When you think of a healthcare risk, you’re probably thinking of an outcome you’d like to avoid. The most widely understood use of risk in healthcare relates to the process of identifying patients at high, intermediate or low probabilities of certain outcomes, like admission to the hospital, dying within 12 months, or accruing high levels of medical utilization and costs.
Given sufficient data, sophisticated algorithms can help identify these patients. Having complete and accurate data is critical. Patients often receive care in many settings, so typically a single electronic health record (EHR) system will not contain complete data. Conversely, claims data cover many places of service but may not contain the level of detail — like lab results — required to fully understand the patient’s risk. Aggregated EHR and claims data offer an algorithm the best chance of successfully identifying at-risk patients.
Once these potentially at-risk patients are flagged by the algorithm, providers usually vet these identified patients to confirm who will benefit from certain services, such as a nurse practitioner home visit, a registered nurse care management phone call, or assignment to a disease management program.
A second meaning of risk is used when a healthcare group or organization takes on financial responsibility for the care of a population. The organization, often categorized under the value-based care model, must work within a certain budget to provide appropriate, quality care for that cohort of patients.
These financially risk-based arrangements require a different mindset for providers. Traditionally, providers have been paid for each service rendered to a patient. In that fee-for-service healthcare economy, providers have no financial risk, other than the risk of not seeing enough patients or not performing enough services. That dynamic can create a tilted incentive to provide “too much care,” with a lessened emphasis on quality.
But as organizations mature and have the systems and confidence to care for patients within a budget, they may take on so-called “upside” and “downside” risk.:
Complete and accurate data is equally important to support organizations grappling with this kind of financial risk, especially those that have fully accountable care organization(ACO) model. Organizations need to understand how their patients are utilizing services which then rolls up to the aggregate total medical expense (TME).
A third meaning of risk relates to both definitions above because an individual patient’s likelihood of negative outcomes impacts how much a provider organization is compensated for the care of that patient. This risk is defined as the combined or aggregate conditions that a single patient accrues over the calendar year
A risk score is used to represent a patient’s conditions numerically, to facilitate easier comparison of patients, panels, or analysis of an entire population. Different methodologies can be used to calculate risk scores; when an organization takes on financial risk, the risk scoring methodology is defined contractually.
A patient’s risk score starts at zero on January 1, and the risk begins to accumulate as codes for visits and procedures are submitted to the payor. Some providers are unaware that the individual patient’s risk reverts to zero on January 1, and all known diagnoses need to be re-entered each year. The importance of this relates to how budgets are calculated.
Though the methodologies differ between Medicare, Medicaid and commercial insurers, the budgets in all global payment arrangements are related to the “aggregate risk” of the patients in a given cohort. For example, for a cohort of 10,000 patients, the aggregate risk includes all the codes entered for all of those patients between January 1 and December 31 of the measurement year. The higher the aggregate risk score, the higher the payments will be relative to the baseline budget.
This fact highlights the importance of complete and accurate coding. In general, doctors and other providers who have been working in a fee-for-service environment have not had any financial incentive to code fully, and hence undercoding is common. Complete and accurate coding can be enhanced by a high functioning analytics system that can identify likely missing diagnoses. Offices then conduct outreach to patients who may not have come in for many months to ensure their care is complete and all relevant codes are entered.
No matter the context, in healthcare, we want to lessen risk whenever and wherever possible. At Arcadia, we’re on a mission to measure, identify, and reduce risk in every sector of healthcare and research. Start the conversation to learn more.