Applying AI to Solve Real-World Problems in Homecare 

Exploring how artificial intelligence could transform care delivery, compliance, and revenue operations in homecare 

Today, “AI-powered” and healthcare technology are almost treated as synonyms. Many companies now describe their products this way, but the term itself is vague and can describe everything from minor feature tweaks to complex algorithms. While there’s undeniable excitement around artificial intelligence (AI) and its potential to transform the industry, we believe it only matters when grounded in reality. Because AI is evolving quickly, this reflects our current stance and our commitment to adapting as it matures. 

At HHAeXchange, we’re committed to developing AI solutions that address real customer challenges. That’s why we are  turning frontline feedback into practical, AI-driven tools that improve compliance, increase efficiency, and uncover new revenue. This is AI rooted in reality, designed to solve everyday homecare challenges. 

HHAeXchange’s Artificial Intelligence Goals 

As we continue developing our AI roadmap, we’re guided by a few core principles. We believe AI should mimic human behavior in helpful ways at this stage in AI’s evolution. That means it should enhance workflows, reduce manual effort, and improve decision-making—without adding complexity. 

Below are real-world use cases that showcase AI’s application within homecare technology and how it will make a meaningful impact on homecare agencies, caregivers, and members. 

1. Electronic Visit Verification (EVV) Compliance 

Predict compliance risks before they happen 

The Problem: 
Agencies are putting in the work—training caregivers, using software—but still fall short of compliance goals. This is an issue as noncompliance can have major consequences for homecare agencies. Agencies that don’t meet their state and payer compliance rates risk fines and loss of funding. Beyond this, accurate EVV improves member care. Human error, unreliable tech, and complex payer rules can all contribute to compliance risks. 

What Gets in the Way: 

• Missed clock-ins and outs 

• GPS failures in rural or complex areas 

• Unclear or inconsistent caregiver schedules 

• Changing payer rules 

How AI Can Help: 

• Predictive modeling uses past data to spot patterns that signal risk. For homecare, it could signal late visits or missing clock-ins based on time, location, and caregiver patterns before they happen. This would let staff act early to prevent non-compliance. 

• Clustering analysis is a technique that groups similar problems (like visit errors) to identify patterns. For example, the system might find that late visits happen more often on Mondays or with a particular service code, helping agencies fix the root cause.  

• AI could also support efforts to prevent Fraud, Waste, and Abuse (FWA), which is an essential part of compliance in homecare. By analyzing who provided the service, when and where it occurred, and what type of care was delivered, AI can help detect anomalies that don’t align with expected patterns. For example, irregularities in clock-ins and clock-outs. 

2. Authorization Utilization 

Turn approved hours into scheduled care 

The Problem: 
Payers authorize care hours, but those hours often go unused due to staffing shortages or scheduling gaps. In certain places, such as Minnesota, Medicaid policy states that authorized PCS hours not used within each six-month period of a service agreement cannot be carried over. While this regulation provides flexibility in hour usage, it can result in a loss of care hours if agencies are unable to staff or schedule services within the designated time frame. Underutilized authorizations leave care undelivered—and revenue on the table.  

What Gets in the Way: 

• Not enough available caregivers 

• Authorizations that expire without the team being alerted 

• Limited visibility into remaining approved hours 

How AI Can Help: 

• Optimization engines are AI tools that could review available caregiver schedules, client needs, and authorization timelines. They could then suggest the most efficient way to schedule visits so that all approved hours are used before they expire.  

• Forecasting models could use historical data—such as how many hours a client has used in the past—to predict how many hours they’re likely to use going forward. If a client is trending toward underuse, the system could flag it early, giving schedulers time to plan additional visits before the authorization runs out. 

• Smart scheduling recommendations could use algorithms that predict the highest likelihood of a caregiver-client match based on things like caregiver specialty, member preferences, distance, and language criteria.  

• Dynamic scheduling adjustments would be able to keep an eye on real-time changes, like canceled visits or no-shows. When a gap is detected between what was planned and what actually happened, staff can take corrective action. 

3. Billing Denials 

Reduce rework and improve cash flow 

The Problem: 
Even with complete documentation, claims can still be denied. According to the KFF Survey of Consumer Experiences with Health Insurance, 18% of insured adults experienced a denied claim in the past year—among Medicaid enrollees it was 12%. While this data spans the broader healthcare landscape, it’s a clear sign of the ongoing abrasion that providers face in the reimbursement process. Many of these denials stem from small technical issues or formatting errors. Fortunately, there are solutions that can help agencies boost first-pass claim acceptance and reduce costly resubmissions. 

What Gets in the Way: 

• Tiny data mismatches or incorrect formats 

• Payer rules that aren’t checked before submission 

• Manual reviews that happen only after denials occur 

How AI Can Help: 

• Generative AI is a type of artificial intelligence that can create natural-sounding summaries. In this situation, it could read a denial message, explain what went wrong in plain language, and suggest next steps. 

• Rule-based learning systems could automatically check each claim against the payer’s rules—like correct codes and formats—before it’s submitted. 

• Natural Language Processing (NLP) is a branch of AI that reads and interprets human language. It could scan Explanation of Benefits (EOBs) and remittance documents to extract useful information and match payments to claims. 

4. Lost Revenue 

Shine a light on what’s missing 

The Problem: 
With disconnected systems and manual tracking, it’s hard to know when payments are missing. Sometimes, agencies don’t realize they’re owed money until weeks later.  Many of our customers share that the difference between expected revenue and actual reimbursement can be a challenging aspect of revenue cycle management. 

What Gets in the Way: 

• Partial payments or paper-based EOBs 

• Billing and accounts receivable systems that don’t sync 

• No real-time visibility into outstanding claims 

How AI Can Help: 

• Automated EOB matching could help match incoming payments to submitted claims with far greater accuracy. Instead of staff manually cross-referencing documents or spreadsheets, AI could handle the bulk of the reconciliation, flagging discrepancies and highlighting what’s been paid, underpaid, or missed entirely. 

• Pattern recognition tools could be used to scan large volumes of payment data to find recurring issues—like a specific payer underpaying certain codes or frequently delaying reimbursements.  

• Claims prioritization logic could sort through unpaid or partially paid claims and identify which ones are most important to follow up on first.  

Making AI Work for Homecare 

Artificial intelligence can deliver real value—but only when it solves real problems. 

At HHAeXchange, our focus is practical and responsible innovation. We’re working hard to build AI tools that reflect the day-to-day needs of homecare agencies, not just industry buzzwords or tech trends. Data privacy, ethics, and safe AI products for healthcare consumers are the cornerstone of our AI mission. 

We see AI as a way to lighten the load for caregivers, schedulers, and billing teams. The goal isn’t to replace people—it’s to help them work smarter, reduce friction, and keep delivering high-quality care.