Ravi Tenneti is the vice president of engineering and business integrations at Olive.
In today’s business world, artificial intelligence isn’t just a term from science fiction—it’s a real tool we use every day. Yet, while many recognize its benefits, turning that potential into action can be a challenge. Many leaders find themselves overwhelmed, unsure of where to start or how to maximize their AI investments.
The world of AI might seem complex, but with a clear plan, it’s entirely navigable. Here are five steps that can help you understand AI implementation at a high level, including key questions and focus areas.
1. Define Your Vision
Begin by understanding your organization’s goals. Are you trying to optimize processes, enhance customer experience or drive innovation? Having a clear vision acts as your North Star, guiding all subsequent steps.
• Key question: What are you trying to accomplish?
• Focus: Clarify your desired vision.
2. Define Specific Use Cases
Not all problems require AI solutions. It’s vital to distinguish between challenges that can be overcome using traditional methods and those where AI can truly make a difference.
• Key question: What are the specific problems or challenges that AI can address?
• Focus: Expected impact (e.g., cost savings, efficiency improvement) of implementing AI for each identified use case.
3. Get The Data Ready
AI thrives on data. The quality, security and relevance of data play a pivotal role in the success of AI implementation.
• Key questions: Is the available data of good quality, well-structured and sufficient for training the AI models? Are there any data privacy or security concerns that need to be addressed?
• Focus: Data preparation, privacy and security.
4. Select Technologies And Talent
Choosing the right technologies and having a skilled team in place ensures the AI solution aligns well with your organizational goals.
• Key questions: What are the most appropriate AI technologies or ML algorithms and tools for each identified use case? What are the required skills and resources for implementing the selected AI technologies?
• Focus: Technology selection and skill and resource assessment.
5. Monitor and Optimize
AI solutions must be regularly monitored and refined based on real-world feedback to ensure sustained performance.
• Key question: What are the key performance indicators (KPIs) that will be used to monitor the performance of the AI?
• Focus: Continuous improvement.
Let’s discuss how this can be applied in healthcare finance. In healthcare systems, revenue cycle management is the complex process that tracks a patient’s journey from booking an initial appointment to paying the final bill. Revenue cycle management encompasses a range of administrative and financial tasks, such as patient registration, prior authorization, eligibility verification, scheduling, charge capture, claims processing, patient collections and accounts receivable management.
By integrating AI into your revenue cycle, you have the opportunity to make huge improvements in efficiency, cost savings, accuracy and overall patient experience.
1. Define Your Vision
What are you trying to accomplish? What is your business vision for introducing AI into revenue cycle management?
Clarify your vision and define your main goal, whether it’s streamlining cost efficiencies, enhancing patient satisfaction or addressing other business challenges. Engage all stakeholders in each stage of the implementation process, starting by gathering feedback from leadership, administrative staff, clinicians and patients.
For example, if you’re a larger hospital aiming to introduce AI in claims management, ask what specifically you want AI to help you accomplish. Is your primary goal to reduce claims denials and days in accounts receivable? How will this contribute to your overall vision of greater time or cost savings?
2. Define Specific Use Cases
Now it’s time to narrow down your focus. You can’t do everything all at once, so start small and scale. Outline one or two specific revenue cycle problems to address with AI technologies, such as prior authorization bottlenecks or inefficient manual processes. Create a business case analysis for each problem, calculating the potential cost savings and efficiency improvements of AI implementation. Analyze the expected impact beforehand to ensure you’re on the right track.
For instance, if prior authorization is taking too long due to inefficient workflows and your goal is to reduce the processing time by 30%, what is the expected impact if you succeed? How does a 30% reduction in time translate into dollar savings for your organization?
3. Get The Data Ready
In revenue cycle management, healthcare data is obviously subject to strict privacy and security concerns. Some initial use cases, such as automating manual processes, might not need a lot of data. Others may be based on advanced machine learning algorithms that require significant amounts of data.
If you wanted to apply AI to calculate the probability of a claim being denied, for example, you would need to supply the model with a lot of historical claims data, which contains patient personally identifiable information. Before using any of the claims data in an AI model, you would have to remove all PII to protect patient privacy.
4. Select Technologies And Talent
Evaluate available AI technologies to find the right fit for each problem you’re trying to solve. Do you want to build capabilities internally, buy pre-built tools or partner with a company to create a customized solution?
Prepare your teams for the changes that will go along with an AI implementation. It’s not just a technology transformation, it’s a cultural shift. Manual processes are now becoming automated, and people will likely need time to adapt. What new skills, resources and talent will you need to support this new technology and culture? Will your current staff need additional training and professional development? Do you need to hire new talent to manage new AI tools?
5. Monitor and Optimize
An AI implementation is not a one-time task. It’s a dynamic activity that demands ongoing monitoring and optimizing. Define key performance indicators to track the performance of AI tools.
Return to the expected impact you outlined in step two, and measure it against your KPIs. What did you intend to do? Is the AI providing the value you predicted? What were your initial efficiency improvements or cost savings goals in the beginning, and how do those objectives align with actual outcomes? Establish a culture of continuous evolution, learning and innovation to get the most out of your AI.
AI is sometimes viewed as a disruptor, but in healthcare revenue cycle management, it’s an enabler. You can use AI to free your staff from the burden of manual, time-consuming tasks—enabling them to do their jobs more effectively and ultimately provide a better patient experience.
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With 17 years in the IT leadership roles, Ravi Tenneti brings a wealth of expertise and passion to the realms of technology strategy, AI, innovation and leadership. His professional journey has led him through an array of sectors such as healthcare, banking, manufacturing, semiconductors and insurance. He is currently focusing on AI in healthcare. Mr. Tenneti holds a bachelor’s degree in computer science and an MBA, and chief technology officer certification program from the Wharton Business School of the University of Pennsylvania. Beyond the world of IT, Mr.Tenneti is an avid writer, speaker and leadership/mindset coach.
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