DRVS Add-On Modules
Optimize the use of DRVS at your organization by adding on modules that will help you better integrate with healthcare ecosystem and provide more extensive care to identified patient populations.
Controlled Substance Abuse Module
The module enables DRVS customers to integrate the treatment of patients with substance use disorders into the primary care practice. It eliminates the need for double documentation and managing your OBOT program in Excel and will help organizations better understand opioid prescribing practices, provide insights on managing patients, and put an end to manual reporting. Users also can access the status of programs through dashboards.
Early identification of patients at risk for alcohol and substance misuse.
Track and manage patients with known alcohol and opioid use disorders (AUD and OUD).
Track and manage Office Based Opioid Treatment (OBOT)
Monitor patients with active opioid treatment medications and/or use of Benzodiazepine and opioids.
Assess the impact of outreach, education and changes to workflow.
Understand the status of programs through DRVS Dashboards.
The risk stratification algorithm that is available in DRVS assists with the consistent identification of high risk patients within or across client health centers. The risk stratification uses diagnostic and clinical data – age, chronic, behavioral health, infectious disease and substance use conditions, social determinants, clinical outcome indicators, medications, and utilization – to identify those patients at risk who might benefit from care management monitoring and intervention by center staff and programs. Patients are stratified into a high, moderate or low risk category which can be utilized across the DRVS platform in Dashboards, Reports, Registries, Patient Visit Planning, Care Management Passport and quality measures.
Identify patients that could benefit from care coordination.
Support NCQA PCMH requirements for population health management.
Efficient and consistent identification of the needy or costly patients.
Match the right resources to the patients need.
Better understand provider panels and comorbidities of specific patient populations.
Compare risk distribution across health centers (Network Risk Algorithm).
Engage in informed conversations with payers and funders with regards to patient risk.
Create programs and build resources specific to population needs.
Monitor program performance and success of the high-risk populations.
The Payer Integration module provides DRVS users with a full suite of reports and features to take advantage of payer enrollment and claims data to get a larger view of patient populations.
Attribution & Enrollment
Match and reconcile attributed health plan members with actual health center patients
Provide lists and counts of unseen members with a single click
Easily see changes in month-to-month enrollment across multiple plans, identifying both newly enrolled and dis-enrolled members
Limit results of DRVS reports to a health plan enrollment group(s)
Provide measure results using full attributed populations
Stratify matched and unseen patients by age and last visit to identify “low hanging fruit” for outreach
Utilization & Total Medical Expense
Reconcile health plan supplied care gaps across clinical & claims data
Track PMPM costs in aggregate and at the patient level
Identify highest costs members
Stratify members by Total Medical Expense, specific costs categories, or service utilization to identify those requiring additional attention
Combine clinical data from the EHR and Practice Management system with enrollment, claims and ADT information for a full view of what is driving utilization trends
Identify emergency department and inpatient trends and frequent fliers
Calculate patient risk using EHR clinical data, claims data or both
Identify highest risk members
Filter DRVS reports, measures and dashboard results based on patient risk level
Utilize client specified risk factor/algorithm criteria such as Social Determinants of Health (SDOH) or deploy industry standards such as Johns Hopkins ACG
Care Gap Reconciliation
Identify discrepancies between payer (claims based) care gaps and EHR (clinical data based) care gaps
Highlight “perceived” care gaps that have been addressed but still require proper documentation
Understand patients overdue for services
Improve both care and performance metrics