As provider organizations continue to transition from fee for service to value based care, it is imperative they accurately document and codify the risk acuity of their patients. This has been a challenge for most provider organizations that struggle to succeed in their risk-based contracts. This is largely due to insufficient focus on risk adjustment coupled with the increased burden on physicians leading to poor adoption. Edifecs’ NLP-enabled Clinical Workflow Suite looks to ease physician overhead while enabling provider organizations to accurately and efficiently capture the risk acuity of their population. Given the varying clinical processes across provider organizations, our comprehensive workflow suite provides a highly flexible and scalable solution for providers to address risk adjustment in a way that best fits their organization’s needs and existing workflows.
Succeeding in Risk Based ContractsView Products
Post Visit Review
Post-Encounter Review ensures that missing diagnoses and incomplete documentation are corrected prior to the claim submission. It is designed for administrative staff (i.e., coding or billing staff) at physician practices to lift the burden of coding efficiently and effectively from providers. Post-Encounter Review standardizes the risk capture process across all patient populations (ACA, MA, Medicare ACO, Medicaid) and provides a streamlined approach to managing all value- and risk-based contracts through one single workflow.
- Perform in-depth, NLP supported reviews of codes prior to submission, enabling Medicaid and ACA RAF capture in all 50 states
- Shorten A/R cycles and forecasting from years to months with increased confidence due to faster and more accurate payment turnaround
- Shift any coding burdens off providers and back into the hands of coding experts
Point of Care Assessment
With newly surfaced, highly likely risk adjustable conditions presented directly at the point of care, providers have a new tool available forpatient care at their fingertips. AI-powered, risk focused chart preparation support refocuses risk adjustment around the patient, changing a traditionally actuarial processes to a real, clinical utility.
- Uncover up to 35% verified codes over retrospective passes for more effective care plans inclusive of all comorbidities
- Impact all risk adjustable populations at the point –of care, freeing physicians from excess administrative time through documentation support within the EHR
- Patient and provider insights that drive performance, population health, condition targeting, patient outreach, and more
Pre Visit Planning
Pre-Visit Planning offers outpatient practices the ability to net fuller, more accurate value-based care reimbursements while facilitating a more thorough level of care. This is accomplished by ensuring that risk scores, an assessment of disease burden and a deciding factor in reimbursements, are routinely evaluated. By flagging suspected risk adjustable conditions for review within the care delivery workflow, providers can quickly evaluate and route diagnosis gaps for closure.
- Go beyond claims-based reconfirmation and add net-new conditions identified from unstructured data
- Submit with confidence knowing each new condition is clinically validated during the encounter
- Improve care and reduce emergent visit costs with NLP supported chart preparation and patient targeting
Provide visibility and insight into a provider’s risk adjustment performance. Using AI-powered insights, providers are able to analyze actual vs suspected risk score projections and analyze gap closure performance. Understanding the impact of a risk adjustment program informs strategies to better align payers, providers, and patients with the best clinical outcomes.
- Track membership and risk score trends over time
- Analyze actual vs project risk score performance and identify high risk patients for intervention
- Manage operational and gap closure performance and identify areas of improvement
Dig deeper into unstructured data to surface conditions not previously diagnosed but anticipated based on our AI model’s clinical evidence review. Findings come from a combination of disparate data sources to identify unrecognized conditions, or undocumented instances of complexity, or comorbidity. In each case, a possible suspect is determined through the blending of criteria that includes available data points and assessment through rules-based and complex logic.
- Generate a more complete and accurate list of suspected conditions with both administrative and clinical data for provider validation
- Throttle suspect volume with confidence scoring, suppression, and filtering at both the global and local level, including care specialization condition targeting
- Confirm up to 20-25% more valid conditions to gain up to a +10% RAF value