Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
J Med Internet Res. 2020 Jul 9;22(7):e15770. doi: 10.2196/15770.
While selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools.
The aim of the study was to examine the impact of using the GRASP framework on clinicians' and health care professionals' decisions in selecting clinical predictive tools.
A controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured.
We received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; t=8.53; P<.001); increased objective decision making by 32%, from 62% (3.11/5) to 82% (4.10/5; t=9.24; P<.001); decreased subjective decision making based on guessing by 20%, from 49% (2.48/5) to 39% (1.98/5; t=-5.47; P<.001); and decreased prior knowledge or experience by 8%, from 71% (3.55/5) to 65% (3.27/5; t=-2.99; P=.003). Using GRASP significantly decreased decisional conflict and increased the confidence and satisfaction of participants with their decisions by 11%, from 71% (3.55/5) to 79% (3.96/5; t=4.27; P<.001), and by 13%, from 70% (3.54/5) to 79% (3.99/5; t=4.89; P<.001), respectively. Using GRASP decreased the task completion time, on the 90th percentile, by 52%, from 12.4 to 6.4 min (t=-0.87; P=.38). The average System Usability Scale of the GRASP framework was very good: 72.5% and 88% (108/122) of the participants found the GRASP useful.
Using GRASP has positively supported and significantly improved evidence-based decision making. It has increased the accuracy and efficiency of selecting predictive tools. GRASP is not meant to be prescriptive; it represents a high-level approach and an effective, evidence-based, and comprehensive yet simple and feasible method to evaluate, compare, and select clinical predictive tools.
在选择用于临床实践实施或临床指南推荐的预测工具时，临床医生和医疗保健专业人员面临着数量庞大的工具。其中许多工具从未被实施或评估过其比较效果。为了克服这一挑战，作者开发并验证了一个用于分级和评估预测工具的基于证据的框架（GRASP 框架）。该框架基于对这些工具的已发表证据的严格评估。
本研究旨在检验使用 GRASP 框架对临床医生和医疗保健专业人员选择临床预测工具决策的影响。
通过基于网络的调查进行了一项对照实验。参与者被随机分配到以下两种情况之一：查看常见创伤性脑损伤预测工具的推导出版物（如描述预测工具开发的研究）（对照组）或查看基于证据的摘要，其中每个工具都使用 GRASP 框架进行了分级和评估（干预组）。要求两组参与者根据最大验证或实施来选择最佳工具。邀请了广泛的国际临床医生和医疗保健专业人员参与调查。测量了任务完成时间、正确决策率、客观决策率与主观决策率以及决策冲突水平。
我们共收到 194 份有效回复。与不使用 GRASP 相比，使用该框架可使正确决策率显著提高 64%，从 53.7%提高到 88.1%（88.1/53.7=1.64；t=8.53；P<.001）；客观决策率提高 32%，从 62%（3.11/5）提高到 82%（4.10/5；t=9.24；P<.001）；基于猜测的主观决策率降低 20%，从 49%（2.48/5）降低到 39%（1.98/5；t=-5.47；P<.001）；基于先验知识或经验的决策率降低 8%，从 71%（3.55/5）降低到 65%（3.27/5；t=-2.99；P=.003）。使用 GRASP 可使决策冲突显著降低 11%，并使参与者对决策的信心和满意度分别提高 13%，从 71%（3.55/5）提高到 79%（3.96/5；t=4.27；P<.001）和从 70%（3.54/5）提高到 79%（3.99/5；t=4.89；P<.001）。使用 GRASP 可将 90 百分位数的任务完成时间缩短 52%，从 12.4 分钟缩短到 6.4 分钟（t=-0.87；P=.38）。GRASP 的平均系统可用性量表非常好：72.5%和 88%（108/122）的参与者认为 GRASP 有用。
使用 GRASP 可以支持并显著改善基于证据的决策。它提高了选择预测工具的准确性和效率。GRASP 并非旨在具有规定性；它代表了一种高级方法，是一种有效、基于证据且全面但简单可行的方法，用于评估、比较和选择临床预测工具。
