CDPH is building a Decision Intelligence Unit

Tomás Aragón, MD, DrPH
Former State Public Health Officer and Director
California Department of Public Health
[email protected] (email)
https://teampublichealth.substack.com/ (blog)

Decision-making is our most important activity

"A decision is a choice between two or more alternatives that involves an irrevocable allocation of resources."¹ "...your decision-making is the single most important thing you have control over that will help you achieve your goals."² Every decision has causal assumptions, predictions, and opportunity costs—the lost benefits of the better option(s) not chosen or not considered.

In service of our values and goals, human decision intelligence (Figure 1) is using ethics, science, and technology to improve team decision-making and outcomes in the face of uncertainty, trade-offs, and time constraints. HDI prioritizes improving human decision-making capabilities.

Figure: Human Decision Intelligence Framework

The CDPH Office of Policy and Planning will have a Decision Intelligence Unit (DIU) to develop, implement, and improve strategic decision-making methods for high stakes, high cost, and/or high impact public health decisions. Methods will include cost-benefit and cost-effective analyses, and decision analysis. These positions have permanent funding. We will secure grants and develop collaborative partnerships with academic institutions.

1. Team leaders and managers as "decision architects": Design and improve of team decision meetings that incorporate fundamentals of cognitive psychology and decision quality. See Annie Duke, 2020² and Spetzler, 2016.¹
2. Methods to optimize policy, budgetary, or prioritization decisions: cost-effectiveness and cost-benefit analyses; and priority setting and resource allocation (PSRA). See Seixas, 2021.³
3. Decision making under uncertainty, including deep uncertainty. See Marchau, 2019.⁴
4. Computational algorithms for decision making (using Bayesian networks (including decision networks [influence diagrams]), Markov Decision Processes (MDPs), Partially Observable Markov Decision Processes (POMDPs), Reinforcement Learning, Agent-based modeling). See Kochenderfer, 2022.⁵

The DIU will have a trans-disciplinary team:

• Health economist with expertise in population health cost-benefit and cost-effectiveness analyses
• Behavioral economist with expertise in decision implementation science
• Computational decision scientist with expertise in programming (Julia, Python, R)
• Population health data scientist
• Biostatistician

The DIU is part of CDPH's longer term strategy to build our capability in population health data science (Table) and to move us to Levels 4 and 5.

Level	Analysis	Description
1	Description	a. surveillance and early detection of events
		b. prevalence and incidence of risks and outcomes
2	Prediction	a. early prediction and targeting of interventions
3	Explanation6	a. discovery and testing of new causal pathways
		b. estimation of intervention efficacy/effectiveness
4	Simulation	a. modeling for epidemiologic or decision insights
5	Optimization7	a. optimizing decision, effectiveness, or efficiency metrics

Visit Team Public Health to learn more about human decision intelligence.

Here is a graphical depiction of the Table.⁸ We aim to embrace our "reasoning" to make better decisions.

To learn more about books relevant to decision intelligence, here is my list.

Appendix

Here is an alternative, but very informative, view of decision intelligence (see two figures below):⁹

Footnotes

Footnotes

1. Carl Spetzler, Hannah Winter, and Jennifer Meyer, Decision Quality: Value Creation from Better Business Decisions (Hoboken, New Jersey: John Wiley & Sons, Inc, 2016), https://www.wiley.com/en-us/Decision+Quality%3A+Value+Creation+from+Better+Business+Decisions-p-9781119144694. ↩ ↩²

2. Annie Duke, How to Decide: Simple Tools for Making Better Choices (Penguin Publishing Group, 2020), https://www.annieduke.com/books/. ↩ ↩²

3. Brayan V. Seixas, François Dionne, and Craig Mitton, “Practices of Decision Making in Priority Setting and Resource Allocation: A Scoping Review and Narrative Synthesis of Existing Frameworks,” Health Economics Review 11, no. 1 (January 7, 2021): 2, https://doi.org/10.1186/s13561-020-00300-0. ↩

4. Vincent A. W. J. Marchau, Decision Making under Deep Uncertainty: From Theory to Practice, 1st ed (Cham: Springer International Publishing AG, 2019), https://link.springer.com/book/10.1007/978-3-030-05252-2. ↩

5. Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making (Cambridge, Massachusetts: The MIT Press, 2022), https://algorithmsbook.com/decisionmaking/. ↩

6. Judea Pearl and Dana Mackenzie, The Book of Why: The New Science of Cause and Effect, First trade paperback edition (New York: Basic Books, 2020), https://www.hachettebookgroup.com/titles/judea-pearl/the-book-of-why/9780465097616/?lens=basic-books. ↩

7. Decision analysis, cost-effectiveness/benefit analysis, mathematical modeling, operations research, etc. ↩

8. Source: https://www.bayesia.com/articles/#!bayesialab-knowledge-hub/1-introduction ↩

9. Source: https://wequity.tech/decision-intelligence-and-design-thinking/ ↩