Healthcare organizations are investing in AI, but launching an AI solution can be harder than building it. Healthcare solutions need to meet clinical requirements, work with the data that's available, and fit into the way people already do their jobs. We partner with healthcare organizations to build AI that fits how their teams actually work, with the people who will use it involved from the start.
Clinical validation, privacy and compliance requirements, fragmented data, and integration with existing systems can all affect whether and how a solution gets deployed.
A successful pilot is only the first step. The technology also has to work within the clinical or administrative process where it will be used. That means accounting for adoption when the solution is designed, rather than relying on training after it has been deployed.
Most AI initiatives fail after the strategy deck is delivered. PivotX stays through execution, adoption, and measurable business outcomes because that's where transformation actually happens.
Natural-language-to-SQL pipelines for analyzing large claims datasets, building and refining cohorts, mapping codesets, and producing patient-journey analyses.
AI-based symptom assessment against clinical pathways over SMS or chat, giving patients access to guided triage outside normal operating hours.
Replace email and Excel-based proof of service collection with a guided workflow embedded in the existing platform, including the documentation and controls required for review.
Automated invoice ingestion, extraction, validation, and routing designed to reduce manual processing across high invoice volumes.
Denial prediction, prior authorization support, and underpayment identification integrated into existing revenue cycle processes.
AI incorporated into existing clinical workflows rather than requiring clinicians to work in a separate system.
We start by understanding the workflow the AI will support and the requirements that come with it. Clinical and operational stakeholders work with our team from the beginning, helping shape the solution as it's built. We map the process, determine what data is available, establish how success will be measured, and identify the requirements the solution needs to meet. Development and validation happen alongside that work, giving the teams who will use the solution an opportunity to test it and provide feedback throughout the process. Our Explore-to-Value process is designed to produce a working solution within ten weeks, along with a roadmap for what comes next.
We map the business and identify where AI could create measurable value. Working with your team, we evaluate potential use cases and narrow the list to the ones worth pursuing. By the end of week four, we know what we're building, why it matters and how we'll measure success.
Solution design and prototyping happen alongside user involvement. The people who will use the solution help shape it as it develops. Working MVPs give the team something real to test and evaluate rather than asking them to react to a finished product.
We deliver the working AI solution, a validated 12-month roadmap, and a quarterly execution plan. We also begin the process of putting the solution into use. At this point, your team has experience with the solution and a clear understanding of what comes next.
Tell us what's preventing your clinical or operational team from using it. We'll tell you how we'd approach the problem.