Optimized Point of Dispensing (POD) Model for Inhalation Anthrax Exposure Response in Union County, Ohio

State: OH Type: Neither Year: 2015

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·          Brief description of LHD- location, demographics of population served in your community   o    The Union County Health Department (UCHD) is located in the Central Ohio Region and is made up of five divisions that serve a population of approximately 53,000 residents to prevent the spread of infectious diseases, promote health and well-being, and assist in healthy development.   §  Describe public health issue   o    Time studies of POD processes such as removing labels from medication bottles had not been performed or published. This resulted in using data that had to be estimated, or extrapolated from data collected from a dissimilar process, when populating data into the RealOpt model. Without empirical data from time studies of POD processes, an accurate, high-confidence, evidenced based and optimized public health point of dispensing (POD) team structure could not be developed to achieve POD operations that serve a population of up to 53,000 residents.   §  Goals and objectives of the proposed practice   o    The goal of this practice was to conduct a time study of screening and dispensing resources not previously used to collect more accurate data. §  Objective: collect data that represents times to complete screening and dispensing operations utilizing o    Another goal of this practice was to create the most optimized Union County point of dispensing team. o    The objective: developing a POD model that, from a theoretical perspective, achieves the greatest optimization rate possible regarding POD operations. Optimization was defined through the following simulation output data: throughput of the county population within 24 hours; minimal patron flow and wait times; optimal numbers of medical and non-medical staff; optimal worker utilization rates; and appropriate queue lengths.   §  How was practice implemented/activities o    A drill was conducted consisting of one screener and 2 dispensers using actual ODH approved NAPH forms and sample medication bottles with exact label replicas as found in the Strategic National Stockpile (SNS). Times were assigned to monitor screening time and dispensing time. o    The data collected from the drill was entered into the RealOpt model software to create a list of staff needed to run a POD site   §  Results/Outcomes (list process milestones and intended/actual outcomes and impacts. o    Were all of the objectives met? §  The results were achieved with the following results: §  Number of NAPH forms: 11 forms serving 78 people using head-of-household protocol §  Screening average time per NAPH form: 23 seconds §  Dispensing average time per NAPH form: 112 seconds §  Screening average time per person: 3 seconds §  Dispensing average time per person: 18 seconds §  Peeling the labels off of bottles accounted for an average of 41% of dispensing time. §  The data collected from the drill was entered into the RealOpt model software and produced a model that was sufficient to support POD operations.   o    What specific factors led to the success of this practice? §  A time study was accomplished involving screening and dispensing positions to collect accurate data for use in the RealOpt software program. Success is contributed to using real NAPH forms and the use of simulate medication bottles that exactly replicate the medication bottles in the Strategic National Stockpile (SNS). §  Success in creating an optimized POD model was based on a number of factors: 1) UCHD had a locally assigned CDC PHAP that was able to perform the project as part of their CDC approved assignment activities; 2) the individual received training to properly utilize the RealOpt Modeling Software Program; 3) the individual was given the time to run multiple models to achieve the desired optimization (14 different iterations of the model were performed over a 6-month period of time to optimize the model to the greatest extent possible).   §  Public Health impact of practice o    Data is now available that was not previously that measures the screening and dispensing rate utilizing official NAPH forms and realistic SNS medication bottle simulators. o    The time study data collected utilized by the RealOpt software to create a highly optimized POD model that UCHD can use to enhance POD operations plans. o    The project also ensures the greatest use of public health resources during full scale exercises for a number of reasons. The use of a scientifically based modeling software increases the potential to “get it right” the first time.   §  Website for your program, or LHD: www.uchd.net
