Patent Application
20220084685
In today's military and emergency response operations, medical planners frequently encounter problems in accurately estimating illnesses, casualties and mortalities rates associated with an operation. Largely relying on anecdotal evidences and limited historical information of similar operations, medical planners and medical system analysts don't have a way to scientifically and accurately projecting medical resources, and personnel requirements for an operational scenario. Inadequate medical logistic planning can lead to shortage of medical supplies, which may significantly impact the success of any military, humanitarian or disaster relief operation and could result in more casualties and higher mortality rates. Therefore, there is an urgent need for the development of a science based medical logistics and planning tool.
Before the development of this invention, some useful, but not comprehensive medical modeling and simulation tools were used in attempts to virtually determine the minimum capability necessary in order to maximize medical outcomes, and ensure success of the military medical plan, such as Ground Casualty Projection System (FORECAS) and the Medical Analysis Tool (MAT).
FORECAS produced casualty streams to forecast ground causalities. It provide medical planners with estimates of the average daily casualties, the maximum and minimum daily casualty load, the total number of casualties across an operation, and the overall casualty rate for a specified ground combat scenario. However, FORECAS does not specify the type of injury or take into account the time required for recovery.
MAT and later the Joint Medical Analysis Tool (JMAT) consisted of two modules. One module was designed as a requirements estimator for the joint medical treatment environment while the other module was a course of action assessment tool. Medical planners used MAT to generate medical requirements needed to support patient treatment within a joint warfighting operation. MAT could estimate the number of beds, the number of operating room tables, number and type of personnel, and the amount of blood required for casualty streams, but was mainly focused at the eater Hospitalization level of care are definitive cares, which comprises of combat support hospitals in theaters (CSH) but does not include the forward medical facilities like the Battalion Aid Station or Surgical companies. Furthermore, MAT treated the theater medical capabilities as consisting of three levels of care, but failed to take into account medical treatment facilities (MTFs) at each level, their spatial arrangements on a battlefield, nor the transportation assets necessary to interconnect the network. Because MAT was a DOD-owned software program, it also did not include a civilian model. As MAT was designed to be used as a high-level planning tool, it does not have the capability to evaluate forward medical capabilities, or providing a realistic evaluation of mortality. JMAT, the MAT successor, failed Verification and Validation testing in August 2011, and the program were cancelled by the Force Health Protection Integration Council. Other simulations were described by in report by Von Tersch et al. [1].
The existing simulation and modeling software provide useful information for preparing for a military mission. However, they lack the capability to model the flow of casualties within a specific network of treatment facilities from the generation of casualties, and through the treatment networks, and fails to provide critical simulation of the treatment times, and demands on consumable supplies, equipment, personnel, and transportation assets. There are no similar medical logistic tools are on the market for civilian medical rescue and humanitarian operations planning.
Military medical planners, civilian medical system analysts, clinicians and logisticians alike need a science-based, repeatable, and standardized methodology for predicting the likelihood of injuries and illnesses, for creating casualty estimates and the associated patient streams, and for estimating the requirements relative to theater hospitalization to service that patient stream. These capability gaps undermine planning for medical support that is associated with both military and civilian medical operations.
An objective of this invention is the management of combat, humanitarian assistance (HA), disaster relief (DR), shipboard, and fixed base PCOFs (patient condition occurrence frequencies) distribution Tables.
Another objective of this invention is estimation of casualties in HA and DR missions, and in ground, shipboard, and fixed-base combat operations.
Yet another objective of this invention is the generation of realistic patient stream simulations for a HA and DR missions, and in ground, shipboard, and fixed-base combat operations.
Yet another objective of this invention is the estimation of medical requirements and consumables, such as operations rooms, intensive care units, and ward beds, evacuations, critical care air transport teams and blood products, based on anticipated patient load.
Common data are data stored in one or more database of the invention, which include EMRE common data, CREstT common data, and PCOF common data. The application contains tables labeling inputs used in different software modules and identify them if they are common data.
Patient Conditions (PCs) are used throughout MPTk to identify injuries and illnesses. The PCOF Tool is used to determine the probability of each patient condition occurring. CREstT creates a patient stream by assigning a PC to each casualty it generates. EMRE determines theater hospitalization requirements based on the resources required to treat each PC in a patient stream. All patient conditions in MPTk are codes from the International Classification of Diseases, Ninth Revision (ICD-9). MPTk currently supports 404 ICD-9 codes. 336 of them are codes selected by the Defense Medical Materiel Program Office (DMMPO). An additional 68 codes were added to this set to provide better coverage, primarily of diseases. In each of the three tools, the user can select to use the full set of PC codes or only the 336 DMMPO PC codes.
PCOF scenarios organize patient conditions and their probability of occurrence into major categories and subcategories, and allow for certain adjustment factors to affect the probability distribution of patient conditions. While baseline PCOF scenarios cannot be directly modified by the user, they can be copied and saved with a new name to create derived PCOF scenarios.
Derived PCOF scenarios, created from any baseline PCOF scenario, also organize the probability of patient conditions into major categories and subcategories affected by adjustment factors, all of which may be edited directly by the user.
Unstructured PCOF scenarios provide the user with a list of patient conditions and their probability of occurrence, but do not contain further categorization and are not adjusted by other factors. MPTk includes a number of unstructured PCOF scenarios built and approved by NHRC, and these may not be directly modified by the user. However, the user may copy and save unstructured PCOF scenarios as new unstructured PCOF scenarios, and these may be modified by the user. Users may also create new unstructured PCOF scenarios from scratch.
Any new derived or unstructured PCOF scenarios are saved to the database, and will appear in the PCOF scenario list with the baseline and unstructured PCOF scenarios that shipped with MPTk.
A scenario includes parameters of a planned medical support mission. The scenario may be created in PCOF, CREstT or EMRE modules. A user establishes a scenario by providing inputs and defines parameters of each individual module.
Casualty count is each simulated casualty in MPTk, which may be labeled and maybe assigned a PC code.
eater Hospitalization level of care are definitive care, which comprises of combat support hospitals in theaters (CSH) but does not include the forward medical facilities like the Battalion Aid Station or Surgical companies.
This invention relates to a system, method and software for creating military and civilian medical plans, and simulating operational scenarios, projecting medical operation estimations for a given scenario, and evaluating the adequacy of a medical logistic plan for combat, humanitarian assistance (HA) or disaster relief (DR) activities.
I. Computer System and Hardware
As shown in
Set of internal components 800 also includes a (read/write) R/W drive or interface 832 to read from and write to one or more portable computer-readable storage devices 936 that can store, but do not transmit, a computer program, such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. MPTk software/database (see
Set of internal components 800 also includes a network adapter or interface 836 such as a TCP/IP adapter card or wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology). MPTk (see
Set of external components 900 includes a display screen 920, a keyboard or keypad 930, and a computer mouse or touchpad 934. Sets of internal components 800 also includes device drivers 840 to interface to display screen 920 for imaging, to keyboard or keypad 930, to computer mouse or touchpad 934, and/or to display screen for pressure sensing of alphanumeric character entry and user selections. Device drivers 840, R/W drive or interface 832 and network adapter or interface 836 comprise hardware and software (stored in storage device 830 and/or ROM 824).
