Partner, Cloud & Digital, PriceWaterhouse Coopers.

GETTY

Opinions abound on what’s right and wrong with our U.S. healthcare system, but there’s one thing most can agree on: There’s a need to transform the experience for patients, providers and payers. The Covid-19 pandemic served as a catalyst for us to relook at and reimagine the digitization of the healthcare system.

The strategic adoption of artificial intelligence (AI) could be transformational, but technology leaders at healthcare organizations are constrained by stringent compliance requirements and security concerns. And their fears aren’t unfounded—any data breach could be catastrophic.

Trust in AI doesn’t come easily—one must tread cautiously, particularly in this industry. AI holds great promise, but questions remain about the ethical adoption of AI in healthcare. These are hurdles data scientists are working to overcome, but we can’t wait.

Where Are The Major Pain Points?

1. Patients: A major challenge today is long wait times. In 2022, the average wait time for a physician appointment in the 15 largest U.S. metro markets has been 26 days. It’s not difficult to connect the dots—the longer the wait time, the higher the risk of illness, mortality and possibly avoidable hospital visits.

2. Physicians: There’s outdated technology, inefficient workflows and a shortage of skilled workers. The outcome—burnout. A study published by Mayo Clinic Proceedings shows that 62.8% of the physicians that responded to a survey said they experienced at least one symptom of burnout in 2021.

3. Payers: Organizations are grappling with improving operational efficiency, reducing the cost of care for members and delivering personalized customer experiences. This, in turn, is driving the focus toward preventative care.

It’s more imperative than ever to bring personalization and efficiency into the patient, physician and operator experience. The positive is that we are now ready with technology powered by AI to speed up this transformation journey.

Three Key Areas For Transformation

1. Improve patient engagement.

Consumer engagement and positive experiences are essential to achieving better patient outcomes, preventing unnecessary trips to the ER and eventually easing the burden on the strained healthcare system. In PwC’s recent customer experience survey results, healthcare ranked in the top three industries in which a significant gap exists between what customers expect versus the satisfaction they’re actually getting. Augmenting the consumer experience with AI could be one way to close that gap.

Organizations are investing in AI-powered intelligent virtual agents (IVAs) to automate routine patient interactions like appointment scheduling and ordering refills. And an integrated contact center that can share information across pharmacies, payers and other health systems can help provide a seamless self-service experience. Research results from Aberdeen show that firms with AI capabilities in their contact centers indicate 3.5 times higher year-over-year increases in customer satisfaction rates and 3.3 times higher gains in client retention rates.

2. Decrease time for diagnostics.

With AI-enabled diagnostics, clinicians can intervene before medical crises occur by using predictive models that analyze patient data and activities. The first step toward this goal is setting up data repositories. For example, Microsoft and PwC recently collaborated with Open Source Imaging Consortium (OSIC) to create a data repository with anonymous imaging data that can be shared. This helps medical professionals make quicker, more accurate diagnoses. Although this project focuses on a rare lung disease, it’s expected that the applications will broaden in the future.

3. Achieve operational efficiencies in billing and records management.

Medical coding and billing can be cumbersome. They require manual documentation, which is often costly and prone to error. The transactional nature of these processes makes them one of the better use cases for the application of AI-enabled software. AI-led automation powered by machine learning (ML) and natural language processing (NLP) can convert physician notes into billable medical codes. It can also conduct real-time audits to identify errors in bills and rectify them. Using ML, providers can identify medical billing cases that require prior authorization, and payers can accelerate the approval cycle by using intelligence in processing. Billing staff can also predict the likelihood of a claim being rejected before it goes to the payer based on past data. More accurate coding and billing translates into fewer claims being reworked and more dollars saved.

Physicians have an administrative burden to create extensive documentation in the electronic health record systems for billing and regulatory compliance. AI-powered NLP solutions can help physicians efficiently and effectively capture clinical documentation from pre-charting through post-encounter, contextualizing it with ambient clinical intelligence and thus improving the quality of documentation without physicians sacrificing time with patients.

Overall, 80% of healthcare providers plan to increase investment in digital health over the next five years, according to research results from HIMSS. And there’s increased focus on industry clouds and healthcare solutions by cloud providers, integrators and software vendors to turbocharge this journey.

Now’s the time to consider crafting or augmenting your AI strategy. You don’t have to do it alone—you might want to choose a partner that combines strategic thinking, industry expertise and technical acumen to help guide you through the principles of ethical AI in healthcare. With an emphasis on transparency, inclusion, accountability, security and resilience, you can build the necessary trust in the technology to reimagine the healthcare experience.