Data sources
Conceptual concept connections only — no systems are integrated.
Illustrative operational scenarios; historical laboratory evidence is identified separately. No live surveillance, integrations or AI models are connected.
Riyadh · Klebsiella pneumoniae
Days 1–9 · illustrative reporting cycle · 7 ICU isolates; association does not prove transmission.
Transparent simulated risk workflow
illustrative EHR/EMR context, LIS susceptibility results and prescription records matched by illustrative case ID.
Python · SVM · Logistic Regression · OCR · NLP · HL7 FHIR · institutional APIs: planned only.
Current concept
Local illustrative data, interactive charts, GIS, role switching and scripted intelligence.
Planned AI & analysis
Python, SVM, Logistic Regression, OCR and NLP — not operational models.
Planned interoperability
HL7 FHIR, EHR/EMR, LIS and institutional APIs — no live integrations.
Future security & validation
Institutional governance, access controls, clinical evaluation and external validation before deployment.
Conceptual sources
8
Across three sectors
illustrative records
2.34m
illustrative volume
Quality checks
96.2%
Illustrative score
Pending mappings
14
Mock tasks
EHR / EMR
482k
Daily conceptual cadence
Laboratory systems
1.28m
Hourly conceptual cadence
Pharmacy systems
316k
Daily conceptual cadence
Public-health systems
84k
Weekly conceptual cadence
Research datasets
48k
Quarterly conceptual cadence
Animal & livestock
21k
Monthly conceptual cadence
Environmental sources
13k
Monthly conceptual cadence
Open data
96k
Quarterly conceptual cadence
Health information exchange
EHR / EMR
In collaboration with local hospitals and PHCs, the Raqeem programme, the open-data platform, NPHIES and the Sehhaty application.
Laboratory information system (LIS)
Culturing methods and conditions, acute and chronic infectious disease context, isolate and serovar counts.
Clinical data warehouse
Lab databases, wearables and population-wide data.
Wasfaty
An electronic service linking hospitals and PHCs with community pharmacies and tracking patient prescriptions.
External sources
CDC, FDA, research institutions and journals including clinical trials, case studies and articles.
Aligned to ISO/TC 215, ISO 27799:2008, ISO 27000 and SAS70 / SSAE 16, with encryption supporting AES or 3DES at minimum.
SIPOC of the surveillance process
Suppliers
Inputs
Outputs
Surveillance determinants
Importation and distribution of antimicrobials
- How many antimicrobials have been imported?
- How many have been distributed?
- Which geographical locations received them?
- Where are antibiotics disposed of?
- How many are in the market?
- What is the percentage of revenue and economic waste?
Consumption of antimicrobials
- Which antimicrobials are consumed by humans and animals?
- Which antibiotic types and doses are used in out-patient and in-patient settings?
- What type of institution dispensed them?
Antimicrobial resistance patterns
- How many infection cases (STI, UTI, URTI, GI, CVS, NMSK)?
- Which bacterial pathogens?
- What symptoms and clinical history?
- What resistance patterns clinically and genomically?
- What is the length of stay?
- What demographics and risk factors?
Antimicrobial resistance research
- Which AMR studies have been implemented across the MENA region?
- How is each study classified?
- Which are analytic and which descriptive?
Risk identification
Analysis of resistance patterns
Risk stratification and identification of organisms with their clinical and genomic resistance patterns, monitored and attached to a healthcare system.
Dashboarding
Dashboard data for surveying antimicrobial resistance patterns extracted from higher agencies and ministries.
Consultation
Assisted consultation for hospitals and PHCs so antimicrobials suit the patient at population and individual level.
Recruitment
Data recruited passively at first, with workforce capacity building following analysis.
Robotic process automation
Planned dashboard scope
- Data analysis of resistant pathogens and antimicrobials used by infection category (STD, UTI, RTI and others)
- Analysis of each hospital and PHC on the application and implementation of stewardship
- Antimicrobial utilisation dashboard (DDD) with demographic and geographic data
- Prescription patterns of antimicrobials by region and healthcare setting
- Saudi Arabia heatmap case study: spatial distribution of Pseudomonas aeruginosa (1998–2015, 15,791 isolates), Enterobacteriaceae (1998–2015, 11,196 isolates) and Acinetobacter baumannii (2005–2015, 5,312 isolates)
Documented local data limitations
Availability of local data
Data is unavailable or unprocurable due to weak local surveillance in healthcare and farm settings.
Limitations
Techniques rely on traditional methods rather than data mining or deep learning.
Representation
Data is not representative and excludes emerging AMR reporting.
Description
Data is not fully descriptive, so risk stratification is limited.
Genome sequencing
Data does not measure genetic resistance patterns; a genomic bank is necessary.
Competitiveness
No grants or incentives are provided for countries with the best care practices.
Operational values are simulated. Historical records and source-listed forecasts retain their original attribution; they are not measured GermQ outcomes.