Bed Turnover Time in Lynfield Mount Hospital

This research focuses on the bed turnover time in Lynfield Mount Hospital and strategies to minimize it. Findings show the use of X-Chart and R-Chart to evaluate the hospital's capability and achieve a turnover time of 120 minutes.

Project 2
Operational management 2

ABSTRACT
Bed turnover time is very essential factor which every hospital needs to consider because
it will directly cause the death of a person. This assessment based on the primary research where
hospital collect 25 samples and calculate the bed turnover time. Further they evaluate that, how
they can resolve this issue or try to minimise time. In addition, ensure that bed will more
frequently available for the patient. This research based on the Linfield Mount Hospital and they
use X- Chart or R- Chart to know about the capability of hospital and able to achieve bed
turnover time around 120 minutes.

Table of Contents
ABSTRACT ....................................................................................................................................2
INTRODUCTION...........................................................................................................................4
MAIN BODY...................................................................................................................................4
CONCLUSION..............................................................................................................................13
REFERENCES .............................................................................................................................14

INTRODUCTION
Operations management is the practices which is used by the many organizations and
ensure that it will helps the business to enhance their operational performance. In order to
improve efficiency as well as effectiveness of the productivity as well profitability (Barati,
Sadeghi and Bahrami, 2019). This report based on Lynfield mount hospital which is England
based health care organization. It is managed by the Bradford District Care NHS Foundation
Trust. This hospital founded in 1913 and it has no emergency department to handle patients. This
report covers the various aspect which help the health care organization to minimise the bed
turnover time. Because excessive time will cause the various issues for the patient and delayed in
the medical procedures.
MAIN BODY
Overview of research:
In the Lynfield mount hospital, availability of bed is very essential because further it can
cause the situation of life or death of patients. Health care organizations need to manage the
process of discharging patient and on the same time, bed will ready for the another one. It can be
referred to the process of bed project turnaround time. If turnaround time is exceeding, then it
will cause many problems and disturb the with patient flow and medical procedure throughout
the hospital.
Management of Lynfield mount hospital should adopt some effective strategies in order to
minimise the long waiting hours for the physicians and patients. Because it will develop
customer dissatisfactions or it can cause the death of an individual. There are various reasons
which can delay the bed turnover time and some incidents already reported (Cerfolio and et. al.,
2019). Such as shortage of clean beds are found 9 incidents, 3 incidences of unavailability of
cleaning staff, unavailability of nurses also registers, lack of wheelchair or communication etc.
Below mention table represent the 25 samples time which taken by the Lynfield mount hospital
to make one bed available for another patient. These data collected by the patient care
association and it mentioned in the below table:

Sample Time 1 Time 2` Time 3

1 127 135 167
2 140 155 122
3 112 128 97
4 223 135 154
5 181 155 160
6 103 158 145
7 146 135 167
8 104 122 115
9 136 158 137
10 145 163 106
11 84 146 125
12 169 152 208
13 216 124 163
14 190 178 103
15 148 205 144
16 157 151 126
17 142 102 95
18 166 178 159
19 177 211 204
20 98 91 158
21 133 160 152
22 212 131 138
23 180 165 134
24 180 126 108
25 95 156 138

Aim:
Main aim of this primary research is to ensure that current operations process is under
control or not. For this, they wanted to reduce bed turnaround time and make sure that it will be
under the limit.
Objectives:
 Main objective of this research is to make people understand about the characteristics of
operations systems by using various approaches.
 Adopt various strategies or methodology in order to control the operation systems and
control over the results.
 Use appropriate technique to control over bed turnaround times.
Formulae of R Chart:
UCL = D4 * R- Bar
LCL = D3 * R- Bar
Formula of X- Bar:
UCL = Average(X) + 3*Sigma(X)
LCL = Average(X) - 3*Sigma(X)
Issues in using control charts:
There are lot of issues which affect the overall results and it should require to consider at
the producing data or formulating charts. Some of the issues are mentioned below:
 Subgroup size affect the control charts because sometimes sample size is common or
sometimes they are in unequal subgroup size.
 Dealing with out of control data also impact the overall research and control charts.
 To set control limit also one of the issues because it will affect the entire result and the
controlling process.

