Dynamic Daily Surgery Scheduling Department of Healthcare Engineering Centre for Health Engineering...

Dynamic Daily Surgery Scheduling

Department of Healthcare Engineering

Centre for Health Engineering

Ecole des Mines de Saint Etienne, France

[email protected]

Centre for Healthcare Engineering

Dept. Industrial Engr. & Management

Shanghai Jiao Tong University, China

[email protected]

Xiaolan XIE

mailto:[email protected]

mailto:[email protected]

- 2 -

Field observation of the operating theatre of Ruijin Hospital

Top 1 hospital in Shanghai

+12000 outpatient visits / day

An integrated operating theatre of 21 OR and a second one under construction

60-70 elective surgery interventions + 10 emergency surgeries / day

- 3 -


No integrated surgery planning but each surgery speciality is given an amount of total OR time

Each speciality decides the surgeries to perform the next day

The operating theatre (OT) is responsible for daily OR assignment and the OR program execution.

- 4 -


Special features of the Ruijin Hospital

Queue of elective patients never empty

Availability of patients to be operated in short notice

Availability of surgeons to operate each day

Large variety of surgeons : top surgeons, senior surgeons, ordinary surgeons

Strong demand to operate at the OT opening in the morning to avoid endless waiting

Strong concern of OT personal overtime

- 5 -


Issues addressed

Promising surgery starting times to meet surgeon's demand for reliable surgery starting

Surgery scheduling/rescheduling to balance between the number of OR team working overtime and the total overtime

- 6 -

Related work

Static scheduling for a single OR

Surgeon appointment scheduling (AS):

Two surgeries: AS solved by a newsvendor model (Weiss, 1990)

A fixed sequence of surgeries: stochastic linear program solved by SAA and L-

shape algo to determine the allowance of each surgery, or equivalently, the

arrival time (Denton 2003).

Others: discrete appointment (Begen et al, 2011), robust appointment (Kong et

al, 2011)

Sequence scheduling: The problem is to jointly determine the position and

arrival time of each surgery (Denton 2007; Mancilla 2012).

- 7 -

Related work

Dynamic scheduling for a single OR

Arrival scheduling: The demand of surgeries is uncertain, surgeries are

processed as FCFS rule. The problem is to dynamically determine the

arrival time upon each application(Erdogan 2011).

Sequence scheduling: The demand of surgeries is also uncertain. The

problem is to jointly determine the position and arrival time of each

surgery upon each application (Erdogan 2012).

• S. Erdogan and B. Denton, "Dynamic Appointment Scheduling of a Stochastic Server with Uncertain Demand," INFORMS Journal on Computing, pp. 1-17, 2011.

• S, Erdogan, A. Gose and B. Denton “On-line Appointment Sequencing and Scheduling”, working paper, Stanford.

- 8 -

Our focus

Multi-OR setting

- 9 -

Our focus

Multi-OR setting

Single-OR

Multi-OR

A1 A2 A3 An

A1/A2 A3 A4 An

No OR assignment

Dynamic OR assignment

- 10 -

Our focus

Two raised problems:

•Determining surgeon arrival times by taking into account OR capacities

and random surgery durations.

•Dynamic surgeon-to-OR assignment of during the course of a day as

surgeries progress by taking into account planned surgeon arrival times.

- 11 -

Assumptions of our work

Assumption 1: Emergency surgeries are assigned to dedicated ORs and hence neglected.

Assumption 2: ORs are all identical and each surgery intervention can be assigned to any OR.

Assumption 3: Each surgeon has at most one surgery intervention each day.

Assumption 4(Starting time planning or proactive problem): At the end of each day, each surgeon of the next day is given a promised surgery starting time or surgeon arrival time.

Assumption 5: Surgeons not available before the promised times.

Assumption 6(Dynamic sugery assignment or scheduling): During the course of the day, at the completion of any surgery, a new surgery is selected as the next surgery on the OR.

