Decision Intelligence 4 Health (di4health)

A project of TEAM Public Health

Tomás Aragón

Embedded in all journeys (life, learning, strategy, innovation, etc.) are the choices we make. Decision making is our most important daily activity. Annie Duke says it best:1 2 3

“… there are only two things that determine how your life turns out: luck and the quality of your decisions. You have control over only one of those two things.”

“… your decision-making is the single most important thing you have control over that will help you achieve your goals.”

Annie Duke, How to Decide: Simple Tools for Making Better Choices. Penguin Publishing Group, 2020.

In spite of its importance, decision quality is rarely taught formally. This is a shame! Some of reasons include the organizational and analytical complexity of decision making. Figure 1 depicts how decision professionals tackle and manage these two dimensions. To address analytical complexity, this site focuses on decision modeling (aka, decision analysis) using popular data science programming languages such as Julia, Python, and R.

Figure 1: Two dimensions of decision competence (Source: Parnell, Gregory S, Terry A. Bresnick, Eric Specking, Steven N. Tani, and Eric R. Johnson. Handbook of Decision Analysis. 2nd ed. Wiley, 2025.

The di4health framework

The di4health framework is a holistic, practiced-based framework for team decision making under real world constraints.

Figure 2 depicts the Decision Intelligence 4 Health framework with four pillars:

  1. 4 dimensions (real world practice),
  2. 4 competency domains,
  3. 4 constraints, and
  4. 4 DEEP decision challenges.

In health and human services, we face 4 DEEP decision challenges:

  1. D — Decision making under uncertainty (information, data, knowledge, future)
  2. E — Ethical decision making (values, moral trade-offs, benefits outweigh risks)
  3. E — Emergency and crisis decision making (time constraints with high stakes)
  4. P — Priority setting and resource allocation (resource investment trade-offs)
Figure 2: Decision Intelligence 4 Health

The 4 DEEP decision challenges bring attention for 4 key constraints:

  1. Information,
  2. Values,
  3. Time, and
  4. Resources.

In practice, these contraints can, and do, occur simultaneously for the DEEP decisions.

Important decisions have 4 dimensions for consideration:

  1. Decision quality,
  2. Strategy execution,
  3. Continuous improvement, and
  4. Ethics, science, and technology.

In public health, strategic decisions do not occur in isolation; they always occur within strategic execution frameworks (eg, incident command system, lean management, PDSA/A3 problem solving, needs-driven innovation, Results-Based Accountability). To ensure decision quality, we must deploy continuous improvement, ethics, science, and technology (eg, artificial intelligence)

Finally, the Alliance for Decision Eduction has developed 4 evidence-based competency domains:

  1. Recognizing and resisting cognitive biases,
  2. Valuing and applying rationality,
  3. Thinking probabilistically, and
  4. Structuring decisions.

Currently, this site focuses on decision modeling. To learn more about the general aspects of Decision Intelligence 4 Health visit TEAM Public Health:

Decision modeling coding examples

See Decision modeling coding examples (this site)

See Decision Analysis in R for Technologies in Health (external link)

Footnotes

  1. Duke, Annie. How to Decide: Simple Tools for Making Better Choices. Penguin Publishing Group, 2020. https://www.annieduke.com/books/.↩︎

  2. Duke, Annie. Quit: The Power of Knowing When to Walk Away. Ebury Edge, 2023. https://www.annieduke.com/books/.↩︎

  3. Duke, Annie. Thinking in Bets: Making smarter decisions when you don’t have all the facts. Portfolio/ Penguin, 2020. https://www.annieduke.com/books/.↩︎