chapter 3 healthcare human resource management. 212 décembre 2015 plan...

Chapter 3Healthcare human resource

management

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Plan

Introduction

Planning/Scheduling

Resource dimensionning or staffing

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Introduction

Hospital

Authorities

Incentives for better healthcare cost control

Users

Increade healthcare demandAging population

Needs to rethink the models and organisations

Quality of care, reduced costs, improved working condition

Merging to benefit from the scale economy

An envisioned strategy

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Introduction

Plateau Médico-Technique (PMT)Center of technical facilities

EndoscopiaEndoscopia

ObstetricsObstetricsIntervention

al radiology

Interventional radiology

SurgerySurgery

AnaesthesiaAnaesthesia

10+ % of hospital budget

Aim of a regional research

project HRP²

Mutualisation of human resources

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Sharing human resources

Serviced’ORL

Serviced’ORL

Serviced’orthopédie

Serviced’orthopédie

Servicede chirurgie

digestive

Servicede chirurgie

digestive

Serviced’ORL

Serviced’ORL Unités

d’hospitalisa-tion

Unités d’hospitalisa-tion

Salles d’intervention

Salles d’intervention SSPI

SSPIServiced’orthopédie

Serviced’orthopédie Unités


Unités d’hospitalisa-tion

Bloc d’orthopédie



SSPIServicede chirurgie

digestive

Servicede chirurgie

digestiveUnités


Unités d’hospitalisa-

tion

Bloc de chirurgie digestive

Bloc d’ORL



SSPI



SSPI

Chirurgie ambulatoire


Services de Chirurgie


Urgences chirurgicales


Unités d’hospitalisation


Soins intensifsRéanimation


Plateau médico-technique

Retour au domicile

Retour au domicile

• ORL• Orthopédie• Ophtalmologie

• Urologie• Obstétrique • …

Services de médecine


• Radiologie• Gastro-entérologie• …



SSPI











Plateau médico-technique

Retour au domicile

Retour au domicile

• ORL• Orthopédie• Ophtalmologie

• Urologie• Obstétrique • …



• Radiologie• Gastro-entérologie• …

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Sharing human resources

RE-ORGANISATION

Monodisciplinary organization Integrated multidisciplinaryorganization

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Approach

Generation of

simulation model

Simulation with Infinite capacity

Characteristicsof PMT

of Processesof organisations

Data collection

Extrapolation

Workload profileFor each resource

Performance evaluation

Proposition of improvement

actions

Ajustment of simulationparameters

Dimensioning Human Resources (workforce per

time slot, working time, start time)

Simulation with Finite capacity

Modifying the model

Decision-aid for resource dimensioning and organization

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Objectives

Mutualisation of human resources

Design

Accompagner la conception de la nouvelle organisation:

• Dimensionner les ressources humaines

• Objectiver les choix d’organisation

Objectives

Control

Aider à la gestion des pools de personnel mutualisés

• Piloter la performance

• Aider à la planification des ressources

humaines

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Sommaire

Introduction

Planning/scheduling

Resource dimensionning or staffing

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Dimensioning human resources

Workforce requirement

Time slots

Workload coverage

Shifts

Workload profile

Phase 1Phase 1

Evaluate workforce requirement by the

workload profile

Phase 2Phase 2

Determine a set of shifts covering the workload

profile

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Phase 1: Deriving workload profile

Phase 1Phase 1

Evaluate workforce requirement by the

workload profile

Prepare the treatment of demandPrepare the treatment of demand

Forecast demand arrivalsForecast demand arrivals

Simulate the system with infinite capacity

Simulate the system with infinite capacity

Modélisation des processus

Valueing the processes

Organisation des ressources

Workload profile

Modélisation des processus

Organisation des ressources

Generic model

Level of mutualisation

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Triage Cons

ultation

llllll llllll

Exam

llllll

1st consulation

2nd consulation

Examples of process models

Emergency department

Birth deliveryStep 1. Birth delivery in an Operating Room by an obstetric physicianStep 2.1 Recovery in a ward for the womanStep 2.2 If type-2 patient, neonatal care for the newbornStep 2.3 If type-3 patient, NICU and then neonatal care for the newborn

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Deriving human resource requirement with a determinstic model

TriageConsultatio

n

llllll llllll

Exam

llllll

1st consulation

2nd consulation

Emergency department1. Activities for each ED patient5 min (triage nurse)15 min (ED physician)0.5 to 2h later5 min with proba. 20% (ED physician)2. Average physician workload per ED patient15 + 5*20% min = 16 min3. Determine arrival rate8h-9h : on average 1112-13 : on average 64. Determine workload profile 8h-9h : 176 min12-13 : 96 min

5 min

15 min

5 min

80%

30 min – 2h

5. Workforce requirement8h-9h : 3 physicians12-13 : 2 physicians

Issues not captured by the simple model1.Uncertainty, 2. Queueing effect

Patients arriving 8-9 are likely to wait much longer and even beyond 9h

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Organisation of the resources

