Office of Reporting, Research, Analytics and Implementation (ORRAI)

ORRAI leads and supports projects that impact DHS as a whole, focus on one or multiple intersecting programs or services, or involve data and topics across multiple state agencies. See projects below for more information about new methods we are utilizing to expand our research and analysis capacity.


Projects

  
Project Summary
Topics
  
2019

Capacity: Identifying Capacity Needs for Children within the Oregon Child Welfare System

Currently in the State of Oregon there are approximately 7,500 children and youth placed in the Child Welfare substitute care system on any given day. Children and youth placements in Oregon have been dictated by placement availability with less focus on child needs and provider capability. This research will estimate the number of placement beds (e.g. foster care, proctor care, residential treatment, etc.) necessary to optimally serve children in substitute care. System experts will recommend a placement type for a random sample of children/youth entering substitute care. Researchers will then use statistical techniques to identify the best placement for each of these same youth; the best placement recognizes the placement type with the optimal child outcome. The differences in these two results will refine estimates of placement capacity to create the optimal continuum of care. The data captured throughout this project will shed valuable light on the root of the problems in families. This information is necessary for sustaining system changes.

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Child Welfare
2019

Data Collection for Program Outcomes Monitoring, Impact Analysis and Research

Lean data collection methods provide organizations with ongoing, systematic, holistic and granular measurement of the range and depth of program impacts and customer experiences. Measures of customer experience, family stability, access to resources, child learning and healthy development and family progress towards goals are currently under development. Systematic, consumer-level data collection across programs are essential to the research agendas for each of the program areas and can improve Oregon state-service customer experience in general.

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Department of Human Services
2019

Dynamic Modeling

Predictive analytics offers information to assist the critical decisions that DHS workers make every day. Once a decision is made, dynamic prediction modeling can continue to assist by monitoring how the decision is playing out in real time. After a critical decision, new information will continue to accrue for the individual or families impacted by the decision. As this new information becomes available in our computerized data systems, dynamic models can update the likelihood profiles of certain outcomes of interest (e.g., whether a trial home reunification will succeed). The nature of the information is important (e.g., whether the caregiver of a child has begun a new drug/alcohol treatment service), as well as the timing of the information (e.g., whether the caregiver began the new treatment one day after reuniting with their child, or one year). In statistical terminology, dynamic prediction modeling takes into account both time-varying coefficients (i.e., “what” is changing), and time-varying covariates (i.e., “when” the changes occur). Dynamic prediction modeling may be implemented 1) as an alert system to notify DHS when a service user’s situation has become more at-risk, or 2) as a decision system to determine which service may be most beneficial to a service user’s situation.

Child Welfare, Dynamic Modeling
2019

Geospatial Modeling

Our research team continues to develop machine learning tools which predict the likelihood of an individual person experiencing an outcome of interest; such tools can aid decision-making processes required of DHS workers every day (see, for example, the Safety at Screening Models --- provide a link here). Additionally, our research team is now beginning to explore the viability of place-based predictive analytics, which is a form of spatial machine learning modeling. Such algorithms treat a region of interest, such as a city, as a grid of cells, much like a chess board. Within each cell, the number of occurrences of an outcome of interest are predicted based on that cell’s proximity to various attributes of the overall grid. For example, each cell could be a city block, and the objective of the algorithm could be to predict the number of children experiencing maltreatment within each city block within the span of a year. Examples of city attributes which may be helpful in making such predictions are each block’s average distance to the three nearest public/private schools, each block’s median household income, each block’s distance to the nearest grocery store, or each block’s average distance to the five nearest reports of domestic violence. Blocks which are predicted to have comparatively large counts could then be paired with appropriate resources in an effort to prevent such outcomes from occurring. The identification of what are appropriate resources for prevention would necessarily be grounded in the input of the identified community.

Geospatial Modeling
2019

Service Matching: Ensuring the Most Appropriate Placement for Children

This research will use historical data to create equations that identify the best placement for each child entering substitute care, based on their unique needs; these equations will identify the likelihood of success for each placement type. Essentially the equations provide the relative effectiveness of each placement for each child or youth. This project is scheduled for 2019.

