How to Register on SAM.gov
Step-by-step guide to registering your business on SAM.gov (System for Award Management). Required for all federal contractors and grant recipients.
Get a UEI Number
Obtain a Unique Entity Identifier (UEI) from SAM.gov. This replaced the DUNS number in April 2022. You can request one during the SAM.gov registration process.
Gather Required Information
Prepare your EIN/TIN, bank account information (for EFT), NAICS codes, business size, and ownership details. Having these ready speeds up registration.
Create a Login.gov Account
Go to SAM.gov and create a Login.gov account. This is the federal government's single sign-on system used for authentication.
Complete Entity Registration
Fill out the entity registration form on SAM.gov. This includes sections on Core Data, Assertions, Representations & Certifications, and Points of Contact.
Submit for Review
Submit your registration for IRS validation and CAGE code assignment. The review process takes 7-10 business days.
Maintain Your Registration
SAM.gov registration must be renewed annually. Set a calendar reminder 30 days before expiration. Let your registration lapse and you cannot receive contract awards.
Frequently Asked Questions
Is SAM.gov registration free?
Yes, SAM.gov registration is completely free. Beware of third-party services that charge fees — you do not need them.
How long does SAM.gov registration take?
The registration process itself takes 1-2 hours to complete. After submission, IRS validation and CAGE code assignment takes 7-10 business days.
Related Resources
Related Opportunities
Electronic Health Records System (EHRS)
Subsequent to Solicitation 20342326Q00005, and pursuant to the authority found in FAR 8.401, Fiscal Service Procurement, on behalf of the Armed Forces Retirement Home (AFRH) of Washington, D. C. has awarded a Delivery Order to Ensoftek, Inc. on a non-competitive basis for an Electronic Health Records System. The SF 1012, Limited Source Justification, is attached. This award has an effective date of 12/01/2025 through 09/30/2026 and a Total Contract Value of $316,519.85.
Electronic Health Management Records Software
Electronic Health Management Records Software
Electronic Health Records
Agency: Montgomery County, MD - Department of Health & Human Svcs
Emerging Anomaly Detection Techniques for Electronic Health Records: A Survey
Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other doma
Continuous-time probabilistic models for longitudinal electronic health records
Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. Here, we present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).. Authors: Kaplan, Alan D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)]; Tipnis, Uttara [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)]; Beckham, Jean C. [Durham Veterans Affairs (VA) Health Care System, NC (United States); VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC (United States); Duke University School of Medicine, Durham, NC (United States)]; Kimbrel, Nathan A. [Durham Veterans Affairs (VA) Health Care System, NC (United States); VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC (United States); Duke University School of Medicine, Durham, NC (United States); VA Health Services Research and Development Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC (United States)]; Oslin, David W. [Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (United States); Univ. of Pennsylvania, Philadelphia, PA (United States)]. DOE Contract: AC5
DOME: Directional medical embedding vectors from Electronic Health Records
Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME
Using electronic health record metadata to predict housing instability amongst veterans
Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.. Authors: Zamora-Resendiz, Rafael [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]; Oslin, David W. [University of Pennsylvania, Philadelphia, PA (United States)]; Hooshyar, Dina [University of Texas Southwestern Medical Center, TX (United States)]; Crivelli, Silvia [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]. DOE Contract: AC02-05CH11231. Subjects: 60 APPLIED LIFE SCIENCES; Document metadata; Electronic healthcare records; Environmental & Occupational Health; Homelessness screening; Machine learning; Public; Veteran health
Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.. Authors: Niu, Haoran [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000155228297); Omitaomu, Olufemi A. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000230787196); Langston, Michael A. [Univ. of Tennessee, Knoxville, TN (United States)] (ORCID:0000000159455796); Grady, Stephen K. [Univ. of Tennessee, Knoxv
INTENT TO SINGLE SOURCE: Merative US L.P. - Annual Subscription to Linked Claims and Electronic Health Record (EHR) Database
NOTICE OF DECISION TO CONTRACT WITH A SINGLE SOURCE: The Federal Aviation Administration (FAA), Mike Monroney Aeronautical Center, intends to award a single source contract to Merative US L. P. for an Annual Subscription to Marketscan (Linked Claims and Electronic Health Record (EHR) Database). Marketscan is owned by Merative and they are the sole distributor/subscription provider for Marketscan access. Purpose of the Announcement: In accordance with the FAA Acquisition Management System (AMS) Policy 3.2.2.4, the purpose of this announcement is to inform industry of the basis of the FAA's decision to contract with a selected source via single source procedures. All questions pertaining to this announcement should be addressed in writing to the Contract Specialist, Andre Casiano at email address andre.casiano@faa. gov.
