Data-Driven Surgical Referrals for Primary Hyperparathyroidism and Associated Surgical Outcomes: A Simulation Study.

Area of Science:

• Surgery
• Health Services Research
• Health Equity

Background:

• Minimizing complications after parathyroidectomy requires directing patients to specialized high-performing facilities.
• This study evaluated the impact of referring patients to higher-quality centers for parathyroidectomy.

Purpose of the Study:

• To assess the effect of referring patients to higher-performing facilities for parathyroidectomy.
• To determine if data-driven facility selection can reduce serious adverse events (SAE) and improve surgical health equity.

Main Methods:

• A simulation study utilized Healthcare Cost and Utilization Project databases for adults undergoing parathyroidectomy for primary hyperparathyroidism.
• Serious adverse event (SAE) rates were generated using Wilson score estimates to stratify surgical facilities into quality quartiles.
• Simulations compared SAE and costs for patients treated at their original facility versus a higher-quality alternative, considering proximity and health system affiliation.

Main Results:

• The simulation predicted a decrease in SAE rates from 3% to 2% (p < 0.001) when patients were referred to higher-quality facilities.
• A slight increase in total cost was observed ($6,351 vs. $6,391; p = 0.032).
• Results were consistent across proximity-based and health system affiliation-based simulations.

Conclusions:

• Data-driven referrals for parathyroidectomy can significantly reduce serious adverse events.
• Implementing data-driven facility selection strategies can enhance surgical outcomes and promote health equity.