Recent developments within artificial intelligence (AI) have demonstrated the scale and power of this technology on business and society. However, businesses need to determine how to structure and govern these systems responsibly to avoid bias and errors as the scalability of AI technology can have costly effects to both business and society.

As healthcare organizations uses different datasets to apply machine learning and automation to workflows, it’s important to have the right guardrails in place to ensure data quality, compliance, and transparency within their AI systems.

IBM can help you put AI into action now by focusing on the areas of your business where AI can deliver real benefits quickly and ethically. Our rich portfolio of business-grade AI products and analytics solutions are designed to reduce the hurdles of AI adoption, establish the right data foundation, while optimizing for outcomes and responsible use.

IBM’s researchers are developing the next generation of advances in AI software and hardware to bring frictionless, cloud-native development and use of foundation models to enterprise AI.

In an instance, Humana’s interactive voice response (IVR) system was transferring far too many calls to human agents, at tremendous cost to the company and its customer satisfaction scores. Administrative staff from healthcare providers contact Humana via phone with inquiries about Humana members’ health plan benefits and eligibility. Humana receives over one million provider calls every month, and most callers were opting out of the IVR system to be directed to outsourced call centers, for which Humana paid by the call. More than 60% of these calls were related to routine, specific, pre-service questions with well-defined answers.

To meet ever-growing user expectations, Humana needed a way to completely rethink how it addressed customer queries. The Humana Provider Services Innovation (PSI) team was charged with finding a solution that could better address these costly pre-service calls and improve the provider experience.

Conversational assistants were not as ubiquitous or advanced as they are now when Humana began this journey in 2016, but the company recognized the profound promise of AI in customer care. Humana chose to work with IBM Watson®, the industry leader in enterprise AI, and began a collaboration with IBM® Data and AI Expert Labs & Learning (DAELL). After a three-month proof of concept (POC), Humana and IBM began development of what became the Provider Services Conversational Voice Agent with Watson®. The solution combines multiple Watson applications in a single conversational assistant, run on IBM Cloud®, while the Watson Assistant for Voice Interaction runs on premises at Humana.

Humana’s Voice Agent with Watson provides a faster, friendlier and more consistent way for administrative staff at healthcare providers to access pre-service, medical eligibility, verification, authorization and referral information without the need to speak with a live agent. The solution relies on AI to understand the intent of a provider’s call, verify they are permitted to access the system and member information, and then determine how best to provide the information requested.

The Voice Assistant uses significant speech customization with seven language models and two acoustic models, each targeted to a specific type of user input collected by Humana. Through speech customization training, the solution achieves an average of 90%–95% sentence error rate accuracy level on the significant data inputs. The implementation handles several sub-intents within the major groupings of eligibility, benefits, claims, authorization and referrals, enabling Humana to quickly answer questions that were never answerable before. In the previous IVR system, a request for “benefits” could lead to a seven-page fax. Now, the Watson solution is able to respond with a specific “point” benefit, such as, “the co-pay for chiropractic visits is USD 100.”

The solution started handling live calls from healthcare providers in April of 2019, with several updates throughout the year to expand functionality and the user base. It has been able to handle inquiries at about a third of the cost of the existing system, and it has also had a higher overall response rate—almost double that of the previous automated IVR system.

Over the course of development, the IBM Watson Expert Services Lab team drove improvements in Watson services with custom training and models better able to understand healthcare terminology in a low-bandwidth call center environment. This has led to several pending patent submissions, and the team has published tools, guides and methods for building, testing and tuning a large Watson Voice Assistant solution. Humana’s Voice Assistant solution pattern recently became formalized in the Watson Assistant for Voice Interaction solution offering.

“Humana’s Voice Agent has greatly improved self-service capabilities for Humana healthcare providers, allowing them to quickly and efficiently get information on patient insurance coverage across a variety of data points. Providers can now call into the Watson-based solution and complete an inquiry in about two minutes without waiting to reach a call center representative. “We’ve got a level of insight that we’ve never had before in these interactions,” says Sara Hines, Director of Provider Experience and Connectivity at Humana.

The solution receives more than 7,000 voice calls from 120 providers per business day, and feedback from users has been incredibly positive. “Humana’s initiative with Watson was three years in development and continues to grow even after implementation,” says Hines. “This is just the beginning of how Humana is enhancing our provider communication, and I’m excited to keep exploring the infinite possibilities of artificial intelligence.”

The Game Changer

IBM watsonx is changing the game for enterprises of all shapes and sizes, making it easy for them to embed generative AI into their operations. This week, the CEO of WellnessWits, an IBM Business Partner, announced they embedded watsonx in their app to help patients ask questions about chronic disease and more easily schedule appointments with physicians.

Watsonx comprises of three components that empower businesses to customize their AI solutions: watsonx.ai offers intuitive tooling for powerful foundation models; watsonx.data enables compute-efficient, scalable workloads wherever data resides; and the third component, watsonx.governance, provides guardrails essential to responsible implementation. Watsonx gives organizations the ability to refine foundation models with their own domain-specific data to gain competitive advantage and ensure factual grounding to external sources of knowledge.

These features—along with a broad range of traditional machine learning and AI functions—are now available to independent software vendors (ISVs) and managed service providers (MSPs) as part of IBM’s embeddable software portfolio, supported by the IBM Ecosystem Engineering Build Lab and partner ecosystem.

The watsonx platform, along with other IBM AI applications, libraries and APIs help partners more quickly bring AI-powered commercial software to market, reducing the need for specialized talent and developer resources.

