Cancer Prediction is the Pathway to Prevention

Examining the benefits of using a systems-based approach to predict and control the evolution of cancer through preventative pathways

Oncology research exposes cancer as a dynamic disease that results from complex interactions between multiple scales of genetic, environmental and constitutional characteristics that are unique to an individual. Currently, the full scope of determinants that cause numerous point mutations to accumulate and/or structural alterations to occur in the process of tumor progression are not fully understood. Shifting from a component to a system-level perspective will help add the context and understanding necessary to make sense of and apply current oncology research innovations.

Preliminary use cases have shown that X-ACT® Health is able to add the missing context of complex cancer processes and promote further understanding of how dynamic interactions between a large number of determinants, such as DNA, pathogens, autoimmune responses, metabolism, environment and aging influence the progression of cancer for an individual patient.

Bridging silos of cancer knowledge will be necessary to help researchers and healthcare providers understand the complex factors that provoke cancer in one individual, and not another. This level of understanding is key to predictively diagnose cancer before any symptoms appear, and would give governments and payers the confidence they need to invest in and standardize personalized preventive pathways.

By using sophisticated algorithms that are computationally efficient and cover many-to-many time dependent relationships, X-ACT Health could revolutionize how we diagnose and treat cancer. The goal isn’t to replace doctors with AI. Instead, we need to build new knowledge and predictive capabilities that enable all healthcare stakeholders—including governments, payers, suppliers and providers—to know when and which actions will help an individual patient avoid cancer. This is the Holy Grail of oncology research and provides the path to cut the long-term costs of cancer care and improve standards of patient care.

 

 

Covid-19: Worldwide Viral Infection Model

Examining the benefits of using Universal Dynamic Engine (UDE) to manage Covid-19 patient risk and critical treatment load.

When news broke of an obscure respiratory disease emerging from the Wuhan market in early January, few imagined that in just over 2 months the world would be facing the worst pandemic since Spanish flu in 1918. With more than 12M people taking commercial flights every day, Covid-19 has spread around our hyper-mobile world with an unprecedented combination of speed and virulence. Further, without widespread testing for Covid-19, it is difficult to know how the pandemic is spreading and how to appropriately respond to it. While it is certain that the total number of Covid-19 cases is higher than the number of known confirmed cases, the total number of Covid-19 cases is not known.

As the Coronavirus spreads, many are predicting that the demand for beds, ventilators and other treatment resources in hospital will far exceed capacity for Covid-19 patients in the near future. Still, public policy and health community responses to Covid-19 are complex decisions that require a delicate balance between protecting health, the economy, and people’s well-being and emotional health. In times of crisis, reliance on theoretical models present challenges for decision makers, because they don’t always replicate real life well—especially when the virus that causes the pandemic is novel.

Covid-19 Modeling: Dynamics Shouldn’t be Ignored

Healthcare systems are not closed loop, linear systems. Therefore, they cannot be accurately modeled without accounting for various elements (components) of the system which continually influence one another (directly or indirectly) to maintain the system in line with the goals of the system. In the case of a pandemic, these goals include slowing the spread of the disease and providing the highest quality of care possible for those infected.

If the relationships between various components of a dynamic system are not well understood, changes made to one part of the chain may affect another in unwanted ways. For instance, treating Covid-19 patients without understanding the influence of pre-existing conditions may place more strain on already overwhelmed medical personnel and hospital resources. A universal mathematical formulation is needed to accurately represent all system dynamics in all cases to provide healthcare system participants with a more complete view of reality. This allows stakeholders to make decisions with more confidence in the outcomes than would be possible if using mechanistic models that may provide a wide range of possibilities and miss new risks due to their dependence on historical data.

UDE and NARS Modeling Capabilities

Universal Dynamics Management (UDE) provides a rigorous process that computes the top-down communicating graphs to deal with the direct and indirect, convergent or degenerative solutions necessary to accurately model the complex, adaptive dynamics of non-linear, open systems. Based on the NARS method of modeling, UDE allows users to accurately represent a high level of interdependent components organized in hierarchy of graphs. Scenario analysis helps modelers construct the remedy and mitigation actions, certify the solution pertinence and discover additional variables and interrelationships that can be dependably used to predict the occurrence of previously known as well as unknown risk behaviors.

