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The document discusses how advanced analytics can help improve child support enforcement outcomes. It describes challenges faced by child support services like high caseloads and limited resources. Analytics provides case workers a holistic view of parents to prioritize cases and identify effective actions. Two examples from Orange County, CA and a State Attorney General's office show how analytics helped increase payments and collections while meeting federal guidelines. Visualizations generated from the analytics include case profiles, ad hoc reports, Sankey diagrams, text analytics, decision trees and forecasts.
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Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future
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Pros and cons of early- versus late-binding.
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Automating ETL processes so data analysts have more time to listen and help end users
Using a metadata repository to enhance data literacy among users and improve trust in data, thus enabling data governance policies
Improving turnaround time for data analysts who support frontline staff who, in turn, monitor interventions based on evidence-based medicine that is constantly changing
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Value-based care and payment reform are prompting hospitals and healthcare providers to more closely manage population health. Hospitals and health systems rely on technology and data to outline the characteristics of their population and identify high-risk patients in order to manage chronic diseases and deliver enhanced preventative care.
Our webinar covered how Cadence Health, now part of Northwestern Medicine, is leveraging the native capabilities of Epic to manage their population health initiatives and value-based care relationships across the continuum of care.
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-Examined the three steps for effective population health management: Collect data, analyze data and engage with patients.
-Covered how access to analytics allows physicians at Northwestern Medicine to deliver enhanced preventive care and better manage chronic diseases.
-Discussed Northwestern Medicine’s strategy to integrate data from Epic and other data sources.
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Historical or intent-to-treat design
Matching comparison design
Randomized control design
All three place a heavy reliance on data and precise, tailored patient registries. Reliable patient registries are one of the most valuable tools in the care management toolbox. And the means to that reliability is an enterprise data warehouse, which essentially gives program managers an all-access pass to stratifying patient risk and leads to a more successful population health initiative.
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Al aha i 10 loe presentation 11 8 13
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CEP • CEI 1BENCHMARKING FoundationEvaluationPra.docx
CEP • CEI | 1
BENCHMARKING
Foundation
Evaluation
Practices
2 3
FOR MORE INFORMATION, CONTACT
Ellie Buteau, Ph.D.
Vice President – Research
617-492-0800 ext. 213
[email protected]
Julia Coffman
Director, Center for Evaluation Innovation
202-728-0727 ext. 116
[email protected]
AUTHORS
This report was prepared by the Center for Effective Philanthropy
(CEP) together with the Center for Evaluation Innovation (CEI). A
number of people contributed to the development of the survey
instrument, analysis of data, and creation of this research report.
From CEP: Ellie Buteau, Jennifer Glickman, and Charis Loh. From
CEI: Julia Coffman and Tanya Beer.
This work is licensed under the Creative Commons BY-NC-ND License.
© 2016. The Center for Effective Philanthropy, Inc. All rights reserved.
CEP • CEI | 5
TABLE OF CONTENTS
Respondent Demographics 8
Foundation Demographics 11
Evaluation Practices 17
Using Evaluation Information 25
Looking Forward 32
Discussion Questions 36
Methodology 39
ABOUT THE CENTER FOR EFFECTIVE PHILANTHROPY
ACKNOWLEDGEMENTS
We are very appreciative of the support that made this work possible.
We are grateful to Marc Holley, Kelly Hunt, Ed Pauly, Diana Scearce,
Nadya Shmavonian, and Fay Twersky for providing feedback on a
draft of the survey used for this research. The survey created for this
research study drew, in part, from a survey originally created by Patti
Patrizi and Elizabeth Heid Thompson in 2009. We are also grateful to
Johanna Morariu for providing feedback on a draft of this report.
The authors would like to thank CEP’s President, Phil Buchanan, for
his contributions to this research, as well as CEP’s Art Director, Sara
Dubois, for her design of the report.
This research is based on CEP and CEI’s independent data analyses,
and CEP and CEI are solely responsible for its content. The report does
not necessarily reflect the individual views of the funders, advisers, or
others listed throughout this report.
For more information on CEP, please visit
www.effectivephilanthropy.org. For more information on CEI, please
visit www.evaluationinnovation.org and
www.evaluationroundtable.org.
ABOUT THE CENTER FOR EVALUATION INNOVATION
MISSION
To provide data and create insight so
philanthropic funders can better define,
assess, and improve their effectiveness—
and, as a result, their intended impact.
MISSION
Our aim is to push
philanthropic and nonprofit
evaluation practice in new
directions and into new arenas.
We specialize in areas that are
challenging to assess, such as
advocacy and systems change.
CEP • CEI | 7
In 2015 and 2016, the Center for Effective Philanthropy and the
Center for Evaluation Innovation partnered for the first time to
benchmark current evaluation practices at foundations. We wanted
to understand evaluation functions and staff roles at foundations, the
relationship between evaluation and foundation strategy, the level of
investment in an.
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- 1. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. USING ADVANCED ANALYTICS TO IMPROVE CHILD SUPPORT ENFORCEMENT Will Jones, MS Child Well-Being Industry Consultant
- 2. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. CHILD SUPPORT SERVICES CHALLENGES • Information overload • High caseloads and limited resources • Inability to pinpoint issues impacting ability to pay • Inaccurate assessment of needs • Case management systems with limited analytic capacity
- 3. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. USING ANALYTICS TO IMPROVE OUTCOMES Analytics • Provides holistic view of parent • Empowers Case Workers • Prioritizes Cases • Identifies Most Effective Services or Actions Outcomes • Better father engagement • Increased child support payments • Decreased delinquent payments • Decrease in child poverty
- 4. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. IMPACT OF ADVANCED ANALYTICS Orange County, CA Child Support Services: Caseworkers can rapidly pinpoint factors that keep parents from being able to pay child support, and then help them get to the point where they can pay. “We use analytics to truly understand what social, economic or structural barriers keep this particular 22-year-old from succeeding. And then we figure out how to help.”
- 5. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. IMPACT OF ADVANCED ANALYTICS State Attorney General’s Office: analyzes and reports on caseloads, collections, paternity establishments and arrears payments. Predictive models and a web-based application deliver results to staff, enabling the agency to meet or exceed federal guidelines. “With the SAS solution, this state is now among the top states, territories and districts for the amount of child support collected, amount collected per employee, and cost-effectiveness. Its child support program is a national leader in productivity and efficiency. It also earned a higher federal incentive payment as a result of superior standing compared to other states’ child support enforcement programs.”
- 6. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. ADVANCED ANALYTICS ARCHITECTURE
- 7. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS CHILD SUPPORT CASE PROFILE REPORT
- 8. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS CHILD SUPPORT CASE PROFILE REPORT
- 9. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS CHILD SUPPORT AD HOC REPORT
- 10. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS CHILD SUPPORT AD HOC REPORT
- 11. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS ANALYTIC VISUALIZATION EXAMPLES • Sankey (Path) Diagram: visualize how cases flow through the system
- 12. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS ANALYTIC VISUALIZATION EXAMPLES • Text Analytics: identify topics and sentiment in unstructured text data
- 13. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS ANALYTIC VISUALIZATION EXAMPLES • Decision tree: visualize predictors of delinquency
- 14. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. VISUAL ANALYTICS ANALYTIC VISUALIZATION EXAMPLES • Forecast: predict the future, perform scenario analysis