Chapter 3
Healthcare human resource
management
Plan
Introduction
Resource dimensionning or staffing
Planning/Scheduling
14 juillet 2017
2
Introduction
Authorities
Users
Incentives for better
healthcare cost
control
Increade healthcare
demand
Aging population
Hospital
Needs to rethink the models and organisations
Quality of care, reduced costs, improved working condition
An envisioned
strategy
14 juillet 2017
Merging to benefit from the scale
economy
3
Introduction
10+ % of
hospital budget
Anaesthes
ia
Surgery
Plateau Médico-Technique (PMT)
Center of technical facilities
Interventio
nal
radiology
Obstetrics
Endoscopi
a
Aim of a
regional
research
project HRP²
Mutualisation of
human resources
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4
Sharing human resources
Urgences
Service
chirurgicale
d’ORL
s
Salles
d’intervention
Chirurgie
ambulatoir
e
Services
Service
de
d’orthopédie
médecine
• Radiologie
• Gastro-entérologie
•…
Services
de
Service Chirurgie
de chirurgie
digestive
• ORL
• Orthopédie
• Ophtalmologie
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Unités
d’hospitalisaUnités
tion
d’hospitalisatio
n
SSPI
Bloc d’ORL
Salles
Salles
d’intervention
d’intervention
SSPI
SSPI
Soins
Unités
intensifs
d’hospitalisa-Réanimation
tion
Plateau médico-technique
Bloc d’orthopédie
Salles
d’intervention
• Urologie
• Obstétrique
•…
SSPI
Retour au
domicile
Unités
d’hospitalisation
Bloc de chirurgie digestive
5
Sharing human resources
Monodisciplinary organization
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R
E
O
R
G
A
N
I
S
A
T
I
O
N
Integrated multidisciplinary
organization
6
Approach
Decision-aid for resource dimensioning and organization
Modifying the model
Ajustment of simulation
parameters
Characteristics
of PMT
of Processes
of organisations
Data collection
Extrapolation
Dimensioning Human
Resources (workforce per time
Simulation with
Finite capacity
Generation
of
simulation
model
Performance
evaluation
Proposition of
improvement
actions
Simulation with
Infinite capacity
Workload profile
For each resource
slot, working time, start time)
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7
Objectives
Mutualisation of human resources
Objectives
Design
Accompagner la
conception de la
nouvelle organisation:
Control
Aider à la gestion des
pools de personnel
mutualisés
• Dimensionner les
ressources humaines
• Piloter la performance
• Objectiver les choix
d’organisation
• Aider à la planification
des ressources
humaines
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8
Sommaire
Introduction
Resource dimensionning or staffing
Planning/scheduling
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9
Dimensioning human resources
Phase 1
Evaluate workforce
requirement by the
workload profile
Phase 2
Determine a set of shifts
covering the workload
profile
Workforce requirement
Workload profile
Shifts
Time slots
Workload coverage
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10
Phase 1: Deriving workload profile
Phase 1
Modélisation
des
processus
Generic
model
Modélisation
des
processus
Evaluate workforce
requirement by the
workload profile
Valueing the processes
Prepare the treatment of demand
Level of mutualisation
Organisation
des ressources
Organisation
des
ressources
Forecast demand arrivals
Simulate the system with infinite
capacity
Workload profile
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11
Phase 1: Deriving workload profile
Examples of process models
Emergency department
llllll
Triage
1st consulation
llllll
Consu
ltation
llllll
2nd consulation
Exam
Birth delivery
Step 1. Birth delivery in an Operating Room by an obstetric physician
Step 2.1 Recovery in a ward for the woman
Step 2.2 If type-2 patient, neonatal care for the newborn
Step 2.3 If type-3 patient, NICU and then neonatal care for the newborn
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12
Phase 1: Deriving workload profile
Deriving human resource requirement with a
determinstic model
Emergency department
1. Activities for each ED patient
5 min (triage nurse)
15 min (ED physician)
0.5 to 2h later
5 min with proba. 20% (ED physician)
2. Average physician workload per ED
patient
15 + 5*20% min = 16 min
3. Determine arrival rate
8h-9h : on average 11
12-13 : on average 6
4. Determine workload profile
8h-9h : 176 min
12-13 : 96 min
5. Workforce requirement
8h-9h : 3 physicians
12-13 : 2 physicians
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15 min
1st consulation
80%
llllll
Triage
llllll
Consul
tation
5 min
llllll
5 min
2nd consulation
Exam
30 min – 2h
Issues not captured by the
simple model
1. Uncertainty, 2. Queueing effect