Through the use of deconstruction theory, it becomes clear that the overall performance of a system (defined in terms of quantity, quality and cost) does not always represent the expected output of the system given the accumulated characteristics of the constituent components, generally due to a loss in energy. Causal deconstruction allows modelers to uncover results that often defy the common wisdom that stops at the wrong level of analysis and usually produces a host of misleading conclusions.

URM Forum Covid-19 Research

URM Forum has published a discussion paper entitled, “Covid-19: Worldwide Viral Infection Model” as part of our ongoing effort to help government, healthcare and business leaders build the UDE capabilities necessary to accurately represent all system dynamics in all cases. The paper includes results for the digital twin of Covid-19 Crisis Management in Germany, referred to as the CV-19 model, to show how UDE can be applied to help provide critical decision insights as needed to guide actions for any country’s or geographic region’s response to the current pandemic as well as monitor and plan activities in order to avoid a future crisis.

The aim of the presented CV-19 model is to show healthcare system participants how they might use UDE to gain a more complete view of reality as needed to make decisions with more confidence in the outcomes. UDE has been successfully applied for a wide range of healthcare, economic and business uses as the preferred way to predictively characterize dynamic systems under specific conditions; identify singularities and metrics, which define the characteristics of the system under different scenarios of increasing volume and structural changes; and validate the applicability of any proposed corrective solutions.

Join Our Efforts to Deliver Improved Risk Modeling Solutions

The dynamics of our mobile and hyperconnected world amplify the risks. Through the participation of a larger number of constituents, the CV-19 model can be expanded to identify thresholds and help support preparedness for a wider variety of parameters and situations as they relate to any dynamic system, which is influenced directly and indirectly by any number of internal and external forces—including travel patterns, weather, government response and/or socio-economics.

If you are interested in joining our efforts to develop scientific-based solutions to society’s biggest risk management challenges, please contact us.

 


Learn more about our Covid-19 Modeling and Healthcare Decision Support research

Read the “Covid-19: Worldwide Viral Infection Model” paper to learn how UDE is being applied to rovide critical decision insights as needed to guide actions for any country’s or geographic region’s response to the current pandemic as well as monitor and plan activities in order to avoid a future crisis.

 

 

 

 

Advancing Economic Forecasting and Risk Analysis Models to Meet Demands of the 4IR

Managing dynamic complexity will pave the way for a more prosperous and efficient economy that operates with less risk and better predictability.

Economic systems that operate through a constantly expanding number of dynamic interactions have become too complex to fully represent using popular econometrics and economic modeling methods. When multiple functions of economy are connected to each other explicitly or through complex topology, the mathematics of these methods grows too complex to deliver reliable predictions. History has shown that an economic crisis, mathematically identified as a singularity, may occur even when classic estimations fail to expose a risk.

From our experience, it is clear that new tools and methodologies are needed to augment traditional risk analysis practices in order to take back control of financial systems. The intended goal is to guide rather than react to market fluctuations in ways that yield the desired outcomes, including the ability to execute a complete system disruption, when and if the dynamics of financial markets no longer meet the goals of society.

Lack of predictability and hyper-risk within financial systems

Today’s economists borrow their risk modeling methods from the core tenants of physical science with a heavy reliance on the concept of entropy to deal with the volatility of financial systems. While these methods did make it easier to represent the approximate behavior of financial systems once upon a time, they have become meaningless in today’s complex environment.

The current lack of predictability and hyper-risk within financial systems is a direct result of dynamic complexity. Dynamic complexity should be considered the enemy of the digital age because its presence erodes stability, hides risk and creates waste within any system that is influenced by the external environment in which it operates. Like an undiagnosed cancer, dynamic complexity consumes valuable resources as it grows without providing any useful return. Traditional forecasting methods are unable to quantify the impact of dynamic complexity, therefore surprises are inevitable because no one can predict how one small change can produce a ripple effect of unintended consequences.

In and of itself, the expansion of subprime loans in the early-to-mid 2000s was considered a manageable risk by economists, policymakers and financial practitioners who took advantage of the Feds interest rate reduction monetary policy to promote new financial instruments, e.g. subprime loans and trading of mortgage-backed securities (MBS). Even as the collapse of the US economy was already happening, the Feds main economic model saw a less-than-5-percent chance that the unemployment rate would rise above 6 percent in two years. The rate actually hit 10 percent, an event that the model said was close to impossible, therefore it was not considered as a plausible risk by policymakers.

