In the past few years, Artificial Intelligence (AI) has been massively transforming the healthcare industry. From advanced AI-powered diagnostic tools to wearable devices that enable data to predict health outcomes, analytics are propelling healthcare innovation.
The healthcare insurance sector is another industry area that is benefiting greatly from the adoption of AI technologies. AI and Machine-Learning (ML) models are being utilized to optimize claims management processes and reduce instances of fraud that cost insurers exponentially.
In Europe, the health insurance market was valued at USD272.78 Billion in 2020, with it being forecast to grow at a CAGR of 6.07% to 2026. In countries such as Spain and Portugal, around 20% of the population has health insurance.
The global pandemic is shining a sharp spotlight on the health insurance sector. Current trends show a dip in claims applications in the short term due to a reduction in elective surgeries. However, at the same time, there has been a trend towards an increase of suspicious claims due to the escalation of financial hardship experienced in the current climate. In addition, claims investigations have been significantly hindered due to reduced mobility.
Experts have predicted that in the long-term, the increased health concerns felt across society and increased barriers to access to public health systems will lead to a greater uptake of private health insurance.
These trends further highlight the need for robust data-driven solutions to help optimize claims processes and detect fraud. Today, digital disruption is taking hold across all sectors of the insurance industry, including healthcare. Deloitte reports that insurance company’s IT departments are gradually moving away from investing in legacy infrastructures towards analytics and AI technologies.
The potential for AI within the health insurance sector is endless. So how is AI enabling the claims optimization process for the healthcare insurance industry?
Claims Management in Healthcare Insurance
For health insurance providers AI can present a real opportunity to strengthen claims management processes.
The traditional claims management process is a labor-intensive, manual process that takes considerable time. When inevitable mistakes are made in the process, such as incorrect billing or errors in patient documentation, the process is even more prolonged. This results in significant delays in claims resolutions, leading to reduced customer satisfaction and monetary losses.
Additionally, the process of claims handling is very dependent on the subjective knowledge and experience of the individual claims handler managing the process. Few insurers have truly developed a standardized approach to claims management.
McKinsey notes that around 1 in 10 claims in health insurance are suspicious or fraudulent; this is a clear headache for insurers.
Claims for services that were not performed, inaccurate reports of diagnoses and procedures, misrepresentation of providers, or waiving of deductibles are common fraud scenarios in healthcare insurance.
McKinsey further notes It is estimated that around 70% of health insurance claims are marked as unusual. However, in reality, only about 10% of these claims are actually suspicious or fraudulent. In essence, handlers manually evaluate substantially more claims than is required, significantly burdening their time and reducing the quality of evaluations.
There is a considerable need for health insurers to optimize the claims management process and create a standardized approach to make evaluations more efficient to reduce costs, improve customer experience, and boost sales.
AI can lift the administrative burden of the claims handling process and limit the number of claims marked as unusual unnecessarily. This leads to fewer claims being sent to the claims handlers, allowing them to focus their energies on claims that are actually suspicious and fraudulent and allowing them to provide a high degree of review.
McKinsey’s profile of the healthcare insurance industry in Germany estimates that insurers can save around 500 million euros each year with optimized AI-led claims management processes.
The advantages of AI-powered technologies provide clear benefits for health insurers across the board.
How to Implement AI in Health Insurance Claims Management
So how do AI systems in healthcare insurance work? Let’s explore:
The first stage of the AI process always begins with data preparation. First, data needs to be cleaned and transformed into defined sets that algorithms can later utilize.
Building the Model
An algorithm is built based on set operational standards. Then, data is fed into the algorithm to train its cognition. Insurance data, along with external data, helps the model to learn and identify patterns.
NLP models extract all meaningful information from documents and text records, screening it automatically, saving considerable time in claims handlers’ administrations.
Built models help analyze variables such as patient details, diagnoses, information on providers, and claims made to establish correlations between data sets.
Metrics are set as benchmarks to build a pipeline that can predict if a claim can be evaluated successfully.
AI scores claims against an evolving library of suspicious scenarios to detect claims that match suspicious patterns established through the model.
The way forward
The insurance claims process is complex. Healthcare insurance claims deal with sensitive matters which can make this process even more difficult to manage. Traditional claims handling processes are made based on settled business rules and claims handlers’ individual experience.
Along with procedural issues, insurers need to deal with customers during stressful and vulnerable moments. Therefore, a fine balance must be struck between handling clients in an empathic way and finding a way to automate processes.
Increasingly AI technologies are taking a leading role in the healthcare insurance industry. These analytical systems help automate the process of claims management, not only reducing the administrative burden on handlers, but also helping to more accurately detect suspicious and fraudulent claims.
Health insurers who implement these processes today will be at a considerable advantage now and in the future.
To learn more about implementing AI into your claims management process, get in touch with Stat-Market today.