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Operations and Information Management University of Connecticut

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1 Operations and Information Management University of Connecticut
Design of Risk Management Strategies in Business Process Information Flow Xue Bai Operations and Information Management School of Business University of Connecticut

2 Outline Motivation and problem definition Methodology
Experimental study Real world application Future research Risk Workshop SAMSI SAMSI Risk Workshop

3 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 Risk Workshop SAMSI SAMSI Risk Workshop

4 An Example of BP with Errors and Risks
An example: a medication process Call back and complain Wrong dosage Medication Pharmacist Prescription Call back and complain Formulary mismatch Adverse Drug Event Prescription Physician Patient Bill Call back and complain Bill Insurer Call back efforts;  Administrative cost;  Patient satisfaction & loyalty & litigation issues.  Patient ends up in ER.  Manual check E-prescribing systems Performance review E-order systems Bills for ER visit.  Risk Workshop SAMSI SAMSI Risk Workshop

5 An Example of BP with Errors and Risks
A medication process Medication Pharmacy Prescription Prescription Physician Patient Bill Bill Insurer Risk Workshop SAMSI SAMSI Risk Workshop

6 Elements of the Model A Business Process (BP) Tasks Information flow
Errors Accuracy Completeness Occurrence Risk exposure Design of Control structure for risk management inaccurate dosage missing information check alert info. check alert info. update medication info. database enter order info. enter order info. database order Manual check E-order mgt. update medication info. The order management process at the pharmacy Risk Workshop SAMSI SAMSI Risk Workshop

7 Outline Motivation and problem definition Methodology
Experimental study Real world application Future research Risk Workshop SAMSI SAMSI Risk Workshop

8 A Simple Sequential Process
Process Structure Affects Error Impact A Simple Sequential Process wrong dosage wrong dosage Update medication information Update medication information Database Database Enter order information Enter order information Check alert info. Check alert info. Factors commonly studied in Control Design are: error generation loss due to undetected errors costs and effectiveness of controls My study is to explore the impact of the process structure the error generation and functioning of control, and ultimately, to the objective to risks management of BP. Next I will explain why. The topology affects Error Propagation because if errors created early in the process, it gets spread to all the downstream tasks, A potential loss is carried by each downstream task when a task is performed on the wrong information whereas if errors created towards the end of the process, the impact of the errors would be smaller. Risk Workshop SAMSI SAMSI Risk Workshop

9 A Simple Parallel Process
Process Structure Affects Control Function A Simple Parallel Process Order of medication control Preparing voucher package Shipping invoice control Process Structure also affects Controls functioning Coz the merging node typically 1 have information coming from multiple sources, thus, it would be easier for controls to detect and correct errors at the merging node, by having more info. available. 2. Process errors of the same type at the same time, will more effective Payment voucher Risk Workshop SAMSI SAMSI Risk Workshop

10 BP as a Graph The precedence matrix: t2 t1 t3 Risk Workshop SAMSI
SAMSI Risk Workshop

11 BP as a Graph 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. t1:Non-Condition node: t1:Condition node: Risk Workshop SAMSI SAMSI Risk Workshop

12 Impact of Error The propagation impact (PI) matrix:
K: the length of the longest path in a process. p(T): the volume transition matrix The propagation potential: t1:Non-condition node: t1:Condition node: Risk Workshop SAMSI SAMSI Risk Workshop

13 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 Risk Workshop SAMSI

14 Error Propagation 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 Risk Workshop SAMSI SAMSI Risk Workshop

15 Loss and Risk Measurement
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) VaR EL CVaR β Risk Workshop SAMSI loss SAMSI Risk Workshop

16 Risk Measures Expected Loss Value-at-Risk Conditional Value-at-Risk EL
CVaR VaR loss β Loss as a function of errors By applying control, we can calculate the risk reduction and formulation the control allocation problem: Given a budget, find the optimal set of x_is that maximizes the net benefit of control This is a convex optimization problem, can be solved optimally using Langranging multiplier approach. I will not show you the mathematics to solving it. Instead, I show you the solution of a simplified version to derive some insights from the pattern shown in the solutions, Which are applicable also to the generalized case. Risk Workshop SAMSI SAMSI Risk Workshop

17 Risk Management: Control Model
Control allocation factor Effectiveness of control the probability of a control catching an error: Deterministic control Stochastic control Cost of control (per period) Control is used to mitigate errors and the error associated risks. We abstract the characteristics of different controls, consider controls as classifiers that detect and correct errors. The total available control resources are considered as a fix pool, which is normalized as “1”. This pool of resources can be divided into any smaller portions and allocate to different task locations. Control allocation factor, $x_i$ represent the portion of control resources allocated to task I, We assume that the portion of control allocated to task I determines the effectiveness and cost of the control. The effectiveness of control in our model is the probability of a control catching an error, given errors exists. We assume diminishing effectiveness as allocation increases. Thus effectiveness function of is modeled as a concave power function of allocation. The parameter g_i represents the maximum effectiveness that the control can achieve at task i. Cost function of is modeled as a convex power function of allocation. Risk Workshop SAMSI SAMSI Risk Workshop

18 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 Risk Workshop SAMSI SAMSI Risk Workshop

19 Outline Motivation and problem definition Methodology
Experimental study Real world application Future research test numerically to go beyond the limiting assumptions of the analytical model. In particular, what is the effect of variation in topological structure? Variation in correlations between errors and controls? Skew in the distribution of costs of errors Risk Workshop SAMSI SAMSI Risk Workshop

20 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 Goal To test the effect of various factors of a process on the optimal control solution. test numerically to go beyond the limiting assumptions of the analytical model. In particular, what is the effect of variation in topological structure? Variation in correlations between errors and controls? Skew in the distribution of costs of errors Risk Workshop SAMSI SAMSI Risk Workshop

21 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 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 ) The optimal amount of control allocation in total decreases Risk Workshop SAMSI

22 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. Risk Workshop SAMSI

23 Outline Motivation and problem definition Methodology
Experimental study Real world application Future research Objective of case study is to show Model is applicable to real scenarios Parameters of the model is obtainable from real data Risk Workshop SAMSI SAMSI Risk Workshop

24 Case Study 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. We collected data about their Order Fulfillment Process from a pharmacy near Pittsburgh. The Data consists of 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. Risk Workshop SAMSI SAMSI Risk Workshop

25 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. 1) Enter order information, ==error generated, error propagation 4) Prove prescription, == cost of errors, error propagation 9) Prepare and send claims to an insurance company or 10) to the responsible party, == cost of fixing errors 13) Update ledgers, == cost of presence of errors Risk Workshop SAMSI SAMSI Risk Workshop

26 Results: Optimal Objective Function Values
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. With moderate control, risks are reduced significantly Risk Workshop SAMSI SAMSI Risk Workshop

27 Outline Motivation and problem definition Methodology
Experimental study Real world application Future research Risk Workshop SAMSI SAMSI Risk Workshop

28 Future research Summary Future research
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. I have proposed a methodology for BP design that brings a holistic view of error impacts in BP, Accounts for process structure in error generation, propagation and mitigation Strikes a balance among the risk factors and provides optimal control design framework to mitigate risk exposure Lending itself to implementation within process modeling workbenches offered by leading software vendors Risk Workshop SAMSI SAMSI Risk Workshop


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