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Rethinking Healthcare

by Michael Rohwer

Healthcare as we currently know it is not sustainable.

Large integrated delivery systems have grown and implemented measures and best practices. Northern Permanente Foundation 1 began the first pre-paid plan in Oregon in 1942. Since that time, the entities that comprise Kaiser Permanente have continued to grow. Multiple HMO plans arose in the early 1970s and continued through the late 1990s. These plans created integrated care and cost containment strategies that have been employed in Medicaid, Medicare, and commercial plans. Case management of high-risk patients is now standard. Redundant and unnecessary care is coming under control. Quality measures are improving. And yet cost continues to rise, as shown in this snapshot from the Peterson-Kaiser Health System Tracker 2

 

 

Moreover, Healthcare expenditures continue to grow faster than the economy. The National Health Expenditure Projections 2014-20243 produced by CMS projects healthcare costs will grow to 19.6% of GDP by 2024.

The workforce is changing, too. The results of a recent survey by Medscape of 35,922 physicians (6880 respondents) followed the trend of similar surveys, showing physician burnout continuing to worsen. All of this is telling us that something is amiss. More of the same won’t produce something different. We need to examine how we think about the problem.

We think using models

As wonderful as the human mind is, the complexity of the world dwarfs our understanding4 . We make sense of it using mental models that are simple enough to be useful. The model we choose must match the system we are working within. As Sandra Mitchell5 points out, even the laws of physics depend on context and contingent conditions. For example, Galileo’s Law of Free Fall depends on earth’s core being mostly Iron. Galileo’s work was refined by Newton’s Law of Universal Gravity, but that is contingent on the gravitational forces not being massive. Massive forces need Einstein’s Law of General Relativity, but it doesn’t apply to the subatomic context where Quantum Mechanics is a better model. We think and understand through models, but all models eventually fail. To succeed we must pick the best model, and that starts with how we think about systems.

The concept of a system is a model, too. A useful definition from Donella Meadows6 is “an interconnected set of elements that is coherently organized in a way that achieves something. Therefore, a system must consist of three kinds of things: elements, interconnections, and a function or purpose.”

Real-world systems coexist and overlap. What separates one system from another is its purpose. The optimal management of any system depends on its ontology7. The two that dominate Healthcare are Ordered8 and Social Complexity9.

Ordered systems are those in which a desired output can be determined in advance. This is achieved through the application of data, analysis, and planning10. These systems are predictable because they are closed and isolated from natural influence, allowing the state of each element or process to be always known. By and large, these are artificial systems that we create apart from nature. For example, accounting is a set of processes that establishes a frame of reference so that values are stable.

Ordered systems are managed using traditional management. The metaphorical model is the machine. While management has evolved over the years, it is based on the work of Frederick Taylor (Scientific Management11) and Henry Ford (Fordism12). The basic principles are:

  • Decompose problems into constituent elements and process.
  • Scientifically study each to determine most efficient way to work.
  • Replace “rules of thumb” and “common sense” with best practices.
  • Match workers to jobs based on capability and motivation and train to maximize efficiency.
  • Ensure workers are using the most efficient way of getting the job done.
  • Ensure managers spend their time planning and training.
  • Standardize process, components, and workers so that they are interchangeable
  • Drive cost down to make products affordable.

These principles are implemented through strategy that is set at the top and translated to implementation goals, tasks, and accountability. Communication is top down. Rules circumscribe discretion. The key to success is tight control and the dynamic is to preserve the gains of the past13. None of this works in nature where complex systems cannot be avoided. 

Complexity is a fundamental attribute of nature where order emerges from disorder at every level of observable existence. The following are characteristics of all Complex Systems.

  • Equilibrium is never achieved.
  • They are self-organizing, which leads to hierarchy development producing emergent behaviors or circumstances.
  • They never repeat, so past actions or patterns will not predict future ones.
  • Output is not predictable based on input.
  • Emergent behaviors are unpredictable but are not random. They follow a probability distribution known as the power law14.
  • The science of a Complex System is probabilistic and not predictive.
When the elements are living entities, we consider them agents and the system becomes adaptive. The patterns of self-organization and emergence are amplified as each element interacts with those around it. When the agents are people, even more dynamic processes begin to influence system behavior15.
  • Humans are intelligent agents that make decisions based on patterns.
  • Humans create and maintain multiple identities.
  • Humans ascribe intentionality and cause where none necessarily exist.
  • Humans have learned how to structure their social interactions to create order.
A Complex Adaptive System confounds traditional mechanical-model management because it is a social system having no effective single point of control.

