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Dr. Klenk, a Principal with Booz Allen Hamilton, has over 20 years of professional experience in the public and commercial Health markets. His professional work spans fields such as Decision Support Analytics, Study Operations, Process Optimization, Scientific Computing, Comparative Effectiveness Research, and Risk Analytics, and he is experienced in employing advanced analytics techniques such as statistical analysis, predictive modeling, simulation, operations research, and Natural Language Processing. Dr. Klenk is leading Booz Allen’s Health Analytics team that is focused on providing advanced analytics services to government, non-profit and commercial health clients to support data-driven planning and decision making. Dr. Klenk holds a Ph.D. in Mathematics and a M.S. in Physics and Mathematics from Tuebingen University, and he holds a Six Sigma Green Belt and a MicroMBA certification. He spent his academic career at Tuebingen University, Yale University, and the Australian National University.
Hello. My name is Juergen Klenk, and I will be presenting to you a project that my team and I are conducting together with the National Institutes of Health, to develop a data-driven, evidence-based decision support framework to assist NIH in the planning, execution, and monitoring of the National Children’s Study, a study of unprecedented size and complexity.
The National Institutes of Health (NIH), through the Children’s Health Act of 2000, was authorized by Congress to undertake on one of its largest and most complex studies ever—the National Children’s Study (NCS). Focused on examining the effects of the environment on the growth, development, and health of children, the goal of the NCS is to improve the health and well-being of children and contribute to understanding the role various factors have on health and disease. NIH must ensure its investment in the NCS produces the best possible results while remaining on time and on budget over the study’s 25-year duration. Challenged by recent setbacks (e.g., the negative outcome of the SELECT trial) and under close scrutiny with recent budget and accountability pressures to generate research results with measurable impact on health at an affordable cost, failure is not an option for a study the size of the NCS. With a budget of just over $3 billion, the NCS will attempt to enroll up to 250,000 mothers and their children at over 175 study locations across the United States and follow them from before birth to age 21 – a huge operational challenge!NIH decided to respond to the challenge by developing a groundbreaking new approach of data-driven, evidence-based study planning, monitoring, and execution. This novel approach, expected to become a standard for study management in the 21st century, will play a crucial role in maximizing chances of study success while minimizing overall cost.
NIH is teaming with Booz Allen to develop the novel analytic foundation that enables data-driven and evidence-based study planning. We worked closely with our client to investigate the scientific question, how can data-driven, evidence-based study planning be accomplished in a rigorous manner? Or in more technical terms, what kind of mathematical tools and techniques would lend themselves to developing an effective, data-driven decision support framework? Our research indicated that a combination of Markov models, stochastic models, predictive models, and discrete event simulation would allow us to simulate the NCS in a virtual environment. This simulation environment could then be used to evaluate operational options in terms of their feasibility, performance, and cost.All Data Analytics efforts need large amounts of data, and the data required to develop the proposed NCS models came from the Vanguard Study, which is a pre-study to the NCS - essentially a small-scale, 1% version of the Main Study. The Vanguard Study was launched in 2009, and Booz Allen supported the selection of data types which needed to be collected, as well as a comprehensive Quality Assurance/Quality Control strategy to ensure correctness and integrity of the data – a cornerstone towards accurate data-driven decision making.
Markov models form the foundation for our selected approach. For example, for the recruitment phase, Markov states represent the different stages of the recruitment process occupied by potential participants, such as ‘sampled’, ‘screen eligible’ and so on until ‘consenting’. Because the population is not homogeneous, a stochastic approach was selected for the implementation. It allows us to not just simulate populations in the aggregate, but individual participants with their unique characteristics such as race, ethnicity, age, income etc. This in turn allows us to represent transitions between states as predictive models, thus correctly representing the observed differentiated transition rates of subpopulations based on their characteristics. The entire model was constructed in Python using an open-source discrete event simulation package.
Booz Allen’s work has already had significant impact on the NCS. The employment of a rigorous modeling approach during the planning phase revealed questions that otherwise might not have been studied. For example, what boost strategies can be employed to shorten the recruitment period, and how does this affect overall cost? Or, what data are required to effectively monitor study operations, and how can these data be collected in sufficient quantity and quality? What sampling unit sizes for study locations are optimal to achieve recruitment targets as quickly as possible, and at the lowest cost? One of the most important questions that Booz Allen’s predictive models helped answer was the actual number of participants required—250,000—to ensure that the NCS can collect enough data to study rare diseases and minorities.The partnership between NIH and Booz Allen to develop the novel analytic foundation that enables data-driven and evidence-based study planning has led to the desired results, to develop a robust plan for the recruitment phase of the NCS. Supported by analytic evidence, the Main Study of the NCS is on track for a successful 2012 launch. Additional planning factors around retention, compliance, and study operations and logistics as well as cost drivers are now being analyzed using the same approach, to develop the study protocol further and beyond the recruitment phase. This effort will ultimately ensure that the NCS can achieve all of its objectives within the allotted budget.