Patients arriving 8-9 are likely to wait
much longer and even beyond 9h
13
Organisation of the resources
Patient of
Patient of
Patient of
OR 1
OR 2
OR 3
horizontal polyvalence
Reception
Transfert
Duty of a personal
Induction
Intervention
…
…
…
Vertical
polyvalence
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14
Phase 2: Shift construction
Phase 2
Determine a set of
shifts covering the
workload profile
Multiple approachs are available
[Partouche, 1998]
Explicit approach
Énumération
Enumerate
all shifts
des vacations
Enumeration algorithm of
shift patterns
Selection
of des
shifts
Sélection
vacations
Set covering model
aij {0,1}
[Dantzig, 1954]
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15
Shift pattern enumeration
Determine the set of shift patterns fulfilling all labor regulation
constraints:
• min and max duration of the shift
•Earlist and latest starting time of the shift
•Duration of a break
•Time window of the break
•Number of hours before and after the break
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16
Shift pattern enumeration
•Min and max duration
(7-8h)
• Earlist and latest date
of the start (7-11h)
•Duration of the break
(1h)
•Time window of the
break (11-14h)
•Number of hours before
the break (2h) and after
(1h)
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avj
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9 10 11 12 13 14 15 16 17 18 19 20 21
1
1
1
0
1
1
1
0
0
0
0
0
0
1
1
1
1
0
1
1
0
0
0
0
0
0
1
1
1
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
1
1
1
1
0
0
17
Set covering model
Integer linear
programming model
Coverage contraints
Number of employees of
shift i
Cost of an employee working
shift i
Mean number of employees
needed for period j
Min % of the workload to cover
P = 100%
14 juillet 2017
P = 80%
18
A hybrid approach
 Observations:
 The workload of the personals is random
 Covering the mean workload does not garantee the
avoidance of:
• Under-capacity due to arrivals greater than average
• Over-capacity due to arrivals less than average
 Interest of a hybrid approach:
 Evaluating the real coverage by simulation
 Integration of two types of costs:
• Personal cost Overtime cost
Determine the
right value of P
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19
Principle of the hybrid approach
Model
parameters
1st P-workload profile
Definition of the
workload profile
1
4
Performance
Modification of the
workload profile
Simulation
model
3
Modified
Pworkload
profile
Evaluation of the
shifts
2
Set of optimal
shifts
Workload coverage
optimisation model
Optimization model
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20
Results
Cleaning personal
CHU de Saint Etienne
Total cost (in K€)
350
Iteration n°1
330
Iteration n°2
Cost saving
21%
310
Iteration n°3
290
270
P (in %)
250
60%
65%
70%
75%
80%
85%
90%
95%
100%
Cost of the hybrid approach
with
Cost of the solution of the
optimisation model with
P = 82,5 %
P = 100 %
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21
Sommaire
Introduction
Dimensioning human resources
Planning human resources
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22
Planning shared resources
Human resources
Better HR planning
IADE
IBODE
ASH
AS
MAR
Better operations PMT
Personal satisfaction
Planning Anaesthesia nurses
IADE - Infirmiers Anesthésistes
Planning Anaesthesists
MAR - Médecins Anesthésistes
MAR
IADE
Planning pharmacy personals
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23
Problems of hospital personal planning
Determine working days
& working time
Staff planning
Meet contraints
Cost
Soft constraints violated
Equity
[Blöchliger, 2004]
Cyclic planning
Noncyclic Planning
Repeat the same weekly or monthly sift pattern
New planning for each period
Easy implementation
<
Flexible
Rigid & weak adaptabily to changes
Time consuming
[Valouxis, 2000]
IADE
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MAR
24
Planning anaesthesia nurses
IADE
 Shared ressources
 In operating rooms
 In recovery rooms
 Both urgent and elective surgeries
 Work of the day and night
 Polyvalent personal running on all duties
CH de Valence
Assign IADE to all day and night activities of a week
Shifts
Contraints
Day-regular (DR)
8 H – 16 H
Demand coverage
Day-urgent ( DU)
8 H – 20 H
Working time regulation
Night-urgent (NU)
20 H – 8 H
Work on night & weekend
Supervision recovery (SR)
9 H – 17 H
Succession of activities
Maximise the equity among employees
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Planning anaesthesia nurses
IADE
 Criteria
 Meet working time regulation and personal preferences
(vacations, ...)