- 12 -

Dilemma of promising surgery starting time

Promise too early

Surgery 1

promised start of surgeon 2

Surgery 2

Surgery 1

promised start of surgeon 2

Surgery 2

Promise too late

surgeon waiting

OR idleOR overtime

Easy if known OR time but OR times are uncerain

- 13 -

Data

J set of surgery interventions or surgeons

N number of identical ORs

T length of OR session

pi() random OR time of surgery i in scenario

bi unit time waiting cost of surgeon i

c1 unit OR idle time cost

c2 unit OR overtime cost

- 14 -

Dynamic Surgery Assignment of Multiple Operating Rooms with Planned Surgeon Arrival Times

Zheng Zhang, Xiaolan Xie, Na Geng

In IEEE Trans. Automation Science and Engineering

- 15 -

Plan

Promising surgery starting times

Real time OR assignment strategies

Some numerical results

Conclusion and perspective

- 16 -

Decision variables

si promised surgery starting time of surgeon i

xir = 1/0 assignment of surgery i to OR r

yij = 1 if surgery i precedes j in the same OR

= 0 if not

Auxilliary random variables

Cir() completion time of surgery i on OR r

Ir() idle time of OR r

Or() overtime of OR r

Wi() waiting time of surgeon i

- 17 -

Model for promising surgery starting times

Assign each surgery to an OR ∑r xir = 1

Relation between assignment & sequencing yij + yji ≥ xir + xjr -1

Promised start before the end of the session si ≤ T

Scenario-dependent completion time xir pi() ≤ Cir ()

Cir () ≤ M xir

Cjr () Cir () + pj() - M (1- yij) - M(2- xir - xjr )

Scenario-dependent OR idle time Cir () ≤ Ir () + iJ xir pi()

Scenario-dependent OR overtime Or () Cir () - T

Scenario-dependent surgeon waiting time rE Cir() = si + Wi() + pi()

OR idle costOR overtime

costsurgeon

waiting cost

min E{c1 ∑r Ir() + c2 ∑r Or() + ∑i biIi()}

- 18 -

Proposed solution

1. Convertion into mixed-integer linear programming model by Sample Average Approximation by using a given number of randomly generated samples

2. Heuristic for large size problem based on

a) Local search for surgery-to-OR assignment optimization

b) Surgery sequencing rule based on optimal sequencing of the two-surgery case

c) Optimal promised start time by SAA and MIP

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Plan





- 20 -

Dynamic surgery assignment optimization

At time 0, start surgeries planned at time 0

At the completion time t* of a surgery in OR r*,

select a surgery i* to be the next surgery in OR r*

among all remaining ones J*

Surgery i* starts at time max{ t*, si* } in OR r* after the arrival of the surgeon at time si*

An Event-Based Framework

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Dynamic surgery assignment optimization

Surgery i* is selected in order to minimize E[ TC(t*, i*, J*)]

where

E[ TC(t*, i*, J*)] is the minimal total cost similar to promised time planning model

by conditioning on all completed surgeries and ages of all on-going surgeries

by scheduling i* as the next surgery on OR r*

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Two-stage stochastic programming approximation

• At k-th surgery completion event at time tk

where J\J(k-1) is the set of remaining surgeries

• The first stage cost is the OR-

idle or surgeon waiting cost induced by surgery l

• lk is the second stage cost, i.e. the total cost induced by

remaining surgeries plus OR overtimes.

\ 1mink lk

l J J klkV g

ˆlk l k l k ls t t sg

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The second stage cost

\ 1 \minlk jlk

j J J k l

where •jlk is the expected stage cost induced by surgery j

•if surgery l is selected at event k and surgery j at event k+1

Jensen's inequality is used to speedup the OPLA rule.

One-period look-ahead (OPLA) approximation

- 24 -

The second stage cost (cont'd)

Min. cost of two dynamic assignment rules:

• Rule 1: Remaining surgeries assigned in the scenario-independent order of minimal expected first stage cost, i.e. the surgery in selected at event n > k minimizes the stage n cost induced by in.

• Rule 2: Remaining surgeries are selected in non-decreasing order of their surgeon arrival times si

Jensen's inequality and another valide inequality are used to speedup the MPLA rule.

