Suppr超能文献

基于医疗需求的人群细分:一种简明临床医生管理工具的验证。

Population Segmentation Based on Healthcare Needs: Validation of a Brief Clinician-Administered Tool.

机构信息

Program in Health Services and Systems Research, Duke-NUS Medical School , Singapore, Singapore.

Department of Medicine (General Internal Medicine), Duke University Medical Center, Durham, NC, USA.

出版信息

J Gen Intern Med. 2021 Jan;36(1):9-16. doi: 10.1007/s11606-020-05962-4. Epub 2020 Jun 30.

Abstract

BACKGROUND

As populations age with increasingly complex chronic conditions, segmenting populations into clinically meaningful categories of healthcare and related service needs can provide healthcare planners with crucial information to optimally meet needs. However, while conventional approaches typically involve electronic medical records (EMRs), such records do not always capture information reliably or accurately.

OBJECTIVE

We describe the inter-rater reliability and predictive validity of a clinician-administered tool, the Simple Segmentation Tool (SST) for categorizing older individuals into one of six Global Impression (GI) segments and eight complicating factors (CFs) indicative of healthcare and related social needs.

DESIGN

Observational study ( ClinicalTrials.gov , number NCT02663037).

PARTICIPANTS

Patients aged 55 years and above.

MAIN MEASURES

Emergency department (ED) subjects (between May and June 2016) had baseline SST assessment by two physicians and a nurse concurrently seeing the same individual. General medical (GM) ward subjects (February 2017) had a SST assessment by their principal physician. Adverse events (ED visits, hospitalizations, and mortality over 90 days from baseline) were determined by a blinded reviewer. Inter-rater reliability was measured using Cohen's kappa. Predictive validity was evaluated using Cox hazard ratios based on time to first adverse event.

KEY RESULTS

Cohen's kappa between physician-physician, service physician-nurse, and physician-nurse pairs for GI were 0.60, 0.71, and 0.68, respectively. Cox analyses demonstrated significant predictive validity of GI and CFs for adverse outcomes.

CONCLUSIONS

With modest training, clinicians can complete a brief instrument to segment their patient into clinically meaningful categories of healthcare and related service needs. This approach can complement and overcome current limitations of EMR-based instruments, particularly with respect to whole-patient care.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02663037.

摘要

背景

随着人口老龄化和日益复杂的慢性病,将人口划分为具有临床意义的医疗保健和相关服务需求类别,可以为医疗保健规划者提供关键信息,以最佳满足需求。然而,虽然传统方法通常涉及电子病历(EMR),但这些记录并不总是可靠或准确地捕捉信息。

目的

我们描述了一种临床医生使用的工具,即简单分类工具(SST),用于将老年人分为六个总体印象(GI)类别和八个复杂因素(CF)类别,以指示医疗保健和相关社会需求的类别。该工具的组内一致性和预测有效性。

设计

观察性研究(ClinicalTrials.gov,编号 NCT02663037)。

参与者

年龄在 55 岁及以上的患者。

主要测量方法

急诊部(ED)患者(2016 年 5 月至 6 月)由两位医生和一位同时看同一个人的护士同时进行基线 SST 评估。普通医疗(GM)病房患者(2017 年 2 月)由其主治医生进行 SST 评估。通过盲法审查员确定不良事件(ED 就诊、住院和从基线起 90 天内的死亡率)。组内一致性通过 Cohen's kappa 测量。使用基于首次不良事件的 Cox 风险比评估预测有效性。

主要结果

GI 的医生-医生、服务医生-护士和医生-护士配对之间的 Cohen's kappa 分别为 0.60、0.71 和 0.68。Cox 分析表明,GI 和 CF 对不良结局具有显著的预测有效性。

结论

经过适度的培训,临床医生可以使用简短的工具将患者分为具有临床意义的医疗保健和相关服务需求类别。这种方法可以补充和克服基于 EMR 的工具的当前局限性,特别是在全人护理方面。

试验注册

ClinicalTrials.gov 标识符:NCT02663037。

相似文献

引用本文的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验