Preventing Fraud, Waste and Abuse with AI in Healthcare

The healthcare industry suffers from enormous losses with fraud, waste, and abuse accounting for $100 billion lost each year. The US’s healthcare system is particularly inefficient, burning through roughly 25% of all its finances - that’s $760 billion annually in the best-case scenario and up to $935 billion annually - with the majority as a result of poor administrative processes.

Leveraging AI will play a crucial role in catching the fraudulent and wasteful behaviours that cost the industry so dearly. By working with the emergency services to facilitate an open API approach, they can successfully leverage voice data within AI and analytics applications. This has significant benefits to the bottom line.

AI’s supporting role during Covid

Unsurprisingly, the emergency services were put under enormous strain during the height of the pandemic with 911 call volumes surging by several hundred-fold, however, AI has enabled organisations to stay abreast of the sheer demand of emergency calls. Indeed, 87% of healthcare industries sped up their adoption of automatic speech recognition (ASR) technology in light of the pandemic.  Being able to capture conversations digitally has never been so important to assist with operations.

This abundance of call recording data and interactions provide a wealth of information that can be fed into AI engines to deliver timely, actionable insights from the analysis of data. Advances in machine learning and artificial intelligence ensure that voice data can be processed at scale, significantly expanding the use cases and value of this data. As healthcare organisations adapt to modern collaboration platforms, it’s essential they are being monitored for conduct standards and risk detection so that electronic health information (EpHI) can be maintained.

Using AI to prevent fraud, waste & abuse

Currently, 80% of global healthcare uses unstructured data, presenting huge opportunities for multiple use-cases across compliance, fraud and risk detection. This is where automated supervision through voice and speech analytics proves incredibly useful. With the combined power of ASR and compliance AI applications such as Theta Lake, calls are automatically exposed to analysis and workflows which flag risk factors to help prioritise reviews. This replaces call sampling and manual review processes which are expensive and expose organisations to more risk.

What’s more, compelling use cases are emerging in healthcare as the industry strains to simultaneously respond and transform in the wake of Covid-19. Using voice biometrics in healthcare offers a way to access sensitive information using something that can’t be lost, stolen, shared or forgotten.

AI-driven technology can also assist in a control room when developed in conjunction with the emergency services for example to triage calls. However, operational intelligence and analytics, coupled with understanding conversations across control rooms and all touch points to provide a holistic view, are key.

Circumnavigating AI challenges

Data silos and a lack of interoperability hinder agility and slow down access to transformative technologies. These data silos are often tied into incumbent vendors, who often limit access to data, charging up to seven figures for export - and not in real-time - resulting in sub-par analytics for AI engines to reason over. Health services need to work with vendors adopting an open API philosophy to successfully leverage voice data within AI and analytics applications. By retaining control of captured data, organisations can choose the right vendors that best meet their requirements, rather than being locked into a single application stack. Our own research suggests that 51% of an organisation’s captured conversations are inaccessible for AI and analytics. It is therefore essential for organisations to expect their data capture solution to be always on, always open and always accessible.

With 76% of healthcare organisations expected to possess capabilities for data exchange with external sources by 2028, the path is being set for a broad range of AI initiatives.  Organisations can ensure they are ready for future spikes in call volumes and fully prepared and enabled to leverage AI by choosing the right technology partners providing the correct tools.