Customer relationship management (CRM) plays a vital role in businesses across many industries and is constantly evolving to let users better it with their business goals. Artificial intelligence (AI) has emerged as a game changer that can revolutionize CRM strategies and help marketers, health experts, and care managers more effectively engage patients.
By analyzing large volumes of data, automating tasks, and providing valuable insights, AI can provide many benefits that enhance CRM practices. This blog explores how organizations can develop their CRM strategy using AI to manage customer relationships.
The key is understanding how these new approaches can outperform traditional methods, contribute to better health outcomes, and save time. AI has already been used successfully in business applications, thanks to its time value and its ability to assist in gathering input for projects.
However, the stakes can be higher in healthcare. The effectiveness of outreach and engagement can impact a patient’s life or quality of life. For AI to be truly valuable in this context, it must surpass the performance of traditional CRMs by delivering exceptional patient experiences.
Over the past 20 years, advancements in computing and big data have allowed AI to be broadly applied. That has made it possible to analyze data, make predictions, and automate tasks, and has transformed many traditional workflows.
Why has interest in AI seemingly exploded overnight? Predictive AI has existed for over two decades, helping organizations predict sales forecasts or flag anomalies in imaging results. However, its use has been limited to a small set of data scientists and subject matter experts while it remained inaccessible to the average user.
But last year, with the launch of OpenAI’s ChatGPT, generative AI was introduced to a wider group of users. Now, AI is available and can be used by nearly anyone in the world. This new approach to AI made it so anyone who has a question or needs help with a task can get it by simply asking.
In a busy hospital with various departments and staff, healthcare marketer Jane is faced with the challenge of delivering personalized experiences and identifying patients with urgent needs. She aims to improve the patient experience while meeting specific metrics. Jane recognizes the potential of AI in addressing these challenges and decides to implement AI-powered solutions.
To achieve this, Jane integrates AI tools that analyze patient data and prioritize outreach to high-risk patients, making her efforts more efficient. As a result, patients can easily access information, schedule appointments, and manage medical records, enhancing the overall patient experience.
Understanding the complexity and time-consuming nature of developing business intelligence programs, Jane leverages AI to streamline the process. The AI system quickly analyzes large datasets, identifies patterns, and provides real-time insights, allowing Jane to make data-driven decisions faster and improve marketing strategies.
Jane also assists the hospital in assigning AI-powered care coordinators to engage with patients and address their healthcare needs. These coordinators utilize AI tools to create individualized care plans and track patients' progress, enabling them to actively listen, provide guidance, and deliver personalized care. With AI handling routine tasks, care coordinators can spend more time building relationships with patients and addressing their specific healthcare needs.
The integration of AI has a significant positive impact on the hospital. Patient satisfaction improves as personalized experiences align with the increasing demand for better customer service in healthcare. Moreover, the assistance provided by AI reduces turnover in the health service call center, enabling care coordinators to focus on more meaningful interactions. Through her innovative use of AI, Jane successfully overcomes healthcare marketing challenges, enhancing patient satisfaction and retention within the hospital.
There are several challenges that can make it difficult to Implement AI in healthcare. Properly implemented requires a secure data infrastructure to handle large amounts of patient data for AI algorithms while ensuring data privacy and regulatory compliance.
Healthcare professionals may be skeptical of AI's capabilities and hesitant to rely on algorithms for patient care decisions. In addition, ongoing training and support are necessary so healthcare professionals can effectively use and interpret AI-generated insights in their clinical practice.
Generative AI has great potential to help healthcare, it must be trustworthy and used responsibly. That means it must be accurate. It must understand healthcare and produce up-to-date evidence-based and clinically-grounded responses that minimize any chance of misinformation, misinterpretation, or harm.
In addition, AI systems must be secure and comply with security and compliance standards, such as the Health Insurance Portability and Accountability Act (HIPAA). That’s why many health system CIOs have blocked or forbidden the use of publicly available generative AI engines for their organizations.
According to a McKinsey study, generative AI is poised to have a high impact in healthcare, more specifically in marketing and sales initiatives, with a significant impact expected in generating content for commercial representatives.
Scaling AI requires significant investments in technology, data, and talent. While data can be enhanced with the use of generative AI, gaps and inefficiencies in the curated data and content it provides, and the technical skills required to use it are factors that can prevent health systems from using the power of AI to its fullest. Required training can be another issue for decision-makers looking to increase the use of AI in their organizations.
An effective CRM infrastructure backed by AI helps call center agents automatically transcribe patient calls, generate summaries, pull appropriate responses from the organization’s knowledge base, and contextualize the suggested responses to the patient’s need.
At Innovaccer, we have invested enormous time, energy, and resources to address the challenges presented by generative and other forms of AI.
We recently introduced Sara for Healthcare: our AI model that powers four assistants for analysts, executives, clinicians, care managers, and care marketers at the Innovation Keynote.
Research indicates that 93% of consumers expect their inquiries to be resolved on the first call, posing a significant challenge for most healthcare systems in the era of consumerization. To address this challenge, introducing Sara for Experience Center, the solution to streamline workflows and enhance consumer engagement in contact centers.
Sara revolutionizes how call center agents work. It automates tasks and optimizes processes to achieve exceptional customer service levels, focusing on improving the first call resolution (FCR) rate, reducing call handling time, and enhancing key contact center metrics. By achieving these goals, consumer satisfaction and loyalty can be significantly increased.
Sara for Experience Center empowers agents to provide faster, enhanced patient experiences by eliminating the need for them to move between multiple systems. Sara does that for them, and quickly provides answers by removing data and system gaps with instant insights and suggested actions. Sara for Experience Center helps health systems enhance customer loyalty with improved experiences.
Sara can be trained on the organization's knowledge base and has unified access to scheduling, ticketing, and documentation systems. As a virtual assistant, Sara actively listens to agent-patient conversations, retrieves data from various systems, and promptly provides the necessary information to agents in real time. This enables agents to complete calls more efficiently and comprehensively.
Moreover, Sara continues to support agents after the call ends. Sara assists with transcribing meetings, summarizing calls, and documenting ticket specifications, allowing tickets to be closed promptly and enabling agents to be ready for their next call sooner.
Through an analysis of 1,000 calls, Innovaccer estimates that Sara can save each call center agent approximately 10 hours per week in documentation time. This time-saving translates to a 25% increase in call volume and improves the quality of agents' workdays by providing relevant information when needed, eliminating the need to search through multiple systems while the patient is on hold.
Implementing Sara results in increased efficiency, happier call center agents, and more patients being quickly and satisfactorily serviced on their first call, every time.
We estimate that Sara for Experience Center can help providers and their agents reduce the time spent on documentation by about 10 hours per week, provide a 25% increase in first-call resolution, a 20% reduction in call handling time, and a 20% increase in outreach.
A Step-by-Step Checklist for Shaping your CRM Strategy to Utilize AI
Use this checklist as you embark on your journey to revolutionize your CRM strategy and improve health outcomes with AI.
Successful implementation of AI in healthcare CRM requires careful planning, integration, and ongoing monitoring to fully leverage the potential of AI capabilities for better patient experiences and operational efficiency.