Crises move faster than decisions

As the pace of innovation accelerates in the Fourth Industrial Revolution (4IR), the cause and effect of economic crisis will become nearly instantaneous. Governments and businesses can no longer afford to wait for the warning signs of an economic turmoil, before taking action. To regenerate prosperity for the betterment of humanity, economists, policymakers and financial practitioners must employ new problem-solving approaches. Eliminating the waste and managing the risk caused dynamic complexity will deliver new opportunities for growth and sustainability. But first, economic stakeholders must tame dynamic complexity and simplify decision cycles.

In the research paper entitled, “Advancing Economic Forecasting and Risk Analysis Models to Meet the Speed, Risk and Sustainability Demands of the 4IR,” URM Forum proposes a new approach to economic forecasting and predictive analysis that allows users to uncover a wide scope of unknown risks. Armed with this knowledge, economic stakeholders will be able to quickly take action with confidence in the outcomes—before imbalances proliferate through financial markets and interdependent subsystems. We believe the suggested use of tensors provides the right formulation to overcome the abused application of entropy and scalar quantities within economics, which neglect the direction of market changes. The utilization of aggregated vectors allows users to more accurately reproduce the magnitude and direction of market behaviors, which in turn allows economists, policymakers and practitioners to predict a wider range of risks and vet which corrective actions will yield the best results.


Learn more about our Economic Forecasting and Risk Analysis Modeling research

Read the “Advancing Economic Forecasting and Risk Analysis Models” paper to learn how economists, policymakers and practitioners to can use our proposed UDE method to predict a wider range of risks and vet which corrective actions will yield the best results.

6 Essential Tips for Managing Supply Chain Performance in the On Demand Economy

Is your supply chain delivering what your customers want, when they want it, while costing your organization as little as possible to accomplish that goal? If your answer isn’t a resounding yes, then you are probably missing opportunities to improve your supply chain and deliver significant value to your organization.

The on-demand economy is all about satisfying the needs of consumers in the most cost-effective, scalable and efficient way. Companies like Uber, Airbnb and Netflix have accomplished this goal by identifying opportunities to create competitive advantage and drive top line revenues through supply chain innovation.

In response, many companies have transformation programs under way to modernize how they deliver products and services to consumers. But identifying and agreeing on the correct path forward has become an insurmountable challenge for many organizations. Bain & Company reports that only 5% of companies involved in digital transformation efforts reported that they had achieved or exceeded the expectations they had set for themselves. For conventional transformations, the success rate only increases to 12%.

While most organizations are well versed in managing distinct supply chain phases, they often lack a clear end-to-end picture of the risks and opportunities for improvement across dynamically complex global supply chains. This makes moving forward with any significant supply chain optimization or digital transformation program difficult.

Fortunately, new business dynamics management (BDM) tools are helping supply chain stakeholders across industries meet the speed, risk and profit performance demanded by the Fourth Industrial Revolution (4IR). By digitally replicating the real world, end-to-end dynamics and interdependencies of global supply chains, organizations can expose the hidden causes of performance problems, prove whether a proposed change program will meet the stated business objectives and better manage risks that traditional supply chain management tools often miss.

If you are looking for ways to improve how you deliver products and services to customers or dramatically reduce supply chain costs, consider these six essential tips for managing supply chain performance in the 4IR.