o    Statement of the problem/public health issue §  Time studies of POD processes such as removing labels from medication bottles had not been performed or published. This resulted in using data that had to be estimated, or extrapolated from data collected from a dissimilar process, when populating data into the RealOpt model. Without empirical data from time studies of POD processes, an accurate, high-confidence, evidenced based and optimized public health point of dispensing (POD) team structure could not be developed to achieve POD operations that serve a population of up to 53,000 residents. o    What target population is affected by problem (please include relevant demographics) §  What is target population size? Approximately 53,000 county residents §  What percentage did you reach? Theoretically, utilizing the data from the time study project, the RealOpt model has demonstrated that 53,000 county residents could be provided medications within required parameters. o    What has been done in the past to address the problem? §  RealOpt modeling software program has been used by Union County in the past but the output of the program was debatable based on the lack of time study data of individual POD process as well as the already mentioned lack of time and personnel resources to conduct a model simulation as thorough as the one developed as part of this model. o    Why is current/proposed practice better? §  The Local impact provided by the highly optimized model will allow for the greatest use of public health resources during full scale exercises. The use of the time study project data in RealOpt modeling provides a theoretical foundation from which to conduct actual mass dispensing operations. This is important in that an exhaustive amount of local resources are necessary to conduct a full scale exercise. The nature of exercise design and execution is time intensive and usually requires at least a year of advance planning to complete successfully. The use of a scientifically based modeling software increases the potential to “get it right” the first time and to improve prediction potential for local jurisdictional planning, which can also be quickly modified based on exercise or life scenario feedback. o    Is current practice innovative? How so/explain? §  Is it a creative use of existing tool or practice: What tool or practice did you use in an original way to create your practice? (e.g., APC development tool, The Guide to Community Preventive Services, HP 2020, MAPP, PACE EH, a tool from NACCHO’s Toolbox etc.)   §  This project is innovative due to the time study conducted of POD screening and dispensing processes that included actually removing labels from medication bottles. §  RealOpt Modeling Software Program is an existing tool that is utilized by individual health departments to develop models that optimize the efficiency and effectiveness of medical countermeasure dispensing site operations. The project is innovative in that the UCHD was unable to find empirical data on these processes and, through research, realized that this type of time study had not been completed and published. The UCHD may be the first health department to conduct a data collecting time study utilizing exact replicas of SNS medication bottles and meticulously documenting the time it took to accomplish screening and dispensing base on the use of these realistic resources. §  The use of the RealOpt modeling software is also innovative in that a single individual was able to be assigned to receive the training and be given the time to put the training into practice by being given ample time to run, analyze and change models to achieve optimization. This is creative and innovative when most health departments lack the resources to commit an extensive amount of time that would allow the software user to become highly proficient at utilizing the software and analyzing the data it produces. §    o    Is the current practice evidence-based? If yes, provide references (Examples of evidence-based guidelines include the Guide to Community Preventive Services, MMWR Recommendations and Reports, National Guideline Clearinghouses, and the USPSTF Recommendations.) ? The goal of this project was precisely to generate data to use within an evidence-based practice model. We collected time parameter data through this study and are already translating this evidence into practice in our own health department’s emergency preparedness activities. Meanwhile, many other health districts in Central Ohio and a couple in the Chicago MSA are using this evidence-based model as well. ? The results are evidence based as a result of empirical data collected from this time study project. From a theoretical perspective as the model output is based on the models produced by the RealOpt software utilizing data collected during real time studies of individual process that had not been done before. Previously, State and Local public health agencies had been advised by national health officials to estimate time parameters, which has led to unacceptable levels of poor prediction standards by previously produced models that have been exercised locally. Unless all 72 MSAs conducting this type of planning are also basing RealOpt input parameters on strong evidence, then we predict that this may be a systematic problem nationwide. Although localized POD responses and designs contain a range of variability, beginning to think about using evidence-based parameters, and sharing successful, evidence-based RealOpt models will initiate a healthy dialogue among health districts conducing this sort of emergency planning. ? As a continuation of the model, future goals and objectives for this practice include: 1) utilizing the model to develop a Point of Dispensing resource type that would be used to conduct pilot tests throughout the United States in order to collect data that proves or disproves the optimized outcome of the model; and 2) establish and validate an optimized POD resource type that creates a public health response standard whereby public health resources can be allocated to public health incidents via EMACs or local mutual aid agreements.  
