Systems Engineering to Improve Traumatic Brain Injury Care in the Military Health System Workshop Summary (2009) / Chapter Skim
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4 Examples of Operational Systems Engineering Applications Relevant to Traumatic Brain Injury Care--William P. Pierskalla
4 Examples of Operational Systems Engineering Applications Relevant to Traumatic Brain Injury Care--William P. Pierskalla
Pages 49-68
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From page 49... ...
• Health-care and health-systems design, including estimates of future resource needs and the deployment of those resources. This chapter is based on the author's presentation and responses to questions during the plenary session of the NAE-IOM workshop on Harnessing Operational Systems Engineering to Improve Traumatic Brain Injury Care in the Military Health System on June 11, 2008.
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. The fourth example illustrates the large-scale application of an OSE simulation to a geographically dispersed health care delivery enterprise
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In Hazen's paper, "Dynamic Influence Diagrams: Applications to Medical Decision Making," he uses dynamic influence diagrams to structure and analyze a continuous chain of decisions related to whether or not a patient should proceed with total hip replacement surgery in a context in which back-stepping loops are possible (Hazen, 2004)
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After being reclassified in the new ACR class, the patient may remain in that class for some time and then, later, transition to another class via infective or aseptic failure of the hip prosthesis or to death from another cause or to a general deterioration of his or her health. Considering all of these possibilities, which can happen randomly over time with certain probabilities, the prognosis of the patient's future quality of life is determined by a reverse analysis from the last point in the patient's life to the beginning point where the decision must be made as to surgery or conservative management.
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A stochastic tree is a graphical modeling tool that allows the explicit factoring of temporal uncertainty -- for example, age-dependent mortality rates -- in a decision tree analysis. An 85-year-old white male has to choose one of two options -- THA or conservative management.
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Applying the same analysis for a 60-year-old white female considering THA, her mean quality of life would be 9 years in Class I and 5 years in Class II (a total of 14 years of high quality) as opposed to 7 years in Class III.
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Similar or different criteria could be used to optimize mTBI treatment plans, locations, and/or personnel. Example 2 Screening Blood for the Human Immunodeficiency Virus Antibody The development and application of a decision analytic model to examine alternative strategies for screening blood for the HIV antibody and making decisions affecting blood donors was presented as having strong parallels to testing for mTBI and making decisions about next steps for wounded soldiers (Schwartz et al., 1990)
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during that period of time Donor subgroup Test Notification 1 1 1 Transfuse 2 2 Discard 2 Removed Retest from Donor 3 3 3 pool donor pool 4 5 8 15 FIGURE 4-2 The decision support model for HIV antibody testing of blood and plasma donors. Source: Schwartz et al., 1990.
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The model provided the panelists with information and a basis for comparing screening regimens. A collection of 15 screening tests was pared down to eight tests that could be used to detect HIV.
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WB-positive donors are informed of the test results. Strategy 4A: All units are tested with an EIA: EIA-negative units are transfused; EIA-positive units are retested with an EIA from a d ifferent cell line.
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Donors with WB-positive results are informed of the results and placed on a registry. Strategy 6: All units are tested with EIAs from two different cell lines.
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EXAMPLE 3 Policy Decision Modeling of the Costs and RESULTS of Medical school Education The purpose of this policy decision model was to support the decision of a board of regents and state legislature on financing state-funded medical education and meeting the state's long-term needs for physicians in rural and urban areas (Lee et al., 1987)
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The variables and parameters in the model were in-state, out-ofstate, urban, and rural students and residents entering medical schools and hospitals in the state over time; all teaching resources necessary to educate medical students and residents (faculty, technology, facilities, and programs) ; financial costs of education; the practice locations and specialties of physicians who had completed their studies; the needs of citizens for physicians by location and specialty; and the goals and objectives of the regents and legislature for medical education in the state.
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The physician-output model was a Markov-chain model. The physician practice location and specialties model was a stochastic forecasting model.
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established for students who remained in the state and practiced in rural areas for a certain number of years. Relevance to TBI Care Management The purpose of this model in the context of TBI care management is to illustrate that a modular OSE model can be used to evaluate various scenarios for achieving overall goals and objectives set by decision makers, even in complex health-delivery situations, such as those of the U.S.
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The purpose of the model was to help MHS address a large number of capacity, organizational, resource allocation, and process change issues. Goals and objectives for TBI patients Policy and Evaluate strategy Implications scenarios Assumptions DECISION SUPPORT SYSTEM DATA INPUTS •TBI patients In-field Out-of-field DOD OUTPUTS •Physicians diagnoses and treatment and •Treatment best practices treatment model locations model •Nurses •Treatment locations •Therapists Financial/cost Forecasting model •TBI patients treated •Families model for future TBI •Resources needed patients •Finances •Costs •Technologies •Facilities •Locations FIGURE 4-5 Decision-support model for developing a strategy and evaluation process Figure 4-5.eps for TBI care management.
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Healthcare Pa "Complex" t ie nt s Population Pat s ie e rn nt s at t lP fe rra Re Telem Population Population Patients e dici ne • Providers Pat ie nt s • Care Protocols • Ancillary Personnel • Ancillary Resources • Information Technology Population ...TO EXPLORE THE IMPACTS ON COST, ACCESS, AND QUALITY FIGURE 4-6 Overview of the Healthcare Complex Model. Source: Bonder, 2005.
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This simulation demonstrated that OSE techniques can, in fact, simulate a large-scale enterprise of health care delivery to address a whole spectrum of issues. EXAMPLE 5 A mixed-integer programming model to locate TRAUMATIC BRAIN INJURY TREATMENT UNITS in the VA Although the model described in this section was only mentioned briefly at the workshop, it represents a direct application of OSE to the management of TBI patients in the VA medical system.
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The results and system outputs were extremely sensitive to the management structure and environment of the TBI treatment units in VISN 8 and suggested that careful consideration be given to the centralization of health care facilities and admission r etention rates, as well as to interactions among these factors, when m aking decisions concerning the location of treatment facilities. VA management could use the results to make informed decisions about the number, location, capacities, personnel, and costs of opening facilities throughout the region to treat TBI patients, with or without family members in attendance.
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2007. A mixed integer program ming model to locate traumatic brain injury treatment units in the Department of Veterans Affairs: a case study.
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