Decision quality, problem solving, and data science

Decisions drive vision, strategy, execution, evaluation, problem-solving, performance, and continuous improvement.1 A decision is an irrevocable commitment of time and resources. Every decision has an opportunity cost—the loss benefit of the better option not chosen or not considered (see SDG.com).

Decision competent managers use and leverage data, information, and knowledge management systems to improve decision quality, problem-solving, and performance improvement. Driven by decision makers’ priorities and questions, data scientists transform data into information and actionable knowledge to inform, influence, or optimize decision making;2 and implementation scientists translate evidence into practice.3

At the San Francisco Department of Public Health (SFDPH) we are asking how do we prepare our workforce to become the best at getting better at decision-making, problem-solving, and performance improvement in era of “Big Data” and data science? Using A3 Thinking, the Population Health Division has proposed an integrated framework to describe how these components are connected (Figure 1).

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Figure 1: Integrated framework for decision-quality, problem-solving, and implementation and data science

At the center is Decision Quality (DQ) which has six requirements, also called the DQ value stream (Figure 2). A decision is only as good as its weakest link.

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Figure 2: The Decision Quality value stream has six requirements (in blue): (1) Frame, (2) information, (3) choices, (4) reasoning (analysis), (5) commitment (people), and (6) prospects (future state, including unintended consequences)

Below is the slide presentation I gave at SFDPH on this topic.

Slide 7 is the most important slide and the contents are displayed in Table 1 below:

Table 1: Decision Quality (DQ) value stream pulls support from lean, data, information, knowledgement management systems, reasoning, and selected sciences
No. DQ value stream Priority areas for development, training, and improvement
1 Frame \(\leftarrow\) decision competency in lean A3 Thinking (problem solving)
2 Information \(\leftarrow\) data and information systems (Epic, Maven, CCMS, etc.)
3 Choices \(\leftarrow\) knowledge management (discovery, translation, and integration)
4 Reasoning \(\leftarrow\) causal and evidential reasoning combined with
\(\phantom{\leftarrow}\) implementation science and data science
5 Commitment \(\leftarrow\) see Frame (involve stakeholders in decisions)
6 Prospects \(\leftarrow\) see Frame (includes visioning/consequences of future state)

Take away: to prepare our workforce to become “the best at getting better” at decision-making and problem-solving in era of “Big Data” and data science we propose focusing on training and/or improvement in five interdependent areas:

  1. Decision Quality value stream (Table 1, column 1)
  2. Lean (A3) thinking (PDSA problem-solving)
  3. Data and information systems (Epic, community, etc.)
  4. Knowledge management (discovery, translation, integration)
  5. Causal and evidential reasoning combined with implementation and data sciences

Footnotes


  1. Spetzler C, Winter H, Meyer J. Decision quality: Value creation from better business decisions. 1st ed. Wiley; 2016.

  2. https://epibiostat.ucsf.edu

  3. https://epibiostat.ucsf.edu/implementation-science-program

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Tomás J. Aragón
Health Officer, City & County of San Francisco; Director, Population Health Division

I exercise legal authority to protect and promote equity and health, and I direct core public health services.

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