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Collaborative Decision-Model System for Healthcare

Posted Mar 04 2009 2:52pm

In our current economy, improving healthcare value is absolutely essential. That means we must reduce healthcare costs, while at the same time increasing its quality. In other words, we have to focus on making healthcare much more cost-effective.

Part of the solution is reforming healthcare economic models, changing how care is paid, and redirecting the way healthcare providers compete because current methods fail to offer high value products and services to consumers. Arguably more important, however, is the need for healthcare professionals and consumers to:

  • Know the most efficient and effective ways to prevent, treat and manage health problems
  • Use this knowledge competently and consistently.

Sadly, while drowning in oceans of information, t he healthcare industry lacks the knowledge and motivation needed to deliver high-value care. Proof includes:

  • The disconnection between quality and cost; research has proven that more expensive care rarely results in better care
  • The fact that healthcare providers often don't know what treatments work best for a particular patient
  • The unaided human mind, no matter how competent, simply cannot focus on all the necessary details nor possess all the knowledge needed for continually making the best clinical decisions
  • Our country hasn't focused enough on making existing healthcare knowledge useful and applicable to clinical practice
  • Obtaining the knowledge to improve decision-making requires a commitment to ongoing clinical outcomes research and a focus on continuous quality improvement, things that the healthcare industry has largely avoided
  • Knowledge about prescription medication safety and effectiveness can be greatly enhanced.

How can this troubling situation be improved? By using and evolving collaborative health decision-models.

Health Decision-Models Described

Let's begin with describing health decision-models and how using them in collaborative environment is essential to increasing healthcare value.

What the Models Do

A good health decision-model increases awareness and understanding about people's health status, threats, problems and care options. It does this by offering personalized recommendations, instructions, feedback, and alerts based on mathematical and logical analyses of comprehensive personal health information. To be truly useful, this information should include data about a person's:

  • Physical and psychological ( body & mind, whole-person ) health and susceptibility, past and present
  • History of treatments, the clinical outcomes (the results of care), and the costs
  • Influences of one's environment and culture
  • Personal preferences.

The model's recommendations and instruction, which should be supported by evidence-based guidelines and sound theory, ought to focus on helping people:

  • Assess (diagnose) and define health risks and conditions
  • Prevent risks from becoming serious health problems
  • Treat and cure existing problems
  • Manage chronic conditions (such as diabetes, obesity, and heart disease), so debilitating complications do not happen.

It’s important to note that models differ in many ways; for example, different models:

  • Take into account different health data
  • Employ different math and logic to analyze the data
  • Make different assumptions
  • Base their recommendations and instructions on different resources
  • Present their information in different ways (including computer software, written manual, video, etc.).

This means that certain models are more useful than others depending on the people utilizing them and the particular circumstances in which they are used. Yet despite these differences, the goal of decision-models used in healthcare should be to increase care cost-effectiveness. And achieving this goal requires collaboration.

Models and Collaboration

Collaboration is essential for bringing ever-greater value to the healthcare consumer by fostering model evolution, and by utilizing models in loosely-coupled collectives. These networks of people collaborate by working together to support shared decision-making and facilitate care coordination, which are two of the most important model-driven processes for high-value healthcare. That's why collaborative health decision-models are so important.

Loosely-Coupled Collectives

A "loosely-coupled collective" refers to people from multiple locations and with different roles, responsibilities and experiences who collaborate to make more valid decisions and competent actions. When such collaboration is among people with wide diversities of knowledge, ideas and points of view, the collective provides a larger collection of intellectual resource, and offers access to a greater variety of non-redundant information and knowledge on which to base decisions and actions. This is unlike a tightly-coupled network that limits participation to people within the same discipline, department, region, etc. who have access to the same information sources and who share similar experiences. Loosely-coupled collectives provide the greatest opportunities for stimulating multifaceted discussions and creative solutions.

Model Evolution through Collaboration in Loosely-Coupled Collectives

Collaborating around models helps evolve (improve) the models by making them more effective in:

  • Increasing people's awareness and understanding of health risks, problems, and remedies
  • Providing useful recommendations, guidance, feedback, and instruction.

Evolving health decision-models in this manner is accomplished by sharing and "playing with" the models. When people share and play with these models, they:

  • Compare and test the models for their ability to reflect reality accurately
  • Manipulate the models to represent different scenarios, such as "what if" scenarios about the probability of future occurrences
  • Discuss the assumptions and results the models produce.

When they find models that disagree or generate invalid results, they:

  • Examine the fundamental assumptions built into the models
  • Look for logical flaws and inconsistencies in the model
  • Question the model's authors' perception of reality
  • Debate about the assumptions and practical value of the model.

By challenging the model's assumptions in these ways:

  • Useful counterintuitive insights often emerge
  • Innovative thought is sparked
  • New questions arise
  • Relationships between collaborators are developed
  • The influence of an organization's culture and politics are revealed
  • Compelling and unexpected management issues are discovered.

This all means that sharing and playing with models is an effective way to evolve health decision-models to increase their validity and usefulness.

Failure to evolve models in this manner can be disastrous! Take, for example, the famous mathematical model that brought Wall Street to its knees. The model was used for the past five years to justify risky mortgage-based investments, even though few understood it and even fewer questioned its assumptions. Invented by a mathematician, the model was based on the faulty assumption that home prices would continue to rise and few homeowners would be unable to pay back their mortgages. While some financial experts realized this was a seriously flawed model, it brought great wealth to many for years, so there was little incentive to criticize it. This very model is a root cause of the world's current financial meltdown.

The healthcare industry ought to learn a valuable lesson from that fiasco!

Enabling Loosely-Coupled Collectives with
Decentralized Communication Architectures

To streamline communications in these loosely-coupled collectives, it's best to deploy a low-cost decentralized (peer-to-peer) architecture. One example is the "mesh" network architecture, which grew out of the need for a distribution network in telephone, power, and water, and oil pipeline businesses. It enables anyone to communicate directly with anyone else at any time, from anywhere. It's like using the telephone to communicate with a single person, or to set up a conference call where each person can communicate to many other people. The mesh network architecture, therefore, provides a simple, flexible, low-cost way to be exchange models.

In my next post, I'll discuss how to use health decision-models in loosely-coupled collectives to provide high-value care by promoting coordinated care, delivering consumer-centered cognitive support, fostering shared decision-making, and advancing evidence-based research.

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