Multi-period look-ahead (MPLA) approximation

- 25 -

Lower bound of the dynamic surgery assignment

• Based on perfect information, i.e. all surgery duration realizations

pj() are known at the beginning of the day

• The lower bound problem is similar to the proactive problem but with

o given promised surgery start times

o scenario-dependent surgery assignment xir() and sequencing

yij()

- 26 -

Dynamic surgery assignment policies

Policy Static: No real time rescheduling OR assignment / sequencing decisions of promised

time planning model are followed

Policy FIFO: Dynamic surgery assignment in FIFO order of surgeon

arrival times

Policy I: Dynamic surgery assignment optimization with OPLA

Policy II: Dynamic surgery assignment optimization with MPLA

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Plan

Background and motivation

Problem setting





- 28 -

Optimality gap

Observations

•Optimality gap is relatively small

•High surgery duration variation degrades the optimality gap

•High workload reduces the optimality gap

•MPLA better than OPLA

GAP = (costX- LB) / LB

(,)GAPI(%) GAPII(%)

Ave. Min. Max. Ave. Min. Max.(0.3,0.75) 7.4 0.1 14.7 6.3 0.1 12.8(0.7,0.75) 8.5 5.1 14.8 7.7 3.8 18.4(0.3,1.25) 5.6 1.3 11.2 4.1 1.0 8.3(0.7,1.25) 7.8 1.9 17.3 6.0 1.6 9.6

(80 3-OR instances)

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Value of dynamic scheduling

OR# (,)VDS (%)

Ave. Min. Max.

3 (0.3,75) 10.6 2.6 22.9

(0.7,75) 14.8 5.5 26.9

(0.3,125) 7.4 3.9 14.1

(0.7,125) 11.1 5.7 15.5

Ave. 11.0 4.4 19.9

6 (0.3,75) 25.4 18.7 31.6

(0.7,75) 29.2 24.7 39.9

(0.3,125) 11.1 7.1 15.5

(0.7,125) 19.1 12.8 24.1

Ave. 21.2 15.8 27.8

12 (0.3,75) 33.6 30.1 37.9

(0.7,75) 36.0 28.9 42.1

(0.3,125) 18.6 17.2 20.4

(0.7,125) 26.1 23.9 30.1

Ave. 28.6 25.0 32.6

Observations

•Dynamic surgery scheduling always helps.

•The benefit is more important for larger OT.

•Dynamic surgery scheduling is able to cope efficiently with surgery uncertainties.

•VDS decreases as the workload of OT increases.

: variation parameter of surgery time: workload

VDS = (costStatic - costDyna) / costStatic

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Value of dynamic scheduling optimization

Observations

•VOS increases as OR# increases.

•VOS increases as increases, i.e. the variance of surgery durations increases.

•VOS decreases as increases, i.e. the workload of OT increases.

OR# (,)VOS (%)

Ave. Min. Max.

3 (0.3,75) 2.8 0.0 14.4

(0.7,75) 5.4 0.0 26.5

(0.3,125) 2.3 0.0 7.0

(0.7,125) 3.1 0.0 10.2

Ave. 3.4 0.0 14.5

6 (0.3,75) 5.4 -0.1 13.6

(0.7,75) 6.0 -0.1 11.3

(0.3,125) 2.9 0.0 5.0

(0.7,125) 5.0 0.6 8.7

Ave. 4.8 0.1 9.6

12 (0.3,75) 7.0 5.8 7.8

(0.7,75) 9.3 6.1 11.8

(0.3,125) 5.0 3.4 6.8

(0.7,125) 6.4 4.7 9.2

Ave. 6.9 5.0 8.9

: variation parameter of surgery time: workload

VOS = (costFIFO - costDynaOpt) / costFIFO

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Value of proactive decisions

Observations

•Proactive decision is very important to dynamic assignment scheduling.

•The arrival times that optimize the proactive model may not be adjustable to the dynamic assignment scheduling.

•Joint optimization of promised start times and dynamic assignment policies is an open research issue.

VOS = (costX - costX) / costX

where costX is the average cost of the strategy X but with promised start times determined with deterministic surgery duration.

(,)VPSI(%) VPSII(%)

Ave. Min. Max. Ave. Min. Max.(0.3,0.75) 7.2 -15.2 23.3 7.0 -20.9 22.6(0.7,0.75) 6.8 -11.1 20.4 6.4 -14.4 20.4(0.3,1.25) 9.8 1.1 23.1 10.0 0.9 21.6(0.7,1.25) 10.1 1.1 19.2 10.1 3.2 17.9

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Plan





- 33 -

Optimal surgery promised starting times for a given OR assignment / sequencing?

