– IMPROVE YOUR EFFICIENCY THROUGH EXPLANATION BASED AUDITING –
PRIVACY TAILORED TO YOUR ORGANIZATION
Every healthcare provider has unique clinical and operational workflows. Using a one-size-fits-all system does not account for these differences, and can leave many accesses left uninspected. EBAS uses patented machine learning algorithms to create tailored explanations for each organization’s data, and automates the monitoring and auditing processes. EBAS learns over time to improve its accuracy.
COMPLETE AUDITS IN MINUTES...NOT DAYS
EBAS can scale to audit millions of accesses in minutes – whether the audit is for a small private practice or an international healthcare system. Using a highly paralleled database architecture, EBAS scales with the resources allocated in order to audit 100,000 to 1 million accesses per minute. Multiple EBAS VMs can work together to complete the job even faster.
TURBO CHARGED ROI
EBAS saves time and resources, allowing healthcare providers to proactively improve their privacy coverage without hiring additional employees.
POWER YOUR MEDICAL MONITORING WITH OUR EXPLANATION BASED AUDITING SYSTEM
Download Whitepapers to Learn More About Technology & Implementation
– SEAMLESS INTEGRATION WITH EXISTING SYSTEMS –
TECHNICAL OVERVIEW & IMPLEMENTATION
3 STEP PROCESS
Data are extracted from EMRs and operational systems and loaded into EBAS. Data types include lists of accesses, patient-hospital encounters (e.g. appointments), ICD-10 Codes, and employee department listings. Maize runs in a virtual machine (VM) so no data leaves your network.
Maize’s data-mining algorithms find commonly occurring connections between patients and the employees accessing their records. Frequently occurring paths are referred to as “explanations” and represent presumptively valid clinical or operational reasons for access.
FILTER APPROPRIATE ACCESS
Maize displays a list of accesses in tables and then filters out appropriate accesses so that there is a smaller number of alerts for manual review.