  1. Earn Board Level Support. Treat your supply chain as a critical business process, not a business function. With board-level support, your supply chain can deliver significant value to your organization. But first, your CEO must understand the opportunities and have confidence in your ability to deliver the promised business value. Use predictive capabilities that have been proven accurate in real world scenarios to explore new models and present innovative strategies that can deliver measurable success. Always communicate performance metrics and the tradeoffs of any critical decisions in terms business leaders can understand—including clear and concise measurements of expected ROI, productivity, quality and costs.
  2. Understand the Impact of Cross Domain Dynamics. The root cause of your supply chain performance problems may be hiding in the gaps between domains. Eliminate supply chain blind spots by shifting away from silos towards a unified methodology that provides visibility across your entire, integrated supply chain ecosystem. Creating and managing an end-to-end model of a highly dynamic global supply chain used to be a complex and cost prohibitive goal, but new emulative technologies that leverage a computer-aided design (CAD) approach now make it easy to model all of your supply chain business processes, applications and infrastructure—including cross domain dynamics—in a matter of weeks and with surprising accuracy.
  3. Make Efficiency and Optimization a Top Priority. As consumer expectations rapidly evolve, your organization must be able to agilely deliver optimized customer experiences at the required speed and cost—all without adding unwanted operational risks or long-term maintenance issues. This means you must have efficient processes and tools in place to identify which transformation strategies will translate into execution plans and end products that support the speed, efficiency and resilience demanded by the 4IR. A predictive model-based approach helps cross-functional teams efficiently communicate, validate and fine tune plans throughout the lifecycle.
  4. Take a Top Down Approach. Too often businesses seek to leverage the latest technologies, like blockchain, IoT, robotics and AI, without first quantifying and then validating whether the proposed changes will actually meet the business’ cost, scale and efficiency requirements. Starting from a strategic level and then drilling down into technology and infrastructure layers makes it easier to clearly communicate how investments into new assets or the sunsetting of legacy models may help or hinder the goals of the business.
  5. Stop Worrying About Probabilities. An outage may prevent you from delivering products to customers, but it’s irrelevant whether the outage was caused an expected or highly improbable event. The solution to the outage will be effective regardless of what caused it. Create an exhaustive list of the conditions, not events, that can disrupt your supply chain (e.g. transaction processing capabilities at the main datacenter fall below a certain threshold), then proactively put into place the appropriate risk management practices. While the unexpected may still happen, you will be better prepared to spot new patterns earlier and respond more appropriately.
  6. Manage Change Using a Step Wise Approach. Transformation programs put enormous pressure on all aspects of an existing business, which must maintain current profits, while pivoting the legacy customer experience, products, services and operations to a new, reimagined future. But digital transformation programs can be executed using an agile approach that enables you to make small improvements that you can build upon. Using a model-based architecture at each step will help ensure that supply chain stakeholders have sufficient information and time to identify and resolve any risks before moving to the next step of system definition or implementation.

Solving Blockchain Distributed Transaction Challenges

Blockchains are ideal for shared databases in which every user is able to read everything, but no single user controls who can write what. By contrast, in traditional databases, a single entity exerts control over all read and write operations. However, issues relating to scalability, enforcement of business constraints, and aggregation may arise when using shared ledger structures for multi-party or distributed transaction models. Augmentation of the blockchain protocol using a collector or aggregator mechanism is necessary to overcome the issues.

Interorganizational Record Keeping

The chain acts as a mechanism for collectively recording and notarizing any type of data, whose meaning can be financial or otherwise. An example is an audit trail of critical communications between two or more organizations, say in the healthcare or legal sectors. No individual organization in the group can be trusted with maintaining this archive of records, because falsified or deleted information would significantly damage the others. Nonetheless it is vital that all agree on the archive’s contents, in order to prevent disputes.

Multiparty Aggregation

This use case is similar to the previous one, in that multiple parties are writing data to a collectively managed record. However, in this case the motivation is different – to overcome the infrastructural difficulty of combining information from a large number of separate sources.

Imagine two banks with internal databases of customer identity verifications. At some point they notice that they share a lot of customers, so they enter a reciprocal sharing arrangement in which they exchange verification data to avoid duplicated work. Technically, the agreement is implemented using standard master–slave data replication, in which each bank maintains a live read-only copy of the other’s database, and runs queries in parallel against its own database and the replica.

Now imagine these two banks invite three others to participate in this circle of sharing. Each of the 5 banks runs its own master database, along with 4 read-only replicas of the others. With 5 masters and 20 replicas, we have 25 database instances in total. While doable, this consumes noticeable time and resources in each bank’s IT department.

Fast forward to the point where 20 banks are sharing information in this way, and we’re looking at 400 database instances in total. For 100 banks, we reach 10,000 instances. In general, if every party is sharing information with every other, the total number of database instances grows with the square of the number of participants. At some point in this process, the system is bound to break down.

Multi-party and Distributed Transaction Challenges

In either a multi-party record keeping or distributed transaction model, a party may want to find and reorder related blocks in chronological order to support a decision or they may want to enforce constraints before taking action. Using patient care as a specific example, a doctor may want to pull all of the medical records for a patient before deciding whether to perform a medical procedure. These records may include hospital records, test results, medical history, insurance authorizations, etc. Proceeding with the patient care may be dependent upon factors such as a primary care doctor referral, appropriate insurance authorizations, and blood test results within 72 hours of the planned medical procedure.