Health Care Delivery Solutions (HCDS) Electronic Health Record Follow-on (MHS GENESIS)
4 Dec 2025 - updated questions and answers provided (HCDS EHR Q and A 4Dec2025). 19 Nov 2025 update DHA Contracting, on behalf of the Defense Health Management Systems Program Executive Office (PEO DHMS), expresses its sincere appreciation to all respondents. We are reviewing the information received. In the meantime, the initial set of questions/ responses are posted (HCDS EHR Q and A 19Nov2025). Questions received will be consolidated every Friday with responses posted by the following Thursday. Please remember this is available at any time for industry to ask questions or provide comments. https://select. bidscale. app 7 Nov 2025 Update: The following link allows the viewing of industry day: https://www. youtube. com/watch? v=c-cFdEajXeo For industry input (RFI) requested (starting on slide 19 of the brief), please submit no later than 17 Nov 2025. Industry Input (RFI): Proprietary information may be submitted; however, RFI respondents are responsible for adequately marking proprietary, restricted or competition sensitive information contained in their response.? If a submission is marked, it will be protected from disclosure outside of Government personnel, unless permission is granted for Government support contractors to view the material. The following companies and individual employees are bound contractually by Organizational Conflict of Interest and disclosure clauses with respect to proprietary information, and they will take all reasonable action necessary to preclude unauthorized use or disclosure of an RFI respondents proprietary data.? RFI responses? MUST? clearly state whether permission is granted allowing the support contractors identified below access to any proprietary information. Boston Consulting Group (BCG) Swingtide Andrew Morgan Consulting, LLC? Greenlight Analytic, LLC Monterey Consultants, INC This RFI is not a solicitation.?? This RFI is for planning purposes only.? It does not constitute an RFP or a promise to issue an RFP in the
All Records
Electronic Health Records System (EHRS)
Subsequent to Solicitation 20342326Q00005, and pursuant to the authority found in FAR 8.401, Fiscal Service Procurement, on behalf of the Armed Forces Retiremen
Electronic Health Management Records Software
Electronic Health Management Records Software
Electronic Health Records
Agency: Montgomery County, MD - Department of Health & Human Svcs
Emerging Anomaly Detection Techniques for Electronic Health Records: A Survey
Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinic
Continuous-time probabilistic models for longitudinal electronic health records
Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods,
DOME: Directional medical embedding vectors from Electronic Health Records
Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments
Using electronic health record metadata to predict housing instability amongst veterans
Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as
Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various cli
INTENT TO SINGLE SOURCE: Merative US L.P. - Annual Subscription to Linked Claims and Electronic Health Record (EHR) Database
NOTICE OF DECISION TO CONTRACT WITH A SINGLE SOURCE: The Federal Aviation Administration (FAA), Mike Monroney Aeronautical Center, intends to award a single sou
Health Care Delivery Solutions (HCDS) Electronic Health Record Follow-on (MHS GENESIS)
4 Dec 2025 - updated questions and answers provided (HCDS EHR Q and A 4Dec2025). 19 Nov 2025 update DHA Contracting, on behalf of the Defense Health Management
Electronic Health Records System (EHRS)
Electronic Health Management Records Software
Electronic Health Records
Emerging Anomaly Detection Techniques for Electronic Health Records: A Survey
Continuous-time probabilistic models for longitudinal electronic health records
DOME: Directional medical embedding vectors from Electronic Health Records
Using electronic health record metadata to predict housing instability amongst veterans
Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms
INTENT TO SINGLE SOURCE: Merative US L.P. - Annual Subscription to Linked Claims and Electronic Health Record (EHR) Database
Health Care Delivery Solutions (HCDS) Electronic Health Record Follow-on (MHS GENESIS)
Ready to find your first government contract?
Search 150M+ federal records across SAM.gov, Grants.gov, USAspending and FPDS.
Get Started Free