Child Welfare
2019

Social Complexity in Medicaid Youth

Literature has shown that social and environmental factors present in a child’s life greatly influence health outcomes as an adult, and that the effects of these experiences are cumulative. Some of these social determinants of health are identifiable through administrative data on service utilization within DHS. This project, in partnership with Oregon Health Authority and the Oregon Pediatric Improvement Partnership, seeks to identify available administrative data sources for social risk factors and provide an index of social risk to Oregon’s coordinated care organizations so they can provide additional services to children most at-risk

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Department of Human Services, Interagency
2019

Treatment Options for Out of State Mental or Behavioral Health Treatment Placements

This project is focused on primary data collection to identify the needs of children that receive acute mental or behavioral health services outside of the state of Oregon through DHS. The project supports the alignment of Child Welfare, Youth Intellectual/Developmental Disabilities and Children’s Mental Health programs and the needs of these vulnerable children experiencing mental health or behavioral challenges.

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Child Welfare, Intellectual and Developmental Disabilities
2019

Workload and Caseload

Current staffing models do not adequately recognize the time required to complete new mandates and new assessments. New random moment surveys are being developed to improve current staffing models. In addition, the analyses will consider how caseload affects client outcomes. Implementation and Reporting are working with each program area to design and implement the surveys, analyze results, and communicate the new survey method to staff.

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Department of Human Services
2018

Adult Foster Homes: Resident and Community Characteristics Report

ORRAI’s reporting and research teams partnered with Institute on Aging at Portland State University to describe the characteristics, staffing types and levels, policies, and monthly charges and fees for a sample of Oregon adult foster homes (AFHs). The study also compares results to prior Oregon assessments and national studies, describes residents’ health and social characteristics, and compares setting types for differences that might affect access, quality, or costs. The study findings are intended to provide information that state agency staff, legislators, community-based care owners, and consumers might use to guide their decisions.

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Aging and People with Disabilities
2018Aging and People with Disabilities
2018

Child Welfare Screening Predictive Analytics Tool

Deciding whether a child abuse allegation should be assigned for investigation by Child Protective Services is a complex, high-stress endeavor. Our Research and Implementation teams worked with Child Welfare professionals throughout DHS and the state to create a predictive analytics tool that assists in real-time decision-making. The tool uses historical data to assess whether a call to the child abuse hotline should be closed at screening or assigned to a caseworker. By decreasing the number of non-abuse cases being assigned, the tool aims to responsibly balance caseload sizes so that workers can focus on families at the highest risk. Read the Safety at Screening Research Brief to learn more.

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Child Welfare, Fairness Correction
2018

Community-Based Care: Resident and Community Characteristics Report on Assisted Living, Residential Care, Memory Care

ORRAI’s reporting and research teams partnered with Institute on Aging at Portland State University to describe the characteristics, staffing types and levels, policies, and monthly charges and fees for three types of community-based care settings: assisted living, residential care, and memory care. The study also compares results to prior assessments and national studies, describes residents’ health and social characteristics, and compares setting types for differences that might affect access, quality, or costs. The study findings provide information that state agency staff, legislators, community-based care providers, and consumers might use to guide policy, reimbursement, quality initiatives, and decisions.

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Aging and People with Disabilities
2018

Human Centered Design

Human Centered Design is one variant in a class of product or service design methodologies that starts with people and ends with solutions tailored to meet their needs. DHS applied Human Centered Design to the inter-agency Parents and Children Thriving Together (PACCT 2 Gen) Project to plan a two-generation approach to improving outcomes for low-income families by simultaneously raising school attendance rates of the most disadvantaged children and youth, helping their parents achieve economic security and supporting parents’ role as caregivers.

