GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Feedback Analysis Solution (FAS)
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 Feedback Analysis Solution is a system that uses CMS or other publicly available data (such as Regulations.Gov) to review public comments and/or analyze other information from internal and external stakeholders. The FAS uses Natural Language Processing (NLP) tools to aggregate, sort and identify duplicates to create efficiencies in the comment review process. FAS also uses machine learning (ML) tools to identify topics, themes and sentiment outputs for the targeted dataset.  

FAS help categorize stakeholder comments or feedback (collected in multiple venues), thereby enabling analysts to use the system to quickly identify comments that may impact program/policy decisions.
12_outputs: Feedback Analysis Solution (FAS) uses AI/ML/NLP tools to identify topics, themes, and sentiments outputs for the target datasets. Groups and offices within CCSQ use the solution in slightly different ways to ingest unstructured data, interpret and structure it for use in ML models after de-duplication of comments.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 9/1/2021
20_date_implemented: 9/1/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 75FCMC22A0018/75FCMC23F0001
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: 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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: IT System Utilization Optimization
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 FFE application usage patterns vary and are dependent on differing environment usage periods. CMS resources are currently manually scaled, not allowing immediate actions in correlation with usage changes.

Automation of application scaling
12_outputs: This use-case will implement Machine Learning to determine optimized infrastructure/application scaling to support system volume.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2019
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 1/1/2020
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: CMS
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
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.
2_use_case_name: Knowledge Management Platform
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 affects involves accessing and using structured and unstructured data sources to enable data-driven decision-making. 

It helps by creating efficiencies, reducing burden, and generating an increased return on investment in the achievement of CMS's mission and business objectives.
12_outputs: It leverages natural language processing, knowledge graphs, and large language models to make the knowledge within structured and unstructured data sources accessible and meaningful to users.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2021
19_date_acq_dev_began: 10/1/2021
20_date_implemented: 11/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: KMP / 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: Risk Adjustment Outlier Analysis
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 RA program spreads the financial risk borne by Issuers due to offering a variety of plans meeting the need of the diverse population. RA payments are distributed based on population risk levels. The FFE uses a distributed data solution to calculate plan average actuarial risk and associated payments and must avoid potential impacts to annual calculations.

Maintain RA Program risk integrity
12_outputs: This use-case will implement Machine Learning to identify outliers in issuer data / behavior that may unduly influence risk adjustment payments.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 6/1/2024
20_date_implemented: 1/1/2020
22_dev_method: Developed with contracting resources.
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: No – agency does not have access to source code.
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.
2_use_case_name: Agent/Broker Fraud Analysis
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: Agent/Brokers (A/Bs) support the consumer enrollment and eligibility process. Because of this, they have learned the intricate details of the FFE for accessing applications, submitting eligibility determinations, and adding enrollments to their line of business, opening up the possibility of fraud. 

Improves the oversight and integrity of Marketplace Applications and Enrollments assisted by Agents and Brokers.  Helps CCIIO identify suspicious behaviors to refer to CPI.
12_outputs: Utilizes Machine Learning to identify suspicious behaviors of Agents and Brokers in the Marketplace
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 1/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
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: No – agency does not have access to source code.
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.
2_use_case_name: CCSQ ServiceNow AI Search
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 ServiceNow AI Search is a product inside ServiceNow (SaaS).  It replaces traditional Zing search tool (exact search), and enables users with more flexible searches to get relevant and actionable answers quickly.

* Improve search relevance
* Promote self-service by empowering users to find information independently, and potential reduce number of cases
12_outputs: AI Search will
* Display most relevant results first
* Support synonyms, auto-corrections, stop words, and auto-completion
AI Search analytics will provide insights into search usage, performance, trends, metrics, and how to improve search experiences.
Plan to do AI Search tune-up next
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 2/1/2024
20_date_implemented: 4/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: This use case is operated in compliance with HHS's agencywide Information Quality Act policy and procedures.
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: ServiceNow knowledge articles, ideas, catalog items data
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: QSC
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: MSP Assignment 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: This impacts automatically retrieving records from a contractor's system, and importing them into an internal OPOLE Access database.  Once populated in the database the record is assigned to staff to review and adjudicate.

Pulls more cases out of BCRC and assigns much faster than an analyst. By allowing BOT to pull and assign cases instead of an analyst, this approach provides maximum time for staff to process cases which decreases the response time.
12_outputs: The Medicare Secondary Payer Operations Group (MSPOG) uses a BOT to pull compromise cases out of BCRC and assign to staff in our internal Management Information System (MIS)
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 3/1/2022
19_date_acq_dev_began: 4/1/2022
20_date_implemented: 8/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: case turn around times
34_data_docs: Documentation is complete
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: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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: Relationships, Events, Contacts, and Outreach Network (RECON)
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: RECON AI for Recommender System| Sentiment Analysis

It has the potential to analyze data faster.
12_outputs: This was “native” Salesforce AI capabilities, not anything custom – and that platform-level AI is not being used in RECON at this time.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 2/1/2024
2_use_case_name: Complaint Analysis POC
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 business process this AI could affect involves the analysis of complaint data used by the OPOLE team.