Management of a CAS uses influence exerted at points of leverage.16

  1. The system exists for a purpose. Directing work toward the purpose is a powerful alignment tool.
  2. The operating paradigm to achieve the purpose sets the agenda for planning. This is the shared social agreement around the nature of what needs to be done.
  3. Self-organization exists in every workplace and throughout nature. It is a powerful engine of growth and innovation. The system is substantially inhibited or enhanced by policies that suppress or enable it.
  4. Rules (incentives, punishments, parameters, and constraints) redirect growth.
  5. Information flows drives growth.
  6. Feedback loops balance or reinforce behaviors.

Healthcare is a human CAS and part of nature. Within it there are ordered and complex subsystems. Thus we need to vary our approach based on the nature of each.

An example of an ordered system within Healthcare is claim payment. The purpose of the system is to accurately pay a claim for a specified service in accordance with a complicated set of policies and an acceptable time frame. The center of the process is a claim, which is a record defined and created to be stable. The inputs, policies, and processes are all known and constant during the adjudication and payment period. Claim payment is like a factory and uses traditional management.

A clinic caring for a patient is a CAS. Each patient has a unique situation that will never be completely knowable. Each practitioner in the clinic has variable knowledge, skills, and temperament which vary unpredictably. Best practices are actually past practices based in prior science or opinion that is applied to situations that cannot be fully understood. Ultimately all decisions are based in human judgement, which is also unpredictable. This is not a factory and needs more effective management tools.

A new management model for Healthcare as a CAS

Complex Adaptive Systems are alive, self-serving, and generate outcomes. Ordered systems are mechanical and create outputs to be used by others. The purpose of Healthcare is to create outcomes not outputs.

  • The member should be part of the system. The alternative, where the system serves a target population, assumes an ordered ontology producing outputs. The natural CAS that is Healthcare includes the member. Our models must reflect this so that our interventions include the member as an active participant.
  • The operating paradigm should maximize value delivered to the member. This seems counterintuitive when you think in terms of an ordered system or industrial processes. A CAS, however, will naturally adapt to create value. The idea that we have a fixed output and limit inputs to just what is necessary does not generate innovation.
  • The purpose of Healthcare is to preserve and enhance individual human health. This is not the three competing goals of the IHI Triple Aim17. Those goals are important and will be achieved through managing the system more effectively.
  • Value delivered to the member is measurable. At the end of the day, what counts is the care delivered to the member. To drive change, that value must be measurable, meaningful, and granular.
  • Value is measured for each individual. The operating paradigm will create problem-centered networks around every member.
  • Value is measured within a local context. Every locality has unique resources and weaknesses. A local CAS with the proper tools will far outperform large national organizations.
  • Value is measured in a problem context. While there are generic values, such as patient satisfaction, real change is accomplished in a problem-specific context. For example, one set of problem contexts might be “Congestive Heart Failure,” “Congestive Heart Failure complicated by renal insufficiency,” and “Congestive Heart Failure complicated by depression and non-compliance.” Each would involve a specific understanding that made sense in the community in a way that supports local initiatives to improve.
  • The problem is described using narrative. People and Complex Adaptive Systems understand the world through story. A narrative description is used so that the measures of value are understandable to members and practitioners. This improves clarity in diagnosing the problem and desired outcomes for both practitioners and members.
  • Practitioner performance is a profile of many measures. No single measure can define the performance of a practitioner. A young mother with healthy children may value timeliness and online appointments, while a senior with severe diabetes may value more time with the provider and systems to help the patient succeed.
  • Practitioner performance is available to members and referring providers. Change depends on creating complex networks that grow and remodel in response to transparent and understandable performance information. Remodeling is driven by informed members and referring practitioners as they use the information to make choices.
  • Recognition and reward of high performance providers will create innovation and competition. This will be enhance when members have more freedom to select excellence and referring providers have more tools to pick their consultants.

System interconnections drive change

Interconnection creates the system. Without it, the elements are isolated and impotent. The nature of the interconnections drives and defines behavior.