The invention also include an non-transitory computer-readable storage medium having stored thereon a program that when executed causes a computer to implement a plurality of modules for generate estimates of casualty, mortality and medical requirements of a future medical mission based at least partially on historical data stored on the at least one database, the plurality of modules comprising:
Various executable programs (such as PCOF, CREsT, and EMRE Modules of MPTk, see
The database 200 comprises PCOF common data, CREstT common data and EMRE common data. The common data are developed based on historical empirical data, and subject matter expert opinions. For example, empirical data were used to develop an updated list of patient conditions for use in modeling and simulation, logistics estimation, and planning analyses. Multiple Injury Wound codes were added to improve both scope and coverage of medical conditions. Inputs were identified as Common Data in tables throughout this application to distinguish from inputs there were user defined or inputted.
For many years, analysts have used a standardized list of patient conditions for medical modeling and simulation. This list was developed by the Defense Health Agency Medical Logistics (DHA MEDLOG) Division, formerly known as the Defense Medical Standardization Board, for medical modeling and simulation. This subset of International Classification of Diseases, 9th Revision (ICD-9) diagnostic codes was compiled before the advent of modern health encounter databases, and was intended to provide a comprehensive description of the illnesses and injuries likely to afflict U.S. service personnel. Medical encounters from recent contingency operations, were compared to the Clinical Classification Software (CCS; 2014), a diagnosis and procedure categorization scheme developed by the Agency for Healthcare Research and Quality, to establish the hybrid database as an authoritative reference source of healthcare encounters in the expeditionary setting.
II. Computer Programs Modules of the Medical Planner's Toolkit (MPTK)
The inventive MPTk software comprises three modeling and simulation tools: the Patient Condition Occurrence Frequency Tool (PCOF), the Casualty Rate Estimation Tool (CREstT) and the Expeditionary Medicine Requirements Estimator (EMRE). Used independently, the three simulation tools provide individual reports on causality generation, patient stream, and medical planning requirements, which can each be used by medical system analysts or logisticians and clinicians in different phases of medical operation planning. The three stimulation tools can also be used collectively as a toolkit to generate detailed simulations of different medical logistic plan designed for an operational scenario, which can be compared to enhance a medical planner's overall efficiency and accuracy.
A. Patient Condition Occurrence Frequency Tool (PCOF)
The PCOF tool provides medical planners and logisticians with estimates of the distributions of injury and illness types for a range of itary operations (ROMO). These missions include combat, noncombat, humanitarian assistance (HA), and disaster relief (DR) operations. Using the PCOF tool, baseline distributions of a patient stream composition may be modified by the user either manually and/or via adjustment factors such as age, gender, country, region to better resemble the patient conditions of a planned operation. A PCOF table can provide the probability of injury and illness at the diagnostic code level. Specifically, each PCOF is a discrete probability distribution that provides the probability of a particular illness or injury. The PCOF tool was developed to produce precise expected patient condition probability distributions across the entire range of military operations. These missions include ground, shipboard, fixed-base combat, and HA and DR non-combat scenarios. The PCOF distributions are organized in three levels: International Classification of Diseases, Ninth Revision (ICD-9) category, ICD-9 subcategory, and patient condition (ICD-9 codes). Example of ICD-9 category, ICD-9 subcategory and patient condition may be dislocation, dislocation of the finger, dislocation of Open dislocation of metacarpophalangeal (joint), respectively. These PCOF distribution tables for combat missions were developed using historical combat data. The major categories and sub-categories for the HA and DR missions were developed using a 2005 datasheet by the International Medical Corps from ReliefWeb (a United Nations Web site). Because the ICD-9 codes from this datasheet is restrictive to that particular mission, the categories, sub-categories, and ICD-9 codes for trauma and disease groups of HA and DR operations are further expanded to account for historical data gathered from
er sources, and modified to be consistent with current U.S. Department of Defense (DoD) medical planning policies. Because the ICD-9 codes are not exclusively used for military combat operations, all DoD military combat ICD-9 codes are used for HA and DR operation planning in conjunction with the additional HA and DR ICD-9 codes in the present invention. The PCOF tool can generate a report that may be used to for support supply block optimization, combat scenario medical supportability analysis, capability requirements analysis, and other similar analysis.
The high level process diagram of PCOF is shown in
Each baseline PCOF table specifies the percentage of a patient type in the baseline. In one embodiment of the PCOF tool, there are five patient-type categories: wounded in action (WIA), non-battle injury (NBI), disease (DIS), trauma (TRA), and killed in action (KIA). The user can alter these percentages to reflect the anticipated ratios of a patient steam in a planned operation scenario. Adjustment factors applied at the patient-type level affect the percentage of the probability mass in each patient-type category, but do not affect the distribution of probability mass at the ICD-9 category, ICD-9 subcategory or patient condition levels within the patient-type category. Changes at patient-type level may be entered by the user directly. Patient Type is a member of the set {DIS, WIA, NBI, A} and PCTDIS, PCTWIA, PCTNBI and PCTTRA are the proportions of DIS, WIA, NBI, and TRA patients respectively.
Then for ground combat scenarios:
and for non-combat scenarios:
The PCOF tool also allows users to make this type of manual adjustment at the ICD-9 category and ICD-9 subcategory levels. At each level, total probability of each level (patient-type, ICD-9 category or ICDR-9 subcategory) must add up to 100% whether the adjustment is accomplished manually or through adjustment factors. In an embodiment, adjustment factors are applied at the ICD-9 category (designated as Cat in all equations). The equation below shows the manner in which adjustment factors (AFs) are applied.
The change in each ICD-9 category is calculated for each adjustment factor that applies to that category. The manner in which this calculation is performed depends on the specific application of the adjustment factor. While some adjustment factors adjust all ICD-9 categories directly, a select few adjustment factors adjust certain ICD-9 categories, hold those values constant, and normalizes the remainder of the distribution. For the adjustment factors who adjust categories directly, the change calculation is performed according to the following:
For the adjustment factors which hold certain values constant, the calculation is performed in the following manner.
Users are able to alter scenario variables from the the graphic user interface (I). The tool calculates the appropriate adjustment factors based on this user input. Not all adjustment factors affect all ICD-9 categories. Furthermore, adjustment factors may not affect all of the injury types within an ICD-9 category. Table 0 displays the adjustment factors that affect patient types by scenario type.
Calculation for each adjustment factors are described in the following sections.
PCOF types affected: HA, DR
Patient types affected: disease, trauma
The age adjustment factor was determined using the Standard Ambulatory Data Record (SADR); a repository of administrative data associated with outpatient visits by military health system beneficiaries. This data is the baseline population in all calculations below. The data were organized by age into four groups:
1) ages less than 5 years, i=1;
2) ages 5 to 15 years, i=2;
3) ages 16 to 65 years, i=3; and
4) ages greater than 65 years, i=4.
The age adjustment factor is determined as follows:
Let i denote the age group, where i∈{1, 2, 3, 4}
Let m denote the index for ICD-9 categories, where m∈{1, 2, . . . , M} and there are M distinct ICD-9 categories.
Let BaselineAgei be the percentage of age group i in the population of the baseline distribution.
Let AdjustedAgei be the user-adjusted percentage of the population in age group i.