Features of surgeries planned to start at OR opening?

Time slacks in promised times vs surgery OR time and waiting cost?

Design of efficient optimization algorithms for promised time planning and real time rescheduling?

Promising time planning under starting time reliability constraints?

Open issues

- 34 -

Simulation-based Optimization of Surgery Appointment Scheduling

Zheng Zhang, Xiaolan Xie

To appear in IIE Transactions

- 35 -

Outline

• BACKGROUND AND MOTIVATION

• SURGERY APPOINTMENT SCHEDULING PROBLEM

• SAMPLE PATH ANALYSIS

• STOCHASTIC APPROXIMATION

• NUMERICAL EXPERIMENTS

• CONCLUSION AND PERSPECTIVE

- 36 -

Our focus

Example : the first released OR is allocated to surgeon 3, the second

released OR is allocated to surgeon 4 and so forth.

Multi-OR

A1/A2 A3 r1An

FCFS assignment

r2 A4

Surgeon appointment optimization for a given sequence of

surgeries assigned to ORs on a FIFO basis.

- 37 -

Outline







- 38 -

Modeling

• Parameters

n surgeries\surgeons and m ORs with capacity T for each OR

pi(): surgery duration with known distribution

/ /i: unit OR idling cost / overtime cost / surgeon waiting cost

• Decisions

Ai: surgeon arrival time with Ai = 0 for i=1,…,m and Ai ≤ Ai+1

- 39 -

Modeling

• Sample path cost function

ri(): the i-th OR releasing time.

ri() is a dependent variable of A and and can be solved using a simple

recursion.

1

1 0

( , )n m

i i m i i i m n pi m p

f A r A A r r T

Waiting cost Idling cost Overtime cost

- 40 -

Modeling

• Expected cost function

• Objective

( ) ,g A E f A

1

min ( )

0, 1,...,

, ,..., 1

A

i

i i

g A

A i mA

A A i m n

- 41 -

Outline







- 42 -

Sample path analysis

LEMMA 1. The sample path cost function f(A,) is differentiable on with probability 1.

PROOF: The non-differentiable points exist at

1.Ai = ri-m()

2.ri()= Ai+m + pi+m() = ri+1()

As pi is in continuous distribution, the probability of pi() = a or pi() -pj() = a is

zero where a is a given constant.

- 43 -


LEMMA 2. If Ai has an increment of ∆, the OR releasing time rj() has an increment

at most ∆ for j > i-m. (Lipshitz continuity of OR release times)

PROOF: Ri() = ri() for j ≤ i-m. Let ci() / Ci() to be the old / new completion time.

For ∆ ≥ 0, we have ci() ≤ Ci() ≤ ci()+ ∆,

1.For j = i-m+1, rj() ≤ Rj() ≤ rj()+∆,

2.If 1 holds, rj() ≤ Rj() ≤ rj()+∆ holds for any j = j+1 by induction.

Similarly, Lemma 2 holds for ∆ < 0.

- 44 -


LEMMA 3. The sample path cost function f(A,) is Lipschitz-

continuous throughout and the Lipschitz constant K is finite.

PROOF: Rewrite f(A,) as

Leading to

where

1 1

1 0 0 1

( , )n m m n

i i m i n p n p ii m p p i

f A r A r T r p

12

max , 1n

m ii m

K m

1 2 1 2 1 2( , ) ( , ) , ,f A f A K A A A A

- 45 -


THEOREM 1 (unbiasednes of sample path gradient). The

objective function g(A) is continuously differentiable on ,and the

gradient of g(A) exists for all A∈with

, ,A AE f A E f A

- 46 -

Sample path analysis : partial derivative at interior point

\{ }

\{ }

\{ }

A:

B:

C: 1

D: 1

i

i

i

i

jj BP i

ji j BP i

jj BP i

f

A

Ai

i BP2(i) j

A.

B.

i

Ai waiting

i BP2(i) BP3(i)C.

Ai

i BP2(i) BP3(i)D.

