WHAT TO LOOK FOR IN THE RIGHT AI & AUTOMATION VENDOR:
1) A focus on healthcare
Most AI technology is not designed for the unique challenges of healthcare. Look for a vendor that understands healthcare and provides the expertise to customize your AI and automation solution to thrive within your existing hospital processes, not add to them.
2) Put healthcare data security first
When you begin your AI journey, industry-specific regulations like HIPAA privacy rules and SOC2 compliance should be the least of your team’s worries. Begin your vendor security evaluation early to ensure your AI solutions can be built with the security complexities of your organization in mind.
3) A partner and trusted advisor
Your vendor should help your team develop a long-term AI and automation strategy focused on achieving the goals of your organization. This means finding a vendor that will educate your team about the tools and technologies available, and help guide your team through evaluation of candidate processes for automation that will provide the largest economic impact on your organization.
4) Driven to provide excellent support
Many AI and automation vendors do not offer support services that cover changes to the applications or updates to the processes you have automated. Ensure you’re covered for growth from the beginning by choosing a vendor that has a long-term strategy for technology integration – one that significantly lessens the burden on your employees and resources is minimized.
As you go into the implementation process, work with your vendor’s team to ensure all stakeholders understand the technology and level of engagement required for successful custom development of your AI technology. As you go through the development and implementation of AI, consider the following :
1) Start with the lowest hanging fruit
There are many processes that can benefit from AI and automation. As you begin, start with workflows that can leverage less complex technologies such as Robotic Process Automation and Computer Vision to provide value and return on investment quickly. These technologies are perfect for many revenue cycle processes such as checking the status of claims, eligibility and benefit verification, and more. Simpler workflows that provide high value returns lay a solid foundation to provide the business case for a larger investment in your long-term AI strategy.
2) Consider when the 80/20 rule applies
It’s possible to automate 100% of a process, however, you may see a diminishing return on investment if you automate the low-volume portions of a larger workflow. For example, checking the status of a claim often requires automating the action of logging into a variety of insurance portals. Instead of trying to automate 100% of the portals immediately, you may find more value in focusing on the 20% of the portals that account for 80% of the total volume as the first priority. You can always revisit workflows in the future if it becomes necessary or valuable to automate the remaining 20% of the volume.
3) Be engaged
Communicate frequently with your vendor team and be sure to ask for demos and updates to allow for feedback during the development process. Remember, you are purchasing AI to execute best practice processes. While the vendor should be delivering updates often, and have the technology and healthcare expertise to deliver an optimized AI solution, frequent evaluation and feedback will help ensure your AI is delivered quickly and successfully.
Going live with your first AI or automation project is a big step towards your long-term AI strategy. You can expect improvements from immediate efficiency gains to reduced errors, to more time for your staff to focus on complex and important tasks. After implementation, it is important to understand the impact AI is having on your organization and use a data-driven approach to evaluate success towards your AI strategy goals. Collaborate with your vendor after implementation to:
1) Establish implementation impact
Work with your vendor to measure the impact of implementation on your business metrics. By comparing the data before and after the deployment of AI, you can evaluate its performance and use the data as a benchmark for what you can expect from other processes as you expand to new workflows. Focus on areas where you are able to evaluate hard data that can be compared directly (efficiency, error reduction, reduction in denials, etc.)
2) Think beyond the initial KPIs
Hard data only tells part of the story. Analyze the overall impact AI has made on the organization by looking at the downstream effects. Ask questions such as: Has AI met my expectations? Are there opportunities to expand workflows to add more value? What does the reduction in errors, decreased days in A/R, or decreased denials mean for the organization? Are we able to reallocate full-time employees to focus on other high priority initiatives? If we could replicate the same efficiency gains from process A to process B, C, D, E, etc. what would the impact be? Ask your vendor for support in helping you prove the value of your AI investment. The right partner will be there for you as a consultant and advisor, helping you to meet your AI goals.
Learn how to build the business case for AI and automation at your healthcare organization here.