ROI Model Companion Paper

nSight’s ROI model is designed to help hospitals understand how objective operating room data can translate into measurable financial impact. The model is an interactive website experience, allowing hospital stakeholders to adjust assumptions, test priorities, and generate a more customized view of potential value based on their own operating environment.

The core purpose of the model is not simply to present a static savings estimate. It is to provide a practical framework for evaluating how nSight can create value across a multi-year deployment. The model connects hospital-specific inputs — such as OR count, case volume, revenue assumptions, staffing costs, and deployment priorities — to the operational and financial outcomes that matter most to perioperative, finance, supply chain, and executive teams.

The model is built around three primary value domains: efficiency, cost containment, and safety. Efficiency value comes from identifying lost OR time, reducing delays, improving turnover, increasing schedule reliability, and creating capacity for additional cases. Cost containment value comes from improving charge capture, identifying opened-but-unused supplies, rationalizing trays, reducing waste, and improving preference cards. Safety-related value comes from reducing avoidable costs associated with surgical site infections, retained-item risk, miscounts, unnecessary X-rays, readmissions, reoperations, and other downstream safety events.

A central feature of the model is configurability. nSight uses baseline assumptions as a starting point, but the model is intended to be updated with customer-specific data. This allows each hospital to evaluate nSight against its own clinical reality, financial structure, service-line priorities, and operational constraints. Rather than forcing every customer into the same default scenario, the model helps answer a more useful question: what could nSight be worth in our specific OR environment?

The interactive model allows users to explore different scenarios directly. Hospital stakeholders can adjust inputs, prioritize different solution areas, and see how deployment sequencing affects projected value over time. This makes the model a valuable planning tool for understanding which opportunities matter most, which modules should be deployed first, and how returns may compound as the platform becomes embedded in daily OR operations.

The companion paper explains that nSight’s value is phased, not instantaneous. Some capabilities can begin creating value early, especially (in the default configuration) those tied to efficiency visibility, charge capture support, and count-related workflows. Other capabilities mature later as the system learns customer-specific inventory, gathers case-level evidence, and supports deeper workflow change. This phased logic is important because it reflects how hospitals actually adopt operational technology: baseline measurement first, early wins second, deeper optimization third.

The model also treats financial impact as a portfolio of solutions rather than a single feature. A hospital may see value from adding more cases, reducing idle time, improving turnover, recovering missed revenue, reducing waste, lowering tray burden, supporting safety workflows, or avoiding downstream costs. Individually, some improvements may look incremental. Together, they can create a meaningful financial case because they act across the full operating room system.

Another important theme is value realization. nSight does not frame ROI as something that happens automatically after installation. The model assumes that realized value depends on measurement, operational adoption, workflow review, and ongoing collaboration between nSight and the customer. For that reason, the companion paper introduces the idea of a Joint Realization Task Force: a shared group responsible for reviewing performance, validating savings, tracking progress against the model, and identifying additional opportunities for improvement.

This structure is meant to turn deployment into a performance partnership. The hospital brings its operational context, clinical priorities, and internal data. nSight brings objective OR data, analytics, workflow tools, and implementation support. Together, the teams can compare projected value to realized value, refine assumptions, and keep the deployment focused on measurable outcomes.

The model also supports executive decision-making. For CFOs and finance leaders, it provides a structured way to evaluate investment, payback, financial impact, and risk. For OR leaders, it connects operational bottlenecks to capacity and throughput. For supply chain teams, it highlights charge capture, tray, waste, and preference-card opportunities. For safety and quality teams, it shows how workflow improvement can also carry financial value when avoidable adverse events and downstream costs are reduced.

The broader argument of the ROI companion paper is that OR intelligence should not be evaluated as a generic software expense. nSight is positioned as a platform that can help hospitals improve the economics of surgical care by creating a better operational record of what happens in the room. When that record is connected to workflows, recommendations, and value tracking, the platform can support both immediate ROI planning and long-term performance improvement.

The model’s greatest strength is that it makes nSight’s value concrete. It translates OR activity into operational drivers, operational drivers into financial assumptions, and financial assumptions into a configurable view of potential return. It makes it easy for hospitals to test the assumptions, understand the levers, and see how nSight may apply to their own surgical environment.