§  Goal(s) and objectives of practice Local goals of this project include: The goal of this practice was to conduct a time study of screening and dispensing resources not previously used to collect more accurate data. Objective: collect data that accurately represents times to complete screening and dispensing operations utilizing Another goal of this practice was to create the most optimized Union County point of dispensing team resource utilizing data collected during the time study of screening and dispensing operations and the RealOpt-POD-v8.0.2© (RealOpt©) modeling program. The objective: developing a POD model that, from a theoretical perspective, achieves the greatest optimization rate possible regarding POD operations. Optimization was defined through the following simulation output data: throughput of the county population within 24 hours; minimal patron flow and wait times; optimal numbers of medical and non-medical staff; optimal worker utilization rates; and appropriate queue lengths. Customization of RealOpt modeling variables through numerous trials resulted in a state of optimization, unparalleled by previous Union County models, providing detailed POD layout design and staffing specifications sufficient to meet CRI specifications and expected POD demands.Another goal of this project was to utilize time study data to develop an optimized POD model to serve as an initial framework from which to build a national public health resource type that would enable the efficient and effective sharing of resources through an EMAC. The objectives include: 1) utilizing the model to develop a Point of Dispensing resource type that would be used to conduct pilot tests in Union County; 2) Collect and analyze the data to prove or disprove the theoretical optimized outcome of the model; 3) utilize the model to develop a Point of Dispensing resource type that would be used to conduct pilot tests throughout the United States in order to collect data that proves or disproves the optimized outcome of the model; and 4) establish and validate an optimized POD resource type that creates a public health response standard whereby public health resources can be allocated to public health incidents via EMACs or local mutual aid agreements.  §  What did you do to achieve the goals and objectives? A drill was conducted consisting of one screener and 2 dispensers using actual ODH approved NAPH forms and sample medication bottles with exact label replicas as found in the Strategic National Stockpile (SNS). Times were assigned to monitor screening time and dispensing time.The data collected from the drill was entered into the RealOpt model software to create a list of staff needed to run a POD site §  What was the timeframe for the practice 12 months§  Were other stakeholders involved? What was their role in the planning and implementation process? §  Local: the UCHD utilized other public health medical subject matter experts to perform the role of screening. Other non-medical public health staff were utilized to perform the dispensing role. §  Regional public health: The CDC public health associate completed POD optimization models for seven health departments in the Central Ohio Region and one health department in Illinois. Accomplishing the additional RealOpt models for other health departments was beneficial to the departments but also further improved the skills of the CDC PHAP operating the software. §  Federal: The CDC public health associate collaborated with a representative of the CDC Division of the Strategic National Stockpile on developing an article that presents the comprehensive POD RealOpt model that utilized the time study project data. In addition, the CDC provided medication bottle simulators at the request of UCHD to ensure there would be enough bottles for the project. §  Any start up or in-kind costs and funding services associated with this practice? Please provide actual data, if possible. Otherwise, provide an estimate of start-up costs/ budget breakdown. Startup costs included $500 of PHEP grant funds used to purchase SNS bottle simulators with exact replica of the labels on the bottle simulator. 