By automating the retrieval and sequencing of events through an aggregator mechanism, we can deliver the necessary information in correct sequence at the right time—without cumbersome manual manipulation of chains or custom coding.

Shared-Ledger Algorithm

We have developed a mathematical algorithm that collects and dispatches the right sequence of events and time sensitive priorities to aggregate multiple domain specific blockchains to form a purpose-oriented blockchain. Tested under a variety of cases to determine its wide applicability, the patented algorithm complements the blockchain protocol to provide necessary aggregation solution for multi-party transaction processes that characterize industries and services of multiple shared blockchains such as:

  • Healthcare: Patient treatment and admission, preventive medicine, research, etc.
  • Government-citizen services
  • Regulations
  • Supply chain management
  • Corporate actions
  • Multiple-suppliers to right time processing
  • Food production
  • Research and development
  • Banking and capital markets

Modeling Economic Dynamics

Examining the Problems with Traditional Risk Modeling Methods

Traditional financial risk management methods were formulated in an analogy with the early foundational principles of thermodynamics. However, traditional economic models are incomplete models of reality because economic systems are not inclined to attain equilibrium states unless we are talking about very short windows of time (similar to meteorological or most nuclear or gravitational systems).

Problems with risk modeling methods based on the laws of thermodynamics:

  • Predictability is limited to short windows, where the initial conditions varies in small amplitudes and in small frequencies
  • Complexities are dealt with once recognized, rather than as a result of structural evolution and systemic behavior of multiple-level interactions
  • Only closed systems that reach equilibrium are dealt with, no adaptive ability to an external or internal modification is allowed
  • Complex systems do not systematically expose equilibrium
  • Using Stochastic models that deal with randomness are difficult to determine 
small resonances and therefore do not tend to a long term representation

A New Way to Look at Economy and Risk

Financial systems are not wholly physical. They do not always behave in an expected manner as predicted from their patterns of past behavior. They are immature. They can sometimes exhibit unexpected and unknown behavior because we do not understand their complexity and how it changes.

To avoid future crisis in the proportions of 2008, we must identify new methods of economic risk analysis that more accurately model the dynamic reality of financial systems. To this end, we promote determinism, which is the view that every event, including human cognition, behavior, decision, and action, is causally determined by an unbroken sequence of prior occurrences.

Determinists believe the universe is fully governed by causal laws resulting in only one possible state at any point in time. Simon-Pierre Laplace’s theory is generally referred to as “scientific determinism” and predicated on the supposition that all events have a cause and effect and the precise combination of events at a particular time engender a particular outcome.

How the impact of dynamic complexity leads to economy non-equilibrium:

  • Different instruments within a portfolio have different dynamic patterns, evolution speeds, producing different impact on risk
  • But also they influence each other: in sharing, affecting, and operating in terms of both frequency and amplitude in the behavior of discriminant factors (econometrics, relation economy/finance, long term repercussion etc.)
  • In addition, each will have different reaction/interaction towards an external/ internal event.

Consequently, modeling economics dynamics is the right foundation to insure predictability of such self-organized evolutionary systems that may prevail towards even several points of singularities and larger number of degrees of freedom than the small number in traditional methods.

Using this method, we will be able to address most of the drawbacks of the traditional methods:

  • Both the need for predictable determinism and the intensive presence of high level of dynamic complexity justifies the use of Perturbation Theory
  • The condition of success to approach an exact solution at any moment of time relies on the use of deconstruction theory that will separate the constituents and find the proper mathematical expression of each prior to the deployment of the perturbed expression (i.e. two-level solution)
  • Evolutionary process guarantees wider window of representativeness and adaptability for the dynamic complexityeconomics
  • Tends to exact solution

Table: Dynamic Complexity versus Traditional Economics

Dynamic Complexity Economics Traditional Economics
Open, dynamic, non-linear in equilibrium Closed, static, linear in equilibrium
Each constituent of the system is model individually then aggregated through Perturbation Theory The system is modeled collectively in one step
No separation between micro and macro level behaviors Separation between micro and macro level behaviors
Evolutionary process guarantees wider window of representativeness and adaptability for the dynamic complexity economics Unstable for wider windows of time
Allows for continuous interactions of external and internal agents Does not allow for continuous interactions of external and internal agents
Optimal control is possible as sub product of dynamic complexity modeling Optimal control is not possible

