Medical School, University of Aberdeen, Aberdeen, UK.
Abertay University, Dundee, UK.
BMC Health Serv Res. 2019 Feb 8;19(1):108. doi: 10.1186/s12913-019-3938-z.
Managing demand for urgent and unscheduled care is a major problem for health services globally. A particular issue is that some patients appear to make heavy use of services, including primary care out of hours. We hypothesised that greater variation (statistical complexity) in reasons for attending primary care out of hours services may be a useful marker of patients at high risk of ongoing heavy service use.
We analysed an anonymised dataset of contacts with the primary care out of hours care for Scotland in 2011. This contained 120,395 contacts from 13,981 high-using patients who made 5 or more contacts during a calendar year. We allocated the stated reason for each encounter into one of 14 categories. For each patient we calculated measures of statistical complexity of reasons for encounter including the count of different categories, Herfindahl index and statistical entropy of either the categories themselves, or the category transitions. We examined the association of these measures of statistical complexity with patient and healthcare use characteristics.
The high users comprised 2.4% of adults using the service and accounted for 15% of all contacts. Statistical complexity (as entropy of categories) increased with number of contacts but was not substantially influenced by either patient age or sex. This lack of association with age was unexpected as with increasing multi-morbidity one would expect greater variability in reason for encounter. Between 5 and 10 consultations, higher entropy was associated with a reduced likelihood of further consultations. In contrast, the occurrence of one or more contacts for a mental health problem was associated with increased likelihood of further consultations.
Complexity of reason for encounter can be estimated in an out of hours primary care setting. Similar levels of statistical complexity are seen in younger and older adults (suggesting that it is more to do with consultation behaviour than morbidity) but it is not a predictor of ongoing high use of urgent care.
管理紧急和非计划性医疗服务的需求是全球卫生服务面临的主要问题。一个特别的问题是,一些患者似乎大量使用服务,包括初级保健服务。我们假设,初级保健服务之外的就诊原因的变化(统计复杂性)可能是预测患者持续大量使用服务的高风险的有用指标。
我们分析了 2011 年苏格兰初级保健服务之外的匿名数据。这包含了 13981 名高使用患者在一个日历年中进行的 5 次或更多次就诊的 120395 次就诊。我们将每次就诊的原因分为 14 个类别之一。对于每个患者,我们计算了就诊原因的统计复杂性指标,包括不同类别的数量、赫芬达尔指数和类别本身或类别转换的统计熵。我们检查了这些统计复杂性指标与患者和医疗保健使用特征的关联。
高使用者占使用该服务的成年人的 2.4%,占所有就诊的 15%。统计复杂性(类别熵)随就诊次数的增加而增加,但与患者年龄或性别关系不大。这种与年龄缺乏关联是出乎意料的,因为随着多病种的增加,人们会期望就诊原因的变化更大。在 5 到 10 次就诊之间,较高的熵与进一步就诊的可能性降低有关。相比之下,一个或多个心理健康问题的就诊与进一步就诊的可能性增加有关。
在初级保健服务之外的就诊中,可以估计就诊原因的复杂性。在年轻和老年患者中,复杂性水平相似(表明这更多的是与就诊行为有关,而不是与发病情况有关),但它不是持续大量使用紧急护理的预测指标。