 Maximise the equity.
Penality score
(pénibilité or arduousness perceived by staff):
Lun Mar Mer Jeu Ven Sam Dim
Day regular
Day Urgency
Ning Urgency
Recovery
1
1,2
1,4
1,6
1
1
1
1,2 1,2 1,2
1,4 1,4 1,4
1,6 1,6 1,6
1
1,2
1,4
1,6
1,4 1,4
1,6 1,6
Minimise the total penality score deviation
Minimise Z  Pmax  Pmin
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Planning anaesthesia nurses
IADE
Contraints




C1:
C2:
C3:
C4:
Hard
Nb IADE needed per time slot
working time per day less than 12h
weekly working time around 38h but less than 48h
no more than 3 nights per employee per week.
Soft
 C5: Saturday DU (resp. NU) implies Sunday DU (resp. NU) and no
work on Monday and Tuesday.
 C6: shift succession constraints to ensure at least 11h rest per day :
• NU during the week implies no working the next day
• DU during the week implies NU or no working the next day (due to twice more
DU shift demand than NU)
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27
Planning anaesthesia nurses
IADE
Variables
Xijk = 1 if nurse i assign to
shift k on day j
Contraints
 C1: # of nurses per shift
per day
N
X
i 1
K
X
 C2: daily working time less
than 12h
k 1
7w
 C3: weekly working time
less than 48h
ijk
 b jk
ijk
K
 n X
j  7 w  6 k 1
k
ijk
1
 Tmax Ri
(Tmax  48h, Ri  1, regime)
 C4: no more than 3 nights
a week.
7w

j  7 w 6
14 juillet 2017
X ij ( NU )  3
28
Planning anaesthesia nurses
IADE
Contraints
 C5: Sat. DU (resp. NU)
X i (7 w1) k  X i (7 w) k  0
implies Sun. DU (resp. NU)
K
and no work on Mon. and
X i (7 w)( JU )  X i (7 w)( NU )    X i (7 w1) k  X i (7 w 2) k   1
Tue.
k 1
 C6 Shift succession
 DU-NU followed by no
working
K
X ij ( JU )  X ij ( NU )   X i ( j 1) k  1
k 1
 DU followed by NU
14 juillet 2017
X ij ( JU )  X i  j 1( NU )  0
29
Planning anaesthesia nurses
IADE
Criterion
min Pmax  Pmin
J
K
Pmax  
j 1 k 1
J
K
Pmin  
j 1 k 1
14 juillet 2017
pk X ijk
Ri
pk X ijk
Ri
30
Planning anaesthesia nurses
IADE
JR = Day Regular
JU = Day Urgency
NU = Night Urgency
SS = Supervision Recovery
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31
Planning anaesthesists MAR
MAR
 Extension of the scope:
 Activitiess pre, per and post operations
 Assignment by half-day
 Need to take into account the competencies
Assign MAR to activities and half-day of a week
Activities
Contraints
Pre-operation: Consultation
Minimum demand coverage
Per-operation: Anesthesia
Daily
and weekly working
time regulation
Contraintes
obligatoires
Post-operation: Visit
No working post-duty
Competency requirment of demands
Minimise the number of
soft constraints violated
No isolated half day working
Contraintes souples
Continuity of post-operation visits
Maximum demande coverage
An integer programming model
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32
Objective
MAR
# MAR assignment outside
their specialty
# of post-visit continuity
violated
Minimise
Weighting factors
# of isolated half working day
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Deviation from the maximum
demand coverage
33
Experimentation: CH de Valence
MAR
 5 « specialties »:
 4 specialist groups
 1 covering all other specialties
 Demands:
