A Novel Mobile App to Identify Patients With Multimorbidity in the Emergency Setting: Development of an App and Feasibility Trial.
A Novel Mobile App to Identify Patients With Multimorbidity in the Emergency Setting: Development of an App and Feasibility Trial.
复制标题
DOI：
10.2196/42970
复制
发表时间：
2023-07-13
影响因子：
2.2
通讯作者：
Kelz, Rachel Rapaport
中科院分区：
文献类型：
--
作者：
Rosen, Claire Barthlow;Roberts, Sanford Eugene;Syvyk, Solomiya;Finn, Caitlin;Tong, Jason;Wirtalla, Christopher;Spinks, Hunter;Kelz, Rachel Rapaport
关键词：
老年人中，多发病与手术结果不佳的风险增加有关;然而，在临床环境中识别多发病可能是一项挑战。我们创建了多病患者识别器应用程序（MMApp），以通过合格合并症集的存在轻松识别多病患者，并测试其在未来临床研究、验证中使用的可行性，并最终指导临床决策。我们通过修改后的德尔菲方法调整了合格合并症集基于声明的多项合并症定义，并开发了MMApp。共有10名住院医师将5种常见于老年人的假设急诊普外科患者情景输入MMApp，并检查了MMApp测试特征，共进行了50项试验。对于MMApp，记录每种情况下选择的合并症，沿着每种情况下正确选择、错误选择和遗漏的合并症数量。使用来自所有场景的复合数据计算使用MMApp识别患者为多病患者的灵敏度和特异性。为了评估模型的可行性，我们使用配对t检验比较了按场景划分的平均任务完成情况与美国外科医师学会国家外科质量改进计划手术风险计算器（ACS-NSQIP-SRC）的平均任务完成情况。在完成所有5个场景后，立即使用18项问卷评估MMApp的可用性和满意度。对于情景A，MMApp和ACS-NSQIP-SRC之间的任务完成时间没有显著差异（86.3秒vs 74.3秒，P=.85）或C（58.4秒vs 68.9秒，P= 0.064），MMapp在场景B中花费的时间更少（76.1秒对87.4秒，P= 0.03）和E（20.7秒对73秒，P<0.001），而场景D的时间更长（78.8秒对58.5秒，P= 0.02）。MMApp识别多聚体的敏感性为96.7%（29/30），特异性为95%（19/20）。用户对MMApp的可用性、效率和实用性的反馈是积极的。MMApp以高灵敏度和特异性识别多发性骨髓瘤，并且在大多数情况下，与常用的基于网络的风险分层工具相比，完成该工具所需的时间并不显著增加。平均用户时间远低于2分钟。居民对该应用程序的可用性和实用性的总体反馈是积极的，即使在急诊普外科环境中也是如此。在急诊普外科环境中使用MMApp识别多发性硬化患者进行验证、研究和最终临床使用是可行的。这种类型的移动的应用程序可以作为其他研究团队的模板，以创建一个工具来轻松筛选潜在注册的参与者。
Multimorbidity is associated with an increased risk of poor surgical outcomes among older adults; however, identifying multimorbidity in the clinical setting can be a challenge. We created the Multimorbid Patient Identifier App (MMApp) to easily identify patients with multimorbidity identified by the presence of a Qualifying Comorbidity Set and tested its feasibility for use in future clinical research, validation, and eventually to guide clinical decision-making. We adapted the Qualifying Comorbidity Sets’ claims-based definition of multimorbidity for clinical use through a modified Delphi approach and developed MMApp. A total of 10 residents input 5 hypothetical emergency general surgery patient scenarios, common among older adults, into the MMApp and examined MMApp test characteristics for a total of 50 trials. For MMApp, comorbidities selected for each scenario were recorded, along with the number of comorbidities correctly chosen, incorrectly chosen, and missed for each scenario. The sensitivity and specificity of identifying a patient as multimorbid using MMApp were calculated using composite data from all scenarios. To assess model feasibility, we compared the mean task completion by scenario to that of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator (ACS-NSQIP-SRC) using paired t tests. Usability and satisfaction with MMApp were assessed using an 18-item questionnaire administered immediately after completing all 5 scenarios. There was no significant difference in the task completion time between the MMApp and the ACS-NSQIP-SRC for scenarios A (86.3 seconds vs 74.3 seconds, P=.85) or C (58.4 seconds vs 68.9 seconds, P=.064), MMapp took less time for scenarios B (76.1 seconds vs 87.4 seconds, P=.03) and E (20.7 seconds vs 73 seconds, P<.001), and more time for scenario D (78.8 seconds vs 58.5 seconds, P=.02). The MMApp identified multimorbidity with 96.7% (29/30) sensitivity and 95% (19/20) specificity. User feedback was positive regarding MMApp’s usability, efficiency, and usefulness. The MMApp identified multimorbidity with high sensitivity and specificity and did not require significantly more time to complete than a commonly used web-based risk-stratification tool for most scenarios. Mean user times were well under 2 minutes. Feedback was overall positive from residents regarding the usability and usefulness of this app, even in the emergency general surgery setting. It would be feasible to use MMApp to identify patients with multimorbidity in the emergency general surgery setting for validation, research, and eventual clinical use. This type of mobile app could serve as a template for other research teams to create a tool to easily screen participants for potential enrollment.
登录
查看更多内容
影响因子：
5.3
作者：
Rutkove, Seward B.;Narayanaswami, Pushpa;Shefner, Jeremy M.
通讯作者：
Shefner, Jeremy M.
影响因子：
3.7
作者：
Quinones, Ana R.;Botoseneanu, Anda;Allore, Heather G.
通讯作者：
Allore, Heather G.
影响因子：
16.9
作者：
Ho, Vanessa P.;Bensken, Wyatt P.;Koroukian, Siran M.
通讯作者：
Koroukian, Siran M.
影响因子：
3.4
作者：
Gale, Stephen C.;Shafi, Shahid;Crystal, Jessica S.
通讯作者：
Crystal, Jessica S.
影响因子：
6.3
作者：
Ho, Vanessa P.;Schiltz, Nicholas K.;Koroukian, Siran M.
通讯作者：
Koroukian, Siran M.