Vertical polyvalenceVertical

polyvalence

horizontal polyvalencehorizontal polyvalence

… … …

Patient of

OR 1Patient of

OR 2Patient of

OR 3

Reception

Transfert

Induction

Intervention

Duty of a personal

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Phase 2: Shift construction

Phase 2Phase 2

Determine a set of shifts covering the

workload profile

Explicit approach

Énumération des vacations

Sélection des vacations

Multiple approachs are available [Partouche, 1998]

Enumerate all shifts

Selection of shifts

Enumeration algorithm of shift patterns

Set covering model

[Dantzig, 1954]

aij {0,1}

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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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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)

avj 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 211 0 0 1 1 1 1 0 1 1 1 0 0 0 0 0 02 0 0 1 1 1 1 1 0 1 1 0 0 0 0 0 03 0 0 1 1 1 1 0 1 1 1 1 0 0 0 0 04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 015 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 016 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 017 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 018 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 019 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 020 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 021 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 022 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 023 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0

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Set covering model

P = 100% P = 80%

Integer linear programming model

Coverage contraintsCoverage 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

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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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Principle of the hybrid approach

Model parameters

Simulation model

Definition of the workload profile

Evaluation of the shifts

Optimization model

PerformanceModification of the

workload profile

Workload coverage optimisation model

1st P-workload profile

Set of optimal shifts

Modified P-

workload profile

1

2

3

4

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Results

250

270

290

310

330

350

60% 65% 70% 75% 80% 85% 90% 95% 100%

P (in %)

Total cost (in K€)

Iteration n°1

Iteration n°2

Iteration n°3

Cost of the solution of the optimisation model with

P = 100 %

Cost of the hybrid approach with

P = 82,5 %

Cost saving 21%

Cleaning personal

CHU de Saint Etienne

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Sommaire

Introduction

Planning human resources

Dimensioning human resources

2321 avril 2023

Planning shared resources

Human resources

Better HR planning

Better operations PMT

Personal satisfaction

Planning Anaesthesia nursesIADE - Infirmiers Anesthésistes

ASH

IADEASIBODE

IADE

MAR

MAR

Planning Anaesthesists MAR - Médecins Anesthésistes

Planning pharmacy personals

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<

Problems of hospital personal planning

Staff planning

Determine working days & working time

Meet contraints

Repeat the same weekly or monthly sift pattern

Easy implementation

Rigid & weak adaptabily to changes

New planning for each period

Flexible

Time consuming

Noncyclic PlanningCyclic planning

Cost Soft constraints violated Equity

[Blöchliger, 2004]

[Valouxis, 2000]

MARMARIADE

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Planning anaesthesia nurses IADE

Day-regular (DR)

Day-urgent ( DU)

Night-urgent (NU)

Supervision recovery (SR)

8 H – 16 H

8 H – 20 H

20 H – 8 H

9 H – 17 H

Demand coverage

Working time regulation

Work on night & weekend

Succession of activities

ContraintsShifts

Assign IADE to all day and night activities of a week

Maximise the equity among employees

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

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Planning anaesthesia nurses

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 1 1 1 1 1Day Urgency 1,2 1,2 1,2 1,2 1,2 1,4 1,4

Ning Urgency 1,4 1,4 1,4 1,4 1,4 1,6 1,6

Recovery 1,6 1,6 1,6 1,6 1,6

IADE

Minimise the total penality score deviation

max minZ P P Minimise

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ContraintsHard

C1: Nb IADE needed per time slot C2: working time per day less than 12h C3: weekly working time around 38h but less than 48h C4: 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)

IADE

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VariablesXijk = 1 if nurse i assign to shift k on day j

Contraints C1: # of nurses per shift

per day

C2: daily working time less than 12h

C3: weekly working time less than 48h

C4: no more than 3 nights a week.

IADE

7

( )7 6

3w

ij NUj w

X

1

1K

ijkk

X

1

N

ijk jki

X b

7

max7 6 1

max( 48 , 1, )

w K

k ijk ij w k

i

n X T R

T h R regime

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Contraints

C5: Sat. DU (resp. NU) implies Sun. DU (resp. NU) and no work on Mon. and Tue.

C6 Shift succession

DU-NU followed by no working

DU followed by NU

(7 )( ) (7 )( ) (7 1) (7 2)1

1K

i w JU i w NU i w k i w kk

X X X X

IADE

(7 1) (7 ) 0i w k i w kX X

( ) ( ) ( 1)1

1K

ij JU ij NU i j kk

X X X

( ) 1 ( ) 0ij JU i j NUX X

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Criterion

IADE

max min

max1 1

min1 1

min

J Kk ijk

j k i

J Kk ijk

j k i

P P

p XP

R

p XP

R

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Planning anaesthesia nurses IADE