Let ICD9_Cat_Agei,m be the percentage of the SADR population in age group i within ICD-9 category m.
The adjustment factors for age are calculated as follows:
PCOF types affected: HA, DR, and ground combat
Patient types affected: WIA, NBI, disease, and trauma
The gender adjustment factor was derived in a manner similar to the age adjustment factor. The data source for the gender adjustment factor was SADR. The data were organized by gender:
Male, i=0
Female, i=1
The gender adjustment factor is calculated as follows:
Let BaselineGenderi be the percentage of the gender group i in the baseline population, i∈{0,1}.
Let AdjustedGenderi be the user adjusted percentage of the population in gender group i.
Let ICD9_Cat_Genderi,m be the percentage of the SADR population in gender group i within ICD-9 category m.
The adjustment factor is calculated as follows:
The “OB/GYN Disorders” major category is adjusted in the same manner as all other major categories. However, in the special case where the population is 100% male, the percentage of OB/GYN disorders is automatically set to zero, and all other major categories are renormalized (Recalculated so the percentages add to 100%.
PCOF types affected: ground combat
Patient types affected: disease
The regional adjustment factor was developed via an analysis of data from World War II. The World War II data was categorized by combatant command (CCMD) and organized into the major disease categories found in the PCOF. The World War II data comprise the baseline population referenced below.
Let CCMDBaseline,m be the percentage of the World War II population comprising ICD-9 category m for the baseline CCMD of the scenario.
Let CCMDAdjusted,m be the percentage of the World War II population comprising ICD-9 category m for the user-adjusted CCMD of the scenario.
The adjustment factor is calculated as follows:
Where AFm is the adjustment factor used to transition an ICD-9 category m from CCMDBaseline to CCMDAdjusted.
PCOF types affected: DR
Patient types affected: disease and trauma
Response phase denotes the time frame within the event when aid arrives. For the purposes of this adjustment factor, response phases were broken down into three time windows and are described below.
1) Early Phase is from the day the event occurs to the following day.
2) Middle Phase is the third day to the 15th day.
3) Late Phase is any time period after the 15th day.
These phases are described in the Pan American Health Organization's manual on the use of Foreign Field Hospitals (2003). Response phase adjustment factors perform two functions. First, they adjust the ratio of disease to trauma. Second, unlike the adjustment factors discussed above, they only adjust the percentages of a small subset of the major categories rather than the entire PCOF. Subject matter expert (SME) input and reference articles were used to develop adjustment factors that adjust the most likely conditions affected by the response phase for both disease and trauma casualties. The conditions are shown in Table 0 and Table 0.
For the major categories, which are adjusted and held constant, the calculations are as follows.
Let k denote the index for ICD-9 categories adjusted by response phase for disease, where k∈{1, 2, 3, 4} and l denote the same for trauma, where l∈{1, 2}.
Let xk be the percentage of major category k, which will be adjusted and held constant.
Let yn be the percentage of major category n, which will be normalized such that the distribution sums to 1, where n∈{1, 2, . . . , N}.
Let ak be the adjustment factor for major category k for disease and let al be the adjustment factor for major category l for trauma. The calculations for the major categories, which are adjusted and held constant, are calculated according to the formulas below (the example is for disease; the same formulation applies to trauma).
The calculations for the major categories, which are normalized so that the distribution sums to 1, are as follows (the example is for disease; the same formulation applies to trauma).
The adjustment factor was developed via SME input and has no closed form. There are unique adjustment factors for each of the six distinctive combinations of baseline and adjusted response phases.
There is also an adjustment to the disease-to-trauma ratio due to a change in response phase. For any change in response phase, the adjustment factor for disease is inversely proportional to the adjustment factor for trauma. Therefore, if the adjustment factor for disease is 8, the adjustment factor for trauma will be
Table 0 denotes the adjustments to relative disease and trauma percentages. These values are then normalized so that they sum to 100%.
PCOF types affected: HA, DR, and ground combat
Patient types affected: disease
The development of the seasonal adjustment factor was performed via the analysis of SADR data for HA and DR scenarios, and from Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) for ground combat scenarios that had been parsed by season. For ground combat PCOFs, the default season is always “All,” implying that the operation spanned multiple or all seasons. For HA and DR PCOFs, the default season is set respective to the season in which the operation took place. For each combination of seasons in HA and DR scenarios, an odds ratio was developed that measures the likelihood of a condition occurring in the user-adjusted season to a reference season (the baseline).
The HA and DR season adjustment factors is calculated as follows: Let SeasonBaseline,k be the percentage of the SADR population comprising ICD-9 category k for the scenario's baseline season. Where k denotes the ICD-9 categories from Table 2 Let SeasonAdjusted,k be the percentage of the SADR population comprising ICD-9 category k for the scenario's user-adjusted season.
and,
AF_HADRSeasonk=Odds_RatioBaseline,k→Adjusted,k
The ground combat season adjustment factor is calculated as follows: Let SeasonBaseline,m be the percentage of the OIF or OEF population comprising ICD-9 category m for the scenario's baseline season.
Let SeasonAdjusted,m be the percentage of the OIF or OEF population comprising ICD-9 category m for the scenario's user-adjusted season.
The ground combat seasonal adjustment factor aligns all of the disease major categories. After adjustment, the major categories are normalized so that the distribution sums to 100%. The HA and DR seasonal adjustment factor, as in the case of the response phase adjustment factor, only affects a specified set of major categories. Specifically, the adjustment factor for season only affects the disease major categories outlined in Table 0. Additionally, as with the response phase adjustment factor, these major categories are adjusted and kept constant while the remainder of the PCOF is normalized.
PCOF types affected: HA, DR, and ground combat
Patient types affected: NBI, TRA
Season is the only adjustment factor which affects PCOFs on the ICD-9 subcategory level. For NBI and TRA patient types, the season adjustment factor changes the relative percentage of the “Heat” and “Cold” subcategories within the “Heat and Cold” top category. Heat injuries are more common during the summer and cold injuries are more common during the winter. As shown in Table 0, the heat and cold subcategory percentages are determined using only the season. Individual PCOFs cannot have heat and cold percentages other than what is shown in the table 5.
PCOF types affected: HA and DR
Patient types affected: disease and trauma (trauma is adjusted through age and gender only)
The selection of a country in the PCOF tool triggers four adjustment factors. The first adjustment factor combines region and climate. Each country is classified by region according to the CCMD in which it resides. Along with this is a categorizing of climate type according to the Koppen climate classification. Each combination of CCMD and climate was analyzed according to disability adjusted life years (DALYs), which are the number of years lost due to poor health, disability, or early death, and a disease distribution was formed. Each country within the same CCMD and climate combination shares the same DALY disease distribution for this adjustment factor.
The region and climate type adjustment factor is calculated as follows: Let Region_ClimateBaseline,m be the percentage of the DALY population comprising ICD-9 category m for the region and climate combination of the baseline country in the selected season. Let Region_ClimateAdjusted,m be the percentage of the DALY population comprising ICD-9 category m for the region and climate combination of the user-adjusted country in the selected scenario.
The second adjustment factor accounts for the impact of economy in the selected country. Each country's economy was categorized according to the human development index. SME input was used to develop adjustment factors for three major categories (Table 0). As in the case of the response phase adjustment factor and HA and DR seasonal adjustment factor, these three major categories are adjusted and held constant while the remainder of the PCOF is renormalized.
