How to Improve Customer Experience in Healthcare with AI

The following is a guest article by Dmitrii Evstiukhin, Director of Managed Services at Provectus

In today’s world, personalized and efficient digital interactions are expected, especially in the healthcare industry. To meet those demands, organizations should prioritize the creation of convenient digital-first environments that satisfy all parties involved in providing health care services. Patients, medical personnel, operations staff, and insurance companies all expect a first-rate experience when dealing with your enterprise.

However, establishing a highly effective customer experience (CX) ecosystem that addresses all needs along the healthcare lifecycle is a significant challenge. In healthcare, an industry known for complex customer journeys, increasing volumes of communication, attrition of front desk and contact center employees, rising customer expectations, and delayed or inefficient adoption of digital solutions have left CX departments scrambling to catch up.

And the stakes are high. When done well, great customer experience improves customer satisfaction. This leads to brand loyalty and advocacy, prevents employee dissatisfaction and talent attrition, and increases productivity and business revenue in the long run.

This article offers a simple five-step framework for adopting AI/ML-powered customer experience solutions in healthcare. We start by discussing current challenges and problems, explain why customer experience should become a top priority, and introduce specific steps you can take to implement AI in your organization.

Customer Experience Challenges and Opportunities in Healthcare

The past couple of years have posed unprecedented struggles for customer care departments. Post-pandemic difficulties in the job market, rampant inflation, and a transition to digital-first communications have created challenges that make it difficult for healthcare organizations to adapt.

  • Diminished Talent Pool: Personnel involved in front desk, contact center, and customer care operations traditionally suffer from high burnout and low job satisfaction. Lack of advancement opportunities and poor work–life balance paint a dismal picture for customer-facing teams. On top of that, many employees now prefer to work from home, creating a vacuum that healthcare companies find hard to fill. Most businesses are simply unable to recruit, hire, and train enough workers to keep pace with the attrition rate of employee turnover.
  • Communication Complexities: With the advance of digital technologies, customers expect to get their questions answered and problems solved in real time. But as customer phone calls, emails, and bot messages increase in volume, the digital customer journey often remains disjointed, and efficient communication at scale becomes an insurmountable task for CX professionals. This problem is especially evident in healthcare, where patients, care providers, and insurance companies must jump through multiple communication hoops to ensure an effective healthcare lifecycle.
  • Inefficiencies of Digital Solutions: Self-service digital solutions play a key role in shifting the workload away from customer care departments, to help customers quickly solve their problems. But while self-service helps by automating common requests, such solutions are often not sufficiently integrated to cover the entire customer journey. By failing to provide cross-channel and omni-channel experiences, they are unable to meet customer expectations, forcing them into live channels, and creating even more transactional, repetitive work for customer care employees.

Considering these challenges, it is no surprise that McKinsey’s 2022 State of Customer Care Survey found that ensuring a great customer experience is a top priority for surveyed customer care leaders. In fact, customer experience is the fastest-growing priority area, with a 19% growth since 2019.

To stay competitive, healthcare organizations must go beyond basic self-service and automation, and address the problem by empowering their staff, improving operations, and elevating technology. In practice, this means roadblocking talent attrition, ensuring a simpler CX throughout the healthcare lifecycle, and adopting data-driven, AI/ML-powered solutions for prediction, personalization, triage, sentiment analysis, and other use cases.

The question is, how do you prioritize investment in all of these areas, and where do you begin?

Five-Step Framework for Adopting AI/ML for Customer Experience in Healthcare

AI and machine learning can help healthcare organizations to dramatically improve customer experience. Not only do they act as potent instruments for streamlining the customer journey, but they also encourage companies to enhance their operations, strengthen their technology foundations, and nurture a culture where all employees are equipped to take advantage of AI/ML insights. In other words, the adoption of AI can organically position your business to fully or at least partially address all challenges mentioned above.

Follow this simple five-step process for adopting CX AI/ML:

  1. Formulate Your Big-Picture Vision for Customer Experience: Before diving deep into AI, it makes sense to formulate a winning CX strategy. Hold brainstorming sessions with leaders, managers, and employees who have a vesting interest in resolving existing problems. Work to develop a concrete plan that includes your future vision, specific goals, and leadership expectations.
  2. Keep the Customer Journey in Mind: While brainstorming, pay special attention to mapping the customer journey. Analyze every touchpoint to get a clear picture of what your customers experience. Keep in mind potential problems, ways customers can solve these problems on their own, and instances where the help of digital solutions or customer care professionals is needed.
  3. Consider Various AI Use Cases: After brainstorming, you should have a better understanding of your customers, their pain points and expectations, and potential changes you can make to improve their experience. To move forward with AI/ML, however, you should take a fresh look at your data, infrastructure, and processes. Do you have enough high-quality data to train models for this use case? Can your infrastructure scale to support that one? Can your workforce be trained to start using AI insights from day one? Regardless of how you answer these questions, opt for an AI use case based on a real business need, with a proven ROI. Your use case should start to generate business value as soon as possible, to gain a buy-in for future organization-wide implementation.
  4. Start Development: AI/ML solutions can be challenging to design and build, and even trickier to support in the long run. A critical first step is to decide if you will develop your solution in-house (take into account your team’s skills and budget) or with the help of a third-party AI vendor (e.g. development from scratch, customization or use of an existing solution, AI/ML implementation via Managed AI Services). Considering the scarcity of AI talent, collaborating with an AI MSP is recommended, to quickly get your AI project off the ground. Begin your project with discovery sessions, proofs of concepts, and pilots.
  5. Ensure Your AI Solution Drives Value Before Scaling: The metrics and KPIs you identified while brainstorming on potential AI use cases will help you to track and measure success over time. Test to see if your solution works quickly; if so, scale and build momentum for other initiatives. But remember that any AI solution is a work in progress: take into account data and model drifts, infrastructure updates, and customer and employee feedback, to continually support and enhance your solution.

It is worth mentioning that any AI solution should be an integral part of your healthcare pipeline; they should not be used as isolated tools. This requires a robust infrastructure for data processing, machine learning, and analytics.

For example, your conversational AI chatbot can be integrated with a triage tool powered by AI and Natural Language Processing (NLP). While the former collects customer requests, the latter tags and categorizes the requests, to automatically resolve a portion of them, and to forward more complex requests for human review. An attached analytics component can look into a customer’s sentiment, alerting the customer care team to quickly and accurately address customer issues, to prevent churn and dissatisfaction.

Conclusion

The healthcare industry is based on the fundamental assumption that all humans are entitled to medical care. However, your business still needs to operate efficiently and cost-effectively. The advancement of AI and machine learning give healthcare organizations the potential to modernize in ways that drive business efficiency, with a human touch.

Exceptional customer experience has become a key competitive advantage in healthcare that can greatly benefit from the adoption of AI/ML. Healthcare organizations should act quickly to enhance the customer journey with AI.

This article explores certain challenges faced by the healthcare industry, and demonstrates how great customer experience powered by AI can be a key part of the solution. It provides a simple framework for adopting CX AI in healthcare, helping your company to improve customer experience, to build deeper, more enduring relationships with clients and partners throughout the healthcare lifecycle.

About Dmitrii Evstiukhin

Dmitrii Evstiukhin is the Director of Managed Services at Provectus. He leads a team of experts who deliver cloud-based solutions to Provectus’ clients and partners. Prior to joining the Managed Services team, he held a Senior Solutions Architect position and was responsible for designing, building, and implementing advanced solutions in the cloud. Dmitrii is passionate about leveraging the latest technologies, encompassing cloud, data, AI/ML, and analytics to help businesses achieve their goals.

   

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