GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Medicare Part C/D Plan Oversight of AI Used for Prior Authorization and Utilization Management
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Medicare beneficiary access to benefits, services, and prescriptions associated with managed care plans and prescription drug plans.

AI tools can increase the capacity to analyze vast data sets based on combined sources. The results can then be further analyzed and refined by SMEs to issue compliance actions, identify plans for program auditing, and help protect the integrity of the Medicare program. 
12_outputs: Evaluate claims, payments, and complaints data to identify outliers. Determine if plans may be out of compliance with CMS rules or are negatively impacting beneficiary health outcomes based on plan use of AI and associated potential bias in system, process, or social determinates of health.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2024
2_use_case_name: Negotiated Drug Predictions
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The Inflation Reduction Act authorizes Medicare to directly negotiate drug prices for certain high expenditure, single source Medicare Part B or Part D drugs. For the first year of the Negotiation Program, the Secretary will select 10 Part high expenditure, single source drugs for negotiation.

Will assist with drug price negotiation for selected drugs
12_outputs: Use historical drug data to predict costs and claim volume for Selected drugs
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2021
2_use_case_name: QPP Admin Bot
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Provides custom reporting and analysis to meet the ongoing and emerging needs to address analytics and reporting needs and supports key business processes in the QPP cycle - such as delivering feedback to providers, calculating final scores, and delivering payment adjustments to the Medicare contractors.

The Admin Bot would benefit the program by increasing availability of data, thus empowering users and informing decisions, through a simple-to-use communication platform.
12_outputs: Powered by Amazon Bedrock, the out-of-the-box solution can interface directly with data lakes like QPP's Universal Data Set (UDS) empowering users to ask very pointed questions on model data and/or model performance.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Operational Efficiency Analytics 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: OHI/MAG is responsible for providing Marketplace consumers with an accurate, efficient and timely appeals adjudication process.

Examples where we can use predictive analytics:
 * Identifying trends in operations for managing and completing appeals processing
* Predicting the number of appeals during open enrollment
* Looking for regional or demographic trends in appeal complexity, completion time, and success
12_outputs: Eligibility Appeals Case Management System (EACMS) - Training a model on the data in our future data lake to be able to do predictive analytics.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
2_use_case_name: ICPG Governance Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The business process it will affect involves the processing and analysis of acquisition plans (APs).

It will help by streamlining the manual steps involved in validating and classifying acquisition plans, reducing the time and effort required. 
12_outputs: It will automate the processing and analysis of acquisition plans and classify them as IT-related or non-IT.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 9/1/2021
19_date_acq_dev_began: 9/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: KMP
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: CMS
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: CEDAR
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: 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: Innovation Center Investment Proposal (ICIP) Model Analysis POC
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: It will affect the process of document analysis, specifically the analysis of ICIP documents.

It will help in  ICIP document analysis, with the goal of extracting of insights and recommendations from these documents.
12_outputs: It will extract relevant insights and recommendations from the ICIPs, making use of a RAG-based architecture, and present them through a natural language interface. This interface will allow users to interact with the documents and retrieve the information they need easily.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: PPMS
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: ICIPs
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: CEDAR
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: 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: OAGM Rate Card
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The business process it will affect involves the analysis of historical price history/trends for labor categories in business proposals for the Office of Acquisition and Grants Management (OAGM).

It will help OAGM make better decisions on future contracts by providing them with a comprehensive and standardized view of historical price data for labor categories.
12_outputs: It will assist in normalizing labor categories, enabling the OAGM team to easily identify the most relevant labor category despite the variations in naming conventions found in business proposals.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 9/1/2021
19_date_acq_dev_began: 11/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: SEAS IT
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: CMS
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: Yes
41_system_name: CEDAR
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: 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: CCSQ Now Assist for CSM 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: CCSQ Now Assist for CSM (Customer Service Management) is a product inside ServiceNow (SaaS).  It integrates Gen AI with CSM, and uses Now LLM (ServiceNow native Large Language Model) to generate contents based on machine learning (ML).  Now Assist for CSM helps agents to improve productivity and efficiency and deliver better services.  

Improve agents' responsiveness and productivity
* Quickly get familiar with a case/chat by getting case/chat summarization
* Quickly resolve the case by using auto-generated resolution notes.
12_outputs: Agents have quick access to 
- Case summarization
- Chat and agent hand-off summarization
- Resolution Notes Generation
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 5/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Not Reported
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Historical ServiceNow case data
34_data_docs: Documentation is missing or not available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: QSC
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: AI Workspace
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The business process this affects involves the ability to easily run AI experiments in a safe and secure environment. 

