’s discharges and keep

everybody apprised of performance. The paper re-

ports that there was an increase in the number of dis-

charges before noon with an earlier average discharge

time over a 13-month period after the intervention. A

further outcome was a reduction in the average LOS

and unexpectedly a reduction in the number of 30-

Day re-admissions. The latter was not considered

statistically signiﬁcant. These two factors were very

positive as there was concern that attempts to keep

patients an extra day to help achieve the discharge

before noon may occur. The other concern was that

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328

patients could be discharged before they were fully

ready to be discharged resulting in them requiring

hospitalisation again due to early discharge. The ﬁg-

ures proved this not to be the case.

A further study (Mustafa and Mahgoub, 2016), had

a similar outcome and improvement rate to the study

above (increase from 7% to 34% versus an improve-

ment above from 11% to 38%). A multi-disciplinary

discharge team was formed to study the problem. A

list of possible causes was drawn and a Pareto chart

compiled to highlight the major sources of delay. A

number of Plan-Do-Study-Act (PDSA) cycles were

conducted over a 26-month period. It was noted in

the report that sustainability was a challenge due to

the requirement for regular feedback which was time

consuming. The suggestion was that with automation,

this feedback would become easier and thus more sus-

tainable. There was no report of LOS improvement

but the report did state that it did not degrade.

(Patel et al., 2017) followed the same pattern. As in

(Mustafa and Mahgoub, 2016), a multi-disciplinary

team was established to identify the key reasons for

delayed discharge. Each quality improvement initia-

tive contained an education, process change and audit

and feedback element using PDSA cycles. The DBN

rate increased from 10.4% before the intervention to

19.7% after 24-months. DBN to skilled nursing fa-

cilities had a more dramatic change from a baseline

rate of 14% to 33.2% after 24-months. The average

LOS (from 5.88 to 5.60) and 30-Day readmission rate

(17.5% to 17.0%) remained relatively stable over the

period of the intervention. The report commented on

the challenges of sustaining the improvements in an

academic environment with rapid staff turn-over, the

commitment of resource to regular meetings and cre-

ation of audit tools and feedback mechanisms.

3.3 Addressing Admission & Discharge

An approach applying interventions in both the ad-

missions and discharge areas of the bed management

process is reported in (Ortiga et al., 2012). The

primary intervention mechanism at admission was

the change to admitting patients on the same day of

surgery for a large percentage of patients. This had

an obvious reduction in LOS due to the elimination

of the pre-surgery stay for these patients. The inter-

ventions at the discharge area were similar to those

reported above. The elective patient LOS reduced

from 4.85 days prior to intervention and 4.54 days

two years later. The global LOS reduced from 8.56

days to 7.93 days over the same period excluding day

surgery patients.

3.4 Communication

Technology has a role to play in bed management

and real-time information through the use of technol-

ogy can aid decision making (Roswo et al., 2003).

This report provides case studies on the implemen-

tation of technology to assist in the bed management

role. A key beneﬁt stated in the report is the ability to

make information available enterprise wide allowing

for quick decision making and feedback. Infosys, an

IT consultancy company promote combining technol-

ogy with process engineering to provide a solution to

the complexity of the bed management role in their

(Balaji and Brownlee, 2009) report. Technology is

referred to in a general way with an emphasis on pro-

cess engineering. A further industrial report on pro-

cess engineering aimed at increasing patient through-

out is presented in (Kobis and Kennedy, 2006). This

is more a high-level view but does emphasise both a

process viewpoint and the need for a organisational

resolve to succeed.

3.5 Modelling

Another approach is to implement modelling to help

with decision making and decision support systems.

One such approach is detailed in (Teraiya and Mak-

wana, 2015) which applies a mathematical model, on

an assumption of a Poisson probability distribution

rate arrival of patients. The approach was to gather

data over a period and apply the data to the model to

predict the arrival rate of patients and their LOS to

help with accommodating them.

A similar approach was adopted (Grifﬁths et al.,

2013), for an CCU, to increase throughput, smooth

daily bed occupancy, predict occupancy levels over

the coming days and identify stafﬁng levels to better

manage costs. This approach again used historic data

as an input to the model.

In (Matos and Rodrigues, 2011), the paper promotes

modelling annual data for prediction and augmenting

the modelling with information technology systems to

provide hospital personnel with the information nec-

essary to increase resource effectiveness. Another

study (Baru, 2015) creates a decision support simu-

lation model and uses hospital data to test a number

of hypotheses and compare actual and simulated LOS

and used statistical techniques to validate the model.

