Kenneth Patterson is a 78 year old gentleman who was admitted to hospital following a fall at his home. X-Rays have confirmed a fractured neck of femur.

Assessment Item – Case study

Length: 2000 words

Past History

Kenneth Patterson is a 78 year old gentleman who was admitted to hospital following a fall at his home. X-Rays have confirmed a fractured neck of femur. On admission to the Emergency Department, Kenneth revealed he had experienced increasing levels of pain in his right hip recently, however had not seen a GP. Surgery has now been scheduled for a Right Total Hip Replacement.

Medical History

Type II diabetes mellitus –

On admission BGL – 15.2

Hypertension –

On admission 170/85

Osteoarthritis

Ex-Smoker (Ceased 5 years ago)

Medications

Metformin – 500mg BD

Paracetamol Osteo – 665mg x 2 BD

Metroprolol – 50 mg BD

Ibuprofen – 400 mg TDS

Esomeprazole – 20 mg OD

Background Information

Kenneth has lived alone for the past two years following the death of his wife, Marie. He has three adult children who are supportive, however they live with their families in the capital, five hours drive away. He does not recall what led to his fall, and was lucky that his neighbour dropped in and found him in the bathroom and called an ambulance.

Your Task

You are to provide a comprehensive nursing care plan for Kenneth that demonstrates the critical reasoning cycle. Your plan MUST be focused on the holistic nursing management of Kenneth and include the pre-operative care and education, as well as the post-operative management.

Your paper must include:

An overview of the pathophysiology of Kenneth’s condition.

Prioritise the nursing interventions in the nursing care plan for Kenneth.

A rationale for each of your suggested nursing diagnoses and interventions based upon evidence and best practice principles.

Demonstrate the ability to reflect by the delivery of a holistic approach to the patient in the case study.

A summary of your findings with some discharge planning for Kenneth.

Double line spacing.

Size 12 font.

A Reference list that adheres to APA presentation guidelines and indicates that you have read widely must be included. The inclusion of only websites does not demonstrate proper research of the topic and as such will incur a loss of marks.

A nursing care template has been provided for you, please ensure you use the template.

NB: Journal article used must be less than 5 years old and textbooks less than 10 years old. Use of websites must be from a reliable source Wikipedia is not acceptable

RESEARCH

146 British Journal of Healthcare Management 2015 Vol 21 No 3

© 2015 MA Healthcare Ltd

Daniel Chalk, Martin Pitt

Fractured neck of femur

patients: Rehabilitation

and the acute hospital

Fractures to the neck of the femur are common

injuries, particularly among the elderly female

population (Stewart, 1955). Typically, fractured

neck of femur (#NOF) patients must be admitted

to an acute hospital and treated surgically

(Parker and Johansen, 2006). Following a period

of post-surgery recovery, it is common for local

#NOF patients to be discharged to a community

hospital for rehabilitation because of the average

age of such patients and the frequency of postoperative

complications. However, until recently

this ‘superspell’, which represents the entire stay

of a patient across all NHS organisations, has

not been widely considered when assessing the

cost of hip fractures (Royal College of Physicians,

2013), and therefore may have affected service

improvement initiatives in this area.

It has been suggested that patients who are

admitted as inpatients to community hospitals

may face longer than necessary -lengths of stay,

as it is often perceived that there is a reduced

urgency to discharge compared to acute hospitals

(Banerjee et al, 2012). However, it has also been

shown that increased hospital lengths of stay

are generally undesirable for elderly patients,

because of the increased risk of developing

complications and loss of independence (Morton

and Creditor, 1993). In addition, length of stay

is the main determinant of cost of care for hip

fractures, and reductions to length of stay for

such patients can improve the cost-effectiveness

of such care (Royal College of Physicians, 2013).

Daniel Chalk

Research fellow in

applied healthcare

modelling and analysis,

NIHR CLAHRC for the

South West Peninsula,

University of Exeter

Medical School, Exeter

Martin Pitt

Associate professor of

healthcare modelling

and simulation, NIHR

CLAHRC for the

South West Peninsula,

University of Exeter

Medical School, Exeter

Email: d.chalk@exeter.

ac.uk

ABSTRACT

Typically, fractured neck of femur patients admitted to an acute hospital are discharged to a

community hospital for a period of rehabilitation after their treatment. However, there is concern

that this might unnecessarily extend the total period of hospitalisation for these patients. Using

data from a local acute hospital, we used discrete event simulation to predict the practicability of

fractured neck of femur patients remaining in an acute hospital for their entire superspell (the

overall length of stay across hospitals). We tested scenarios in which patient superspell duration

was shortened, as well as a scenario in which no reduction in superspell length was observed.