With the help of above mention table, it has been analysed that health care organization
such as Lynfield Mount Hospital used sample size of 25 which is collected by the patient care
association (Greenwood, 2019). There are three different time which is used to calculate X- Bar
or R- Bar. By using excel software for this calculation, R- Bar of 25 sample size is 49.8 and X-
DBar is 146.7866666667. Values of X- bar or R- bar has huge fluctuations and the average value
of both Bar also has difference.
Calculation and graphical representation of X- Chart:
Sample X-Bar Range CL UCL LCL
1 143 40 145.56 216.305 74.814
2 139 33 145.56 216.305 74.814
3
112.333
3 31 145.56 216.305 74.814
4
170.666
7 88 145.56 216.305 74.814
5
165.333
3 26 145.56 216.305 74.814
6
135.333
3 55 145.56 216.305 74.814
7
149.333
3 32 145.56 216.305 74.814
8
113.666
7 18 145.56 216.305 74.814
9
143.666
7 22 145.56 216.305 74.814
10 138 57 145.56 216.305 74.814
11
118.333
3 62 145.56 216.305 74.814
12
176.333
3 56 145.56 216.305 74.814
13
167.666
7 92 145.56 216.305 74.814
14 157 87 145.56 216.305 74.814
15
165.666
7 61 145.56 216.305 74.814
16
144.666
7 31 145.56 216.305 74.814
17 113 47 145.56 216.305 74.814
18
167.666
7 19 145.56 216.305 74.814
19 197.333 34 145.56 216.305 74.814

3
20
115.666
7 67 145.56 216.305 74.814
21
148.333
3 27 145.56 216.305 74.814
22
160.333
3 81 145.56 216.305 74.814
23
159.666
7 46 145.56 216.305 74.814
24 138 72 145.56 216.305 74.814
25
129.666
7 61 145.56 216.305 74.814
X bar chart used to monitor the arithmetic means of the available samples which denoted
with the m. This type of chart used to prepares for the measurement of various scales such as
temperature, weight, time, thickness etc. In order to prepare X- Bar chart, they need to calculate
control limited and it has two types such as upper control limit ( UCL ) and lower control limit
( LCL ). As we can see in the above mention table, there are three separate columns and it named
as UCL, CL or LCL (Hassmiller and Bilazarian, 2018). This calculated data used to prepare
control chart and it further help the people to understand the results.
The formula which is used to calculate it is given by:
Cp = (US – LS)
6 σ
For our data, the calculations were done using Microsoft Excel with:
US = 135
LS = 105
σ = 23.58176
Cp = ( 135 – 105)
6∗23.58176
= 0.212028307
Since the CP value is less than 1, it can be concluded that the process is not viable
Cpk= min of [ X −LS
3 σ , US−X
3 σ ].
Using Microsoft Excel, the following values were input;
X = 145.56
LS = 105

US = 135
σ = 23.58176
which is Cpk = min of [ 145.56−105
3∗23.58176 , 135−145.56
3∗23.58176 ], yielding a Cpk of -0.149267928
The negative value of Cpk which is -0.149267928 indicates that it is much far from target value
and needs some implementation to narrow gap between cp and cpk.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
0
50
100
150
200
250
X-Chart
Average CL UCL LCL
Above mention graph shows the plotted points which represent the averages value of each
subgroup. UCL denote the upper control limit, LCL indicate the lower control limit and CL used
for control limit. In addition, X- Bar represent in the graph with the blue line and it has huge
fluctuation and the rest of the items are constant (Lindenbratenand et. al., 2019). UCL or LCL is
very close to the control limit, so management need to build effective strategies which helps in
controlling X- bar with the control limit.
Calculation and graphical representation of R- Chart:
From the above mention table it has been analysed that for the R Chart, researcher need
to calculate UCL or LCL and plot these point on the graph for the better understanding. UCL and

