Preliminary Learnings: Big Data Office Ergonomics Field Study

Ron GoodmanRemedy Interactive

Arnold NeustaetterPacific Gas & Electric

Main Ergonomics Goals

Reduce injuries and optimize injury prevention

• Step 1: Identify the greatest risks

• Step 2: Mitigate those risks

Typical Methods to Identify Risk

Typical Methods to Identify Risk

In-PersonAssessment

Typical Methods to Identify Risk

In-PersonAssessment

Remote Assessment

Typical Methods to Identify Risk

In-PersonAssessment

Self Assessment

Remote Assessment

Typical Methods to Identify Risk

In-PersonAssessment

Self Assessment

Remote Assessment

Combination of Methods

Typical Methods to Identify Risk

In-PersonAssessment

Self Assessment

Remote Assessment

Combination of Methods

EqualsEmployee Risk Status

“Before the computer age, progress in science was achieved mainly by: gathering empirical data and crafting [hypotheses to explain] our observations...

This theory-observation-refine (TOR) cycle has provided many of our most profound insights into how the universe works.

It has not worked so well, however, for developing our understanding of complex systems.”

Christoph Adami, Professor of Microbiology &Molecular Genetics, Michigan State

Are there Better Ways to Predict Risk?

PG&E Study

• Step 1: Collected as much risk factor data as practical, using an epidemiological study model, with premise that we don’t know which factors influence risk or why

• Step 2: Using predictive analysis tools (a la Netflix) to consider each factor separately and in combination with others to see where factor(s) predict risk

• Step 3: Using these results to create an algorithm that accurately predicts risk of discomfort and time-to-onset of discomfort

What We Learned

• Factors with predictive value aren’t necessarily intuitive

• We can use predictive analysis to quantitatively guide the degree to which an ergonomics program should consider different factors

Understanding Optimal Risk Factors

Initial Findings – Example 1

• Disc = Discomfort• OR = Odds Ratio

Key Take-away:

An employee’s perception of how often they take breaks is a significant predictor of risk of injury

Initial Findings – Example 2

• Disc = Discomfort• OR = Odds Ratio

Key Take-aways:

• Not all questions are valuable risk predictors (surprisingly, this one wasn’t)

• Since this is a survey question, this doesn’t mean that external devices aren’t important (could be the question, or inaccurate reporting)

Initial Findings – Unexpected Predictors

What question would you imagine results in this discomfort distribution?

• Disc = Discomfort• OR = Optimal

Risk

Key Take-away:

• It’s important to consider all factors without bias as to which will be the strongest risk predictors

Future Use of Predictive Data• Shorten assessments to focus on

questions with significant predictive value

• When possible, use automatically collected data to predict risk (timeliness, easier on employee)

• Focus on interventions that are shown to reduce discomfort incidence

• Look at multiple factors (e.g. notebooks + exposure hours)

• Rely on software solutions to automatically take measures to reduce detected risks

Our Preliminary Learnings

Collect as much ergonomic data as possible before making any assumptions about what factors cause risk

What your data reveals may surprise you!

Questions?

Thank you!

Arnold Neustaetter

Ron Goodman