It could help the OPOLE team to understand the main issues and potential root causes of the complaints, enabling them to address these issues more efficiently and effectively.
12_outputs: It utilized data science techniques and an LLM to analyze complaint data.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 2/1/2024
24_hisp_support: No
2_use_case_name: Data Lake/Load-Extract-Transform-Load (L-ETL)
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: CMS is using Security Data Lake to modernize the Load-Extract-Transform-Load (L-ETL) pipelines and data tooling. There is no actual ML/AI work being done here today, rather, we are beginning work on the scaffolding that will open up these opportunities in 1-2 years time. 

Building on top of a modern data platform will provide opportunities to experiment with machine learning model development against this data--solving any number of problems that require decisions to be made about inferences over time series data. 
12_outputs: CMS will be enhancing Agency security to bring together more system, telemetry and program data in one place with a unifying governance model.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024
2_use_case_name: FOIA Document Review/Redaction 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: OIT & OSORA partnership: Internal business process of reviewing and redacting CMS business documents subject to FOIA requests.

Reduces burden on FOIA staff to manually redacting business documents subject to FOIA requests.
12_outputs: Utilizes GenAI technology to automate review and redaction of CMS documents per FOIA requests/process.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
24_hisp_support: No
2_use_case_name: Help Desk Responses
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: The Division of Issuer Management and Operations has talked with their contractor, LMI, about using an AI tool for the Help Desk contract.

This would reduce the amount of time staff contractors need to generate answers for SME review and approve responses to QHP issuers and other external entities that ask questions to the help desk.   
12_outputs: This AI feature in the issuer help desk would help generate responses to common questions from issuers and external organizations based on previously cleared material.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 12/1/2023
2_use_case_name: 508 Accessibility Automation for Documents
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: OIT: Internal business process at individual employee level to automate review, identification, and correction of potential 508 accessibility issues in documents.

Reduces burden associated with making documents 508 compliant (for individual CMS employees).
12_outputs: Utilizes GenAI technology to automate review and generation of corrected documents compliant with 508 accessibility rules.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2023
2_use_case_name: Brand vs Generic Market Share
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 Drug Data Processing System (DDPS) needs to predict changes in Medicare Part D costs 

Understanding beneficiary changes from Brand to Generic drugs allows for more accurate financial estimates.
12_outputs: Analyze generic drugs compared to brand drugs over time and forecast future market shares based on Part D claims volume
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2021
2_use_case_name: Chat Client - Resource Library
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: Continue to assess and refine content present in both the informational public side of QPP, Resource Library, Webinar Library, and Top Navigation as the site continues to grow.

The Chat Client would benefit the program by improving user experience through easier access to information, increased program understanding, and ability to communicate in multiple languages. 
12_outputs: Powered by Amazon Bedrock, by loading all resources, files, references etc. available to the QPP user community, we can establish a knowledge base that allows the bot to scan all available program documentation to construct a message that accurately answers a user's question. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Chatbot within Hub
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: Currently the following help support activities are handle by a person:
Personalized FAQ’s; 
Clarify questions with schema and onboarding etc.; 
Handle - Where is “My file” or “My Request” inquiries
Provide a - Talk to Agent feature
Scheduling a testing window
Provide data summarization and reporting for internal stakeholders
Report operational health of the system
Include training materials, Q/A about the system and such.

Help support team in day-to-day communication with external partners
12_outputs: Build a chatbot that will address some help support activities through:
Access to Internal Knowledge base Including RAG (Retrieval Augmented Generation).
Personalized FAQ’s & Contextual generation.
Interaction with Live Agent.
Chat & Talk Feature.
Ability to query custom data source for File or Case status.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2024
2_use_case_name: Drug cost anomaly detection
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: CMS Division of Payment Reconciliation is responsible for monitoring Prescription Drug Event  (PDE) data for accuracy

Allows for identification of outliers in PDE which can be corrected through Part D plan outreach in order to prevent overpayments.
12_outputs: Identify anomalies in drug costs on Part D claims
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2021
2_use_case_name: Drug Cost Increase 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 Drug Data Processing System (DDPS) needs to predict changes in Medicare Part D costs 

Machine Learning model uses multiple methods to predict changes to Part D drug costs based on market changes.
12_outputs: Use Historical drug costs increases to predict future increases
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2021
2_use_case_name: Medicare Part C/D Marketing Material Review
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: Drug and Health Plan Operations (DHPO) staff reviews both advertising mediums and channels materials that includes over 22  different types of media marketing materials ranging from written, digital, and more. The marketing materials are submitted to CMS from either the Medicare Health Plans and/or their third party marketing organizations. DHPO staff reviews these marketing materials to ensure they meet Medicare Marketing Guidelines. 

The current process is time consuming and resource intensive. This reduces the efficiency and quality of staff reviews. AI can improve the efficiency and quality of the marketing materials reviewed; with human validation of results. Incorporating AI into current review process can reduce the time it takes to review marketing materials, improve efficiency and allow existing FTE resources to be utilized more efficiently, and potentially reduce the volume of marketing misrepresentation complaints.  Ideally this will improve efficiency and quality, help beneficiaries access to care, and also ensure marketing materials meets Medicare Marketing Guidelines.
12_outputs: The AI tool required has not been identified as we still need to develop a proof of concept.  We anticipate AI would learn the various requirements for Marketing materials to help identify issues and compliance with applicable policies and regulations.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024