LCL both are constant for the period (Pohl and et.al., 2018). By using excel software research
able to produce R chart for the better understanding of the results.
Rang
e
LC
L R- Bar UCL
40 0 48.44
124.7
3
33 0 48.44
124.7
3
31 0 48.44
124.7
3
88 0 48.44
124.7
3
26 0 48.44
124.7
3
55 0 48.44
124.7
3
32 0 48.44
124.7
3
18 0 48.44
124.7
3
22 0 48.44
124.7
3
57 0 48.44
124.7
3
62 0 48.44
124.7
3
56 0 48.44
124.7
3
92 0 48.44
124.7
3
87 0 48.44
124.7
3
61 0 48.44
124.7
3
31 0 48.44
124.7
3
47 0 48.44
124.7
3
19 0 48.44
124.7
3
34 0 48.44
124.7
3
67 0 48.44
124.7
3
27 0 48.44 124.7

3
81 0 48.44
124.7
3
46 0 48.44
124.7
3
72 0 48.44
124.7
3
61 0 48.44
124.7
3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
0
20
40
60
80
100
120
140
R-Chart
Range = Max - Min CL
UCL LCL
Above mention graph represent that UCL, LCL or CL constant but R Bar has huge fluctuation,
which is clearly mentioned, and it is denote with the blue line. Bed turnover time is high at the
sample size of 4, 13, 14 and 22. Along with this, turnover time lower control limit is on 8 or 18
(Teksen and Anagun, 2018). So, hospital need to maintain their bed turnover time under control
and try to be near with yellow line that is control limit.

CONCLUSION
From the above discussion it has been concluded that bed turnover time required to reduce or try
to make beds available morefaster for the patients. Because in the health care sector such as
hospital, it is very important factors and it cause the various problems such as delay in medical
procedure can affect the life of a person.
With the help of X- bar chart or R chart, management able to understand the performance and
how they make decisions as per the results. In addition, with the help of Z test they evaluate that
operations process of the hospital is under control or not. But, it is observed that final result
which evaluated with the help of Z- test is 1.9599639845 that is less than 3. it means bed
turnover time process is under control.

REFERENCES
Books & Journals
Barati, O., Sadeghi, A. and Bahrami, M. A., 2019. The Effect of Management Contract
Implementation on Public Hospitals’ Performance: A Case Study in Iran. Evidence
Based Health Policy, Management and Economics. 3(3). pp.212-221.
Cerfolio, R. J. and et. al., 2019. Improving operating room turnover time in a New York City
Academic Hospital via Lean. The Annals of thoracic surgery. 107(4). pp.1011-1016.
Dexter, F., Jarvie, C. and Epstein, R. H., 2018. Lack of generalizability of observational studies'
findings for turnover time reduction and growth in surgery based on the state of Iowa,
where from one year to the next, most growth was attributable to surgeons performing
only a few cases per week. Journal of clinical anesthesia. 44. pp.107-113.
Greenwood, S., 2019. The Impact of Role Changes in the Operating Room on Turnover Time
between Surgical Cases. Saint Francis Medical Center College of Nursing.
Hassmiller, S. and Bilazarian, A., 2018. Patient Engagement from Both Sides of the Bed. NEJM
Catalyst. 4(6).
Lindenbraten, A. L. And et. al., 2019. October. Planning of Trauma Orthopedist Population.
In International Conference on Health and Well-Being in Modern Society (ICHW 2019).
Atlantis Press.
Pohl, A. and et.al., 2018. Possible patterns of marine primary productivity during the Great
Ordovician Biodiversification Event. Lethaia. 51(2). pp.187-197.
Suman, G. and Prajapati, D., 2018. Control chart applications in healthcare: a literature
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Teksen, H. E. and Anagun, A. S., 2018. Different methods to fuzzy X¯-R control charts used in
production. Journal of Enterprise Information Management.
Wang, R. F. And et. al., 2018. Economic design of variable-parameter X-Shewhart control chart
used to monitor continuous production. Quality Technology & Quantitative
Management. 15(1). pp.106-124.