“Whatever else it produces, an organization is a factory that manufactures judgments and decisions. Every factory must have ways to ensure the quality of its products in the initial design, in fabrication, and in final inspections.”
Daniel Kahneman (2002, Nobel Prize in Economics; author of Thinking, Fast and Slow )
This blog entry is based on a talk entitled “Decision quality, problem-solving, and data science — Lean concepts for human-centered, data-informed decision-making”.
Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.— Daniel Kahneman 
Read working paper here: http://bit.ly/ph-thinking
Introduction: data science and decision quality Population health is a systems framework for studying and improving the health of populations through collective action and learning . Population health data science (PHDS) is the art and science of transforming data into actionable knowledge to improve health .
Introduction At the San Francisco Department of Public Health, Population Health Division, we promote Decision Quality (DQ) [1,2]. DQ starts by knowing what a good decision looks like. A good decision is built with six quality requirements (Table 1).
Table 1: Decision quality requirements: A decision is only as strong as its weakest link Name Quality requirements Key question Frame Appropriate frame What are we deciding and why?
View or download full article: https://escholarship.org/uc/item/2r7298jr
Abstract Public health principles are based on promoting dignity, equity and compassion for all. San Francisco has made great strides towards “Getting to Zero” HIV infections, deaths, and stigma. However, we face new challenges with persons who inject drugs (PWID), and increases in substance use disorder, mental illness, and homelessness. Residents and visitors are concerned about (a) an increase in people injecting drugs in public and (b) an increase in discarded syringes on the streets of San Francisco.
In Part 1 we introduced directed acyclic graphs (DAGs)  as a better way to represent program theory . A DAG is a Bayesian network where each directed arrow represents a causal link, not merely a probabilistic dependency .
Program theory has three components:
Theory of causation (causal model) Theory of change (selected strategy) Theory of action (selected intervention that activates change) Program theory is often called “theory of change.
Every public health intervention has a program theory; however, very few can actually describe the program theory supporting their primary programmatic activity or research. Can you? If not, read on.
I too could not describe the program theories supporting my own work until I read Funnell Rogers’ book Purposeful Program Theory . It turns out that program evaluators not only live and breath program theory, but they call it by different names: logic model, program logic, theory of change, causal model, results chain, intervention logic, etc.
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