§  What did you find out? To what extent were your objectives achieved? Please re-state your objectives. The goal of this practice was to conduct a time study of screening and dispensing resources not previously used to collect more accurate data. The objective was to collect data that accurately represented the time it took for screening and dispensing operations to be completed.The objective was fully achieved. Valuable data was collected that showed screening and dispensing timeframes. An additional data set was collected that shows the amount of time necessary to peel the labels off of the medication bottles.Another goal of this practice was to create the most optimized Union County point of dispensing team resource utilizing data collected during the time study of screening and dispensing operations and the RealOpt-POD-v8.0.2© (RealOpt©) modeling program. The objective: developing a POD model that, from a theoretical perspective, achieves the greatest optimization rate possible regarding POD operations. Optimization was defined through the following simulation output data: throughput of the county population within 24 hours; minimal patron flow and wait times; optimal numbers of medical and non-medical staff; optimal worker utilization rates; and appropriate queue lengths. Customization of RealOpt modeling variables through numerous trials resulted in a state of optimization, unparalleled by previous Union County models, providing detailed POD layout design and staffing specifications sufficient to meet CRI specifications and expected POD demands.The objective was fully achieved. The time study data was extremely valuable for conducting simulations utilizing the RealOpt software to find the most optimized POD operational model.  o Our objectives were achieved in that we gathered time data for an emergency post-exposure prophylaxis screening and dispensing station that our county plans to utilize as a core function of it POD preparedness serving 53,000 individuals of Union County, part of the Columbus MSA. o These time data were inputted into the RealOpt model according to the model’s accepted format that uses minimum, maximum, and mode time points. Our data from this time study informs the “screening” and “dispensing” rows of data (highlighted in yellow) in the following chart Table 2: Time Distribution Tab InformationPOD Station Name Type Distribution Min Time (minutes: seconds) Mode Time (minutes: seconds) Max Time (minutes: seconds)Greeting/Wellness Triage Process Triangular 0:05 0:15 0:30Fill out Forms Delay Triangular 2:48 1:38 7:54Medical Evaluation/Mental Health Process Triangular 1:00 1:30 5:00Screening Process Triangular 0:06 0:09 0:30Doctor Screening and Dispensing Process Triangular 1:04 1:36 7:20Dispensing Process Triangular 0:40 1:00 3:20Quality Control Process Triangular 0:10 0:30 0:50 §  The time parameters for the other POD stations listed in this table were drawn from previous models included as part of the RealOpt software package, however it is unclear how these time values were derived. §  Based on these time parameters, the resulting RealOpt model for dispensation of prophylactic medications in response to a hypothetical anthrax exposure scenario show that serving Union County’s 53,000 population at a single POD site requires a total of 17 medical and 84 non-medical personnel per 12 hours shift.  §  Did you evaluate your practice? §  List any primary data sources, who collected the data, and how (if applicable) One primary data source for the project was the time study that was conducted at the Union County Health Department.Another primary data source was the RealOpt software output reports.§  List performance measures used. Include process and outcome measures as appropriate. Process measures: The time study allowed for the RealOpt software operator to modify the program in order to achieve process optimization such staff utilization rates of 85%, staff requirements for POD processes and stations, and queue length and wait times. Outcome measures: The time study allowed for the RealOpt software operator to modify the program in order to generate a simulation model that achieved the outcome measure of dispensing medications to the entire population of Union County within a 24-hour period.§  Describe how results were analyzed The results of the time study were placed into excel spreadsheets. Excel formulas were used to assemble the raw data into averages, medians, minimums, and maximums for individuals and head-of-household to be screened and receive medications. The spreadsheets were extremely helpful in distilling the raw data to a medium that was needed for use in the RealOpt software.§  Were any modifications made to the practice as a result of the data findings? ? Our study evaluated not only time information for the screening and dispensing process, but also screening and dispensing accuracy data, noting the kinds and types of errors that were made in dispensing medications. Analysis of these data encouraged Union County planners to add an additional POD station (depicted in the previous chart) of “quality control,” providing for an additional staff member to check compiled medications with the screening paperwork and client names, to ensure that each POD patron received the right type of medication, and that each family picking up medications received the bag of medications meant for them.  • Additionally, based on the time parameter results from this study, we realized that screeners completed tasks at a much faster rate than dispensers, due to differences in job responsibilities. Thus, we decided to use a system of one screener for 4 dispensers and to include 1 quality control worker for each system of screener and dispensers. Additionally, we provided for one additional quality control worker to stand by as a “floater” who can provide assistance to multiple screening and dispensing stations.  