The model predicts that—even assuming that the superspell of fractured neck of femur patients

could be significantly reduced—bed occupancy levels at the acute hospital would increase to

operationally infeasible levels. Therefore, it is unlikely that fractured neck of femur patients

could remain in a typical acute hospital unless there were sufficient increases in available resources.

Key Words: Fractured neck of femure • acute hospital • bed occupancy • length of stay

RESEARCH

British Journal of Healthcare Management 2015 Vol 21 No 3 147 ©

2015 MA Healthcare Ltd

An acute hospital local to our research group

wanted to explore the possibility of conducting

an empirical pilot study that assessed the

impact on length of stay of keeping fractured

neck of femur patients in the acute hospital,

with no subsequent discharge to a community

hospital unless made necessary by a patient’s

comorbidities. However, the hostpital wanted to

investigate the operational feasibility of such a

scheme in terms of resultant bed occupancy levels.

Discrete event simulation is a modelling

technique that is useful for assessing the impact

of process changes and service reconfigurations

(Babulak and Wang, 2010). In a healthcare

context, discrete event simulation is often used

to predict the impact of changes to a clinical

pathway (Cardoen and Demeulemeester,

2007; Sobolev et al, 2011; Monks et al, 2012).

This type of simulation also allows clear

visual representations of current pathways

and proposed changes to them, and can be a

helpful means to facilitate understanding of

the model for non-specialists, allowing them

to challenge elements of the model that they

feel do not adequately represent the real world

system. In this article, we describe how we used

discrete event simulation and data from the

acute hospital to build a model of the current

admissions of fractured neck of femur patients,

and then adapted the model to predict the impact

of undertaking the entire superspell (total length

of stay across hospitals) in the acute hospital for

these patients.

Methods

The data

The acute hospital in this study requested that

their anonymity to be retained when publishing

these results. Therefore, we shall refer to the

hospital simply as ‘the acute hospital’, and the 13

surrounding community hospitals by letters A–M.

We obtained anonymised patient data for all

patients either admitted to the acute hospital, or

to one of the surrounding community hospitals

following an admission to the acute hospital,

between 10 January 2006 and 21 January 2013.

This represented 1 022 577 unique episodes for

253 227 unique patients. We identified #NOF

patients as those with an ICD-10 code prefix of

S72 recorded as their primary diagnosis, which

represented 7759 episodes, or 0.76% of the

dataset.

The trauma ward of the acute hospital is the

ward into which #NOF patients are admitted if

there is sufficient capacity. In the seven years

of data, there were 10 783 admissions to the

trauma ward, and 6241 #NOF admissions to the

acute hospital, of which 3042 (48.74%) were

admissions to the trauma ward. On average,

there were 2.84 #NOF admissions per day to the

acute hospital, and 3.5 non-#NOF admissions

per day to the trauma ward. The inter-arrival

time of patients represents the time between the

arrivals of patients. Therefore, the average interarrival

time is 0.35 days for #NOF patients and

0.29 days for non-#NOF patients.

The average length of stay of #NOF patients

in the acute hospital was 7.48 days, and 7.45

days specifically for those admitted to the

trauma ward. The average length of stay of

non-#NOF patients in the trauma ward was 3.39

days. Figures 1 and 2 show the distribution of

lengths of stay of #NOF and non-#NOF patients

admitted to the trauma ward, respectively. The

superspell of #NOF patients was calculated

as the sum of their length of stay at the acute

hospital and any subsequent length of stay

at a community hospital, where the patient’s

discharge from the acute hospital was on the

same day or the day prior to their admission to

a community hospital for a primary diagnosis of

#NOF. The average #NOF superspell length was

20.07 days. 19.51% of #NOF admissions to the

acute hospital resulted in a subsequent discharge

to a community hospital with a primary

diagnosis of #NOF.

The model

In a discrete event simulation model, we simulate

patients flowing through a series of individual

processes, each of which takes a certain amount

of time, and may require a number of resources.

If a process is full to capacity, a queue for the

process may form. In the context of our model,

our processes represent stays in beds in the ward

and community hospitals. Patients are either

#NOF or non-#NOF patients. Figure 3 shows an

overview of the structure of the model.

RESEARCH

148 British Journal of Healthcare Management 2015 Vol 21 No 3

© 2015 MA Healthcare Ltd

time of their arrival, otherwise they are sent to

another ward and effectively exit the model,

because their admission to the hospital would

not affect the bed occupancy of the trauma

ward. We only model those non-#NOF patients

who are admitted to the trauma ward, and

their arrival is also determined by a Poisson

distribution, but with mean inter-arrival time

of 0.29 days. Non-#NOF patients in the model

will queue for a bed in the trauma ward until one

becomes available, because they represent the

real-world blocking of bed capacity in the trauma

Poisson distributions are used to calculate

the probability of something happening that is

independent from the outcomes before or after.

