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Type II diabetes mellitus essay assignment
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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 hospitalization 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 hospital 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 anonymized 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 #NOFpatients 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
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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.
Type II diabetes mellitus
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
Type II diabetes mellitus
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
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Type II diabetes mellitus
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
Type II diabetes mellitus
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
Type II diabetes mellitus
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 anonymized version of the full data used to parameterize the model, along with the full outputs of the model, may be provided on request. BJHCM
References
Babulak E, Wang M (2010) Discrete Event Simulation:
State of the Art. Discrete Event Simulations Aitor Goti
Available at: www.intechopen.com/books/discreteevent- simulations/discrete-event-simulation-state-ofthe- art (accessed 27 February 2015) Bagust A, Place M, Posnett JW (1999) Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ 319: 155–8 Banerjee J, Conroy S, O’Leary V, Rawstorne S, Wenman J, Walker A et al (2012) Quality Care for Older People with Urgent and Emergency Care Needs. http://www. bgs.org.uk/campaigns/silverb/silver_book_complete. pdf Cardoen B, Demeulemeester E (2007) Evaluating the capacity of clinical pathways through discrete-event simulation. Available from: https://lirias.kuleuven.be/ bitstream/123456789/120479/1/KBI_0712.pdf Jones R (2011) Hospital bed occupancy demystified. BJHCM 17(6): 242–8
Malviya A, Martin K, Harper I et al (2011) Enhanced recovery program for hip and knee replacement reduces death rate: A study of 4,500 consecutive hip and knee replacements. Acta Orthopaedica 82(5): 577–81 Monks T, Pitt M, Stein K, James M (2012) Maximizing the population benefit from thrombolysis in acute ischemic stroke. Stroke 43: 2706–11 Morton C, Creditor MD (1993) Hazards of hospitalization of the elderly. Ann Intern Med 118: 219–223 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 review of the use of computer simulation modeling of 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 1(4915): 698–701 Wolff RW (1982) Poisson arrivals see timea. Operations
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