There is also an adjustment to the disease-to-trauma ratio due to a change in income. The disease and trauma percentages will be adjusted when the selection of a new country changes the income group. 0 denotes the adjustments that will be applied to the disease patient type percentage. After the disease percentage is multiplied by the adjustment factor, the disease and trauma percentages are renormalized to sum to 100%.
Finally, adjustment factors are applied for the change in age and gender. These adjustments are performed in the same manner as user-input changes to age and gender distribution (described above). However, instead of a user-input age or gender distribution, the age and gender distribution of the user-chosen country is used.
B. Casualty Rate Estimation Tool (CREstT)
The Casualty Rate Estimation Tool (CREstT) provides user estimate casualties and injuries resulting from a combat and non-combat event. CREstT may be used to generate casualties estimates for ground combat operations, attacks on ships, attacks on fixed facilities, and casualties resulting from natural disasters. These estimates allow medical planners to assess their operation plans, tailor operational estimates using adjustment factors, and develop robust patient streams best mimicking that expected in the anticipated operation. CREstT also has an interface with the PCOF tool, and can use the distributions stored or developed in that application to produce patient streams. Its stochastic implementation provides users with percentile as well as median results to enable risk assessment. Reports from CREsT may be programed to present data in both tabular and graphical formats. Output data is available in a format that is compatible with EMRE, JMPT, and other tools. The high level process diagram of PCOF is shown in
Baseline ground combat casualty rate estimates are based on empirical data spanning from World War II through OEF. Baseline casualty rates are modified through the application of adjustment factors. Applications of the adjustment factors provide greater accuracy in the casualty rate estimates. The CREsT adjustment factors are based largely on research by Trevor N. Dupuy and the Dupuy Institute (Dupuy, 1990). The Dupuy factors are weather, terrain, posture, troop size, opposition, surprise, sophistication, and pattern of operations. The factors included in CREstT are region, terrain, climate, battle intensity, troop type, and population at risk (PAR). Battle intensity is used in CREstT instead of opposition, surprise, and sophistication factors to model enemy strength factors.
Casualty estimates for ground combat operations in CREstT are calculated using the process depicted in
The CREstT baseline rates are the starting point for the casualty generation process. There is a WIA baseline rate which is dependent on troop type, battle intensity, and service and a DNBI baseline rate which is dependent only on troop type.
Baseline WIA casualty rates based on historical data are provided for the Army and Marine Corps. Sufficient data does not exist to calculate historic ground combat WIA rates for the other services. Table 0 displays the baseline WIA rate for the Marine Corps for each troop type and battle intensity combination. Values are expressed as casualties per 1,000 PAR per day. WIA rates for combat support and service support are percentages of the combat arms WIA rate. The combat support rate is 28.5% of the combat arms rate and the service support rate is 10% of the combat arms rate. Peace Operations (Peace Ops) intensity rates are based on casualty rates from Operation New Dawn (Iraq after September 2010). Light intensity rates were derived from empirical data based on the overall average casualty rates from OEF 2010. Moderate intensity rates are derived from the average casualty rates evidenced in the Vietnam War and the Korean War. Heavy intensity rates are based on the rates seen during the Second Battle of Fallujah (during OIF; November 2004). Lastly, “Intense” battle intensity is based on rates sustained during the Battle of Hue (during the Tet Offensive in the Vietnam War).
Table 12 displays the baseline WIA rate for the Army for each troop type and battle intensity combination. Army rates are still under development, so the Army rates are currently set to the same values as the Marine Corps rates.
If the user selects the “User Defined” battle intensity, then the user defined WIA rate will be used rather than a rate from the above tables. The disease and nonbattle injury (DNBI) baseline rates are determined only by troop type, independent of battle intensity and service. Table 0 displays the three DNBI baseline rates. As with WIA rates, values are in casualties per 1,000 PAR per day.
The DNBI baseline rate calculation process produces two sets of outputs, the respective WIA and DNBI baseline rates for each user-input selection of troop type and battle intensity (if applicable).
The formula for adjusted casualty rates for both WIA and DNBI are:
WIA Adjustment Factor for Region
Affected casualties: combat arms, combat support, and service support
CREstT allows the user to adjust the region or CCMD in which the modeled operation will occur. A previous study was performed to determine specific variables that influenced U.S. casualty incidence (Blood, Rotblatt, & Marks, 1996). The results of this study were aggregated for CCMDs during CREstT's development. Table 0 lists the adjustment factors by region.
WIA Adjustment Factor for Terrain
Affected casualties: combat arms, combat support, and service support
Previous modeling efforts by Trevor N. Dupuy (1990) have demonstrated that terrain and climate have the potential to impact the numbers of casualties in an engagement. Terrain factors previously derived by Dupuy were adapted for the development of terrain adjust factor seed in this tool. The multiplicative factors for each terrain description were averaged in the aggregated category. The “Urban” terrain type serves as the baseline value. The average factors for each category were scaled so that Urban would have a value of 1.0. Table 0 describes each of the factors used by Dupuy and the adjustment factors found in MPTk.
WIA Adjustment Factor for Climate
Affected casualties: combat arms, combat support, and service support
Climate adjustment factors were also derived from the work of Dupuy. Climate descriptions were aggregated into larger groups similar to the process described in the Adjustment Factor for Terrain section. It should be noted that the aggregated values are adjusted so that the “Temperate” climate serves as the baseline with a value of 1. This is performed by adjusting the “Temperate” climate average to a value of 1 and adjusting each of the other aggregate values by the same multiplier.
WIA Adjustment Factor for Troop Strength
Affected casualties: combat arms, combat support, and service support
The troop-strength adjustment factor is derived from the user-input unit size. However, if the unit size is greater than the PAR, the PAR will be used. Unit size will default to 1,000 unless adjusted by the user. If the user inputs a unit size of zero, the PAR will be used for the troop strength adjustment factor calculation.
The hazard-rate step function is as follows:
Where:
Affected casualties: combat arms, combat support, and service support
DNBI regional adjustment factors were developed via an analysis of World War II data aggregated by both disease and NBI occurrences within each region. Disease and NBI each have an individual adjustment factor. The adjustment factors are as shown in Table 0.
The application of the adjustment factors yields two sets of outputs: the adjusted rate for WIA casualties and the adjusted rate for DNBI casualties. Table 0 describes the outputs.
The inputs to the WIA casualty generation process are shown in table 21 and the logic used to generate WIA casualty generation process is shown in
All CREstT casualties are generated via a mixture distribution. First, a daily rate (DailyWIAt) is drawn from a probability distribution that has the adjusted casualty rate (WIATroop) as its mean. As described in detail below, this distribution will be either a gamma or exponential distribution. The daily rate (DailyWIAt) is then applied to the current PAR and used as the mean of a Poisson distribution to generate the daily casualty count (NumWIATroop). The underlying distributions for WIA casualties are determined by the baseline WIA casualty rate (BRWIA,Troop). Rates corresponding to Moderate battle intensity or lower will use a gamma distribution, while those corresponding to Heavy or above will use an exponential distribution. Table 0 displays the cutoff point between the two distributions.
