Data science for service change Presented by Datasf

Service Issue
Data Science
Service Change
Result
Expected: Reduce the dropout rate of moms, infants and children, leading to healthier outcomes for both
Since 2011, DPH has seen an increase in mothers dropping out of their nutrition program. Which moms are most at risk of dropout?
Built a predictive model that identified moms and infants who are at greatest risk for dropping out
Using the high-risk client profiles to conduct targeted interviews to identify program barriers and make service changes
DPH WIC: Help moms and babies stay in nutrition program
Flag “stuff” early
Full write up at datasf.org/showcase/datascience/
Service Issue
Data Science
Service Change
Result
Expected: Reduction in high cost clients and use of high cost emergency services
A small fraction of mental health patients use a large % of resources. Can we identify high users early to improve their outcomes and reduce costs?
Build predictive model to identify clients at greatest risk for becoming high users
Expected: Targeted service model to direct high users to more stable and preventative services
DPH BHS: Improve results and reduce costs in mental health care
Find the needle in the haystack
Flag “stuff” early
Service Issue
Data Science
Service Change
Result
Improved response rate by 17%. TTX continuing to apply BIT principles to other taxpayer communications
TTX wanted to use behavioral economics and A/B test to increase effectiveness of collection letter for unsecured personal property (a difficult type to collect on).
DataSF helped organize a Behavioral Insights Training (BIT) workshop and provided guidance on A/B test
Use whichever letter gets the best response
TTX: Increase response to tax letter
A/B test something
Full write up at datasf.org/showcase/datascience/