Such distributions can be used to model the

arrival of patients, because the arrival time of

one patient is not typically linked to the arrival

time of the patients arriving before or after them

(Wolff, 1982). Therefore, the inter-arrival time of

#NOF patients into our model is determined by

a Poisson distribution, with a mean of 0.35 days

between arrivals. #NOF patients are admitted

to the trauma ward if there is a free bed at the

14.00%

12.00%

10.00%

8.00%

6.00%

4.00%

2.00%

0.00%

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62

% of #NOF patients admitted to trauma ward

Length of stay (days)

40.00%

35.00%

30.00%

25.00%

20.00%

15.00%

10.00%

5.00%

0.00%

% of non-#NOF patients admitted to trauma ward

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90

Length of stay (days)

Figure 1. #NOF distribution

Figure 2. Non-#NOF distribution

RESEARCH

British Journal of Healthcare Management 2015 Vol 21 No 3 149 ©

2015 MA Healthcare Ltd

ward from the perspective of #NOF patients.

Currently there are 35 beds in the trauma

ward, and we represent this in the model. The

length of time a patient stays in the trauma ward

is dependent on whether they are a #NOF or a

non-#NOF patient, and is drawn randomly for

each patient from the relevant length of stay

probability distribution extracted from the data

(see Figure 1 for #NOF distribution and Figure

2 for non-#NOF distribution). Therefore, those

lengths of stay that occurred more frequently in

the data have a higher probability of being drawn

as the length of stay of a patient in the model.

In the base case scenario, 19.51% of #NOF

patients in the model are discharged to a

community hospital after their stay in the trauma

ward. The specific hospital to which they are

discharged is randomly drawn according to the

distribution of discharge destinations obtained

from the real-world data.

We developed the model using Simul8

software (SIMUL8; SIMUL8 Corporation,

Boston, MA; www.Simul8.com) and ran the

simulation for two years for each tested scenario,

taking results only from the second year to allow

the simulation model sufficient time to ‘warm

up’ from a starting state in which the ward is

empty. Each scenario was also run five times,

with average results taken over these runs. Please

see Figure 3 for an overview of the structure of

the model. #NOF patients are admitted to the

trauma ward if a bed is free, otherwise they are

moved elsewhere. Non-#NOF patients queue for

a bed in the trauma ward. On discharge, #NOF

patients may be discharged to a community

hospital in the base case scenario.

‘What if’ analysis

In order to predict the potential impact on

bed occupancy levels in situations where the

entire #NOF superspell takes place in the acute

hospital, we simulated a number of potential

future scenarios in the model. In these scenarios,

no #NOF patients are discharged to a community

hospital, and their length of stay in the trauma

ward represents their total superspell. We looked

at varying assumed reductions in superspell

length (including a scenario in which there

would be no reduction in superspell length).

In addition, at the time of the study the trauma

ward had recently lost five beds. We therefore

looked at how the predicted results would change

if these beds were returned. Table 1 contains

details of the eight scenarios we tested.

Results

Table 1 shows the predicted bed occupancy

levels for each tested scenario. The base case

Admitted to

another ward

Non-#NOF

patient

arrivals

Queue for

trauma ward

Non-#NOF

patient

discharges

#NOF patient

discharges

Discharged to

community hospital

Discharged to

other

#NOF patient

arrivals

Trauma ward

Figure 3. Overview of the structure of the model

RESEARCH

150 British Journal of Healthcare Management 2015 Vol 21 No 3

© 2015 MA Healthcare Ltd

(Scenario 1), predicts beds in the trauma ward

are occupied around 86% of the time, which

staff at the acute hospital felt was an accurate

reflection of their actual bed occupancy levels

in the trauma ward. For the simulated scenario

where #NOF superspell is undertaken entirely in

the trauma ward of the acute hospital, the model

predicts that beds would be occupied 100% of

the time if there was no associated reduction is

superspell length (Scenario 4), and would remain

extremely high at 97.5% even if total superspell

length could be reduced by 8 days (Scenario 2).

If the five removed beds were returned to the

trauma ward, there would be a small reduction

in bed occupancy levels to 79% if #NOF patients

continued to be discharged to community

hospitals (Scenario 5), but undertaking the

entire #NOF superspell would still result in

very high bed occupancy levels, ranging from

94.2% if superspell length was reduced by 8

days (Scenario 8) to 99.8% if no reduction in

superspell length was observed (Scenario 6).