Ai

waiting

waiting waiting

waiting waiting overtime

[i-m]1 …

[i-m]1 …

[i-m]1 …

[i-m]1 … BP4(i)

waiting

= unit OR idling cost

overtime cost

i = surgeon waiting costBusy Period approach

- 47 -

Sample path analysis : directional derivative at boundary point

- 48 -

Sample path analysis : improving direction

- 49 -

Outline







- 50 -

Stochastic approximation

- 51 -

Outline







- 52 -

Convergence of stochastic approximation

THEOREM 2. There exist sample paths on which the sample path cost function is

not quasiconvex.

DATA: p() = {9, 4, 4, 1}; m=2 ORs with capacity T=10; Idle time penalty is 1; No

overtime penalty; Unit waiting penalty with 3=1, 4=3.

Two sets of arrival times: x=(4, 7.5); y=(6, 8.5).

f(x,) = 1.5, f(y,) = 3.5, f(0.5x+0.5y,) = 4

- 53 -


By randomly perturbing p around {9, 4, 4, 1}, we implement the stochastic

approximation algorithm.

Evolution of arrival times visited by the stochastic approximation algorithm in

Example 1, when applying it over 200 sample paths.

- 54 -


- 55 -


[i-m] iB. 1 …

shiftingAi

t[i-m]

[i-m]

Ai

A. i

shifting

1 …

t[i-m]

- 56 -

Convergence of stochastic approximation: numerical evidence

Log normal distribution Uniform distribution

var, wkload 0.3,0.75 0.7,0.75 0.3,1.25 0.7,1.25 0.3,0.75 0.7,0.75 0.3,1.25 0.7,1.25

Initial dispersion

3-OR 5.0 4.9 6.5 7.0 5.4 4.8 6.6 6.8

6-OR 6.5 6.7 8.5 9.5 6.5 6.6 10.3 9.8

9-OR 8.0 7.4 11.2 10.5 7.9 7.7 10.5 10.5

Final dispersion

3-OR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

6-OR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

9-OR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Final grad

3-OR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

6-OR 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.1

9-OR 0.0 0.2 0.1 0.3 0.0 0.2 0.2 0.3

- 57 -

Allowances of Multi-OR vs single OR settings

- 58 -

Allowances of Multi-OR vs single OR settings

Optimal allowance shapedome shape in 1-OR, zigzag shape in 2-OR

2-OR vs 1-ORsmaller allowances, half total allowance, highly uneven

Increasing surgery duration variabilitysmoothing 2-OR allowances, increasing 1-OR allowance variability

Higher waiting costlarger allowances in both settings but rather insensitive in the 2-OR setting

- 59 -

Allowances vs OR#

- 60 -

Allowances vs OR#

- 61 -

Value of dynamic assignment and proactive solution

Three strategies

Strategy I : no dynamic surgery-to-OR assignment

Strategy II : same surgeon appointment times, FIFO surgery-to-OR assignment

Strategy III : same surgeon arrival sequence, FIFO surgery-to-OR assignment, simulation-based optimized appointment times

Value of dynamic assignment (VDA)percentage improvement of strategy II over strategy I

Value of proactive anticipation and dynamic assignment (VPD)percentage improvement of strategy III over strategy I

- 62 -

Value of dynamic assignment and proactive solution

VDA > 0, VPD > 0 , VPD > VDA : dynamic assignment and the proactive anticipation of dynamic assignments always pay

Higher OR number : increasing VDA and VPD due to scale effect and benefit of well planned arrivals.

Higher duration variability: increasing VDA and VPD implying the importance of careful appointment planning and dynamic scheduling.

Higher waiting costs: higher VPD but smaller VDA implying the importance of appointment time optimization.

Higher workload: smaller VPD and VDA due to unimprovability of overloaded system.

Impact of case-mix: •larger VPD when surgeries are identical due to their interchangeability. •smaller VDA when surgeries are identical due to suboptimal appointment times

- 63 -

Outline







- 64 -

Summary

A more realistic model of AS which has m servers; patients are served

in a pre-determined order but are flexible to any server.

Our aim is to proactively optimize the arrival times under the FCFS

dynamic assignment strategy.

We formulate a simulation-based optimization model to smooth integer

assignments, and derivate a continuous and differentiable cost

function.

