Zia Ul Haq Muhammad, Hornback Andrew, Harzand Arash, Gutman David Andrew, Gallaher Bradley, Schoenberg Evan D, Zhu Yuanda, Wang May D, Anderson Blake
Noncommunicable Diseases and Mental Health Department, World Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt.
Bio-MIBLab, School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Front Artif Intell. 2024 Dec 3;7:1422208. doi: 10.3389/frai.2024.1422208. eCollection 2024.
The electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers' cognitive load, impeding effective care delivery and contributing to burnout.
To understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia. We quantified the burden of messages interacted with by each health worker type and visualized the communication patterns using graph theory. Our analysis included 76,694 conversations comprising 144,369 messages sent between 47,460 patients and 3,749 health workers across 85 healthcare specialties.
On average, nurses/certified nursing assistants/medical assistants (nurses/CNA/MA) interacted with the most messages (350), followed by non-physician practitioners (NPP) (241), physicians (166), and support staff (155), with the average conversation involving 10.51 interactions before resolution. Network analysis of the communication flow revealed that each health worker was connected to approximately two other health workers (average degree = 2.10). In message sending, support staff led in closeness centrality (0.44), followed by nurses/CNA/MA (0.41), highlighting their key role in fast information spread. For message reception, nurses/CNA/MA (0.51) and support staff (0.41) also had the highest values, underscoring their vital role in the communication network on the receiving end as well.
Our analysis demonstrates the feasibility of applying graph theory to understand communication dynamics between patients and health workers and highlights the burden of EHR-based messaging.
电子健康记录（EHR）极大地扩展了患者与医护人员之间的医疗沟通。然而，EHR信息的数量和复杂性增加了医护人员的认知负担，阻碍了有效的医疗服务提供，并导致职业倦怠。
为了了解EHR沟通带来的这些潜在危害，我们分析了佐治亚州亚特兰大市的大型学术医疗系统埃默里医疗中心患者与医护人员之间发送的EHR信息。我们对每种医护人员类型所处理的信息负担进行了量化，并使用图论可视化了沟通模式。我们的分析包括76,694次对话，这些对话包含了47,460名患者与3,749名医护人员之间发送的144,369条信息，涉及85个医疗专业。
平均而言，护士/注册护理助理/医疗助理（护士/CNA/MA）处理的信息最多（350条），其次是非医师从业者（NPP）（241条）、医生（166条）和辅助人员（155条），平均每次对话在解决之前涉及10.51次互动。对沟通流程的网络分析表明，每位医护人员与大约另外两名医护人员相连（平均度数 = 2.10）。在信息发送方面，辅助人员的接近中心性最高（0.44），其次是护士/CNA/MA（0.41），这突出了他们在快速信息传播中的关键作用。在信息接收方面，护士/CNA/MA（0.51）和辅助人员（0.41）的值也最高，这同样强调了他们在接收端的沟通网络中的重要作用。
我们分析证明了应用图论来理解患者与医护人员之间沟通动态的可行性，并突出了基于EHR的信息传递的负担。