The parameterization of the gamma distribution used in CREstT is as follows.
Shape Parameter
Scale Parameter
Generate a random number U=uniform(0,1)
Gamma(α,β)=Gamma.Inv(U,α,β)
As described above (in Table 0), heavy and intense battle intensities use the exponential distribution. The exponential distribution can be characterized as a gamma distribution with shape parameter α=1. Therefore, the parameterization of the exponential distribution is as follows:
Random variates of the exponential distribution are calculated as follows:
For combat support and service support troop types, the daily casualty rate (DailyWIAt) for day t is calculated by generating a random variate with mean WIATroop from either a gamma or exponential distribution using the procedures described above.
An underlying assumption of the CREstT casualty model is that combat arms WIA rates are autocorrelated. This autocorrelation indicates that the magnitude of any one day's casualties is related to the numbers of casualties sustained in the three immediately preceding days. Therefore, CREstT uses an autocorrelation function for the generation of combat arms casualties. Combat support and service support are not modeled using autocorrelation. The autocorrelation computation is as follows.
Where:
If BRWIA,Troop is above cutoff (Table 0):
Where:
During the first three days of the simulation (days 0, 1, and 2), casualty rates for three previous days are not available to perform the autocorrelation. This limitation is overcome by assuming that the three days prior to the start of the simulation all had rates equal to WIATroop.
This has the effect of canceling out terms in the autocorrelation equations above that do not apply. For example, on day 0 with heavy battle intensity, the autocorrelation equation would reduce to:
Once the above calculations have been performed, either in the presence or absence of autocorrelation, the resulting rate (DailyWIAt) is used in a Poisson distribution to generate a daily casualty estimate. The parameterization of the Poisson distribution's probability mass function is as follows:
To generate the daily WIA casualty estimate, the previously generated rate (DailyWIAt) is multiplied by the current PAR divided by 1000 and used as the mean (λ) of a Poisson distribution.
Decrement the PAR after WIA and KIA
After WIA and KIA casualties have been generated, but before generating DNBI casualties, the PAR must be decremented. If the “Daily Replacements” option is selected for this troop type and interval, then the PAR is not decremented. The inputs for decrementing the PAR after WIA and KIA generation are as follows.
If KIA casualties are generated, all KIA casualties are removed from PAR. The WIA casualties are adjusted so that only the casualties that are expected to require evacuation to Role 3 are removed. This adjustment assumes that all casualties that can return to duty after treatment at Role 1 or Role 2 are not removed from PAR and all casualties that are evacuated beyond Role 2 are permanently removed and not replaced.
Generate DNBI Casualties
The inputs for the DNBI casualty generation process are shown in table 28.
The logic to generate DNBI casualties is displayed in
The underlying distribution used to create DNBI is the Weibull distribution. This distribution is standard across all troop types and battle intensities. The mean rate is the only value that changes. The parameterization for the Weibull distribution includes a shape parameter (α) and scale parameter (β). In CREstT, it is assumed that the shape parameter is 1.975658. This value is used to solve for the scale parameter. The parameterization of the Weibull distribution used in CREstT is as follows:
Shape Parameter α=1.975658
Scale Parameter
Where:
Γ( ) indicates the gamma function
Random variates of the Weibull distribution are calculated as follows:
Generate a random number U=uniform(0,1)
Thus the daily DNBI rate is:
As in the case of WIA casualties, the daily DNBI rate (DNBIt) is multiplied by the current PAR divided by 1000 and used as the mean (λ) of a Poisson distribution. The Poisson distribution is simulated, as described above for WIA casualties, to produce integer daily casualty counts.
CREstT generates the number of DNBI casualties per day as described above. It then splits the casualties according to the user input for “NBI % of DNBI.” The calculations are as follows:
Decrement the PAR after DNBI
After DNBI casualties have been generated, but before moving to the next day, the PAR must be decremented. If the “Daily Replacements” option is selected for this troop type and interval, then the PAR is not decremented. The inputs for decrementing the PAR after DNBI generation are as follows.
The DIS and NBI casualties are adjusted so that only the casualties that are expected to require evacuation to Role 3 are removed. This adjustment assumes that all casualties that can return to duty after treatment at Role 1 or Role 2 are not removed from PAR and all casualties that are evacuated beyond Role 2 are permanently removed and not replaced.
Disaster Relief
CREstT includes two modules that allow the user to develop patient streams stemming from natural disasters. These patient streams can subsequently be used to estimate the appropriate response effort. The two types of DR scenarios currently available in CREstT are earthquakes and hurricanes. The following sections provide descriptions of the overall process and describe the algorithms used in these simulations.
The CREstT earthquake model estimates daily casualty composition stemming from a major earthquake. CREstT estimates the total casualty load based on user inputs for economy, population density, and the severity of the earthquake. This information is used to estimate an initial number of casualties generated by the earthquake. The user also inputs a treatment capability and day of arrival. CREstT decays the initial casualty estimate until the day of arrival. After arrival, casualties are treated each day based on the treatment capability until the mission ends. The specific workings of each subprocess are described in the following sections.
Calculate Total Casualties
The first step in the earthquake casualty generation algorithm is to calculate the total number of direct earthquake related casualties. This is a three-step process:
calculate the expected number of kills,
calculate the expected injury-to-kills ratio, and
calculate the expected number of casualties.
The inputs for these calculations are as follows.
The number of kills is calculated as follows:
The injury-to-kills ratio is calculated as follows:
Finally, the total number of casualties is calculated according to the following:
The single output from this process is the total number of casualties.
Decay Total Casualties until Day of Arrival
The next step in the earthquake algorithm is to calculate the number of casualties remaining on the day of arrival. The inputs into this process are as follows.
The initial number of direct earthquake casualties decreases over time. The rate at which they decrease is dependent on several unknown variables. These can include but are not limited to: the rate at which individuals stop seeking medical care; the number that die before receiving care; and the post disaster capability of the local health care system. A shaping parameter, lambda, is a proxy for these non-quantifiable effects. The model makes an assumption that a nation's economic category is closely correlated with its ability to rebuild and organize infrastructure to respond to disasters. Additionally, since larger magnitude earthquakes produce exponentially greater casualties, the model assumes that earthquakes greater than 8.1 have a slower casualty decay. Therefore, a separate lambda is provided for each economic level and magnitudes ≤8.1 and >8.1, as follows.
The outputs for this portion of the algorithm are as follows.
Calculate Residual Casualties
The next step in the earthquake algorithm is to calculate the residual casualties in the population. Residual casualties are diseases and traumas that are not a direct result of the earthquake event. For example, residual casualties can be injuries sustained from an automobile accident, chronic hypertension, or infectious diseases endemic in the local population. Non-disaster related casualties initially represent a small proportion of the initial causality load (Kreiss et. al., 2010). Over time the percentage of non-disaster related casualties increases until it reaches the endemic or background levels extant in the population.
The calculation for the daily number of residual casualties is:
Generate Earthquake Casualties
Beginning on the day of arrival, trauma and disease casualties are generated based on the number of initial casualties still seeking treatment and the daily number of residual casualties. After the day of arrival, casualties waiting for treatment are decayed in a manner similar to how they were decayed before they day of arrival.
