“Fraud, waste and abuse [FWA] are essentially symptoms of outdated coding structures and obsolete claims systems that are in themselves wasteful in their inefficiency and abusive in their delayed, and all too often detrimental, financial impact on small healthcare practices in particular,” says Chantal Viljoen, chief executive officer of Knowledge Objects (KO). KO is a South African-born global technology company specialising in advanced administration and risk management systems through the application of artificial intelligence to manage claims risk for healthcare funders globally.

Outdated claims processes are causing unnecessary friction at the expense of medical scheme members and healthcare practitioners relying on legacy systems, placing unheeded financial pressure on smaller practices and potentially delaying treatment [Image: Rawpixel].

“Indeed, medical schemes have a duty to protect their members’ funds, and a responsibility to defend the integrity and sustainability of the broader healthcare system through, among others, the mitigation of FWA,” adds Agility Health chief executive officer Dr Tebogo Phaleng.

“Effective claims management processes should be able to ensure payment of claims for appropriate care. Members of medical schemes and their healthcare providers appreciate and expect the certainty of immediate approval of claims upfront.

“Where a medical scheme’s administrator does not apply the full benefits of technology to streamline this process, it unnecessarily creates the sort of friction between schemes and healthcare providers that can potentially impede members’ access to much-needed care.”

“Instead, the advanced rule-based technology we employ to manage clinical and business risks allows us to proactively check that the treatment is both clinically sound and that it falls within the member’s benefit entitlement,” Viljoen adds.

“In a complex environment where more than 70 medical schemes have their own rules and exclusions, real-time claims approval helps remove the administrative burden for the healthcare provider and assists with a sound relationship between consumers and their treating providers.”

Machine learning puts health needs first

Powered by big data and constant self-refinement through machine learning, new patterns and trends are identified to improve clinical risk management continually, putting members’ healthcare needs first. “Simultaneously and instantaneously, each line item is assessed on its own merit and any fraudulent or wasteful claims are detected at this granular level, and are flagged as inappropriate before payment is granted,” Viljoen explains.

Any queries on claims or pre-authorisations are automatically sent to the healthcare provider for clarification without the need for time-consuming email or telephone exchanges, allowing for immediate resolution and approval.

The artificial intelligence rules engine system developed by Knowledge Objects processes claims in real-time, proactively checking against individual members’ clinical risks to ensure funding of appropriate care, protecting their health by preventing contra-indicated treatments.

“This ensures that scheme funds are efficiently used only for clinically valid claims and significantly reduces the potential for human error interference, consequently reducing inappropriate healthcare costs, operational expenses and the administrative burden on both the medical scheme and healthcare providers,” Dr Phaleng notes.

“This protects medical scheme members’ funds and ensures that resources are optimally used to fund legitimate claims for the sustainability of the medical scheme.

“The primary focus is on confirming that quality, appropriate care is provided at all times, and to support healthcare practitioners to ensure better clinical outcomes for their patients,” Dr Phaleng concludes.