A platform prioritized for enterprise AI

IBM is focused on helping organizations create business value by embedding generative AI. Watsonx provides the functionality enterprise developers need most, including summarization of domain-specific text; classification of inputs based on sentiment analysis, threat levels or customer segmentation; text content generation; analysis and extraction (or redaction) of essential information; and question-answering functions. The most common use cases from partners often combine several of these AI tasks.

ISVs need the flexibility to choose models appropriate to their industry, domain and use case. Watsonx provides access to open-source models (through the Hugging Face catalog), third-party models (such as Meta’s Llama 2) and IBM’s own Granite models. IBM provides an IP indemnity (contractual protection) for its foundation models, enabling partners to be more confident AI creators. With watsonx, ISVs can further differentiate their offering and gain competitive advantage by harnessing proprietary data and tuning the models to domain-specific tasks. These capabilities allow ISVs to better address their clients’ industry-specific needs.

Exceptional customer care through AI solutions

Today, customers expect seamless experiences and fast answers to their questions, and companies that fail to meet these expectations risk falling behind. Customer service has leapfrogged other functions to become CEOs’ top generative AI priority. Given this trend, companies should be looking for ways to embed generative AI into their customer care portals. To accelerate this process, companies can implement AI-infused customer care commercial solutions. IBM’s embeddable AI technology, such as IBM watsonx Assistant and watsonx.ai, allows ISVs to quickly and easily build AI into their solutions, which in turn helps them to reduce time to market and reach their customers sooner.

Watsonx allows enterprises to effortlessly generate conversation transcripts with live agents or automate Q&A sessions. With watsonx.ai, they can obtain concise conversation summaries, extract key information and classify interactions, such as conducting sentiment analysis to gauge customer satisfaction. This information will further refine and improve the information available to the agents.

WellnessWits is using watsonx Assistant to create a virtual care solution that connects patients to chronic disease specialists–from anywhere. The platform features an AI-powered chat functionality that can help patients gather information and answers about their chronic disease and facilitates personalized, high-quality care from physicians that specialize in their condition.

Ubotica is leveraging IBM Cloud and watsonx.ai in its CogniSAT platform, enabling developers deploy AI models to satellites for a wide variety of observational use cases such as detecting forest fires or space junk. CogniSAT improves the efficiency with which data is stored and processed, providing edge-based analysis onboard satellites.

IBM solution provider Krista Software helped its client Zimperium build a mobile-first security platform using embedded AI solutions. The platform accelerates mobile threat defense response by automating ticket creation, routing and software deployment, reducing a 4-hour process to minutes.

Artificial intelligence is being used in healthcare for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals.

As health and fitness monitors become more popular and more people use apps that track and analyze details about their health, they can share these real-time data sets with their doctors to monitor health issues and provide alerts in case of problems.

AI solutions—such as big data applications, machine learning algorithms and deep learning algorithms—could also be used to help humans analyze large data sets to assist in clinical and other decision-making. AI could also be used to help detect and track infectious diseases, such as COVID-19, tuberculosis and malaria.

AI can help connect disparate healthcare data

One benefit the use of AI brings to health systems is making gathering and sharing information easier. AI can help providers keep track of patient data more efficiently.

One example is diabetes. According to the Centers for Disease Control and Prevention, 10% of the US population has diabetes. Patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team. AI can help providers gather that information, store and analyze it, and provide data-driven insights from vast numbers of people. Leveraging this information can help healthcare professionals determine how to better treat and manage diseases.

Organizations are also starting to use AI to help improve drug safety. The company SELTA SQUARE, for example, is innovating the pharmacovigilance (PV) process, a legally mandated discipline for detecting and reporting adverse effects from drugs, then assessing, understanding and preventing those effects. PV demands significant effort and diligence from pharma producers because it’s performed from the clinical trials phase all the way through the drug’s lifetime availability. Selta Square uses a combination of AI and automation to make the PV process faster and more accurate, which helps make medicines safer for people worldwide.

In some cases, AI could reduce the need to test potential drug compounds physically, which is an enormous cost-savings. High-fidelity molecular simulations can run on computers without incurring the high costs of traditional discovery methods.

AI also has the potential to help humans predict toxicity, bioactivity, and other characteristics of molecules or create previously unknown drug molecules from scratch.

Future and potential of AI in the healthcare ecosystem

AI provides opportunities to help reduce human error, assist medical professionals and staff, and provide patient services 24/7. As AI tools continue to develop, there is potential to use AI even more in reading medical images, X-rays and scans, diagnosing medical problems and creating treatment plans.

AI applications will continue to help streamline various tasks, from answering phones to analyzing population health trends (and, likely, applications yet to be considered). For instance, future AI tools may automate or augment more of the work of clinicians and staff members. That will free up humans to spend more time on more effective and compassionate face-to-face professional care.

IBM and AI in healthcare

When patients need help, they don’t want to (or can’t) wait on hold. Healthcare facilities’ resources are finite, so help isn’t always available instantaneously or 24/7—and even slight delays can create frustration and feelings of isolation or cause certain conditions to worsen.

IBM watsonx Assistant AI healthcare chatbots can help providers do two things: keep their time focused where it needs to be and empower patients who call in to get quick answers to simple questions.

IBM watsonx Assistant is built on deep learning, machine learning and natural language processing (NLP) models to understand questions, search for the best answers and complete transactions using conversational AI.