Discussion

Our model predicts that—even if there were

clinical benefits to keeping #NOF patients in the

acute hospital for the duration of their postsurgical

rehabilitation—it would be operationally

infeasible for the trauma ward of the acute

hospital to do this, at least with the availability of

resources to which the hospital realistically has

access, and given the competing demands for the

Table 1. Details of scenarios tested in the model, and the corresponding predicted average (and 95%

confidence interval) bed occupancy, expressed as the average percentage of time beds are occupied.

Scenario # Description of scenario Bed occupancy (as average %

of time beds are occupied)

95% confidence

interval (CI)

1 Base case scenario. 35 beds in trauma ward,

patients discharged to community hospital for

rehabilitation, 20 day mean superspell.

85.9% 82.6% to 89.2%

2 35 beds in trauma ward, superspell entirely

at acute hospital, 8 day reduction in mean

superspell

97.5% 95.9% to 99%

3 35 beds in trauma ward, superspell entirely

at acute hospital, 4.5 day reduction in mean

superspell

99.6% 99% to 100%

4 35 beds in trauma ward, superspell entirely

at acute hospital, 0 day reduction in mean

superspell

100% 100% to 100%

5 40 beds in trauma ward, patients discharged to

community hospital for rehabilitation, 20 day

mean superspell.

79% 74.4% to 83.6%

6 40 beds in trauma ward, superspell entirely

at acute hospital, 8 day reduction in mean

superspell

94.2% 90.9% to 97.6%

7 40 beds in trauma ward, superspell entirely

at acute hospital, 4.5 day reduction in mean

superspell

97.8% 96.5% to 99.1%

8 40 beds in trauma ward, superspell entirely

at acute hospital, 0 day reduction in mean

superspell

99.8% 99.3% to 100%

RESEARCH

British Journal of Healthcare Management 2015 Vol 21 No 3 151 ©

2015 MA Healthcare Ltd

Key Points

n A discrete event simulation model was built to predict the

impact of #NOF patients remaining in an acute hospital for their

rehabilitation

n The model predicts that such a scheme would lead to operationally

infeasible bed occupancy levels in the trauma ward of the acute

hospital in our study

n Bed occupancy levels would remain infeasible even if superspell

length could be significantly reduced

n It is likely that other hospitals would see similar results, because

the study hospital is currently operating at recommended bed

occupancy levels

trauma ward from non-#NOF patients. Typically,

it is recommended that hospitals operate at a

maximum bed occupancy of 85%, to allow for

variability arising from fluctuations in demand

(Bagust et al, 1999; Jones, 2011). Therefore, bed

occupancy levels of 95% and above are likely to

be infeasible, and lead to severe problems during

periods of higher demand.

Furthermore, superspell reductions of 8

days would represent a significant reduction

for #NOF patients and may not be realistic—

particularly given there would be a minimum

level of recovery required post-surgery, although

enhanced recovery strategies could help

(Malviya et al, 2011). However, even assuming

such significant reductions, the predicted bed

occupancy levels in the trauma ward would

remain infeasible.

While our model only predicts the operational

impact for the specific acute hospital studied in

this project, it is likely that other hospitals would

see similar results, since the acute hospital in our

study is currently operating at the recommended

level of bed occupancy, and resource constraints

are common across acute hospitals in the NHS.

Nevertheless, we would recommend others

considering a similar scheme to investigate

discrete event simulation modelling as a means

of predicting the potential impact on their own

trauma wards, in order that an evidence-based

decision can be made.

Acknowledgements: This study was funded

by the National Institute of Health Research

(NIHR) Collaboration for Leadership in Applied

Health Research and Care for the South West

Peninsula. The views and opinions expressed

in this article are those of the authors, and

not necessarily those of the NHS, the National

Institute for Health Research, or the Department

of Health.

An anonymised version of the full data used

to parameterise the model, along with the

full outputs of the model, may be provided on

request. BJHCM

References

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State of the Art. Discrete Event Simulations Aitor Goti

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art (accessed 27 February 2015)

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Banerjee J, Conroy S, O’Leary V, Rawstorne S, Wenman

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pdf

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Parker M, Johansen A (2006) Hip fracture. BMJ

333(7557): 27-30

Royal College of Physicians (2013) National Hip Fracture

Database Annual Report 2013. RCP, London

Sobolev B G, Sanchez V, Vasilakis C (2011) Systematic

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patient flow in surgical care. J Med Syst35(1): 1-16. doi:

10.1007/s10916-009-9336-z

Stewart IM (1955) Fractures of neck of femur. BMJ

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