GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Genetic distance computation method for comparing complex multi-locus parasite (Cyclospora) genotypes
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Investigating the similarity of infections used during epidemiologic investigations of cyclosporiasis outbreaks.

It helps produce a clustering output for massive datasets comprising genotypes that are too complex to be analyzed using traditional genetic distance computation methods and aid in identifying similarities of infections when tracking cyclosporiasis outbreaks and other potential parasites.
12_outputs: The AI algorithm compares sets of haplotypes sequenced from a set of clinical samples and computes a set of genetic distances. It performs these comparisons across thousands of genotypes to identify infections that are closely related or unrelated. The resultant genetic distances are then clustered to define groups of closely related infections. This information is used routinely to complement epidemiologic investigations of cyclosporiasis (Cyclospora) outbreaks, and traceback investigations performed by state public health agencies and FDA. While this AI has mostly been applied to Cyclospora, we have also applied it to other parasites, including malaria, and certain parasitic worms.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2018
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 9/1/2019
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: used Cyclospora sequence data that was generated by CDC, State Public Health Labs, and the Public Health Agency of Canada following a protocol for amplifying 8 genotyping markers. This protocol was developed by CDC. All sequence data generated by CDC and State Public Health Labs are publicly available through NCBI (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA578931/). The sequence data from the Public Health Agency of Canada can typically be found on NCBI as well (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA796535/).
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.
39_code_link:  
40_has_ato: Yes
41_system_name: Advanced Molecular Detection Scientific Computing Platform
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 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: MedCoder - Coding literal text cause of death information reported on death certificates to ICD-10
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: MedCoder is a subsystem of CDC's National Center for Health Statistics (NCHS) National Vital Statistics System for coding causes of death indicated on the death certificates to the International Classification of Diseases 10th Revision (ICD-10) codes.

With the implementation of MedCoder, the percentage of deaths that can be automatically and accurately coded has risen from 70-75% using the previous coding system to over 85%. This improvement translates into substantial cost savings, amounting to hundreds of thousands of dollars that would have been spent on manual coding. Additionally, it significantly enhances the timeliness of data related to urgent public health concerns such as COVID and drug overdose deaths, enabling near real-time surveillance.
12_outputs: MedCoder assists with the identifying cause of death codes from death certificates using ICD-10 Underlying cause codes and automatically identifies and rejects complex causes of death as well as frequently miscoded ones, requiring manual coding or review. Additionally, in production, MedCoder utilizes natural language processing (NLP) to cleanse and standardize literal text cause of death statements before performing coding.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2017
19_date_acq_dev_began: 2/1/2018
20_date_implemented: 6/1/2022
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is widely available
35_demo_features: Sex/Gender
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: National Vital Statistics System (NVSS)
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: No
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: NCIRD SmartFind ChatBots - Public and Internal 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Internal partner mailbox email management including managing knowledge base. Previously, this included public facing chatbots but the public ChatBots are no longer active. This uses previously cleared FAQs and other reviewed information accessible publicly as the knowledge base.

The internal Knowledge-Bot's SharePoint component is helping program staff manage emails from partners more efficiently and effectively including creation and maintenance of a knowledge base that can be utilized across staff managing the mailbox. 
12_outputs: Develop conversational ChatBots (Public Flu, Public COVID-19 Vaccination, Internal Knowledge-Bot) that analyze free text questions entered by the public, healthcare providers, partners, and internal staff, and provide agency-cleared answers which best match the question. Developed in collaboration with Microsoft staff during COVID-19 pandemic using their Cognitive Services, Search, QnA Maker, Azure Healthcare Bot, Power Automate, SharePoint, and webapps. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 3/1/2024
20_date_implemented: 12/1/2024
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: For the public-facing chatbots, we use public-facing FAQs.
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.
39_code_link:  
40_has_ato: Yes
41_system_name: CDC OD ATO
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: NIOSH Industry and Occupation Computerized Coding System (NIOCCS)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Replaces manual coding of industry and occupation text to standardized codes so that they can be used for research and analysis.

Reduces the high cost of manually coding industry and occupation data while promoting increased coding speed, accuracy, and consistency.
12_outputs: Uses machine learning models to translate industry and occupation text into standardized codes that can be used for research and analysis.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
20_date_implemented: 1/1/2024
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
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: Yes
33_agency_data: STLT's death record data (received via NCHS) and BRFSS survey 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.
39_code_link:  
40_has_ato: Yes
41_system_name: Modernization Platform NIOSH (MPN)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
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: 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: Nowcasting Injury Trends 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Predicting current injury rates and real time estimates of injuries and deaths. The internal dashboard that will be created as a result of this effort will enhance the understanding of injury trends and expedite surveillance and research activities. This initiative will have a direct impact on the speed at which emerging trends in injuries resulting in deaths are identified and investigated.

Gold standard data take time to process and be made available. Real-time insight of injury trends when gold standard data are not available can be a resource resulting in timelier situational awareness to inform injury surveillance efforts. 
12_outputs: An internal-facing, interactive dashboard incorporating multiple traditional and non-traditional datasets and a multi-stage machine learning pipeline to 'nowcast' injury death trends nationally on a week-to-week basis. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 1/1/2022
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Emergendy Department data from the National Syndromic Surveillance Program
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.
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
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: Risk Assessment Module (RAM) for the National Diabetes Prevention Program (National DPP) Operations Center.
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Tracking program progress. This AI assists in determining if an organization that is currently operating within the National DPP is at risk of improperly starting, at risk of going inactive, or at risk of not achieving the goals necessary to continue successfully in CDC’s Diabetes Prevention Recognition Program.