Conclusion

From a scientific standpoint, the subject of financial dynamics and the best risk analysis method is still open and further mathematical, physical and engineering as well as economic risk analysis developments are necessary. A great body of contributions, covering a wide spectrum of preferences and expertise and from deeply theoretical to profoundly pragmatic, currently exists today. All show the interest, but also the urgency, to find a solution that can help us avoid the singularities that occurred in 2008. To progress, we must continuously seek to recognize the failures of past methods and strive to find solutions.

Blockchain: Navigating the Disruption

After years of theoretical debates and abstract use cases, it is no longer a question of if blockchain will cause market disruption, but rather when and how widely the impact will be felt. Now is the time to remove any outstanding doubts about blockchain applicability and strategically manage the business and operational risks that inevitably come with innovation. The main barrier being how to best plan for and manage the disruption. In all cases, correctly quantifying the threats and opportunities is a requirement for success.

Using a range of specific cases across various markets and industries, including financial services, supply chain and healthcare, we are actively conducting research and collaborating with other industry leaders to verify how to best apply a scientific method of predictive emulation to reveal where the capabilities of blockchain are best suited to solve business problems, quantify the expected improvements and manage the risks in delivering the solution.

Blockchain technology is best known for being the magic behind Bitcoin, but there are scores of other industries that can benefit from this revolutionary technology. The benefits include driving costs savings by reducing labor-intensive processes and eliminating duplicate efforts, as well as creating new markets by exposing previously untapped sources of supply.

Funded by eager venture capitalists, start-ups can easily pursue blockchain initiatives, but convincing stakeholders of global corporations and financial institutions to go all-in on a new technology that could overturn the very fundamentals of the business and the organization’s biggest profit drivers is not easy. Alternatively, taking a wait and see approach may place laggards at a significant competitive disadvantage as the move to blockchain requires a well devised plan and sufficient time for its execution.

Given the dynamic complexity of modern systems, it can be difficult to identify across operations the right plan to manage disruption and create a sustainable business model. With technological innovations, often the most dangerous risks are posed by the unknowns that cannot be predicted with historical reference models and often escape the imagination of risk committees. A scientific method to predictively quantify opportunities and universally manage risks can help stakeholders strategically time, justify and manage a disruptive move.

X-Act® OBC Platform is useful in these cases as it is the only mathematical dynamic complexity emulator that can realistically model business services and infrastructures. We use X-Act OBC Platform to replicate the dynamics and complexity of business implementations—allowing us to predictively compute system behaviors at different points in time and under various operational conditions. These insights can then be used to plan for and manage a disruptive move by supporting the series of complex decisions necessary to make the right trade-offs between sometimes conflicting objectives, allow acceptable time to market and preserve business continuity.

Using the emulator capabilities of X-Act OBC Platform, we tested various blockchain scenarios under different patterns of initial conditions and dynamic constraints to identify the conditions under which risk will increase, as well as the possible mitigation strategies.

By modifying the parameters of each scenario within the emulator, one by one, by group, or by domain, to represent possible changes, we are able to extrapolate each time the point at which the system will hit a singularity and use the corresponding information to diagnose the case. Additional scenarios can be created to explore viable and proactive remedial options that secure an acceptable risk mitigation strategy.

Understanding a System through Deconstruction

A system—organizational, industrial, biological, environmental, or IT—is composed of components, objects, or members, each of which have specific properties that characterize its behavior in space and time. All members interact, impact, serve, and receive from other members in time and space. We can think of this as the connectivity or more specifically the time and space connectivity from which many possible combinatorial and dependencies result. Depending on the intensities of such intra- and inter-relations among components and their configuration, the overall system will expose behavior patterns and characteristics.

From this we can produce a set of quantitative and qualitative metrics that will provide a synthesis of what happens. This set of metrics will show the global characteristics of the system, but the ultimate target is contribution of each individual component and their interactions. This knowledge will allow us to properly identify the causal configuration. In this case, deconstruction theory becomes important to our goal of identifying the component, or components, that expose the system to a risk—in terms of limits beyond which the system will no longer work, service quality, or cost. Basically, if you want to understand the behavior of a system, you must deconstruct it and look at its components.

