Gisladottir Undina, Nakikj Drashko, Jhunjhunwala Rashi, Panton Jasmine, Brat Gabriel, Gehlenborg Nils
Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, United States.
Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, United States.
JMIR Hum Factors. 2022 Apr 29;9(2):e29118. doi: 10.2196/29118.
There is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks.
Our main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4).
We designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool.
In total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators.
We identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization.
在知情同意过程中,对于向即将接受手术的患者告知哪些风险以及如何告知,目前尚无共识。使这一过程变得复杂的是,患者的偏好可能与临床假设不同,而且在讨论中往往未被考虑。这种差异可能导致困惑和不满,增加法律诉讼的可能性。为了克服这些问题,我们提出了一种可视化的同意工具,该工具纳入了患者的偏好,并使用数据可视化向患者传达个性化的风险。我们使用这个平台来识别用于传达个性化手术风险的关键有效视觉元素。
我们的主要重点是了解如何使用数据可视化来最好地传达个性化风险。为了将患者对主要问题的回答置于背景中,我们研究患者在手术前如何感知风险(研究问题1),可视化同意工具能够多合适地呈现个性化手术风险(研究问题2),我们的可视化如何很好地传达这些个性化手术风险(研究问题3),以及可视化同意工具如何改进知情同意过程以及如何使用它(研究问题4)。
我们设计了一种可视化同意工具以实现我们的研究目标。为了计算和列出个性化手术风险,我们使用了美国外科医师学会风险计算器。我们使用先前确定在风险沟通中很受欢迎的视觉元素创建了多个可视化模型。在患者术后进行了半结构化访谈,每个模型都在我们的可视化同意工具设计的背景下独立呈现和评估。访谈内容被转录,并进行了主题分析以识别主要主题。我们还应用了定量分析方法来评估我们工具中呈现的可视化的不同认知的普遍性。
总共采访了20名患者,中位年龄为59岁(范围29 - 87岁)。主题分析揭示了影响风险认知的因素(手术程序、患者的认知能力和同意的时机;研究问题1);影响风险可视化感知价值的因素(对罕见事件沟通的偏好、对风险可视化的偏好以及与平均值比较的有用性;研究问题3);以及可视化同意工具的感知有用性和用例(研究问题2和4)。最重要的是,我们发现患者更喜欢可视化同意工具而不是当前基于文本的文件,并且对风险可视化没有统一的偏好。此外,我们的研究结果表明,现有风险计算器中并不常体现患者的担忧。
我们确定了在围手术期环境中影响有效视觉风险沟通的关键因素,并指出了现有计算器在解决患者担忧方面的局限性。患者偏好差异很大,应影响关于风险呈现和可视化的选择。