RESOURCE CAPACITY ALLOCATION OF INPATIENT CLINICS AMC Academic Medical Centre ...

93 Pages·2012·1.98 MB·English

RESOURCE CAPACITY ALLOCATION OF INPATIENT CLINICS AMC
Academic Medical Centre Amsterdam
September 2011 – May 2012
F.J. Mak BSc
S0092991

Supervisors

E.W. Hans PhD MSc UUnniviveresristiyt yo of fT Twwenentet e
A. Braaksma MSc AAcacdaedmemici cM Medeidciacl aCl Cenetnretr eA Ammstsetredradmam
N. Kortbeek MSc UUnniviveersristiyty o of fT Twweenntete

University of Twente
School of Management and Governance
Department of Industrial Engineering and Business Information Systems

Management summary
An ageing population, more advanced treatments and a high standard of care led the past decade
to an enormous increase in demand for care and costs. Health care managers face the challenging
task to organize their processes more effectively and efficiently [17]. Within the Academic Medical
Centre of Amsterdam (AMC) the sense of urgency to change is gradually accepted. Different types
of research projects are started in order to improve the overall performance and to provide insight
in the relations of complex hospital processes.
High fluctuations in the demand for care and beds in the clinical wards of the surgical division of
the AMC have led to the development of two models. The model of Smeenk et al. [29] makes it
possible to predict the number of beds that are occupied each hour of the day given the Master
Surgical Schedule (MSS). The model of Burger et al. [9] uses the output of the model of Smeenk
to determine the optimal number of dedicated nurses per ward and the number of nurses per flex
pool. A flex pool consists of nurses that still need to be assigned to a ward at the start of a shift
given the dedicated nurses already assigned and the number of patients present.
The models of Smeenk et al. and Burger et al. focus on the clinical wards while the MSS is
created in the OR department. In this research we develop an integral method that encompasses
resourcecapacityplanningdecisionsintheORdepartmentandtheclinicalnursingwards. Wehave
formulated the following research objective:
To develop a method which determines the best combination of patient case mix, OR capacity, care
unit and nurse staffing decisions in such way that total cost margins are maximised while satisfying
production agreements and resource, capacity, and quality constraints.
We express our research objective as a mathematical optimisation problem in which we minimise
the resource usage in the OR department and clinical wards, while selecting the most profitable
case mix. We define several quality and resource constraints. To evaluate the total costs of the
objective function we have defined several cost parameters.
The solution method we present encompasses a decomposition approach in which we use several
models and optimisation tools based on state of the art literature. Our solution approach consist
of the following six steps:
1. Set the desired patient case mix and the length of the MSS.
2. SolveanIntegerLinearProgram(ILP)tocreateamastersurgicalscheduleandassignelective
and acute patient types to wards, while minimising the number of ORs, wards, and the
expected number of nurses and beds required.
3. EvaluatetheaccesstimeservicelevelofthecreatedblockschedulewiththemodelofKortbeek
et al. [19].
4. Determine the number of beds required per ward while satisfying target rejection and mis-
placement rates with the model of Smeenk et al. [29].
5. Iteratively use the model of Burger et al. (Step 6) to determine the best flex pool-ward
combination.
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6. Determine the optimal number of dedicated nurses per ward and the total number of nurses
in a flex pool given various target service levels with the model of Burger et al. [9].
To test our approach we performed experiments with real data obtained from the surgical division
within the AMC. Our experiments show that our solution approach reduces variation in demand
for beds and thereby levels the workload. When we consider a cyclic MSS of four weeks we can
reduce the number of beds by 5.2% compared to our model representation of the current situation.
From our results we conclude that nurses can be utilised more efficiently by considering less wards
with more beds per ward. When we consider three wards with at most 50 beds we require 11.1%
less FTE nurses compared to our model representation of the current situation. When we consider
a flex pool of nurses between two wards we can achieve an additional reduction of 1.7% in FTE
compared to our model representation of the current situation. The benefits of a flex pool mainly
depend on how the MSS is organised, the flex pool-ward assignment and the chosen values of the
service levels.
Our solution approach encompasses a large variety of resource capacity planning decisions that are
related to each other. Due to the large number of planning decisions and the complexity between
them it is very ambitious to find one optimal solution. The MSS that results from solving our
ILP does reduce the expected number of beds and thereby reduces variation in demand for care.
PossibleimprovementslieinthedevelopmentofanMSSthatfurtherimprovesalignmentindemand
for beds with the required number of nurses and a tool to automatically select the optimal case
mix. The patient-to-ward assignment can be improved by taking the surgery, and, admission and
discharge distributions into account.
To conclude, the approach we present provides hospital managers with a tool to evaluate and
optimise the resource requirements in the OR department and the clinical wards given a patient
case mix and the length of the MSS. This tool can be used to (re)design, evaluate and improve
currenthospitalprocessesandis,duetoitsgenericnature,applicableinawidevarietyofhospitals.
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Preface
I am proud to present this graduation report, which contains my research carried out at the Aca-
demic Medical Centre (AMC) Amsterdam. This report is the last piece of a puzzle, completing my
Master’s degree in Industrial Engineering and Management. Almost nine months ago, when I first
came to the AMC I had high expectations. After a cumbersome first three months, in which I had
difficulties defining the scope and accepting an uncertain outcome, I finally found my way with as
end result this graduation report. I would like to thank several people that supported me during
this project.
First, I thank Erwin Hans for providing the opportunity to perform my assignment in the AMC
and his role as first supervisor. I enjoyed your enthusiasm and your constructive feedback during
the various meetings we had. I thank Nikky Kortbeek and Aleida Braaksma of the AMC for their
extensive supervision. Both have encouraged my academic thinking and helped to improve the
quality of this research. I enjoyed the weekly sessions and appreciated the discussions we had. I
especially thank Aleida for her detailed feedback regarding my report, which definitely improved
after each revision. Next, I thank Piet Bakker and Delphine Constant for the possibility to execute
myresearchintheAMCandtheircontributionduringthemonthlymeetings. Ithankallco-workers
at KPI for the pleasant time. I enjoyed the cosy atmosphere and the famous "tweede donderdag
van de maand" drinks.
Finally, I thank my parents for their continuous support throughout my student career. I am glad
that you always encouraged me to make my own choices. Last, but certainly not least, I thank my
girlfriend,Jojanneke,forsupportingmethroughoutthisproject. Youwerealwaysthereformeand
helped me stay motivated.
Amsterdam, May 2012
Frank Mak
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Contents
Management summary i
Preface iii
1 Introduction 1
1.1 Research context: AMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Research objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Research demarcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Context analysis 9
2.1 Division B: surgical specialties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Patient flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 OR department . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Inpatient care units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Literature 19
3.1 Techniques for resource capacity planning . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Methods OR department . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Methods clinical wards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 Decomposition approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4 Solution approach 25
4.1 Optimisation problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Decomposition approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3 Software implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.4 Verification & validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Computational results 41
5.1 Data gathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.2 Demarcation experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.4 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
6 Conclusions & recommendations 59
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
6.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.4 Further research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Bibliography 62
A Mathematical optimisation problem and ILP 67
A.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
A.2 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
B Data analysis 71
C Financial parameters 73
D Class diagram Delphi 75
E Detailed results 77