Design of Risk Management Strategies in Business Process Information Flow Xue Bai Operations and...

Design of Risk Management Strategies in Business Process Information Flow

Xue Bai

Operations and Information ManagementSchool of Business

University of Connecticut

Risk Workshop SAMSI 2

Outline

• Motivation and problem definition

• Methodology

• Experimental study

• Real world application

• Future research


Motivation

• Impact of errors in corporate business processes – “10 percent to 30 percent of the data flowing through corporate

systems is bad…” (CFO magazine 2003)

• Impact of errors in healthcare processes– More than 8.8 million ADE’s occur each year in ambulatory care,

cost at least $5,000 per ADE. Medication errors account for 1 out of 131 ambulatory care deaths (Washington: eHealth Initiative 2004).

– Health care data quality: accuracy 67%, completeness: 30.7% (Stein et al. 2000)

• Legal mandates– Sarbanes Oxley Act (2002)– HIPAA (1996), Medical malpractice laws


Physician Patient

Pharmacist

Insurer

Prescription

Prescription

Bill

Medication

Bill

An Example of BP with Errors and Risks

Call back and complain



Formulary mismatch

Wrong dosage

Adverse Drug Event

Call back efforts; Administrative cost; Patient satisfaction & loyalty &

litigation issues.

Patient ends up in

ER.

An example: a medication process

Bills for ER visit.

Manual check

E-prescribing systems

Performance review

E-order systems


Physician Patient

Pharmacy

Insurer

Prescription

Prescription

Bill

Medication

Bill

An Example of BP with Errors and Risks

A medication process


• A Business Process (BP)– Tasks– Information flow– Errors

• Accuracy• Completeness• Occurrence

– Risk exposure

– Design of Control structure for risk management

enter order info.

check alert info.

update medication info.

order database

inaccurate dosage

missing information

The order management process at the pharmacy

Manual check

E-order mgt.

check alert info.

update medication info.

databaseenter order info.

Elements of the Model


Outline


• Methodology



• Future research


Check alert info.

Update medication information

Enter order information

DatabaseCheck alert info.

Update medication information

Enter order information

Database

wrong dosage wrong dosage

A Simple Sequential Process

Process Structure Affects Error Impact


A Simple Parallel Process

Order of medication

Shipping invoice

Payment voucher

Preparing voucher package

control

control

Process Structure Affects Control Function


BP as a Graph

t1

t2

t3

The precedence matrix:

otherwise.,0

, precedesdirectly task if ,1][

jitT ijij

000

100

110

33T


t1

t2

t3

The volume transition matrix:

p(T): the ratio between the volume output by task i and the volume fed to task j, given tij =1.

000

100

110

)( 33Tp

000

100

0

)(1312

33

rr

Tp

t1:Non-Condition node: t1:Condition node:

BP as a Graph


• The propagation impact (PI) matrix:

– K: the length of the longest path in a process.

– p(T): the volume transition matrix

• The propagation potential:

kK

kTp

1

)(

jiji ][

000

100

210

000

100

110

T

000

100

110

)(Tpt1:Non-condition node:

000

100

0 23

21

000

100

110

T

000

100

10

)(21

Tpt1:Condition node:

Impact of Error


Error Generation

• Error correlation structure• Models for error generation processes – hierarchical

sampling schema

• Controlling for dependence/independence due to the homogeneity/ heterogeneity of operations and resources involved

– Within a task

– Across tasks

,~,| Mi ormaldLogisticNGeneralizep

)( ~| imimim pBernoullipe Mm ,,1Ni ,,1


• The number of errors of type m at task i:

– : number of errors of type m that show up at task i– : number of errors of type m that arrive at task i– : occurrence of errors of type m generated by task i– : average number of eim

im

N

j jmjiimimim eeeee ~~~1

ime

ime

imeime~

Error Propagation


Loss and Risk Measurement

• Loss:cim: cost of an error of type m at task i.