The RAM module will help program managers synthesize large amounts of organization-level data in making decisions on how best to assist organizations with their programs, ultimately leading to increased program participation and improved health outcomes in program participants. 
12_outputs: The RAM is a reporting tool within the National DPP Operations Center which already has an ATO. The RAM module ingests large amounts of organization-level data including National DPP participant enrollment records, demographics, and specific risk factors to perform supervised learning and model generation. This machine learning model is used to predict and quantitatively rank those organizations that are at highest risk of failing to meet program objectives. This information, both input and RAM output, is currently fully sequestered to associates of the National DPP of the CDC. In the future, with continued development and validation, the plan is to allow external-to-CDC State Quality Specialist (SQS) users access to the outputs of RAM for additional assistance with improving organization success.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2023
19_date_acq_dev_began: 9/1/2023
20_date_implemented: 8/1/2024
21_date_retired:  
22_dev_method: Developed with contracting resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: DDT DPRP Portal - historical data from organization's 6-mth submissions of participant attendance of Lifestyle change classes.
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.
39_code_link:  
40_has_ato: Yes
41_system_name: DDT DPRP Portal
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: 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: Semi-Automated Nonresponse Detection for Surveys (SANDS)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: SANDS is a fine-tuned large language model (LLM) designed to assist with human-in-the-loop procedures specifically for open-ended survey responses. Open-ended survey responses vary in quality and are challenging to use effectively because they are labor-intensive to review and therefore cost-prohibitive to use at scale. SANDS is intended to reduce the burden by filtering out clear and obvious non-response such as  "This is so dumb, why I am I wasting my time on such a stupid question" or "Because It is true" which provide no value to researchers. This LLM is available on HuggingFace and is not routinely updated, but it can be used like any open-source LLM through secure instances of Python.

Manual curation of open-ended survey responses is time-consuming, often requiring extensive hours to identify themes and review outputs. SANDS significantly reduces this manual burden by providing scores for responses, enabling researchers to quickly compile an initial high-quality dataset for qualitative research. SANDS also flags responses needing further examination, streamlining the review process.
12_outputs: CDC's National Center for Health Statistics (NCHS) has developed and released a model to detect nonresponses in open-text survey responses. This helps improve survey data quality and question and questionnaire design. The system is a natural language processing (NLP) model that has been fine-tuned on a custom dataset of survey responses. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2021
19_date_acq_dev_began: 9/1/2021
20_date_implemented: 9/1/2022
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: 3,000 labeled open-ended responses to web probes on questions relating to the COVID-19 pandemic gathered from the Research and Development Survey or RANDS conducted by the Division of Research and Methodology at the National Center for Health Statistics
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.
39_code_link:  
40_has_ato: No
41_system_name:  
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: 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: TowerScout: Automated cooling tower detection from aerial imagery for Legionnaires' Disease outbreak investigation
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: During Legionella outbreaks, the National Center for Immunization and Respiratory Diseases (NCIRD)/Division of Bacterial Diseases (DBD) needs to identify cooling towers that could potentially be spreading Legionella bacteria. 

Using AI object detection, TowerScout detects cooling towers approximately 600 times faster than manual searches, allowing researchers to more efficiently respond during outbreaks. 
12_outputs: TowerScout uses object detection and image classification models to detect cooling towers within aerial imagery.  
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
20_date_implemented: 5/1/2021
21_date_retired:  
22_dev_method: Developed with contracting resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/TowerScout/TowerScout
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
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: 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: Creation of synthetic Survey-like Insurance Names for use coding NHIS private insurance responses
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This solution will impact the process of manual coding and reviewing text errors in the National Health Interview Survey 

Previous efforts to augment the manual coding have proven ineffective. Given the complex nature of insurance programs, this results in over 200 FTE hours a year being spent on just the initial coding of open text insurance fields. Additional hours are spent reviewing the manual coding for errors. By creating survey-like insurance responses, this could theoretically save over 100 hours of staff labor a year, greatly increasing efficiency and timeliness.
12_outputs: The National Health Interview Survey calculates insurance coverage rates among the US non-institutionalized resident population, including statistics of the percentage of people with private insurance coverage. The collection of information on insurance coverage is accomplished in two parts. First, survey respondents self-identify the type of insurance they have. Secondly, to aid in the verification of insurance type, survey respondents also provide the name of the health plan or program they have in an open text field. The information collected in the open text field may include misspellings, acronyms, and rarely match exactly the true complete insurance plan name. 

This leads to a time intensive process as staff may need to decipher the plan name in the open text field mentioned by the respondent and/or abbreviated by the interviewer and if that plan name confirms the correct type of insurance initially indicated during the interview process. By creating AI generated known acronyms as data points for projects, as an example Blue Cross Blue Shield of Alabama to BCBS of AL and BC/BS of AL, this will create a more representative set of information for staff to use when coding potential saving up to hundreds of FTE hours reviewing responses. This also works in combination with existing efforts to augment manual coding with AI and Machine Learning to increase efficiency.

16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 6/1/2024
22_dev_method:  
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review: