Roadmap for Implementing Practical AI Uses in Home Healthcare

There’s a common trap that businesses can fall into when it comes to technology – everyone wanting everything, all at once. At a time when new, cutting-edge platforms and services are introduced at a lightning pace, it’s easy to get distracted by the “shiny new object” that may not have practical application for your business – leading to wasted time and resources. 

Leaders across all levels and functions of a company know they need to have technology to enable their processes. But for those who aren’t steeped in this space, it can be difficult to grasp the big picture of where new tech will be useful, and even what’s available to choose from. An ability to bridge the gap between understanding business needs and technology is becoming critical.

Determining What’s Practical

Healthcare has well-defined use cases and increasing inflection points – like labor shortages, administrative burdens and regulatory requirements – that necessitate innovative approaches. For home healthcare in particular, technology already plays a big role. But with more AI solutions coming to market, how do we make sure they solve practical issues and build on, not replace, care decisions made by a highly skilled healthcare professionals?

The first step is setting realistic expectations. AI isn’t new, but many of its new applications, like ChatGPT, are getting a lot of headlines. It’s important to remember that AI isn’t replacing the judgement of a healthcare professional, but instead augmenting their workflow to speed up decisions, reduce errors, and give back time in their day to engage people on what they really need to look at.

The reality is that there are many ways to use AI, and most of those are still maturing and not yet ready for widespread adoption within healthcare. But several have promise for the home healthcare industry and are actively being deployed in the space with great results.

Some of these tangible benefits include:

1. Streamlined Coding Processes: AI can help coders quickly identify the correct ICD-10, CPT, and HCPCS codes based on documentation, reducing the time spent on manual coding tasks. AI’s natural language processing (NLP) capabilities can extract key medical information from clinical notes to recommend accurate codes, ensuring correct coding practices and better payment accuracy.
2. Enhanced Billing Accuracy: AI systems can analyze patient data and clinical notes to automatically generate the most accurate billing codes. This reduces the risk of human error, ensuring compliance with ever-changing regulations and improving reimbursement rates.
3. Improved Quality Assurance: AI tools can audit claims in real-time, flagging potential coding discrepancies, inconsistencies, or compliance issues before they become a problem. By automating these QA processes, agencies can proactively address errors and improve claim accuracy, reducing costly rejections and delays.
4. Increased Efficiency: By automating repetitive tasks like code assignment, claim review, and auditing, AI allows a team to focus on more strategic initiatives. This leads to faster turnaround times for claims processing and quicker reimbursements, all while maintaining high standards of accuracy and compliance.
5. Cost Savings: AI-driven automation minimizes administrative overhead, reduces the need for extensive manual labor, and enhances productivity – leading to significant cost savings in the billing and coding process. Agencies can allocate resources more effectively and reinvest savings into patient care and operational improvements.
6. Better Patient Support: Ultimately, AI enables healthcare professionals to concentrate on providing enhanced care to patients and spending less time on administration, all while maintaining control over AI’s output by a review and approval process to ensure accuracy.

Assessing Impact and Success

Once the technology is rigorously vetted and implemented, it’s important to validate its purported capabilities. A key metric here is accuracy, particularly over time. Is the technology enabling error-free automated coding processes and catching potential errors? As time goes on, also assess how integrated the AI tools are with the base applications that contain patient records, such as EMR systems, which directly feed into its computational abilities.

The second metric is cost. Is the company truly more efficient with these systems in place? Look for between 30% and 50% cost savings that go all the way down to the individual office level, not just at corporate headquarters. At the end of the day, companies should be seeing a reduction in claims write-offs for something that was missed, and a greater percentage of claims that are paid for.

Lastly, assess the acceptance, adoption, use of and approval by the people who directly interact with these tools every day. While implementation of any new system takes time, AI’s ability to impact the workplace is directly tied to its usage and trust among staff. Healthcare professionals should feel their work is augmented by AI, not displaced – and their clinical judgement is supported rather than jeopardized. Furthermore, continually determine if there is a substantive, positive change in time spent at the bedside versus on administrative tasks.

While healthcare has a reputation for being laggards on innovation, AI is one area where it’s advantageous to be ahead – with the proper guardrails in place. Replace the wait-and-see or trial-and-error approach with a measured, strategic implementation roadmap and watch as the success stories compound over time.

About Jim Glueck

Jim Glueck is Executive Vice President and Chief Information Officer for Caring Brands International and its subsidiaries—Interim HealthCare Inc., Bluebird Care and Just Better Care. With more than 25 years of business and technology experience, he is an expert in leveraging technology to transform and scale organizations in multiple industries.