The disaster casualties on day i after the earthquake (h0i) for the day of arrival is initialized to ArrivalCas and the starting interval counter for the decay shaping parameter (k) is initialized to either 5 or a percentage of the initial casualties. The delta parameter is defined in the same manner as it was before the day of arrival. The scaler parameter is defined as a function of the casualties remaining on the day of arrival (ArrivalCas).
For each day in the casualty generation process, Trauma and Disease casualties are generated using one of three methods, depending on the number of remaining casualties, the treatment capability, and the level of residual casualties. MPTk will display results beginning with the day of arrival, which will be labeled as day zero. The trauma and disease casualties on day j after arrival (Traj and Disj) are calculated using the index j=i−Arrival.
The CREstT hurricane model is similar to the earthquake model. It estimates daily casualty composition stemming from a major hurricane. Similar to the earthquake model, CREstT estimates the total casualty load based on user inputs for economy, population density, and hurricane severity. This information is used to estimate an initial casualty number. The user also inputs a treatment capability and day of arrival. CREstT decays the initial casualty estimate until the day of arrival. After arrival, casualties are treated each day based on the treatment capability until the mission ends.
Calculate Total Casualties
The first step in the hurricane casualty estimation process is to determine the total number of casualties. This process is performed in a similar fashion as described in the corresponding process in the earthquake algorithm. The steps required to perform this process are as follows:
The next step in the hurricane algorithm is to calculate the number of casualties remaining on the day of arrival. The inputs into this process are as follows.
Similar to the earthquake model, the initial number of direct disaster related casualties decreases over time. The rate at which they decrease is dependent on several unknown variables, to include but not limited to: the rate at which individuals stop seeking medical care; the number that die before receiving care; and the post disaster capability of the local health care system. A shaping parameter, lambda, is a proxy for these non-quantifiable effects. The model makes an assumption that a nation's economic category is closely correlated with its ability to rebuild and organize infrastructure to respond to disasters. Therefore, a separate lambda is provided for each economic level as follows.
Calculate Residual Casualties
The next step in the hurricane algorithm is to calculate the residual casualties in the population. Residual casualties are diseases and traumas that are not a direct result of the hurricane event. For example, residual casualties can be injuries sustained from an automobile accident, chronic hypertension, or infectious diseases endemic in the local population. Non-disaster related casualties initially represent a small proportion of the initial causality load (Kreiss et. al., 2010). Over time the percentage of non-disaster related casualties increases until it reaches the endemic or background levels extant in the population.
The calculation for the daily number of residual casualties is:
Generate Hurricane Casualties
Beginning on the day of arrival, trauma and disease casualties are generated based on the number of initial casualties still seeking treatment and the daily number of residual casualties. After the day of arrival, casualties waiting for treatment are decayed in a manner similar to how they were decayed before they day of arrival.
The disaster casualties on day i after the hurricane (h0i) for the day of arrival is initialized to ArrivalCas and the starting interval counter for the decay shaping parameter (k) is initialized to either 5 or a percentage of the initial casualties. The delta parameter is defined in the same manner as it was before the day of arrival. The scaler parameter is defined as a function of the casualties remaining on the day of arrival (ArrivalCas).
For each day in the casualty generation process, Trauma and Disease casualties are generated using one of three methods, depending on the number of remaining casualties, the treatment capability, and the level of residual casualties. MPTk will display results beginning with the day of arrival, which will be labeled as day zero. The trauma and disease casualties on day j after arrival (Traj and Disj) are calculated using the index j=i−Arrival.
The humanitarian assistance casualty generation algorithm generates random daily casualty counts based on a user-input rate. For each interval, the inputs for this process are as follows.
The first step in the HA casualty generation algorithm is to calculate the parameters of the lognormal distribution. The parameters μ and σ2 are selected so that the lognormal random variates generated will have mean λ and standard deviation 0.3λ.
For each day, if the HA mission is considered “in transit”, then no casualties are produced. Otherwise, random variates are produced by first generating a lognormal random variate, then generating two Poisson random variates. The calculations are as follows for casualties on day i.
Fixed Base
The fixed base tool was designed to generate casualties resulting from various weapons used against a military base. The tool simulates a mass casualty event as a result of these attacks. Along with generating casualties, the tool also creates a patient stream based on a patient condition occurrence estimation (PCOE) developed from empirical data. This tool gives medical planners an estimate of the wounded and killed to be expected from a number of various weapon strikes.
Front End Calculations
The area of the base must first be converted into square meters to simplify future calculations in which weapons are involved. These calculations are as follows:
Finally, the total area and PAR must be split amongst each of the sectors according to their characteristics. The calculations for this are as follows.
Assign Hits to Sectors
The next step in the simulation process is to stochastically assign each weapon hit to individual sectors based upon their probability of being hit. The inputs for this process are shown in Table 0.
The first step in this process is to build a cumulative distribution of each of the sector's PHits. The cumulative probability for each sector is calculated according to the following:
select the sector from the cumulative distribution corresponding with the smallest value greater than or equal to U.
Calculate WIA and KIA
Once individual weapon hits have been assigned, the simulation calculates the number of WIA and KIA casualties for each weapon strike. The inputs for this process are shown in Table 0.
These calculations are performed for each weapon strike, and the PAR is decremented prior to the calculations for the next weapon strike. Once all of the calculations have been performed, the total number of WIA and KIA are summed together. These are the outputs for this portion of the simulation.
Shipboard
The shipboard casualty estimation tool was designed to generate casualties resulting from various weapons impacting a ship at sea. The tool, similar to the fixed base tool, generates a mass casualty event as a result of these weapon strikes. Shipboard casualty estimation tool can simulate attacks on up to five ships in one scenario. Each ship can be attacked up to five times, but it can only be attacked by one type of weapon. Each ship is simulated independently. The process below applies to a single ship and should be repeated for each ship in the scenario.
Front End Calculations
The front end calculations in shipboard calculate the WIA and KIA rate for a specific combination of ship category and weapon type. The inputs to this process are shown in the following table.
The following three tables show the values of E[WIA]Class,Weapon, E[KIA]Class,Weapon, and DefaultPARClass. The default PAR for a CVN includes an air wing. The default PARs for other ships include ship's company, but not embarked Marines. These values are stored in the CREstT common data.
The WIA rate and KIA rate are calculated by dividing the expected number of casualties by the PAR of the ship.
The outputs of this process are as follows:
Casualty counts in Shipboard are generated using an exponential distribution. The parameterization of the exponential distribution is as follows:
Calculate WIA and KIA
Once the casualty rates have been calculated, they are used to simulate the number of casualties caused by each hit. Each ship can be hit up to five times by the same type of weapon, and the PAR is decreased after each hit by removing the casualties caused by that hit. The inputs to this process are shown in the following table.
The calculation of WIA and KIA casualties is performed according to the following process.
Total KIA and WIA for each ship are the sum of KIA and WIA from each hit:
Assignment of ICD-9 Codes
The previous sections described the procedures used by CREstT to produce counts of casualties on a daily basis. In addition to these casualty counts, CREstT also produces patient streams, which assign ICD-9 codes to each patient. This process is common to all of the casualty generation algorithms within CREstT.
To assign ICD-9 codes, the PCOF is first converted into a CDF (cumulative distribution function). This allows CREstT to randomly select a ICD-9 code from the distribution via the generation of a uniform (0,1) random number.