JR = Day RegularJU = Day UrgencyNU = Night UrgencySS = Supervision Recovery

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Contraintes obligatoires

Contraintes souples

Planning anaesthesists MAR MAR

Assign MAR to activities and half-day of a week

Pre-operation: Consultation

Per-operation: Anesthesia

Post-operation: Visit

Minimum demand coverage

Daily and weekly working time regulation

No working post-duty

No isolated half day working

ContraintsActivities

Minimise the number of soft constraints

violated

An integer programming model

Extension of the scope: Activitiess pre, per and post operations

Assignment by half-day Need to take into account the competencies

Competency requirment of demands

Maximum demande coverage

Continuity of post-operation visits

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Objective MAR

# MAR assignment outside their specialty

# of isolated half working day

# of post-visit continuity violated

Deviation from the maximum demand coverage

Minimise

Weighting factors

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Experimentation: CH de Valence

Resolution of 5 problems, over 7, 14 and 28 days

By two solvers CPLEX GLPK

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

MAR

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

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Example of results: Problem 1

Lun Mar Mer Jeu Ven Lun Mar Mer Jeu VenSpécialité MAR am pm am pm am pm am pm am pm Spécialité MAR am pm am pm am pm am pm am pmSpécialité 1 2 per 0 0 0 0 0 0 0 0 0 0 Spécialité 5 1 pre 0 0 0 0 1 1 0 0 0 0

post 0 1 0 1 0 1 0 1 0 1 per 0 0 0 0 0 0 1 1 0 0Chirurgie 3 per 0 1 0 0 0 0 1 1 0 0 Toutes 2 pre 1 0 0 0 1 0 1 0 1 0viscérale post 0 0 0 0 0 0 0 0 0 0 spécialités per 0 0 1 0 0 0 0 0 0 0et urologie 4 per 1 0 0 0 1 1 0 0 0 0 3 pre 1 0 1 1 1 0 0 0 1 0

post 0 0 0 0 0 0 0 0 0 0 per 0 0 0 0 0 1 0 0 0 15 per 0 0 0 0 0 0 0 0 0 0 4 pre 0 1 1 1 0 0 0 0 0 0

post 0 1 0 1 0 1 0 1 0 1 per 0 0 0 0 0 0 0 0 0 1Spécialité 2 6 per 0 0 1 0 1 0 1 0 0 0 5 pre 0 0 0 0 0 0 1 0 0 0

post 0 1 0 1 0 1 0 1 0 1 per 0 0 0 0 0 0 0 0 0 0Orthopédie et 7 per 0 0 0 1 0 1 0 0 0 0 6 pre 0 0 0 0 0 0 0 0 0 0neurochirugie post 0 0 0 0 0 0 0 0 0 0 per 1 0 0 0 0 0 0 0 1 0

8 per 0 0 0 0 0 0 0 1 0 0 7 pre 0 0 0 0 0 0 0 0 0 0post 0 0 0 0 0 0 0 0 0 0 per 1 1 1 0 1 0 0 1 1 1

Spécialité 3 10 per 1 0 0 0 0 0 0 0 0 0 8 pre 0 1 0 0 0 0 0 0 0 0post 0 1 0 1 0 1 0 1 0 1 per 1 0 0 1 1 1 1 0 1 0

ORL, 11 per 0 0 0 0 0 1 0 0 0 0 9 pre 0 0 0 0 0 0 0 1 1 1Ophtalmologie post 0 0 0 0 0 0 0 0 0 0 per 1 1 1 1 0 0 1 0 0 0et chir. Ambu 12 per 0 1 0 0 1 0 0 0 0 0 10 pre 0 0 0 0 0 0 0 0 0 0

post 0 0 0 0 0 0 0 0 0 0 per 0 0 1 0 1 0 1 0 1 0Spécialité 4 4 per 0 0 0 0 0 0 0 0 1 0 11 pre 0 0 1 0 0 0 0 0 0 0

post 0 0 0 0 0 0 0 0 0 0 per 0 1 0 1 0 0 0 0 0 1Maternité 8 per 0 0 1 0 0 0 0 0 0 1 12 pre 1 0 0 0 0 1 0 0 0 0gynécologie post 0 0 0 0 0 0 0 0 0 0 per 0 0 0 0 0 0 0 0 0 0obstétrique 11 per 0 0 0 0 0 0 0 0 1 0 13 pre 0 0 0 0 0 0 1 0 0 0et pédiatrie post 0 0 0 0 0 0 0 0 0 0 per 0 0 0 0 0 0 0 0 0 0

13 per 1 0 1 0 1 0 0 0 1 0 14 pre 0 0 0 0 0 0 0 0 0 0post 0 1 0 1 0 1 0 1 0 1 per 0 0 0 0 0 0 0 1 0 0

14 per 1 1 1 1 1 1 1 0 0 0post 0 0 0 0 0 0 0 0 0 0

15 per 0 1 1 1 0 0 1 1 1 1post 0 0 0 0 0 0 0 0 0 0

Objective = 21

MAR

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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)

3721 avril 2023


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)

3821 avril 2023


Example of data

chapter 3 healthcare human resource management. 212 décembre 2015 plan...

Documents

h later5

workload profileprepare

average physician workload

resource dimensioning

neonatal care

ed patient5

ed physician0

ed patient15