Transitioning to value-based health care isn't a question of when; it's a question of how. If you can better understand episodes of care, you can treat patients holistically, and build value into the care continuum. SAS Episode Analytics lets you tap into services data to flexibly construct clinical episodes. Identify variations and opportunities for improvement. And assess your financial risk in value-based reimbursement agreements.
Start with standard definitions for episodes of care.
Analyze all services data – such as procedure, encounter or hospitalization data in claims or other formats – to identify signals that indicate a condition. Then automatically construct episodes of care based on either standardized or your own customized episode definitions. Associations between episodes are detected automatically, giving you a holistic patient view that enables better treatment decisions and prevention of potential complications.
Understand the true cost of episodes to better manage new payment models.
Effectively manage value-based payment models, such as bundled payments, by determining expected episode costs and adjusting them to account for heterogeneity and condition risk propensity. Then compare your expected, risk-adjusted costs with actual costs by provider, condition and patient. You can identify potentially avoidable costs (PACs) and the reasons behind them so you can target improvements in both quality of care and costs.
Identify potentially avoidable complications.
Analyze episodes and reimbursements associated with care at both the population and patient levels. As a result, you can identify which services are related to a given episode, as well as determine whether they are typical or indicative of a complication. By differentiating between typical, complication and unrelated services – e.g., at the provider level – you can look for ways to reduce preventable complications and provide better quality of care.
Assign accountability to providers and compare their performance.
Examine provider performance by correctly attributing PACs and typical reimbursements for individual episodes to providers. Our solution provides multiple attribution options, by class and for specific conditions, giving you additional control over the attribution method you want to use. You can compare provider performance on similar episode types, and measure adherence to protocols and evidence-based guidelines.
- Automatic episode definition. Examine episodes created by condition signals based on standard or custom definitions. Capture all services related to an episode to see the entirety of care across the episode.
- Potentially avoidable complication identification. See which services, procedures and medical events are relevant to a condition, identify hierarchical relationships as typical or complication, and then allocate claim dollars to the most appropriate episodes.
- Clinical association between episodes identification. Find potential clinical interactions of various episodes, and identify if a connection is typical or a complication to understand the true clinical status of a patient.
- Patient-severity/risk-adjusted claim dollar comparisons. Obtain expected, total costs of episodes of care and adjust them to account for heterogeneity and condition risk propensity in patient populations.
- Identify and analyze focused subsets of patients. Combine clinical, non-medical and other data with claim-based episodes to identify actionable patients.
- Provider attribution to measure cost and quality. Assign episodes to an organization or individual provider to understand if a conducting provider is meeting cost and quality measures for an episode.