A discrete event simulation to evaluate impact of radiology process changes on emergency department computed tomography access

Innovate and Validate: Design-Led Simulation Optimization to Test Centralized Registration Feasibility in a Multispecialty Clinic

OBJECTIVE

This study utilizes a design-led simulation-optimization process (DLSO) to refine a hybrid registration model for a free-standing outpatient clinic. The goal is to assess the viability of employing DLSO for innovation support and highlight key factors influencing resource requirements.

BACKGROUND

Manual registration in healthcare causes delays, impacting patient services and resource allocation. This study addresses these challenges by optimizing a hybrid centralized registration and adopting technology for efficiency.

METHOD

An iterative methodology with simulation optimization was designed to test a proof of concept. Configurations of four and five registration options within a hybrid centralized system were explored under preregistration adoption rates of 30% and 50%. Three self-service kiosks served as a baseline during concept design and test fits.

RESULTS

Centralized registration accommodated a daily throughput of 2,000 people with a 30% baseline preregistration rate. Assessing preregistration impact on seating capacity showed significant reductions in demand and floor census. For four check-in stations, a 30%-50% preregistration increase led to a 32% seating demand reduction and a 26% decrease in maximum floor census. With five stations, a 50% preregistration reduced seating demand by 23% and maximum floor census by 20%.

CONCLUSION

Innovating introduces complexity and uncertainties requiring buy-in from diverse stakeholders. DLSO experimentation proves beneficial for validating novel concepts during design.

Intended and unintended consequences of strategies to meet performance benchmarks for rheumatologist referrals in a centralized intake system

RATIONALE

Timely assessment of a chronic condition is critical to prevent long-term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge.

METHODS

We applied a system simulation approach to model a high-volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change.

RESULTS

The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178-269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178-43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit.

CONCLUSION

An MWTG policy can result in intended and unintended consequences-ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.