Sustainability is determined by the availability of adequate resources. In addition, the practice should be designed so that the stakeholders are invested in its maintenance and to ensure it is sustained after initial development (NACCHO acknowledges that fiscal crisis may limit the feasibility of a practice's continuation.) §  Lessons learned in relation to practice The major lesson learned was that the process involving peeling the labels from the medication bottles accounted for 41% of the dispensing process time.  §  Did you do a cost/benefit analysis? If so, describe. An informal cost/benefit analysis was accomplished. It was determined that the time study data was critical for modifying the RealOpt model in order to optimize Union County POD operations. The impact of the time study data did not add any additional costs to the Union County Health Department because the increase in optimization resulted in lower staffing and supply requirements. In addition, actual POD plans needed very little change as the POD operations concept remained the same, only the quantity of resources needed were adjusted. The adjustment was typically for less resources. §  Is there sufficient stakeholder commitment to sustain the practice? §  Describe sustainability plans   §   Union County Health Department is currently the only stakeholder. However, the optimized model could potentially benefit any health department with a population of approximately 50,000. Greater potential for mutual aid and EMAC support when agencies utilizing a standard POD model would make the project and RealOpt model sustainable. In addition, a POD model developed utilizing the time study data would allow for other jurisdictions to run addition tests of the model utilizing the RealOpt software. These tests would either validate the model as the most optimized possible or would identify gaps that need improvement. Ultimately, the model would essentially go through a continuous quality improvement process that would enhance POD operations and plans for any public health jurisdiction that utilized the time study data and the RealOpt model created by the Union County Health Department. The enhancements could potentially be achieved at little, to no costs to the jurisdiction, or could be cost shared between partnering jurisdictions. The Cities Readiness Initiative (CRI) mandates preparedness planning for biological hazards across 72 MSAs nationwide. However other health districts that are not part of the 72 MSA’s selected by CRI are also conducting similar planning. This CRI planning is required to integrate RealOpt modeling as a component of developing evidence-based POD designs, however previous RealOpt modeling conducted was based on estimated figures by public health preparedness planners, yet were not based in any concrete data. This study provided that data that we can now use in our RealOpt prediction modeling for resource allocation at POD sites. ? RealOpt models will still need to be evaluated with live drills with predicted throughput and staff utilization figures compared to “real world” exercises and events. Presumably, live scenarios contain additional complexities that we may not have accounted for in our modeling. However close observation and documentation of exercises can provide a feedback loop of information to inform the next round of RealOpt prediction models. §  The Local impact provided by the highly optimized model will allow for the greatest use of public health resources during full scale exercises for a number of reasons. Most notably is that the RealOpt modeling software provides a theoretical foundation from which to conduct actual mass dispensing operations. This is important in that an exhaustive amount of local resources are necessary to conduct a full scale exercise. The nature of exercise design and execution is time intensive and usually requires at least a year of advance planning to complete successfully. The use of a scientifically based modeling software increases the potential to “get it right” the first time. §  The successful results of the project, from a theoretical perspective, supports that public health community as a whole would sustain the POD modeling that resulted from the time study to include: 1) utilizing the model to develop a Point of Dispensing resource type that would be used to conduct pilot tests throughout the United States in order to collect data that proves or disproves the optimized outcome of the model; and 2) establish and validate an optimized POD resource type that creates a public health response standard whereby public health resources can be allocated to public health incidents via EMACs or local mutual aid agreements.  §  Another sustainment would be to alter the current RAND data collection process to support time studies of other POD micro-processes that exist in intake, screening, and dispensing and exit areas of the POD. RAND data collection would also support sustainability with little or no additional costs. However, a RealOpt model and subsequent POD team structure would need to be created and accepted by participating health jurisdictions to ensure like data is collected based on standard practices. ? We hope that grounding prediction model in evidence will help support the use of RealOpt as a beneficial tool for public health agencies, not simply a requirement that must be checked off to meet grant deliverables, yet not based on solid, operational standards.
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