ICD-9 code assignment for each casualty consists of the following two steps:
Combined scenarios allow the user to combine the results of multiple individual CREstT scenarios into a single set of results. Each individual scenario is executed according to the methodology for its mission type. The combined results are then generated by treating each component scenario as its own casualty group. For mission types with multiple casualty groups, the results for the ‘Aggregate’ casualty group are sent to the combined scenario.
C. Expeditionary Medical Requirements Estimator (EMRE)
The Expeditionary Medical Requirements Estimator (EMRE) is a stochastic modelling tool that can dynamically simulate theater hospital operations. EMRE can either generate its own patient stream or import a simulated patient stream directly from CREstT. The logic diagram showing process of EMRE is shown in
The EMRE tool is comprised of four separate algorithms:
EMRE has two different methods for generating casualties: use a CREstT scenario or generate casualties using a user defined rate. In each case, MPTk will generate casualty occurrences then probabilistically determine which of those occurrences will become admissions at the theater hospitalization level of care. These two methods of generating casualties are described in detail below.
When a CREstT patient stream is used, all casualties from CREstT are considered. However, the patient stream generated by CREstT must be adjusted to account for the fact that many of the casualty occurrences generated by CREstT will not become admissions at the theater hospitalization level. The inputs to this process are shown in the table below.
The procedure for adjusting casualty occurrences to arrive at theater hospital admissions is as follows:
Casualty Generation Using a User Defined Rate
The first step when generating casualties from a user defined rate is to determine the number of admissions on each day, k, for each replication,j, (NumAdmj,k). This number is determined by a random simulation of the Poisson distribution with a mean equal to the user input number of patients per day (λ). As is the case throughout MPTk, Poisson random variates with means greater than 30 are generated using the rejection method proposed by Atkinson (1979). For means less than 30, Knuth's method, as described by Law, is used (2007).
EMRE then generates a patient stream that consists of the ICD-9 codes for each admission that occurs on each day for each replication. To accomplish this, EMRE generates casualty occurrences from the given PCOF. It then randomly determines if each occurrence becomes an admission using the same procedure used with CREstT casualty inputs in EMRE. This is repeated until the proper number of casualties has been generated (NumAdmj,k). The procedure is as follows.
For each replication j and day k:
The result of this process is the set of ICD-9 codes for every theater hospital admission on each day of each replication (ICD9i,j,k). The process for generating the ICD-9 codes of casualty occurrences (Occ_ICD9i,j,k) is described in detail below. EMRE first stochastically assigns the patient type of each casualty occurrence using the user-input patient type distribution (P(type)). The user-input patient type distribution is converted into a CDF (cumulative distribution function) for random selection. This allows EMRE to randomly select a patient type from the distribution via the generation of a uniform (0,1) random number. EMRE then generates a random number for each casualty and selects from the cumulative distribution. After generating a uniform (0,1) random number, EMRE selects the injury type corresponding to the smallest value greater than or equal to that number.
Injury type assignment for each casualty consists of the following two steps:
Once the patient type is assigned, the casualty is randomly assigned an ICD-9 code using the user specified PCOF. The manner in which ICD-9s are assigned is identical to the process used to assign ICD-9 codes within CREstT.
Calculate Initial Surgeries
The Calculate Initial Surgeries algorithm stochastically determines whether casualties will receive surgery at the modeled theater hospital. EMRE does this based on its common data, which contains a probability of surgery value for each individual ICD-9 code. These values range from zero (in which case a particular ICD-9 code will never receive surgery) to 1 (where a casualty will always receive surgery). EMRE randomly selects from the distribution similarly to how injury types and ICD-9 codes are assigned.
Determining surgery for each casualty consists of the following two steps:
This process creates a single set of outputs-a Boolean value for each casualty describing whether they received surgery.
These variables can be used to calculate the number of surgeries on a given day or replication. As an example, the calculation for the number of Surgeries on rep j=1 day k=1 is as follows:
Calculate Follow-Up Surgeries
The logic diagram showing how follow-up surgery is calculated is shown in
The next step in the EMRE process is to calculate the time in surgery for each of those casualties who required surgery in the previous two processes. EMRE's common data contains values by ICD-9 code for both initial and follow-up surgery times. If the casualty was chosen to have surgery, a value is randomly generated from a truncated normal distribution around the appropriate time. The inputs for this process are shown below.
Surgery times are drawn from a truncated normal distribution where the distribution is bounded within 20% of the mean surgical time. The standard deviation is assumed to be one fifteenth of the mean.
The total amount of OR time a patient uses for their initial surgery (ORTimeIniti,j,k) is the simulated amount of time necessary to complete the surgery plus the OR setup time.
A similar calculation is used to calculate the amount of OR time that is required for follow-up surgery.
Random variates are simulated from the truncated normal distribution as follows: The percentiles of the normal distribution that are associated with the minimum and maximum of the truncated normal distribution (p1 and p2) can be calculated from the CDF of the normal distribution. Because the standard deviation is a constant ratio of the mean, these values will be the same for every ICD-9 and only need to be computed once.
To generate a random variate from this distribution, generate a uniform random number.
Where Norm.Inv evaluates the inverse of the Normal distribution cumulative distribution function at x.
The total number of load hours needed each day k, in a given replication j, (LoadHoursj,k) is the sum of the times necessary to complete all initial and follow-up surgeries that occur on that day.
The outputs for this process are the total OR load for each day of each replication, and are described in the following table.
Calculating OR Tables
The calculation of the required number of OR tables is a simple extension of the process for calculating OR load hours. EMRE calculates, for each day, the necessary number of OR tables to handle the patient load. This calculation is based upon the following inputs.
The calculation is the ceiling of the daily load hours divided by the operational hours. This process produces a single output—the number of required OR tables on each day of each replication
Determining Patient Evac Status
The next step in the high-level EMRE process is to determine the evacuation status and length of stay in both the ICU and the ward for each patient. The inputs for this process are shown below.
There are two decision points for this logic. First, casualties are split according to whether they required surgery. Their length of stay for both the ICU and the Ward is then determined. Next, if the total length of stay is greater than the evacuation policy, the casualty will evacuate; otherwise, they will return to duty.
As a convention, a patient's status is always determined at the end of the day. For example, a patient that arrives on day 3, stays for 3 nights in the ward, and then evacuates will generate demand for a bed on days 3, 4, and 5. On day 6, they will be counted as a ward evacuee, but they will not use a bed on day 6 because they are not present at the end of the day. The outputs for this process are as follows.
Calculating Number of Beds and Evacuations
The next step in the EMRE process is to determine the number of beds, both in the ICU and the ward, required to support the patient load on a given day. Coupled with this is the calculation of the evacuations, both from the ICU and the ward, on any given day. Casualties that evacuate from the ward are also counted towards demand for staging beds. The inputs for this process are as follows.
This process is broken down into two subprocesses. First, the calculations are performed for casualties who were designated for evacuation in the Determining Patient Evac Status section. Next, a different process is performed for patients who were designated to return to duty.
Calculating Blood Planning Factors
The final process in an EMRE simulation is the calculation of blood planning factors. This process simply takes the user-input values for blood planning factors, either according to specific documentation or specific values from the user, and applies them to specific casualty types. The inputs are displayed in Table 87.