The proposed stochastic approximation algorithm is able to solve

realistic-sized instances and significantly improve the initial solution.

- 65 -

What next?

Joint optimization of surgery sequence and surgeon

appointment times.

simulation-based discrete optimization + stochastic approximation

Chance constraints of surgery starts

Dynamic control of overtime allocation

Surgeon behavior

Joint scheduling of inpatient and day surgeries

- 66 -

Relevant previous work

Planning operating theatres with both elective and emergency

surgeries

M. Lamiri, X.-L. Xie, A. Dolgui and F. Grimaud. "A stochastic model for operating room

planning with elective and emergency surgery demands", European Journal of Operational

Research, Volume 185, Issue 3, 16 March 2008, Pages 1026-1037

Mehdi Lamiri, Xiaolan Xie and Shuguang Zhang, "Column generation for operating theatre

planning with elective and emergency patients," IIE Transactions, 40(9): 838 – 852, 2008

M. Lamiri, F. Grimaud, and X. Xie. “Optimization methods for a stochastic surgery planning

problem,” International Journal of Production Economics, 120(2): 400-410, 2009

- 67 -

Healthcare engineering lab

At

ENSM.SE & SJTU

- 68 -

Mission statement

Develop quantitative methods for modeling, simulation and

optimization of health care systems & health services

Explore the integration of medical knowledge and patient

health information in operations management of health care

systems

in close collaboration with hospitals

Stochastic modeling and optimization in the face of random events and changing system dynamics

- 69 -

Theme I : Engineering health care systems & services

To develop scientific methods for performance evaluation and design of health care delivery systems and new health services.

Examples of work done :

•Performance analysis of patient flows with UML and Petri nets•Simulation and capacity planning of Emergency departments

•Process improvement of hospital supply chains by RFID•Health care logistics with mobile service robots

•Designing home healthcare networks•Design and operations of perinatal care networks

•Permance evaluation of Hospital Information Systems•Blood collection optimization

- 70 -

Dynamic perinatal network reconfiguration

Context•3 types of neonatal cares (OB = obstetrics care, Neo = basic Neonatal Care, NICU)•3 types of maternity services (OB, OB+Neo, OB+Neo+NICU)•Demographic evolution•Immediate admission of random arrivals

Dynamic capacity planning and location of hierarchical service networks under service level constraints, IEEE Transactions on Automation Science and Engineering, 2014.

Perinatal Network of North Hauts-de-Seine

(Type-3)

H. Louis Mourier

H. Beaujon

(Type-1)

H. FOCH

(Type-2)

CH Neuilly(Type-

2)

H. Franco Britan

(Type-2)

H. Nanterre(Type-1)

Challenge: •Determine optimum reconfiguration of perinatal networks to meet demographic changes and equal service level of care

Solution & results:

•Erlang loss-queueing model for admission probability evaluation;•Original hierarchical service network with nested hierarchy of patients and maternity services•Network reconfiguration by opening/closing services, capacity transfers, hiring/firing•Large-scale nonlinear optimization models solved with original linearization techniques•5% increase of admissions at the 1st choice hospital.

- 71 -

Traceability in biobanks

Research questions

Performance evaluation of traceability technologies

Design supply chains of drugs and medical devices with RFID

New operation management problems (re-warehousing of bio-banks, skill/quality monitoring, ...)

Infoerrors

InventoryerrorCurrent situation

Samples stored in nitrogen tanks (77°K)

“Cold Chain” constraints Resistance of the tags?

Hand-made inventories, data-base updates, cryotube numbering or label edition…

Problems: Error probabilities(Hand-copy, inventory, picking, computerization…)

Impacts of Radio-Identification on Cryo-Conservation Centers, TOMACS, 2011.

- 72 -

Engineering health care : Design blood collection systems

Research questions

Human resource capacity planning

Donor appointment scheduling

Annual planning mobile collections

Backgrounds

Increasing demand for blood products

Dilemma of donor quality of service & efficiency of blood collection systems

Uncertain and dynamic donor arrivals

Goal: decision aid tools for design of blood collection systems

Modeling and simulation of blood collection systems, HCMS, 2012.

