GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: NLQuery- As Data or Pulse
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: AHRQ
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: As a feature to clinical dashboards available on a secured page on the Patient Safety Organization Privacy Protection Center website for PSOs who submit data, provides capability to allow user to type a question in common language and instantly get a response. Answers come in the form of automatic data visualizations, with no need to manually drag-and-drop fields or understand the nuances of user's data’s structure.

Improved functionality of the clinical dashboards.  Potential to encourage additional reporting of data in light of benefit to user.
12_outputs: Provide capability to allow user to type a question in common language and instantly get a response . Answers come in the form of automatic data visualizations, with no need to manually drag-and-drop fields or understand the nuances of user's data’s structure.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2021
2_use_case_name: Quality and Safety Review System AI-enabled automated abstraction
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: AHRQ
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The aim of this AI use case is to enhance or eliminate the need for manual abstraction of patient records and manual data entry into the QSRS tool, thereby streamlining the data entry process.

This AI use case has the potential to significantly help by increasing the volume of cases abstracted while maintaining or lowering government expenditure, thereby enabling more work to be done at the same or reduced cost. Additionally, it can maintain the number of cases abstracted with improved efficiency, thus also reducing government costs by achieving the same output at a lower cost. Furthermore, it will improve abstraction accuracy and expand AI/ML capabilities at both the national and local hospital levels while ensuring data quality improvements so there is trusted and reliable data for reporting purposes.
12_outputs: This AI use case is designed to automate the data abstraction process once the OCR system has transformed PDF images into text. It also aims to automate data entry into the Quality and Safety Review System (QSRS), thereby increasing efficiency and accuracy.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75Q80122F80006
31_data_catalog: No
34_data_docs: Documentation is missing or not available
40_has_ato: Yes
41_system_name: AHRQ AWS Enclave
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Document Q&A Interface 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The SCCT consolidates a number of documents for every response that it monitors, and often analysts have questions about response context stored in those documents

Increase an analyst / user on Protect's knowledge of a specific response or MCM by allowing them to "ask questions" of associated / uploaded  SCCT media
12_outputs: Large Language Models are used to query existing documents for answers based on a user's query
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 000HCVL1-2022-66353
31_data_catalog: No
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Emergency Inventory Redistribution Model
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: Optimization models are needed to identify efficient preparedness and responses strategies for inventory management and distribution during an emergency.

This can be used to identify possible interventions.
12_outputs: This linear programming (constrained optimization) model can identify the optimal strategy for meeting demand for medical supplies during an emergency event.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2023
19_date_acq_dev_began: 8/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: emPOWER AI
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Public health authorities, first responders and others across the emergency management spectrum indicated that they had to be able to more rapidly access emPOWER publicly available data, particularly in unstable internet conditions, during disasters. emPOWER AI, an  Amazon Alexa Skill, was created to allow anyone with a smartphone to be able to request the data and informational resources. 

First responders, public health authorities, emergency managers, health care providers, and other community partners can use emPOWER AI prior to, during, and after an incident, emergency, or disaster to anticipate, plan for, and respond to the access and functional needs of at-risk populations in their communities using publicly available emPOWER data, informational resources and training.
12_outputs: emPOWER AI gives the user publicly available data from the HHS emPOWER Map on the number of electricity-dependent Medicare beneficiaries at the national, state, territory, county, and ZIP Code levels. It also provides information on other HHS emPOWER Program tools and resources that collectively support emergency preparedness, response, recovery, mitigation, and resilience activities. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2018
19_date_acq_dev_began: 9/1/2017
20_date_implemented: 1/1/2019
22_dev_method: Developed with contracting resources.
23_contract_piids: GSA Schedules
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is complete
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Not Reported
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Entity Resolution Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: SCCT receives order information that references thousands of manufacturers and customers. These data need to be standardized.

This allows analysts to aggregate data by manufacturer or customer to better characterize order and delivery patterns.
12_outputs: This tool uses embedding models, hierarchical clustering, and large language models to identify groups of duplicate entity names and assign a new standardized name.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 7/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Executive Report Generation AI Copilot 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: SCCT produces executive summaries and commercial product awareness reports (CPARs) on supply chain disruptions, weather events, shortages, etc. These utilize a large amount of SCCT data that needs to be summarized and explained in detail.

Used to decrease the amount of time needed to produce an executive report, reducing the need for SCCT analysts to create them from scratch every time
12_outputs: Large language models are utilized to summarize existing documents and data in order to produce the base text for an executive report. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 2/1/2024
20_date_implemented: 3/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 000HCVL1-2022-66353
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Flow Model Edge Prioritization Estimation Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: This model is needed to investigate potential impacts of supply disruptions.

This is one component in a simulation tool used to quantify the impact of supply chain disruptions on customers.
12_outputs: This model uses linear regression to summarize patterns in preferential order fulfillment and predict future fill rates.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2023
19_date_acq_dev_began: 9/1/2023
20_date_implemented: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Generalized Demand Model Sensitivity Analyzer
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The generalized demand model is used by SCCT to create demand projections for critical medical products.

This is a diagnostic tool used to help analysts understand and refine their models.
12_outputs: This constrained regression model is used to identify the primary drivers of uncertainty in demand models and quantify their impact.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2023
19_date_acq_dev_began: 12/1/2023
20_date_implemented: 2/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Google Search Model Alerts
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Publicly available information can enhance the ability to identify supply chain disruptions.

This provides automated alerts that can help trigger review of the relevant data by analysts.
12_outputs: This model, based on exponentially weighted moving averages, identifies rising trends in timeseries related to google search volume for keywords of interest.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 4/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Google Search Model Rising Trend Entity Extraction
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Publicly available information can enhance the ability to identify supply chain disruptions.

The list of products produced by this model helps analysts identify products that may be in shortage.
12_outputs: This tool uses language models to identify the object in shortage from a list of shortage related rising search terms identified by Google.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 4/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Manufacturer Name Standardization
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The SCCT receives many manufacturer name variants from its many distributors that need to be consolidated and standardized

This allows analysts to have more standardized manufacturer names for reporting
12_outputs: An advanced grouping algorithm groups similar manufacturer names together, and a Large Language Model recommends a standardized name based on all variants
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 4/1/2024
20_date_implemented: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 75A50122C00052
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Model based open source reporting search and summarization
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: SCCT collects open source information for monitoring supply chain events.

Language models provide an initial summary that analysts can use to select topics of interest in order to focus on relevant events.
12_outputs: Large language models are used to search open source information and summarize topics related to supply chain events.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 000HCVL1-2022-66353
2_use_case_name: PPE Product Classification Model
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: ASPR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: SCCT receives information about thousands of products from distributors.  This is used to predict if the product falls into one of the following 7 categories: 1) Gloves – Surgical & Exam, 2) Needles & Syringes, 3) Gowns – Isolation & Surgical, 4) Masks – N95, 5) Masks – Surgical/Procedural, 6) Eye / Face Shields, 7) Test Kits.

These classifications are used to aggregate data in order to make it possible to analyze trends.
12_outputs: This model uses bayesian anomaly detection and random forests to assign products to a list of predefined product classifications.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 1/1/2023
20_date_implemented: 1/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 000HCVL1-2022-66353
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: SCCT Distributor Data
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.