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Human Centered Design, Self Sufficiency Programs
2018

Impact of Estimating Fairness, Minimizing the Effects of Bias

The ORRAI Research Unit is using historical child welfare data and predictive analytics to develop risk estimates for different decision-making points throughout investigations into child abuse and safety. When using historical data, predictive equations have the potential to perpetuate biases embedded in historical data. Researchers developed a way to minimize such biases while largely preserving the accuracy of the predictive risk assessment tool. Currently this procedure is incorporated in the Child Welfare Screening Predictive Analytics Tool.

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Child Welfare, Fairness Correction
2018

Impact of Estimating Fairness, Minimizing the Effects of Bias

Our research team is using historical child welfare data and predictive analytics to develop risk estimates for different decision-making points throughout investigations into child abuse and safety. When using historical data, predictive equations have the potential to perpetuate biases embedded in historical data. Researchers developed a way to minimize such biases while largely preserving the accuracy of the predictive risk assessment tool. Currently this procedure is incorporated in the project.

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Child Welfare, Fairness Correction
2018

Parents and Children Thriving Together (PACCT 2 Gen)

In 2016, Oregon received a National Governors Association (NGA) and the Center for Law and Social Policy (CLASP) a technical assistance and peer networking grant to create a two-generation (2Gen) plan to align efforts across human services, workforce, and education policy areas to better meet the needs of low-income families. Oregon’s inter-agency team used Human Centered Design in five Oregon communities to create a statewide plan that leverages current initiatives in the state - including the Oregon Department of Education’s Statewide Education Plan – and promotes cross-agency alignment and collaboration. The plan’s 2Gen objectives are:

  • Supporting children’s learning and healthy development by raise school attendance rates of the most disadvantaged children and youth while simultaneously
  • Helping their parents achieve economic security, and
  • Supporting their parents’ role as caregivers.

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Interagency, Self Sufficiency Programs
2017

Nursing Facility Care in Oregon

Reporting collaborated with Oregon State University College of Public Health and Human Sciences to produce The State of Nursing Facilities in Oregon in, 2016 to assist in local and statewide planning and policy-making efforts in long-term care services.

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Aging and People with Disabilities
2017

Social Policy Simulation

In Oregon and across the nation, low-income families rely on many government programs to help make ends meet, yet decision-makers often lack a way to see how these programs work together. By integrating nearly 100 formulas into a single tool, the Oregon Enterprise Data Analytics (OEDA) Social Policy Simulation gives a holistic view of the financial supports available to Oregon’s families in need. You can compare scenarios and view results in dynamic data visualization displays with the simulation and summary below.

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Department of Human Services, Interagency
2016

Oregon Project Independence – Describing Differences between Oregon Project Independence & Medicaid Long-Term Care Consumers

This study seeks to improve our understanding of how enrollment in Oregon Project Independence (OPI) improves the independence of the Oregonians it serves. It includes profiles of OPI consumers and compares them with Medicaid Long-Term Care consumers. This study also investigates whether participation in OPI delays entry into Medicaid Long-Term Care and whether this program is cost-effective.

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Interagency
2016

Student Mobility Impacts on Student Achievement

Children with high transience may exhibit a significant achievement gaps when compared to students with greater family stability. This study investigates how social services to families and destabilizing events (such as moving) intersect with children’s educational outcomes such as absences and graduation from high school and longer-term outcomes such as teen pregnancy and employment. It also explores key strategies to provide early support for students who move frequently and face challenges at home that require social services.

Interagency
2016

The State of Nursing Facilities in Oregon

Reporting also collaborated with Oregon State University College of Public Health and Human Sciences to produce The State of Nursing Facilities in Oregon in, 2016 to assist in local and statewide planning and policy-making efforts in long-term care services.

Aging and People with Disabilities
2016

Two-Year Employment Outcomes for Adults in the Temporary Assistance to Needy Families Program

One of the statutory goals of the Temporary Assistance to Needy Families (TANF) program is to 'end dependence on welfare by promoting job preparation and work.' To better understand the TANF program's impact towards achieving this goal, Oregon Enterprise Data Analytics used over twelve years of administrative data from the Integrated Client Services data warehouse to uncover if adults have higher incomes and/or work more hours after participating in the program.

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Self Sufficiency Programs