Cost-efficiency

- 73 -

Theme II: Planning and logistics of health care delivery

To develop optimization methods for operations management of healthcare delivery and its supply chains.

Example of work :

•Planning and scheduling operating theatres subject to uncertainties•Capacity planning control MRI examinations of stroke patients•Stochastic optimization for hospital bed allocation

•Inpatient admission control•Dynamic outpatient appointment scheduling

•Operation management of outpatient chemotherapry •Capacity planning and patient admission for radiotherapy

•Robust home healthcare planning•Home healthcare admission planning&control

•Management of winter epidemics (flu, bronchitis, gastroenteritis)•Long-term care planning & scheduling

- 74 -

Optimization of outpatient chemotherapy

ICL Loire Cancer Institute

Major challenges of further research: • Integration of decisions different levels and different time scales

(medical planning, patient assignment, appointment scheduling)• Modeling treatment protocols with rich medical knowledge• Modeling the dynamics of health conditions based on rich patient data• High uncertainties of patient flow and patient's health care requirement

Large variation in bed capacity requirement in actual planning

20% reduction of peak bed requirement in the optimized planning

bed requirement

Planning oncologists of ambulatory care units. Decision Support Systems. (To appear)

- 75 -

Capacity planning of diagnostic equipment (MRI)

MRI examination of stroke patients

Expensive (over 1 million $) -> high utilization

Demand uncertainties and demand diversity (both elective and emergency)

Goal: Reduce waiting time for stroke patients without degrading MRI utilization

Actual waiting times of 30-40 days for MRI examination

2 - 10 days with the optimized reservation and control strategy。Monte Carlo optimization and dynamic programming approach for managing MRI examinations of stroke patients. IEEE Transactions on Automatic Control, 2011

- 76 -

Some projects

• Management of winter epidemics (flu, bronchitis, gastroenteritis) (ANR-TECSAN project HOST)

• Engineering home health care logistics

• Planning home health care admissions (ARC2, Rhone-alps region)

• Planning home health care activities (Labex IMOBS 3)

• Planning home health care logistics (Labex IMOBS3)

• Performance modeling & evaluation of HIS (DGOS-PREPS e-SIS)

• CIFRE-Heva : Patient pathway mining with national database

• Care pathway optimization of elderly people

• CLARA – Procan : Cancer care delivery & chemotherapy at home. 2008-

• FP6-IST6-IWARD on mobile & reconfigurable robots for hospital logistics. 2007-2010 (1 thesis)

- 77 -

Planning and optimisation of hospital resources

5-year project funded by National Science Foundation of China (2012-2016)

Consortium: IE, B. School, Ruijin hospital all from SJTU

Four major research tasks:

Planning / scheduling of key clinical resources (human + beds)

Capacity planning / preventive maintenance of diagnostic & treatment equipment

Coordination / cooperation mechanism design

Modelling / simulation of hospital emergency responses

- 78 -

Process Mining of patient pathways

PhD thesis funded by HEVA company(2014-2016)

Goal:

extract the process model with what patients actually endured instead what is recommended.

- 79 -

Process Mining (1/3)

6

concept : -Business Process Management-Based on knowlege extracted from an event log (national hospital care data base)

Example: 3 patients Consultation appointment application.

Data Mining

Business Process

Process Mining

1 2

Données BrutesDonnées mises en forme

Découverte du processus sous-jacent

- 80 -

Process Mining + PMSI (2/3)

7

PMSI Process MiningPathway

patient

Device implementation

01/01/2006 (15j)

Patient- ID = 73

- Age = 45 ans

Heart failure28/03/2006 (4j)

Device infection03/06/2006 (8j)

CHU d’Amiens

Clinique privé d’Amiens

Clinique privé d’Amiens

ID = 98, 101, 106, …

…

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Process Mining + PMSI (3/3)

Brut data : 16,931 hospital stays from 2006 - 2013

Diagramme spaghetti

Implantation

Complication post- opératoire

Suivi régulier

Remplacement

Décès

8

Sortiedu PMSI

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Event clustering +process mining

Date post:	17-Jan-2016
Category:	Documents
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Dynamic Daily Surgery Scheduling Department of Healthcare Engineering Centre for Health Engineering...

Documents