Holleman D R, Bowling R L, Gathy C
Medical Service, Lexington Veterans Affairs Medical Center, KY 40511, USA.
J Gen Intern Med. 1996 Apr;11(4):237-9. doi: 10.1007/BF02642481.
We studied the association between calendar and weather variables and daily unscheduled patient volume in a walk-in clinic and emergency department. Calendar variables (season, week of month, day of week, holidays, and federal check-delivery days) and weather variables (high temperature and snowfall) forecasted clinic volume, explaining 84% of daily variance and 44% of weekday variance. Staffing according to predicted volume could have decreased overstaffing from 59% to 15% of days, but would have increased understaffing from 2% to 18% of days. Models using calendar and weather data that forecast local utilization may help to schedule staffing for walk-in clinics and emergency departments more efficiently.
我们研究了日历和天气变量与一家无需预约的诊所及急诊科每日非预约患者量之间的关联。日历变量（季节、每月的周数、星期几、节假日和联邦支票递送日）和天气变量（高温和降雪）可预测诊所就诊量，解释了每日差异的84%以及工作日差异的44%。根据预测就诊量进行人员配置本可将人员过剩的天数从59%减少至15%，但会将人员不足的天数从2%增加至18%。利用预测当地医疗服务利用率的日历和天气数据构建的模型，可能有助于更高效地安排无需预约的诊所及急诊科的人员配置。