The calculation of the blood products is simple. If a casualty has the patient type WIA, NBI, or trauma, he receives the blood products according to the user-input quantities. Therefore, it is simply a multiplier of the total number of WIA, NBI, and trauma casualties and the quantities for the blood planning factors. As an example, below is the calculation for red blood cells. The calculations for each of the other planning factors are calculated similarly.
The Medical Planners' Toolkit (MPTk) is a software suite of tools (modules) developed to support the joint medical planning community. This suite of tools provides planners with an end-to-end solution for medical support planning across the range of military operations (ROMO) from ground combat to humanitarian assistance. MTPk combines the Patient Condition Occurrence Frequency (PCOF) tool, the Casualty Rate Estimation Tool (CREstT), and the Expeditionary Medical Requirements Estimator (EMRE) into a single desktop application. When used individually the MPTk tools allow the user to manage the frequency distributions of probabilities of illness and injury, estimate casualties in a wide variety of military scenarios, and estimate level three theater-medical requirements. When used collectively, the tools provide medical planning data and versatility to enhance medical planners' efficiency.
The PCOF tool provides a comprehensive list of ROMO-spanning, baseline probability distributions for illness and injury based on empirical data. The tool allows users to store, edit, export, and manipulate these distributions to better fit planned operations. The PCOF tool generates precise, expected patient probability distributions. The mission-centric distributions include combat, humanitarian assistance (HR), and disaster relief (DR). These mission-centric distributions allows medical planner to assess medical risks associated with a planned mission.
The CREstT provides the capability for planners to emulate the operational plan to calculate the combat and non-combat injuries and illnesses that would be expected during military operations. Casualty estimates can be generated for ground combat, ship attacks, fixed facilities, and natural disasters. This functionality is integrated with the PCOF tool, and can use the distributions developed in that application to construct a patient stream based on the casualty estimate and user-selected PCOF distribution. CREstT uses stochastic methods to generate estimates, and can therefore provide quantile estimates in addition to average value estimates.
EMRE estimates the operating room, ICU bed, ward bed, evacuation, and blood product requirements for theater hospitalization based on a given patient load. EMRE can provide these estimates based on a user-specified average daily patient count, or it can use the patient streams derived by CREstT as EMRE is fully integrated with both CREstT and the PCOF tool. EMRE also uses stochastic processes to allow users to evaluate risk in medical planning.
The MPTk software can be used separately or collectively in medical logistics and planning. For example, the PCOF module can be used individually in a method for assessing medical risks of a planned mission comprises. The user first establishes a PCOF scenario for a planned mission. Then run simulations of the planned mission to create a set of mission-centric PCOF distributions. The PCOF stores the mission-centric PCOF distributions for presentations. The user can use these mission-centric PCOF to rank patient conditions for the mission and thus identifying medical risks for the mission.
In another embodiment, the MPTK may be used collectively in a method for assessing adequacy of a medical support plan for a mission. The user first establishes a scenario for a planned mission in MPTk. The user then stimulates the planned mission to create a set of mission-centric PCOF using PCOF module. The user then can then use the CREstT module to generate estimated estimate casualties for the planned mission and use the EMRE module to calculate estimated medical requirements for the planned mission. The results from the simulation in three modules can then be used to assess the adequacy of a medical support plan. Multiple simulations may be created and run using different user inputs, and the results from each simulation compared to select the best medical support plan, which reduces the casualty or provides adequate medical requirements for the mission. The MPTk software can also be used in a method for estimating medical requirements of a planned mission. In this embodiment, the user first establishes a scenario for a planned mission in MPTk or only in EMRE. Then the user run simulations of the planned medical support mission to generate estimated medical requirements. The estimated medical requirements may be stored and used in the planning of the mission. In an embodiment of the inventive method for estimating medical requirements medical requirements of a planned mission, medical requirements estimated including but not limited to:
IV. Verification and Validation of MPTk Software
A MPTk V&V Working Group were designated by the Services and Combatant Commands in response to a request by The Joint Staff to support the MPTk Verification and validation effort. The members composed of medical planners from various Marine, Army, and Navy medical support commands. Each member of the Working Group received one week of MPTk training conducted at Teledyne Brown Engineering, Inc., Huntsville, Ala. The training was provided to two groups; the first group receiving training 28 Apr.-2 May 2014 and the second group from 5-9 May 2014. During the training, each member of the Working Group received training on MPTk, to include detailed instruction on the PCOF tool, CREstT, and EMRE as well as training on the verification, validation, and accreditation processes. Specific training on the V&V process included the development of acceptability criteria, testing methods, briefing formats, and the use of the Defense Health Agency's eRoom capabilities, which served as the information portal for the MPTk V&V process.
Towards the end of each week, initial testing began using the same procedures that would be used throughout the testing to familiarize each of the Working Group members with the process. The major validation events of the V&V process occurred on the Defense Connect Online (DCO), report calls that were conducted during the validation phase of the testing. On each of the DCO calls during validation testing of the model, Working Group members were presented briefings on topics they had selected on validation issues by the software developers. The Working Group members then discussed validation issues. The major issue identified during the validation phase of the testing was a recommendation to add the ability for the user to select a service baseline casualty rate (vs. a Joint baseline casualty rate) and a use redefined baseline casualty rate. The MPTk V&V Working Group members determined this was a valid concern and the capability was added to the model and thoroughly tested. Once this capability was added, the Working Group members were satisfied with the validation phase of the testing.
Comparison testing on MPTk was conducted on DCO calls on 6 Aug. 2014 and 13 Aug. 2014. Testing was conducted comparing MPTk results to real world events, and also to output from another DoD medical planning model, JMPT. Working Group members identified several issues during the comparison testing of MPTk, all of which were corrected and retested. At the conclusion of the testing, all Working Group members were satisfied with the results of the comparison testing.
Multiple iterations of the changes made have recently been incorporated into MPTk. These include:
Based on the foregoing, a computer system, method and software have been disclosed for medical logistic planning purpose. However, numerous modifications and substitutions can be made without deviating from the scope of the present invention. Therefore, the present invention will be disclosed, the DETAILED DESCRIPTION section, by way of example and not limitation.
The tables below (Tables 89-91) show the data used by EMRE to support the previously described processes. All variables with a source listed as “EMRE common data” are defined here. Some values may be stored at a greater precision in the MPTk database and rounded for display in these tables.
This application is a continuation of U.S. patent application Ser. No. 15/004,022, filed Jan. 22, 2016, which is a continuation-in-part application of patent application Ser. No. 14/192,521 filed on Feb. 27, 2014 (U.S. Pat. No. 10,706,129), and claims priority to U.S. Provisional Application No. 62/107,072 filed on Jan. 23, 2015.
This invention was made with Government support under contracts W911QY-11-D-0058 and N62645-12-C-4076 that were awarded by the OSD DHA, OPNAV (N81), and the Joint Staff. The Government has certain rights in the invention.
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| 61769805 | Feb 2013 | US | |
| 60107077 | Nov 1998 | US |
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| Parent | 15004022 | Jan 2016 | US |
| Child | 17535613 | US |
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| Parent | 14192521 | Feb 2014 | US |
| Child | 15004022 | US |
