MedCity Influencers, Health IT

Risk adjustment sets the stage for proactive care

Technologies such as machine learning and data analytics can transform risk adjustment processes.

imageHealthcare is on the path toward more proactive and personalized care, and an accurate narrative about patient healthcare is the foundation for understanding what works and what does not.

The Affordable Care Act (ACA) ushered in new regulation, one that many other countries take as table-stakes: payers and providers cannot deny care or coverage based on a patient’s pre-existing conditions. Post-ACA, the group of people who receive health insurance in this country has changed from a relatively healthy subset to a more representative sample.

Equity in insurance coverage comes with its own challenges, though. Thirty-four percent of Americans are considered obese and chronic conditions such as diabetes are growing at an alarming rate. The Department of Health and Human Services estimates that five percent of the population is responsible for almost 60 percent of all healthcare spending. Given these numbers, it’s clear the level of wellness amongst the U.S. population is not evenly distributed amongst insurance or provider groups that care for them. This puts significant strain on resources and complicates efforts to treat patients at scale.

Now, payers and providers are required to deliver care and coverage for a patient cohort that is more diverse than ever. As such, generalized approaches to patient care no longer suffice. Healthcare providers must transition to a proactive care mindset that delivers highly individualized care.

Risk Adjustment Sets the Stage for Proactive Care

Concentrating on delivering personalized treatment and care will transform quality outcomes. To ensure that sick patients get the individualized care they need and providers get reimbursed the adequate amount to provide top-quality care, provider groups have turned to risk adjustment.

Nearly 60 million Americans are covered by an insurance plan that requires risk adjustment, which extracts information from health records to define the wellbeing of individuals and sets how much money an insurance company or healthcare provider will receive to care for them. The risk adjustment process is based on Hierarchical Condition Category (HCC) codes that measure the health of each individual over the course of the year by tracking a series of conditions or disease states.

At the end of the year, each insurance company and provider with members enrolled in plans that require risk adjustment submits the disease codes of their membership, which then get added up to create a risk adjustment score — an overall measure of the health and wellness of their membership. Payments are then adjusted according to how sick or well their membership is in relation to all the others.
This data provides a comprehensive picture of patient’s health and illness and enables health organizations to correctly target resources. It’s also a crucial metric for organizational success because payment for increasing a patient’s health should reflect how sick the patient originally was; healing a high-risk patient is a greater accomplishment than preserving the health of a low-risk patient. Yet, if the accuracy of risk adjustment is jeopardized, so is the quality and effectiveness of the healthcare ecosystem.

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How Risk Adjustment Can Deliver on its Potential

For risk adjustment to function and ensure efficient and effective allocation of healthcare resources, it requires one thing: accurate and long-term risk assessment. The problem is that assessing a patient’s overall wellness has historically been a manual task. Coders go through hundreds of pages of documents to identify disease categories and HCC codes on a per-patient basis, for millions of patients, every year.

This manual process is inherently inaccurate and leaves room for missing and inaccurate codes. Data analytics technology that has been applied for the past 15 years to innovate the Internet, e-commerce and advertising industries can be leveraged within healthcare to reduce use of costly and time-consuming manual processes involved in determining risk scores. Linguistic analysis and machine learning-based algorithms can identify and properly document conditions attributed to individual patients within clinical text and patient notes.

These technologies can transform the risk adjustment processes into simple straightforward workflows, reducing errors and closing gaps that lead to improved care accuracy. If organizations employ these technologies to accurately and efficiently compute risk adjustments scores and manage risk, they can succeed under the Affordable Care Act and deliver more targeted and effective care.

Photo: Flickr

Mark Scott has more than 18 years of medical technology and health care provider marketing experience. Mark has driven several highly successful global re-branding and marketing communications efforts for such diverse healthcare organizations as Scripps, Carefusion, Alere and Masimo. He eats, sleeps and breathes marketing and brand. His expertise covers all the bases—from brand development, positioning and messaging; to brand identity, packaging and labeling; public relations; content marketing, website development; internal/employee communications; and global brand-launch activations. Mark has a Bachelors and a Masters Degree from the University of Western Ontario and won a number of awards for marketing and communications stuff over the years.

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