Promising algorithms to perilous applications: a systematic review of risk stratification tools for predicting healthcare utilisation.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Christopher Oddy, Joe Zhang, Jessica Morley, Hutan Ashrafian
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Abstract

Objectives: Risk stratification tools that predict healthcare utilisation are extensively integrated into primary care systems worldwide, forming a key component of anticipatory care pathways, where high-risk individuals are targeted by preventative interventions. Existing work broadly focuses on comparing model performance in retrospective cohorts with little attention paid to efficacy in reducing morbidity when deployed in different global contexts. We review the evidence supporting the use of such tools in real-world settings, from retrospective dataset performance to pathway evaluation.

Methods: A systematic search was undertaken to identify studies reporting the development, validation and deployment of models that predict healthcare utilisation in unselected primary care cohorts, comparable to their current real-world application.

Results: Among 3897 articles screened, 51 studies were identified evaluating 28 risk prediction models. Half underwent external validation yet only two were validated internationally. No association between validation context and model discrimination was observed. The majority of real-world evaluation studies reported no change, or indeed significant increases, in healthcare utilisation within targeted groups, with only one-third of reports demonstrating some benefit.

Discussion: While model discrimination appears satisfactorily robust to application context there is little evidence to suggest that accurate identification of high-risk individuals can be reliably translated to improvements in service delivery or morbidity.

Conclusions: The evidence does not support further integration of care pathways with costly population-level interventions based on risk prediction in unselected primary care cohorts. There is an urgent need to independently appraise the safety, efficacy and cost-effectiveness of risk prediction systems that are already widely deployed within primary care.

从有前途的算法到危险的应用:对用于预测医疗保健使用情况的风险分层工具的系统回顾。
目的:预测医疗保健利用率的风险分层工具已被广泛整合到全球的初级医疗保健系统中,成为预测性护理路径的关键组成部分,其中高风险人群是预防性干预的目标。现有工作主要集中在比较模型在回顾性队列中的表现,而很少关注在全球不同环境下使用该工具在降低发病率方面的功效。我们回顾了支持在真实世界环境中使用此类工具的证据,从回顾性数据集性能到路径评估:方法:我们进行了一次系统性检索,以确定报告在未选定的初级保健队列中开发、验证和部署预测医疗保健利用率模型的研究,这些模型可与当前的实际应用进行比较:结果:在筛选出的 3897 篇文章中,发现有 51 项研究对 28 个风险预测模型进行了评估。其中一半经过了外部验证,但只有两个模型经过了国际验证。未发现验证背景与模型区分度之间存在关联。大多数真实世界评估研究报告称,目标群体的医疗保健利用率没有变化,甚至显著增加,只有三分之一的报告显示了一些益处:讨论:虽然模型判别对应用环境的稳健性令人满意,但几乎没有证据表明,准确识别高危人群可以可靠地改善服务提供或发病率:有证据表明,在未经选择的初级保健队列中,不支持根据风险预测进一步整合护理路径和昂贵的人群干预措施。目前迫切需要对已在初级医疗中广泛应用的风险预测系统的安全性、有效性和成本效益进行独立评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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