It is important to perform deconstruction in such a way that allows the shortest path to the identification of the risk component(s), the dynamic signature of what happens or may happen, the conditions under which a component will reveal the risk, and above all the actions required to proactively fix the problem while there is still an opportunity for a possible solution.

Over the last 10 years, we have been able to confirm that this approach yields significant contributions to the determination of risk and risk management in comparison to traditional methods. The suggested process of causal deconstruction has been applied many times on different business, industrial, economic, and services activities, and the results have been significant and exhaustive.

A Complex System under Optimal Control

By combining causal deconstruction theory and perturbation theory, a dynamic complexity problem can be accurately solved with the right level of representation and a good level of certainty on the reproducibility. This method shows great promise as a powerful process for risk identification, evaluation, management, and avoidance.

To determine the performance and accurately identify risky components within an open structure involving multiple orders perturbations, we use a layered hierarchical process based on the causal deconstruction to feed a mathematical hierarchy of specialized algorithms, which are computed and aggregated following the capabilities of perturbation theory. Through this approach, the behavior of a component determines its status that, with respect to others, will determine the characteristics of the component, its ability to deliver its service to the system, and to what extent. The environment is composed of the ensemble of components, the demand structures from each to all components, and the possible combinations that deliver a service based on multiple interactions.

From this point, the solution can be extended to meet the goals of optimal business control (OBC). In this case, a knowledge base and other automation technologies are used to observe the system in operation to identify dynamic characteristics that may lead to a risk. The ambition of these methods are to place the system under permanent control, so that it becomes possible to slow down the adverse effects of dynamic complexity or prepare for the avoidance of an eventual risk.

Identifying Cost Saving Opportunities in Healthcare

The goal of many governments is to continuously improve the public health system efficiency by increasing preventive and proactive intervention, reducing any unnecessarily overhead due to multiple analysis and even diagnosis for the same case, and consolidating patient history to improve preparedness.

In a client case involving a government sponsored healthcare program, each citizen was attached to a binder that included all of his or her health and drug history, time series analyses, medical procedures, medical attributes and some projections that could help with follow-up tracking. This binder was available to medical and pharmaceutical personnel and was synchronized in case multiple doctors were involved. The gigantic national infrastructure necessary to support this level of information sharing formed one of the earliest applications of big data—(even before the popularization of the term).

Once the binders were implemented, the next challenge was to improve the speed of record updates and allow access to patient records 24/7 from anywhere in the medical network grid (around 37 large academic hospitals organized in 12 groups and tens of clinics and nursing homes). The introduction of smart cards allowed the goals to become a reality. With this improvement, the risks associated with late diagnosis, surveillance and patient record management efforts were reduced.

X-Act OBC Platform predictive emulation and risk management were used to evaluate the cost efficiency of a network of public hospitals serving a large metropolitan area and its suburbs. The hospital system offered healthcare to more than 7 million individuals with 5 million external consultations, 1.2 million beds, 1.1 million urgent care visits (1 every 30 seconds), 38,000 new births, and 1,200 organ transplants each year. Ninety thousand professionals, including 22,000 doctors, 51,000 hospital personnel, 16,000 nurses and 15,000 administrative personnel, served the needs of the constituents.  Additionally, an average of 2,700 research projects in biomedicine with strong connection to the academic world were included under the same management structure.

The risk in this environment is predominantly operational. However as the system involves human safety, management of possible pandemic, and professional errors, legal, economic, reputation and administrative risks are present which require strong predictive analytics to control and alert stakeholders of any performance problems. Using our X-Act OBC Platform technologies and optimal business control (OBC) methodologies, we were able to help the government reduce the cost of healthcare by 9% and have plans for an additional reduction of 10% through the smart use of a universal database.

Through this project, we were able to construct a predictive platform that allowed the management to test decision scenarios and explore options to implement right-time control and surveillance. The technologies allowed stakeholders to anticipate risk and enhance mitigation plans as the system dynamics evolve or change. Having proven the solution through this project, it is our ambition to generalize the approach to cover the whole country. The expanded usage would allow a host of studies and research projects to take place in order to understand the origin, evolution, risk factors and correlations to internal and external influences of both rare and more recognizable maladies.