 Pre-operation: min and max
 Per-operation: according to the surgery planning (rule: one MAR
for 2 operating rooms)
 Post-operation: fixed according to the workload profile
 List of duties
 Resolution of 5 problems, over
7, 14 and 28 days
 By two solvers
 CPLEX
 GLPK
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Weights
λ1
λ2
λ3
λ4
Problem 1
1
1
1
1
Problem 2
1
2
1
1
Problem 3
2
2
1
1
Problem 4
1
1
1
2
Problem 5
1
4
1
1
34
Example of results: Problem 1
Lun
Spécialité
MAR
Spécialité 1
2
Chirurgie
viscérale
et urologie
3
4
5
Spécialité 2
6
Orthopédie et
neurochirugie
7
8
Spécialité 3
10
ORL,
11
Ophtalmologie
et chir. Ambu 12
Spécialité 4
4
Maternité
gynécologie
obstétrique
et pédiatrie
8
11
13
14
15
14 juillet 2017
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
per
post
Mar
Mer
Jeu
Ven
am pm am pm am pm am pm am pm
0 0 0 0 0 0 0 0 0 0
0 1 0 1 0 1 0 1 0 1
0 1 0 0 0 0 1 1 0 0
0 0 0 0 0 0 0 0 0 0
1 0 0 0 1 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 1 0 1 0 1 0 1 0 1
0 0 1 0 1 0 1 0 0 0
0 1 0 1 0 1 0 1 0 1
0 0 0 1 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0
0 1 0 1 0 1 0 1 0 1
0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 1 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0
1 0 1 0 1 0 0 0 1 0
0 1 0 1 0 1 0 1 0 1
1 1 1 1 1 1 1 0 0 0
0 0 0 0 0 0 0 0 0 0
0 1 1 1 0 0 1 1 1 1
0 0 0 0 0 0 0 0 0 0
MAR
Lun
Spécialité MAR
Spécialité 5
1
Toutes
spécialités
2
3
4
5
6
7
8
9
10
11
12
13
14
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
pre
per
Mar
Mer
Jeu
Ven
am pm am pm am pm am pm am pm
0 0 0 0 1 1 0 0 0 0
0 0 0 0 0 0 1 1 0 0
1 0 0 0 1 0 1 0 1 0
0 0 1 0 0 0 0 0 0 0
1 0 1 1 1 0 0 0 1 0
0 0 0 0 0 1 0 0 0 1
0 1 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0
1 1 1 0 1 0 0 1 1 1
0 1 0 0 0 0 0 0 0 0
1 0 0 1 1 1 1 0 1 0
0 0 0 0 0 0 0 1 1 1
1 1 1 1 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 1 0 1 0 1 0 1 0
0 0 1 0 0 0 0 0 0 0
0 1 0 1 0 0 0 0 0 1
1 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0
Objective = 21
35
Planning pharmacy personal
 Motivated by the restructuring of CH Villefranche (2
times bigger)
 Need of a decision aid tool to generate pharmacy
personal planning
 Personals: 21 employees (4 pharmacists, 7 pharmacy
assistants for preparation)
 Various duties :
• gestion, appro et distribution des médicaments
(armoires informatisées ou non)
• Guichet
• Préparation chimio
• Gestion de gaz médicaux
 Objectives: robust planning, reactivity to
perturbations, equity between personals (rotation on
all duties)
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36
Planning pharmacy personal
 A set of n tasks on a H days horizon
 Parameters of a task:
 Task duration pi
 Frequency Ti
 Contraints of the days
 Date of execution ti (if fixed)
 Earliest Date ri
 Latest Date di
 Min delay between two executions
 A set of m resources of different competencies
 Bij = 1 if resource j can execute task i
 Soft contraints: breaks, workload balancing
 Decisions :
 Assign tasks to resources
 Planning the execution scheduling (day-date)
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37
Planning pharmacy personal
 Example of data
14 juillet 2017
38