Reasons Why We Need Disease Discovery for Risk Adjustment Coding
Posted On
Disease Discovery is a necessary process that should be included as part of Risk Adjustment Coding. It will help you to determine what illnesses are occurring in your population and how severe they are so that you can ensure that your providers are reimbursed based on the severity of their conditions. This process will also allow you to identify any illnesses that may not have been coded and are, therefore, potentially incurring costs. If you want to learn more about disease discovery, you have come to the right place. In this article, we’ll discuss how to use the finding of diseases to make better risk adjustment coding decisions. You’ll also learn about some of the challenges associated with this approach and the steps you can take to improve your practice.
Table of Contents
Incentivizes Them To Find Innovative Ways To Help Patients
Risk adjustment is a statistical method that predicts how much a healthcare service will cost. While it can be intimidating to learn about, it’s essential to understand that it’s a powerful tool that can benefit insurers and providers.
When properly implemented, risk adjustment can help ensure that contracted providers have the resources to provide patient care. Making payments based on the patient’s health and other risk factors can minimize the potential for discrimination.
Under the Affordable Care Act, commercial health plans are required to include risk adjustments in their payment formulas. Health plans also have to account for age distribution and metallic tier. However, the ACA’s formula is complex and could create winners and losers. The key to successfully managing a risk adjustment strategy is a strong health plan and provider execution.
Risk scores will likely increase as health plans transition to value-based reimbursement models. In turn, they will also have incentives to care for sicker patients. This can result in increased profits and extra benefits for plan members.
Disease-based risk adjustment should be central to a health plan’s risk adjustment strategy. It encourages physicians to make accurate diagnoses and capture chronic conditions.
Develop A National Database For Health Services Research
The healthcare industry has a vast amount of data at its disposal, and the ability to make sense of it all would be a good thing. With the right tools and technologies, it should be possible to glean insights that could lead to better care and management. Unfortunately, most of this information is untapped. Hence, the healthcare industry must fully utilize its rich data streams. This includes data mining, data analytics, and the analysis of text, images, and videos.
A good example is the Clinical Classification System (CCS). It consists of two related classification systems, one of which includes more than 3,900 procedure codes. As the name implies, it is a database containing the most pertinent information about diagnosis and treatment codes.
In particular, a multi-level CCS is useful for evaluating large aggregations of conditions and treatment types. However, this complex undertaking requires technical expertise and data infrastructure. Fortunately, the Healthcare Cost and Utilization (HCU) Project is in the business of doing just that. By way of background, the HCU is a federal-state-industry partnership aimed at improving healthcare quality and reducing costs in the U.S. Various regulatory and legislative frameworks govern the project and provide data.
Include Historical Clinical Information Beyond 1 Year
Incorporating historical clinical information beyond one year in the disease discovery process could help predict risk. However, there are several questions regarding whether risk adjustment algorithms can do so. This is particularly relevant for the Nosos model, which incorporates a variety of data sources, including health insurance status, patient registry status, pharmacy data, and medical history. The study aims to assess whether adding historical clinical information improves the fit of risk adjustment scores and improves the ability to predict the outcome of clinical events.
While incorporating historical clinical data may help predict the risk of certain conditions, the study found no substantial gain in predicting the risk outcome. Moreover, including prior years of clinical data was only partially effective in correcting for missing data. For example, the study found that including a three-digit ZIP code in a five-digit ZIP code resulted in no material gain in risk adjustment.
Educate Clinicians On The Importance Of Accuracy
Achieving accurate risk adjustment coding requires a partnership between health plans and providers. This relationship has a significant impact on the financial viability of a health plan. It is also necessary to provide members with the best care possible.
Effective risk adjustment programs take into account the time physicians and staff dedicated to documenting patient conditions. It can be challenging to achieve accurate coding without the proper documentation.
Accuracy is more important than precision. The goal is to capture the full scope of a patient’s clinical history. Missing or inaccurate diagnoses skew a patient’s profile. Not reporting a diagnosis can negatively affect funding for the patient’s treatment.
Physicians need to be educated on the importance of accurate coding. This will help them improve the quality of their practice. As a result, they will be more likely to be adequately reimbursed.
Health plans have a variety of tools they can utilize to help physician practices improve their coding and billing workflow. These include technology, educational materials, and on-site coaches. A practical approach is to develop a regular cadence for monitoring coding quality.
