The US goes on to allocate a larger portion of its budget in case of healthcare as compared to any other developed country across the world. In 2021, US national health spending figured at 18.3% of its GDP. But projections indicate that by 2030, this percentage is most likely to increase to almost 20%, which would amount to near about $6 trillion.
But data goes on to indicate that nearly 25% to 30% of healthcare spending is classified as waste and happens to have the potential to be eliminated. Fraud, waste, as well as abuse, along with inefficiencies like human errors and administrative bottlenecks, contribute majorly to the total healthcare expenditure.
Risk adjustment in healthcare happens to be a methodology employed to evaluate patients’ health needs, anticipate their probability of utilizing healthcare services, and also determine the cost of their care. Making sure of a fair compensation for healthcare providers happens to be crucial to recognizing the importance of the care they provide to patients.
For example, individuals who happen to be 65 years and above are eligible to register themselves for Medicare, a government-funded health insurance program that is managed by the Centers for Medicare & Medicaid Services- CMS. As of March 2023, the number of people who happen to be enrolled in Medicare is more than 65 million. The CMS makes use of risk adjustment techniques so as to estimate the expenses that are associated with treating a patient in a specific year. These anticipations take into account numerous factors, ranging from diagnoses to comorbidities, that may impact the entire cost of care. Providers happen to receive higher reimbursements for patients who have greater medical issues as compared to patients who are regarded as healthier and are less likely to want any sort of service.
Challenges and Solutions in Risk Adjustment
Risk adjustment happens to be a highly complex process that involves considering numerous data sources and also patient factors. This process goes on to pose significant challenges, mainly when applied to a large population involving millions of patients. CMS as well as health insurance companies have traditionally made use of a range of tools for risk adjustment, like zip code analysis as well as algorithmic evaluation of population risks. But this approach frequently requires to have a manual intervention and human supervision. Doctors as well as nurses may have to intricately review numerous pages of the medical records in order to locate the needed information. This process happens to be time-consuming, laborious, and, at the same time, susceptible to mistakes.
It is well to be noted that AI provides robust solutions that go on to effectively streamline the risk adjustment process in this domain. AI-driven algorithms have the capacity to swiftly as well as accurately identify pertinent information within medical records, thereby eliminating the need for individuals to look for a needle in a haystack. AI happens to have the ability to automate the review process, which can effectively lessen the workload for human reviewers and also greatly decrease the chances of errors.
The fact is that AI has the ability to be trained to identify specific data points that are extensive datasets. For instance, rather than individuals spending hours searching across pages to find pulmonary function test results and thereafter determining if they happen to match recorded comorbidities as well as risk codes, AI could locate an apt PFT result within seconds by way of one click. An AI tool very well has the potential to promptly highlight evidence, or even the absence of it, so as to assist humans in making more informed decisions.
In developing AI solutions in the healthcare gamut, it is witnessed firsthand the benefits when it comes to implementing AI-driven risk adjustment. By saving valuable time as well as resources, while at the same time enhancing accuracy and minimizing bias, this methodology proves to be very effective. If one is looking out to implementing AI solutions within healthcare organizations, it is always suggested to take into account the following factors:
Healthcare data privacy and security: happen to be crucial aspects of the healthcare industry. Medical data is very sensitive and also requires careful handling so as to ensure privacy, security, as well as compliance. Publicly available large language models may not be suitable for this task. To accomplish it, one will need to utilize clinical natural language processing- CNLP along with a reduced large language model- LLM that can be taught on private healthcare datasets.
Training on diverse datasets: is essential. Coming up with AI models internally can be a demanding and time-consuming process, primarily because of the need for training models on multiple datasets that are both extensive as well as diverse. Healthcare data can go on to differ between regions, and medical providers could utilize distinct terminology. For example, heart failure can be described in many ways by various doctors or health systems, such as cardiac failure or congestive heart failure, CF, HF, or even CHF. To make sure of precision and consistency, it is important to train models on a diverse range of datasets from numerous locations of the country.
Contextualization: Context happens to be important when dealing with healthcare data. In case of reviewing a patient’s chart for congestive heart failure, it is crucial to determine the exact year in which the event took place. Context as well as temporal time frame are critical factors in terms of documentation as well as risk adjustment. AI tools should not be restricted to basic optical character recognition but they should be enhanced by sophisticated clinical natural language processing abilities, that enable them to not only gauge temporal aspects but at the same time contextualize information more effectively.
Achieving greater accuracy as well as efficiency in risk adjustment is a significant endeavour, but it can be done with the appropriate tools and partnerships. By embracing solutions that are AI-driven and working with experts in the field, healthcare organizations can make a significant shift in their existing processes which can lead to improved opportunities when it comes to delivering cost savings along with better outcomes for patients.