Delivering Unemployment Assistance in Times of Crisis

Angell, Mintaka; Gold, Samantha; Howison, Mark; Kidd, Victoria; Molitor, Daniel; Burns, Casey; Johnson, Chris; Kahn, Matthew E.; Venzke, Stuart; Deneault, Sandra; Doweiko, Donald; Dziembowski, Stephen; Kumar, Bijay; O’Donnell, Patrick; Reidl, Andrew; Tardiff, Brian; Hastings, Justine S.; Jensen, Scott A.; Pellegrino, Angelika; Roberts, Amelia; Sarathy, Rahul

doi:10.1145/3428125

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…RIPL's system was developed, tested, and deployed within just 10 days, making Rhode Island the first state in the nation to collect, validate, and pay PUA claims. 6 The Rhode Island Department of Labor and Training (RIDLT) is now launching new tech-based solutions using their Secure Data Enclave to help displaced workers reskill for economic recovery through career recommendations powered by data and artificial intelligence. 12 exchanging data between state agencies to reduce benefit processing time.…”

Section: Use Casesmentioning

confidence: 99%

“…This automated verification allowed Rhode Island to "pre-validate" PUA claims and avoid downstream costs and delays associated with manual verification, contributing to Rhode Island being the first state in the country to pay PUA claims. 6 This use case also illustrates "evaluation as a service," a model enabled by the Secure Data Enclave architecture where other parties can partner with government to use administrative data to evaluate the impact of policies and programs while maintaining data confidentiality between all parties.…”

Section: Use Casesmentioning

confidence: 99%

“…A real-world example of this solution is our organization's recent use of a cloudbased Secure Data Enclave for processing Pandemic Unemployment Assistance claims as part of the State of Rhode Island's emergency response to the COVID-19 pandemic. 6 That system followed the reference architecture described here and allowed the state to safely share sensitive claims data with contractors for claims processing and fraud detection.…”

Section: Introductionmentioning

confidence: 99%

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Protecting Sensitive Data with Secure Data Enclaves

Howison¹,

Angell²,

Hicklen³

et al. 2021

Preprint

Self Cite

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A Secure Data Enclave is a system that allows data owners to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party’s access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave and provide a reference architecture for its implementation on the Amazon Web Services platform using managed cloud services.

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Section: Use Casesmentioning

confidence: 99%

Section: Use Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Protecting Sensitive Data with Secure Data Enclaves

Howison¹,

Angell²,

Hicklen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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Predicting Divertible Medicaid Emergency Department Costs

Hastings

Howison

2022

Digit. Gov.: Res. Pract.

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Lowering health care costs is a policy priority for public health insurance programs. Policies that divert Emergency Department (ED) care to less costly, more effective health services are a promising avenue for cost savings. Using comprehensive, anonymized administrative data from the State of Rhode Island, which includes Medicaid and other social insurance programs, we demonstrate how government can identify and conduct efficient outreach to Medicaid recipients at risk of becoming high-cost ED users for potentially divertible care. The top predictors from our models include age, Medicaid enrollment and eligibility factors, and prior medical procedures. Our predictive models capture more future divertible spending than existing methods for identifying ED “super-utilizers” based on multiple prior ED visits. By using comprehensive administrative data that includes all the state's social insurance programs, we can also establish connections between predicted high-cost ED users and existing case management in non-Medicaid programs. Policymakers could use these models to improve their identification of divertible spending and reduce the need for de novo outreach and case management programs.

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Protecting Sensitive Data with Secure Data Enclaves

Howison,

Angell,

Hastings

2024

Digit. Gov.: Res. Pract.

View full text Add to dashboard Cite

A Secure Data Enclave is a system that allows data owners, such as governments and private firms, to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party's access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave, provide a reference architecture for its implementation on Amazon Web Services using managed cloud services, and describe four use cases of this architecture in partnerships with state governments to control access to sensitive administrative data.

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Delivering Unemployment Assistance in Times of Crisis

Cited by 3 publications

References 1 publication

Protecting Sensitive Data with Secure Data Enclaves

Protecting Sensitive Data with Secure Data Enclaves

Predicting Divertible Medicaid Emergency Department Costs

Protecting Sensitive Data with Secure Data Enclaves

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