It helps by reducing barriers to AI experimentation, supporting strategic AI infrastructure enablement, and providing hands-on experience for teams to learn and understand the potential risks and benefits of AI.
12_outputs: It utilizes data science tooling, large language models, and other approved AWS services such as Sage Maker and Bedrock to provide a secure and supportive environment for AI experimentation and prototype development.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2024
19_date_acq_dev_began: 3/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: SEAS IT
24_hisp_support: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: CEDAR
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: 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: Citation Analysis and Survey Assistant (CASA - Nursing Home Survey CMS 2567)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: 2567 (CASA) enhances the efficiency and effectiveness of monitoring and reviewing nursing home surveys across the US. It enhances how the Quality, Safety, and Oversight Group (QSOG) and Survey Operations Group (SOG) assess how State Survey Agencies (SSAs) are citing nursing home deficiencies, reported on CMS Form 2567, by employing advanced natural language processing and machine learning technologies.

 The system would support the Nursing Home Survey CMS Form 2567 survey work allowing QSOG and SOG staff to follow up on cases of under and over citations at the facilities that are flagged by the system and sent to the State Agencies
12_outputs: It will allow for real-time interaction with survey data, facilitating a deeper understanding of trends and individual case specifics. By flagging discrepancies in citations and providing a robust platform for detailed analysis. CASA supports CMS’s mission to ensure high standards of care and accountability in nursing homes
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2023
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: CMS
34_data_docs: Documentation has been partially completed
40_has_ato: Yes
41_system_name: CDRAP
42_dev_tools_wait: 6-12 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: 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: Hazards & Threats Awareness Data Processing Automation
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: EPRO is responsible for developing agency-wide situation reports from various open web and email materials. 

More effective prioritization and processing of materials for situational awareness, faster delivery of usable work product to decision-makers agency-wide. The manual process takes 2-3 hours per day, and the AI system will reduce the work to a human quality assurance/inspection step estimated to take approximately 30 minutes.
12_outputs: The AI will scrape known websites and monitor relevant resource mailboxes, use a categorization algorithm, and summarize and format the significant events each morning.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2024
19_date_acq_dev_began: 11/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: SEAS IT
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: EPRO
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Hazard and Threat Awareness
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: 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: Independent Dispute Resolution (IDR) Eligibility Rules Engine
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The current Independent Dispute Resolution (IDR) Technical Assistance (TA) process is very manual and time intensive which limits throughput.  The rules engine should significantly expedite processing and expand capacity. Automating more of the process may also increase consistency across recommendations and result in a more predictable timeframe for the workflow by better positioning disputes for analyst review.

The AI tool will help automate the eligibility review process, reducing time-intensive manual steps and increasing consistency of results.  
12_outputs: Use of artificial intelligence (AI) models to identify the presence or absence of necessary data points within documentation.  AI tool searches documentation to identify and store necessary data points such as document title, file type, payment date, service code, claim number, and date of service. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Federal Independent Dispute Resolution (IDR) Dispute Data
34_data_docs: Documentation is complete
40_has_ato: Yes
41_system_name: AWS
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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: Medicare Fee for Service Requirements Modernization (MFRM)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: EIG is developing a business process to parse, curate, and simplify Medicare coverage and coding requirements by medical condition using AI LLMs. 

After rigorous testing for accuracy, AI output will be uploaded to the MFRM application to be used to enhance CMS and healthcare operational efficiencies, including organization of guidance documents, and identification of source complexities to be triaged with owners.

12_outputs: Utilizing an LLM (Anthropic’s Claude 3.5) to curate current, accurate, and reliable Medicare requirements information sourced from FFS policy and guidance documents.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
31_data_catalog: No
33_agency_data: Medicare Fee-for-Service coverage and coding information
34_data_docs: Documentation has been partially completed
40_has_ato: No
42_dev_tools_wait: 6-12 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: 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: Review Regulatory Comments
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Division of Issuer Management and Operations is considering having our contractor, AIR, use an AI tool for comment analysis in the Letter to Issuers (LTI) and Notice of Benefits and Payment Parameters (NBPP). This would streamline review of stakeholder comments. 

This tool would reduce the amount of time and resources CCIIO and contracting staff would have to review the LTI and NBPP by 25%. 
12_outputs: The AI tool would identify trends in responses from stakeholders in policy and operational issues proposed by CMS.  
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 12/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: Not Reported
24_hisp_support: No
33_agency_data: CMS
40_has_ato: Yes
41_system_name: Not Reported
2_use_case_name: Improved Data Quality Checks
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Improve consumer experience by providing an asynchronous solution to detect and provide near real time feedback via outreach to the consumer shortening the overall return cycle time without requiring UI changes. 

Improvement of user experience
12_outputs:  develop a POC classifier model to identify incorrect document upload types / low-quality images through use of OCR.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 1/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
31_data_catalog: No
33_agency_data: CMS
34_data_docs: Documentation is widely available
40_has_ato: Yes
41_system_name: FFM
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: 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.