In 2001, Paul Plsek described Complex Adaptive Systems and their importance to Healthcare in Appendix B of the IOM publication, “Crossing the Quality Chasm.” Why hasn’t it improved Healthcare? There are many reasons, but the most important is that Complex Networks were not understood. Now examples of lightweight and powerful networks are present in other industries to guide us.

We need lightweight, rapidly configurable connections. These allow elements and processes to rapidly reorganize and adapt. Healthcare is an information-and-knowledge business. We shouldn’t anchor everything to a brick-and-mortar infrastructure.

Interoperability is more important than features. Complex Adaptive Systems grow and adapt using shared information. While each element of the system may want specific features, the strength and resilience of the system is based on sharing and interoperability.

Services network must be reconfigurable and resilient. The interconnecting services that support the system can be easily reconfigured. This requires standardization of applications and services so that multiple vendors are available for any given function. Having multiple options and a standardized framework enables faster innovation and greater reliability. 

Enabling technology

To allow all the services to interoperate, a core set of technologies is needed. These are components that can be assembled in many forms to yield new solutions.

  1. Process map with associated software Application Programming Interface (API): This enables new developers to enter the marketspace with best-of-breed smaller products. It also ensures that the system is reconfigurable and resilient. By using an API, a single process is supported by multiple options and potential vendors.
  2. Workflow/process management: This enables small products to be incorporated into multiple processes and facilitates new ideas without costly development engineering.
  3. General policy management/automated decisions: Throughout the system, we need to be able to automate decisions and redirect processes. This is essential to reconfiguration and community-level innovation.
  4. Member value measurement and tracking: A healthy Healthcare system is grown from its member roots. To focus on the system’s purpose, the value delivered to each member is measured. This allows local communities to define and track the value in multiple important contexts. This can be connected to other services, such as payment. It can also be connected to other services available on the network to maximize that value within the given budget.
  5. Systems to implement scale-free organizations: This enables organizations or collaborations to be defined in the system and operations applied to them. These operations can be any operation normally applied to a system element. The organization can be generated on the fly or specifically to match an existing business entity. This allows elements of the community to rapidly reorganize in order to optimize results.

Getting there

Implementation is incremental and should use using lean startup cycles to build early value and growth. This cycle relies on small learning experiments producing small products that have value and support growth.

 

 

Each step supports two essential concepts. The first is the specialized interconnecting network described above to support change. The second is the measurement and use of value delivered to the member also described above.

Development requires a supporting business network of customers. The technology necessary to create change requires a receptive environment exist because it is a catalyst and not the initiator of change. In our case, we will use our existing CCO customer base. Existing goals of customers and PH Tech are in alignment with the first steps of this initiative. Subsequently the same communities may migrate their capabilities to other insurers.

Decisions regarding development priorities require broad input and evaluation. The stakeholders include existing customers and community organizations working on similar or supporting solutions. The vehicle for this is a new not-for-profit to provide evaluation, collaboration, and coordination of activities.

We can change healthcare. By working at the bottom and creating the ability of every community to innovate, we will make it better.

 

 

1.http://www.oregonencyclopedia.org/articles/kaiser_permanente_in_oregon/#.VoCupOgrLqw
2. http://www.healthsystemtracker.org/interactive/health-spending-explorer
3. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/proj2014.pdf
4. See Herbert Simon concepts of bounded rationality and Sterman referenced earlier
5. Sandra D. Mitchell, “Unsimple Truths, Science Complexity and Policy”
6. https://en.wikipedia.org/wiki/Donella_Meadows “Thinking in Systems: A primer”
7. D J Snoden, “Multi-ontology sense making”, Management today Yearbook 2005
8. Ordered simple and complicated ontologies
9. D J Snoden, “Multi-ontology sense making”, Management today Yearbook 2005
10. Snoden, David J “Multi-ontology sense making, Management Today Yearbook 2005
11. https://en.wikipedia.org/wiki/Scientific_management
12. https://en.wikipedia.org/wiki/Fordism
13. Taken from a blog by Gary Hamil, Harvard Business Review, “Bureaucracy must die”, Nov 4, 2014
14. https://en.wikipedia.org/wiki/Power_law
15. Snoden, David J “Multi-ontology sense making, Management Today Yearbook 2005
16. “Thinking in Systems”, Donella Meadows,
17. Institute for healthcare improvement (http://www.ihi.org)