Ann N. Jan 04, 2024

Antimicrobial resistance database: Considering factors in implementation

In the battle against infectious diseases, the emergence of antimicrobial resistance poses a dreadful challenge to healthcare systems worldwide. To establish our defenses, the implementation of robust antimicrobial resistance (AMR) databases stands as a frontier. These databases serve as guardians of invaluable information, offering insights into resistance patterns, guiding treatment decisions, and shaping public health strategies. 


However, the journey towards effective AMR databases demands careful consideration of many factors. 


Which information goes into an antimicrobial resistance database


The information typically included in an AMR database covers various aspects of resistance patterns and associated factors. Here are the key types of information that may go into an AMR database:


Microorganism data

Information about the microbial species, including bacteria, viruses, or fungi, that are subject to antimicrobial susceptibility testing. Details about specific strains or isolates, such as their source and origin.


Antimicrobial susceptibility test results

Information on the methods used for susceptibility testing, including disk diffusion, agar dilution, or broth microdilution.

Quantitative data indicating the lowest concentration of an antimicrobial agent that inhibits the growth of a microorganism.

Results from disk diffusion tests, showing the diameter of the zone where bacterial growth is inhibited around an antimicrobial-impregnated disk.


Antimicrobial agents

List of antimicrobial drugs on the specific antimicrobial agents tested against the microorganisms.

These databases also have drug classes - classification of antimicrobial agents into categories such as antibiotics, antivirals, antifungals, etc.


Geographic and temporal data

The geographical origin shows the location or region where the microorganism was isolated.

Also, it contains temporal information, including the date of sample collection or isolation of the microorganism.


Patient and clinical data

Patient demographics will be stored in AMR databases, with basic patient information, such as age, gender, and other demographics.

Additionally, other details about the patient's medical history, previous antimicrobial treatments, and underlying health conditions are also collected to this database.


Genomic and molecular data

Molecular information related to resistance genes or genetic determinants of resistance.

Genomic Sequencing Data: Sequencing information that may provide insights into the genetic basis of antimicrobial resistance.


Resistance mechanisms

Details about the specific mechanisms through which microorganisms exhibit resistance to antimicrobial agents.


Epidemiological information

AMR database will use epidemiological typing, a technique used to classify microorganisms based on their epidemiological relatedness, such as pulsed-field gel electrophoresis (PFGE) or multilocus sequence typing (MLST).


Surveillance data

Trends and patterns in antimicrobial resistance over time, aiding in the identification of emerging resistance issues.


Treatment outcomes

Information on the clinical outcomes of patients treated with antimicrobial agents, including treatment success or failure.


Type of database for antimicrobial resistance data


The type of database used for storing antimicrobial resistance (AMR) data can vary based on the specific requirements of the healthcare or research organization. Different types of databases may be employed, each with its own characteristics and advantages. Here are some common types of databases used for managing AMR data:


Relational databases

These databases organize data into tables with predefined relationships between them. They use structured query language (SQL) for querying and managing data.

Relational databases can ensure data consistency and integrity through defined relationships and are well-suited for large datasets and complex queries.


NoSQL databases

NoSQL databases are designed to handle unstructured or semi-structured data and can be more flexible than traditional relational databases.




  • Suited for distributed and horizontally scalable architectures.
  • Adaptable to different types of data structures.

Performance: Can provide fast and efficient access to large volumes of data.


Graph databases

Graph databases represent data as nodes and edges, making them ideal for managing complex relationships and interconnected data.


  • Well-suited for representing relationships between different elements in AMR data.
  • Efficient for traversing relationships in a network.


Document stores

Document-oriented databases store data as documents, typically in JSON or BSON format. Each document contains key-value pairs or other structures.


  • Easily accommodates diverse data types and structures.
  • Scales horizontally to handle growing datasets.


Data warehouses

Data warehouses are designed for the analysis and reporting of large volumes of data. They often consolidate data from multiple sources.


  • Well-suited for complex analytical queries and reporting.
  • Centralized storage of data from different sources.


Spatial databases

Spatial databases manage data with spatial components, making them suitable for scenarios where geographical information is crucial, such as tracking the spread of resistant microorganisms.


  • Supports the storage and retrieval of geospatial information.
  • Enables mapping and analysis of spatial patterns.


The choice of the database type depends on factors such as the nature of the data, the complexity of relationships, scalability requirements, and the specific goals of the AMR data management system.


Suggesting functions for antimicrobial resistance database


Data collecting

AMR should be able to collect data from different sources such as hospitals, laboratories, medical facilities, and other related organizations. Collected data includes information on bacteria, antibiotics, drug resistance results and other related factors.


Data storage and management

The AMR system must provide an efficient and secure data storage mechanism. Antimicrobial resistance data needs to be organized, managed and protected to ensure integrity and privacy.


Retrieving and searching information

AMR systems need to provide the ability to retrieve and search information quickly and easily. Users need to be able to search for information about bacteria, drug resistance, drug resistance results, and other related factors through query interfaces and search engines.


Data analysis

AMR systems should provide data analysis tools to better understand drug resistance trends and variation patterns. These tools can include histogram analysis, correlation analysis, timing analysis, and other methods to extract useful information from resistance data.



Offering suggestions and guidance

Based on drug resistance data, the AMR system can provide recommendations and guidance on the use of antibiotics and other treatments. This helps health professionals make accurate decisions about antibiotic use and resistance management.


Updating information and reports

AMR systems should provide information and reports on drug resistance, resistance trends and control measures. Through reports, the system can provide information to health providers, researchers and health policy decision makers.


User interaction

AMR systems should be able to interact with users through an intuitive and user-friendly interface. This helps users easily access and use functions and information in the system.


Healthcare systems integrate with antimicrobial resistance database


Efficient management of antimicrobial resistance (AMR) requires seamless integration of healthcare systems with dedicated databases. By integrating with other healthcare systems, providers can access comprehensive information to make informed decisions and enhance antimicrobial stewardship.


Electronic Health Record (EHR) systems

Antimicrobial resistance data can be integrated directly into EHR systems, providing clinicians with real-time access to patient-specific resistance profiles.

EHRs can display historical resistance patterns, recent susceptibility test results, and past antimicrobial treatments, enabling a holistic view of a patient's resistance status.



Hospital information systems

HIS can serve as a centralized repository for AMR data, consolidating information from various departments within the hospital.

Antimicrobial resistance databases and HIS integration allows for the inclusion of both inpatient and outpatient resistance data, supporting continuity of care.


Health information exchanges

HIEs facilitate the exchange of AMR data between different healthcare facilities, promoting collaborative efforts in combating resistance.

Health information exchanges enable regional surveillance by aggregating data from multiple organizations, providing a broader perspective on resistance trends.


Clinical decision support systems

Clinical decision support systems can incorporate AMR data to offer real-time guidance on antimicrobial treatment choices based on local resistance patterns.

AMR integrates with CDSS allows for the generation of alerts when prescribing decisions that align or conflict with known resistance profiles.


Pharmacy information systems

Pharmacy information systems can integrate with AMR databases to streamline medication reconciliation processes, ensuring that prescribed antibiotics align with resistance data.

Integration supports optimizing antibiotic inventory by considering local resistance patterns, avoiding unnecessary stockpiling of less effective antibiotics.


Final words


By recognizing and addressing the factors integral to a robust AMR database, we can have a look into the future where these databases become indispensable allies in our ongoing battle against microbial adversaries. The journey is undoubtedly challenging, but the promise they hold in safeguarding public health and ensuring the continued effectiveness of antibiotics is a hope guiding us toward a resilient and responsive healthcare future.