Digital Health Tools for the Emergency Department Before, During, and After COVID-19

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Increasing accessibility and efficiency in emergency departments (EDs) nationwide has been an active area of quality improvement. In the last decade, technological advances have been implemented to bridge gaps in access, standardize quality of care, anticipate extremes in patient census, and improve communication. The COVID-19 pandemic has underscored the need for creating operational efficiencies to prepare for surges. What is the role that digital health technologies may play in the ED to help accomplish these goals?

Digital health solutions in the ED have been implemented in a variety of ways, including:

• Artificial intelligence (AI) and machine learning (ML) tools for managing surge capacity
• Communications systems
• Telehealth for ED follow up

AI and ML for Surge Capacity Planning

In 2016, Johns Hopkins University became an early adopter of the command center model, designing Judy Reitz Capacity Command Center to address patient surge management using AI tools for decision support.(1) The center integrates data from 14 different IT systems and real-time staff reporting to a dedicated 24 member team to predict resources needed to triage, treat, and discharge patients.(2) The AI-based models and simulations utilized in this command center combine discrete event simulation (DES) and agent based simulation (ABS). DES models imitate the items that move through a healthcare system like lab tests or patient transport, while ABS imitates the behavior and decision-making of those with active roles such as patients and clinical staff.(3)  Preliminary results from the Judy Reitz Capacity Command Center included nearly 20% reduction in ED boarding, 80% reduction in operating room hold time, and a 60% increase in interhospital patient transfer throughput.

However, solutions like these have had slow marketplace penetration due to high capital costs ranging from $5-20 million.(4) The scalability utilizing this solution appears to be mainly viable in institutions that have more than a 200 bed capacity. These AI-enabled throughput systems serve to help large institutions mitigate patient safety issues and optimize resource preparedness through high and low census periods.

Communications Systems

Communication gaps occur in busy settings, and the ED is no different. In order to address this, tools have been developed to provide patients with digital care updates during what may be long wait periods in the ED.(5)  Such tools have been shown to have the ancillary benefit of improving patient satisfaction and reducing load on staff to provide frequent updates.(6) (7) Communications between ED staff and patients during the COVID-19 pandemic may also be hampered by efforts to limit the number and duration of face-to-face encounters, and by the mere fact that when encounters do happen, faces are obscured by masks and other personal protective equipment. The use of telepresence robots (devices that provide two-way video and can be stationed or driven into patient rooms remotely), enables patients to see a human face, while limiting clinician exposure and excess use of personal protective equipment.(8) One step further is the telenursing robot which can also perform tasks.(9) While such devices may mitigate exposure and limit PPE use during outbreaks, little evidence demonstrates whether they improve outcomes, or save time, both of which are critical in surge settings.

Digital Health for ED Follow Up

Digital telehealth solutions have also been used for post-ED follow up, potentially helping to reduce ED re-visit and hospital admission rates.(10) (11) Some systems have turned to these tools for post-ED patient self-monitoring of COVID symptoms, in order to help those patients without emergency department needs to stay at home.(12) Finally, some tools leverage what is known about social determinants of health to help match patients discharged from the ED with optimally tailored community-based resources.(13)

Emergency departments are busy environments, and in many institutions have been stretched beyond capacity during the coronavirus pandemic. While the number of use cases for digital health in the ED is growing, these tools must be able to provide evidence-based value in a manner that is highly efficient and fully compatible with the fast-paced workflow of this environment. Therefore, as more digital solutions are evaluated for efficacy, endpoints including staff and patient satisfaction, as well as efficiency remain critical to demonstrate.

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NODE.Health is pleased to cross post this article giving examples of digital health solutions for emergency medicine applications. NODE.Health encourages its readers to be diligent with selecting such tools and understand the evidence. As more evidence comes out on the use of such tools for COVID-19 and beyond, NODE.Health will keep its readers informed about the latest developments. Interested in learning more about the Network of Digital Evidence (NODE.Health)? Click here

References:

1. Murphy, Kieran. “The US Hospital Run by Artificial Intelligence.” Global Innovation Index, Global Innovation Index , 3 Dec. 2019, www.globalinnovationindex.org/gii-blog/the-us-hospital-run-by-artificial-intelligence–b205.
2. Kane EM, Scheulen JJ, Püttgen A, et al. Use of Systems Engineering to Design a Hospital Command Center. Jt Comm J Qual Patient Saf. 2019;45(5):370‐379. doi:10.1016/j.jcjq.2018.11.006
3. Siebers, Peer-Olaf & Macal, C. & Garnett, Jeremy & Buxton, D. & Pidd, Michael. (2010). Discrete-event simulation is dead, long live agent-based simulation!. J. Simulation. 4. 204-210. 10.1057/jos.2010.14.
4. Advisory.com. 2020. Johns Hopkins Created 16 Beds’ Worth Of Capacity Without Adding A Single Bed. Here’s How.. [online] Available at: <https://www.advisory.com/daily-briefing/2018/06/11/command-center> [Accessed 18 May 2020].
5. Agarwal AK, Sangal RB, Hahn L, et al. Digital Care Updates in the Emergency Department: A Feasibility Study. Acad Emerg Med. 2020;27(3):236‐239. doi:10.1111/acem.13905
6. Larson CO, Nelson EC, Gustafson D, Batalden PB. The relationship between meeting patients’ information needs and their satisfaction with hospital care and general health status outcomes. Int J Qual Health Care. 1996;8(5):447‐456. doi:10.1093/intqhc/8.5.447
7. “EDCAHPS Early Adopter Study.” Press Ganey.  2019. Available at: https://www.pressganey.com/resources/white-papers/edcahps-early-adopter-study . Accessed May 14, 2020
8. IEEE Spectrum: Technology, Engineering, and Science News. 2020. [online] Available at: <https://spectrum.ieee.org/automaton/robotics/medical-robots/telepresence-robots-are-helping-take-pressure-off-hospital-staff> [Accessed 18 May 2020].
9. IEEE Spectrum: Technology, Engineering, and Science News, spectrum.ieee.org/automaton/robotics/medical-robots/medical-robots-future-outbreak-response.
10.  Papanagnou D, Stone D, Chandra S, Watts P, Chang AM, Hollander JE. Integrating Telehealth Emergency Department Follow-up Visits into Residency Training. Cureus. 2018;10(4):e2433. Published 2018 Apr 5. doi:10.7759/cureus.2433
11.  Koziatek C, Klein N, Mohan S, et al. Use of a telehealth follow-up system to facilitate treatment and discharge of emergency department patients with severe cellulitis [published online ahead of print, 2020 Feb 1]. Am J Emerg Med. 2020;S0735-6757(20)30073-5.
12. Nychealthandhospitals.org. 2020. NYC Health + Hospitals Launches COVID-19 Text Message-Based Symptom Monitoring Program | NYC Health + Hospitals. [online] Available at: <https://www.nychealthandhospitals.org/pressrelease/nyc-health-hospitals-launches-covid-19-text-message-based-symptom-monitoring-program/> [Accessed 18 May 2020].
13.  “Press Releases.” Www.ahn.org, Allegheny Health Network, 28 Apr. 2020, www.ahn.org/newsroom/press-releases?page=2&prvstartrow=16&maxrow=20&zeus=59#!release/highmark-ahn-and-gateway-health-launch-online-tool-to-meet-critical-needs-during-covid-19-pandemic?page=2&prvstartrow=16&maxrow=20&zeus=59.