The NCS is one of the most important enterprises at the NIH. It has its own line item in the NIH budget, and as such it has the attention of the NIH Director, Dr. Francis Collins. The novel data-driven, evidence-based approach to the planning, execution, and monitoring of the study—proposed by NCS Director Dr. Steven Hirschfeld—is viewed not only as a guarantor for success, but also as a way to plan and conduct studies in the 21st century. Booz Allen’s innovative use of Advanced Analytics techniques such as predictive modeling has provided NIH with an analytic platform that realizes the desired data-driven approach of study management and demonstrates its overall value. Moreover, Booz Allen designed the analytic platform so that it is easily customizable for other studies and trials. Based on the NCS planning successes to date, NIH has now asked Booz Allen to determine if the data-driven approach can be extended from operational to scientific planning of studies. With the amount of available data growing exponentially, data-driven approaches to decision making will become increasingly important for NIH and for biomedical research at large, complementing traditional hypothesis- or expert-driven decision making. Booz Allen’s Advanced Analytics Team is ready to help NIH accomplish this transformation to a data-driven decision making paradigm.
Using Advanced Analytics for Data-Driven Decision Making
Helping Organizations Improve Study Planning andExecution Using Advanced Analytics for Data-Driven Decision MakingJuergen A. Klenk, PhDPrincipal, Health AnalyticsMarch 2012
Table of contents• The Challenge• Our Solution• Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 2
The National Children’s Study – an Enterprise ofunprecedented size and complexity• Congress authorized the National Institutes of Health (NIH) with the Children’s Health Act of 2000 to undertake the National Children’s Study (NCS)• Focus of the NCS is to examine the effects of the environment on growth, development, and health of children• The NCS attempts to enroll and follow at least 100,000 and up to 250,000 women and their children, from before birth to age 21, at a minimum of 100 and up to 175 study locations across the United States• The NCS is expected to generate data that form the basis of child health guidance, interventions, and policy for generations to come• NIH saw an opportunity to pioneer novel techniques for data- driven, evidence-based study planning, to manage the daunting task of planning the execution of the NCS and maximize chances of success
Table of contents• The Challenge• Our Solution• Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 4
Modeling and Simulation as Analytic Techniques that formthe Foundation for Data-Driven Decision Making• Booz Allen conducted research to identify the best analytical approach to developing a data-driven planning and decision making framework• Key analytic techniques employed include • Markov Models • Stochastic Models • Predictive Modeling • Discrete Event Simulation• The resulting model was calibrated against real-world data and used to evaluate alternative scenarios of study operations• Data needs could be met by the Vanguard pre-study to the NCS, and specific attention was given to data understanding, preparation, and QA/QC
Table of contents• The Challenge• Our Solution• Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 6
Select Analytic Techniques are combined into a decisionsupport framework• We are using a Markov models, implemented as Resource Independent Transition stochastic models using a discrete event simulation Resource Dependent Transition Active State tool Final State• Potential participants are represented Sampled Enumeration as populations in various states• Dynamics are determined Not Screen Screen Eligible by the rates of transition Eligible from one state to another Follow up Not Pregnancy• Transition rates are (Not Preg.) Eligible affected by pregnancy Follow up rates, consent (Preg.) rates, allocation of staff Consent Eligible resources, etc., and are implemented as predictive Not Consenting, Not Consenting, models Consenting Soft Hard
Table of contents• The Challenge• Our Solution• Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 8
Key planning questions could be answered• Sample planning questions studied and answered • What boost strategies can be employed to shorten the recruitment period, and how does this affect overall cost • What data are required to effectively monitor study operations, and how can these data be collected in sufficient quantity and quality • What sampling unit sizes for study locations are optimal to achieve recruitment targets as quickly as possible, and at the lowest cost• One of the most important questions answered was to calculate the number of participants required to successfully conduct the Study: 250,000• The NCS Main Study is scheduled to launch in 2013• The same innovative approach will now be applied to plan beyond the recruitment phase, to include planning factors such as retention, compliance, study operations, logistics, and cost
Table of contents• The Challenge• Our Solution• Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 10
Helping Booz Allen’s Clients be Ready for What’s Next• The novel data-driven, evidence-based approach to study planning has helped NIH to successfully plan for and get ready to launch one of its most important enterprises, the NCS• The novel approach of data-driven planning is viewed as a way to plan and conduct studies in the 21st century• The approach, developed for study operations planning, could be extended to scientific planning• This work helps NIH be ready to leverage critical information contained in large quantities of data for study planning, and thus accomplish a transformation to a data-driven decision making paradigm
Learn More about our Advanced Analytic Capabilities www.boozallen.com/analytics Juergen A. Klenk, PhD Principal, Health Analytics email@example.com Phone (703/377-7205)