AI is revolutionizing healthcare, addressing acute challenges for providers, payers, and patients. From documentation and coding to diagnostics and pharmaceutical R&D, the industry is embracing AI’s potential.
However, while many companies focus on algorithms and models to alleviate administrative burdens, the true breakthrough lies in pairing AI’s power with effective workflows. A prime example is how health systems manage specialist referrals. AI and machine learning can streamline this process to ensure that referrals are prioritized so that patients are seen at the right time, but it won’t yield results unless it is integrated with the right workflow.
Why Workflow Matters as Much as the AI Algorithm
In a recent Q&A with the Wall Street Journal, Kaiser Permanente’s AI chief highlighted that developing AI algorithms is the easy part; the real value lies in redesigning workflows to fully leverage your AI insights.
Maximizing AI’s potential requires processes and workflows that transform AI insights into actionable workstreams. To that end, traditional methods must be re-evaluated and redesigned to fully leverage AI investments.
How Care Continuity Maximizes AI Outcomes through Workflow Redesign
Care Continuity uses AI and machine learning models to assign a Navigation Score to each patient referral, factoring in over 50 variables to prioritize cases and determine the optimal navigation pathway. A higher score indicates a greater need for clinical assistance and a higher likelihood of accepting navigation help.
However, the score alone doesn’t improve outcomes. It’s like having an AI tool for your morning commute, and having it suggest a 7:15 AM departure without specifying the best route. Effective patient navigation requires not only identifying high-yield patients but also pairing that information with patient-centric workflows to achieve meaningful results.
Redesigning the Patient Navigation Workflow
Expanding Priority Queuing: Many health systems rely on EMRs with limited options for prioritizing referrals. This means that on any given day, your referral management teams may have thousands of referrals to process with little direction on which cases should take precedence. Care Continuity leverages machine learning to prioritize patients based on their Navigation Score and design tailored workflows, ensuring navigation efforts are focused on the right patients at the right time.
Modifying Navigation Workflow Based on Scoring: Prioritizing specialist referrals is just the first step. To fully optimize your network, you need to match each patient with the optimal navigational workflow based on their characteristics, needs, and any capacity constraints within the network.
The Impact of AI-Enabled Patient Navigation
AI-driven patient navigation aligns network design with growth, quality, and patient satisfaction goals. It leads to reduced time to appointments, less patient outmigration, improved outcomes, and increased loyalty.
Care Continuity’s Proven Results
Care Continuity has delivered measurable results for leading health systems. Within 120 days of implementation, clients see 15 to 20% growth in procedure volumes, a 50% increase in appointment completion rates and a 20 to 30% decrease in unnecessary repeat visits and readmissions.
To learn more about how Care Continuity can leverage AI to transform your patient navigation program, contact us today.
