Designing Referral Network Structures and Decision Rules to Streamline Provision of Urgent Health and Human Services*
通过计算机模拟研究转诊网络属性与决策规则对紧急服务（如疫苗分发）效率的影响，发现适度合作与概率选择能显著提升服务覆盖率。
ABSTRACT This study uses fully factorial computer simulation to identify referral network attributes and referral decision rules that streamline the routing of people to urgent, limited services. As an example of a scenario, the model represents vaccine delivery in a city of 100,000 people during the first 30 days of a pandemic. By modeling patterns of communication among health care providers and daily routing of overflow clients to affiliated organizations, the simulations determine cumulative effects of referral network designs and decision rules on citywide delivery of available vaccines. Referral networks generally improve delivery rates when compared with random local search by clients. Increasing the health care organizations’ tendencies to form referral partnerships from zero to about four partners per organization sharply increases vaccine delivery under most conditions, but further increases in partnering yield little or no gain in system performance. When making referrals, probabilistic selection among partner organizations that have any capacity to deliver vaccines is more effective than selection of the highest‐capacity partner, except when tendencies to form partnerships are very low. Implications for designing health and human service referral networks and helping practitioners optimize their use of the networks are discussed. Suggestions for using simulations to model comparable systems are provided.