Designing surgical coordination at an academic hospital

Team project management · Stakeholder research · Model development

I led a graduate consulting team and built Bayesian and Monte Carlo models to explore surgical workflow constraints. With nurses on the team informing intervention design, we translated stakeholder needs into coordination proposals and a measurement plan.
Author

Ivan De Jesus Rueda

Role: Team Project Manager and Model Developer in a graduate healthcare consulting engagement.

I led the graduate consulting team’s stakeholder research and process mapping, and built its Bayesian and Monte Carlo models. Nurses on our team informed intervention design with their clinical experience.

Confidentiality: Numerical values in this example are mock data used to illustrate the modeling approach. The hospital’s actual data is not shown.

Start with the people

Stakeholder research and peer-hospital benchmarking shaped design criteria for patients, surgical teams, anesthesiology, OR nurses, and hospital operations.

Five stakeholder groups connect to design considerations, proposed workflow changes, and measurement priorities: patients, surgical teams, anesthesia teams, OR nurses and teams, and hospital operations.
Design synthesis: stakeholder considerations connected to proposed responses and process measures.

Redesign the handoffs

We proposed shared scheduling, a clinical-coordination role, and staged patient communication to address fragmented planning and limited pre-operative visibility.

Current findings show separate patient preparation, surgery scheduling, and anesthesia availability streams meeting at day-of-surgery reconciliation. The proposed workflow joins them through shared readiness and scheduling before the OR handoff, with three earlier patient touchpoints.
Reconstructed service blueprint. The shared workflow and patient touchpoints were proposed changes.

Build a model around evidence and uncertainty

The recorded outcomes did not establish causes. I combined Bayesian inference with assumption-driven workflow simulation to explore coordination options and identify measurement gaps.

I chose these methods to connect workflow design to the evidence we had:

  • The network made assumed relationships explicit: how coordination, scheduling, patient readiness, anesthesia, and the OR team connected to recorded outcomes.
  • Bayesian inference combined prior assumptions with outcome data while retaining uncertainty about factors we could not observe directly.
  • Monte Carlo simulation carried that uncertainty through repeated draws, allowing us to explore a range of possible workflow outcomes under stated assumptions.
Recorded operating-room outcomes and literature-informed assumptions feed Bayesian inference on five hidden operating factors. Factor distributions and assumed step probabilities and durations inform exploratory workflow simulation, design-option comparisons, and proposed pilot measurements.
Conceptual reconstruction of the exploratory model. Process links and scenario changes were assumptions; intervention effects were not validated.

Reconstructed network with a working mock Bayesian update. Play the walkthrough to follow priors, evidence, updated beliefs, and joint posterior draws. The numerical example is simplified; it does not reproduce the original fitted model.

Open the model demonstration in a full page

Plan implementation and measurement

We delivered models, coordination recommendations, and a phased implementation roadmap that was well received by hospital stakeholders. The roadmap linked clinical workflow changes, technology, and measurement through staged review gates.

A proposed implementation roadmap progresses through foundation and alignment, workflow redesign, a technology pilot, and rollout and improvement. Each phase includes proposed activities and a review gate. No calendar dates or completed deployments are shown.
The roadmap was well received. Phases and review gates show the proposed approach; implementation and realized benefits remain unverified.