• Risk Measures– Expected Loss (EL), Value-at-Risk (VaR), Conditional Value-at-

Risk (CVaR)

imim

N

j jmjiim ceel ~~

1

EL CVaR VaR

loss

β


Risk Measures

– Expected Loss

– Value-at-Risk

– Conditional Value-at-Risk

),(:min )(l

lxlxr

dlxlfxxr

)(),(

-1

1 )(

dlxlfx

0

),()(E

EL CVaR VaR

loss

β

)(xr )(x)(E x


• Control allocation factor

• Effectiveness of control

–the probability of a control catching an error:• Deterministic control

• Stochastic control

• Cost of control (per period)

Risk Management: Control Model

NixxN

iii ,,1,1,10

1

.0,1

,))(()( 1

1

ii

bii

N

jjiii

db

xdtpx i

.10,10 ,)( iia

iii gaxgx i

.10,10

)Bernoulli(~)(

ii

aiii

ga

xgx i


Model Formulation I, II, and III

– Design problem: Given a budget B,

– Model I: “Expected-Loss-Optimal” Control Structure

– Model II: “β-VaR-Optimal” Control Structure

– Model III: “β-CVaR-Optimal” Control Structure

.,...,1,10,1,)(..

)(min

11

1

NixxBxts

x

i

N

ii

N

iii

N

iii

)(xL

,),(:min )(l

lxlxr

)()( xrlx


Outline


• Methodology



• Future research


Experimental Study

• Experimental design– Topological variation

• Sequential, parallel, arbitrary

– Process size• Small (4 tasks), medium (10 tasks), large (25 tasks)

– Cost of control vs. Loss per error ( )• Expensive: (500, 1000, 2000, 4000, 10000), inexpensive: (25, 50,

100)

– Tolerance level of risks (β)• β = 0.90, 0.95, 0.99

– Error correlation• Independent, dependent

ii cd /


Experimental Results• As the process size increases, (Table 113, 115, 117, 119, 121,

and 123)– The optimal amount of control allocation in total increases– The optimal amount of control allocation at each task decreases– For sequential structure, the objective function value increases

exponentially; for parallel structure, the magnitude remains the same. • As the tolerance level of risks (β) changes (Table 2 and 10;

Table 22 and 30), – The optimal amount of control allocation in total increases– The impact of β at the task level depends on characteristics of the loss

distributions• In the range of β value and loss distributions tested, the impact is

insignificant.

• As the ratio cost of control / loss per error ( ) increases (Table 153-159)– The optimal amount of control allocation at each task decreases– The optimal amount of control allocation in total decreases

ii cd /


Experimental Results (continue)

• Optimal control allocations depend on risk objectives.– The relative importance of each task location changes accordingly– Tradeoffs when consider multi-risk objectives

• For processes with sequential structure, holding other factors constant, – The highest control allocations occur at tasks towards the center of the

process.

• For processes with parallel structure, holding other factors constant, – The highest control allocations occur at the merging tasks of the

process.


Outline


• Methodology



• Future research


• An Order Fulfillment Process The Data: 15 tasks, 13 internal tasks, 46 errors that occur in different tasks, costs

per error per type, frequencies of error occurrences, cost factors of controls, based 1200 orders per month.

Case Study


The tasks:0) Clients place order, 1) Enter order information, 2) Check payer and insurance info. 3)

Create/update contracts, 4) Prove prescription, 5) Prepare prescribed items, 6) Dispense from alternative source, 7) Submit drug orders to wholesaler, 8) Deliver medication, 9) Prepare and send claims to an insurance company or 10) to the responsible party, 11) Collect payments, 12) Post payments and prepare vouchers, 13) Update ledgers, 14) insurer/clients pay bills.

Results: Optimal Allocation of Control Resource


The tasks:0) Clients place order, 1) Enter order information, 2) Check payer and insurance info. 3)

Create/update contracts, 4) Prove prescription, 5) Prepare prescribed items, 6) Dispense from alternative source, 7) Submit drug orders to wholesaler, 8) Deliver medication, 9) Prepare and send claims to an insurance company or 10) to the responsible party, 11) Collect payments, 12) Post payments and prepare vouchers, 13) Update ledgers, 14) insurer/clients pay bills.

Results: Optimal Objective Function Values


Outline


• Methodology



• Future research


• Summary– Risk management models for error associated risks in business process

information flow

• Future research– Sensitivity analysis of the effect of other factors on optimal

control allocations and risk objectives• Loss per error, Control effectiveness, Cost structure of controls,

Topological redesign, Analytic solution for CVaR

– Managerial problems• Multi-Objective Optimization• Find the maximum confidence level β for a given value-at-risk • Given the output errors, identify the most probable error sources • Many others.

Future research

Design of Risk Management Strategies in Business Process Information Flow Xue Bai Operations and...

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