A slightly different approach is taken in (Schmidt

et al., 2013) for a decision support system for bed

management, where cost factors include ward occu-

pancy, change of ward occupancy assignment delay

and an afﬁnity cost that allows an administrator to

deﬁne a cost. A simulation was created from ac-

Hospital Bed Management Practices: A Review

329

tual hospital data and the output compared to the ac-

tual assignment. One of the limitations noted is that

the software looked at only one resource, bed capac-

ity, whereas in reality multiple other resources would

need to be factored in as part of the placement plan-

ning.

The concept of modelling is taken a step further in

(Bolt and Sparks, 2013) where a surveillance tree

methodology is used to predict changes in presenta-

tions in hospital emergency rooms to allow imple-

mentation of the management of the change. The

surveillance tree methodology is shown to be more

robust in unknown sub-populations over an exponen-

tially weighted moving average (EWMA) model for a

similar population.

Modelling is also used to manage the allocation of

beds in clinical specialisations in a hospital. A multi-

attribute value theory model is proposed in (Tsai and

Lin, 2014) to improve the quality of patient-bed as-

signment in terms of ward specialisation compliance.

This too is the subject of a proposed model by (Lee

et al., 2017) to partition clinic services in the hospi-

tal to provide an efﬁcient means of allocating beds to

the medical specialisations. They address the issue

of how many specialisations to form, the number of

beds to allocate to each specialisation and how to par-

tition services amongst the specialisations using a two

stage framework. The method provide balanced mod-

els for the trade-off between pooling capacity and fo-

cused care using a initial clustering of services while

the subsequent stage performs optimisation of the fea-

sible solutions returned by the initial stage.

4 PEOPLE, PROCESS &

TECHNOLOGY

In the previous sections, a number of initiatives have

been highlighted with regard to improving patient

throughput in hospitals, in an environment of reduc-

ing bed numbers. The sustainability of such issues

was reported as challenging due to the commitment

and effort required to keep the initiatives going while

performing a primary role in caring for patients.

A argument is put forward (Sherer et al., 2017) that

implementing coordination in healthcare is more dif-

ﬁcult due to the many external inﬂuences including

payment and regulatory mechanisms, privacy con-

straints and the level of professional control amongst

others.

The report introduces the control theory interdepen-

dence types of ‘pooled’, ‘sequential’ and ‘recipro-

cal’, in order of complexity and the associated coor-

dination mechanisms of ‘standardisation’, ‘plan’ and

‘mutual adjustment’. The report goes on to state

that while care monitoring is achievable at the pooled

level, for care coordination a reciprocal level of in-

terdependence needs to be achieved. This is only

achieved through a very high level of information

sharing.

A comparison in this report, between industry’s suc-

cessful coordination re-engineering and the additional

challenges associated with implementation in health-

care is echoed in (Husby, 2012). In this second study,

the adoption of the ‘lean’ concept used so success-

fully in manufacturing and software is promoted us-

ing hoshin. The paper states that the adoption of the

lean techniques has potential to facilitate coordina-

tion in the healthcare environment but needs signiﬁ-

cant capabilities and management to achieve success.

The study proffers a practical approach for undertak-

ing a hoshin initiative. It also underpins the critical-

ity of improving coordination in people process tech-

nology as increased technology deployment is driving

increasing health costs without a matched impact in

care.

5 CONCLUSION

This paper has examined bed management and shown

it to be a key area to increase patient throughput thus

making a scarce resource available to more people.

The trend for decreasing bed numbers, with increas-

ing population can only serve to increase pressure on

the bed resource. An increased efﬁciency can help to

counter this pressure.

There is a constant application of initiatives to work

towards a more efﬁcient use of bed resource but the

effort of sustaining them is an issue.

This paper’s contribution is to highlight the need for

sustainable solutions in addressing bed management.

It further suggests, a holistic approach to looking at

people, processes and technology to create greater co-

ordination in the healthcare sector as successfully ap-

plied in manufacturing and software could be the key

to providing the sustainability required to maximise

efﬁciency of the bed resource.

ACKNOWLEDGEMENTS

This work was supported with the ﬁnancial support

of the Science Foundation Ireland grant 13/RC/2094

and co-funded under the European Regional Develop-

ment Fund through the Southern & Eastern Regional

Operational Programme to Lero - the Irish Software

Research Centre (www.lero.ie)

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330

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