Development of a novel information and communication technology system to compensate for a sudden shortage of emergency department physicians

Abstract

Background: A sudden shortage of physician resources due to overwhelming patient needs can affect the quality of care in the emergency department (ED). Developing effective response strategies remains a challenging research area. We created a novel system using information and communication technology (ICT) to respond to a sudden shortage, and tested the system to determine whether it would compensate for a shortage.

Methods: Patients (n = 4890) transferred to a level I trauma center in Japan during 2012-2015 were studied. We assessed whether the system secured the necessary physicians without using other means such as phone or pager, and calculated fulfillment rate by the system as a primary outcome variable. We tested for the difference in probability of multiple casualties among total casualties transferred to the ED as an indicator of ability to respond to excessive patient needs, in a secondary analysis before and after system introduction.

Results: The system was activated 24 times (stand-by request [n = 12], attendance request [n = 12]) in 24 months, and secured the necessary physicians without using other means; fulfillment rate was 100%. There was no significant difference in the probability of multiple casualties during daytime weekdays hours before and after system introduction, while the probability of multiple casualties during night or weekend hours after system introduction significantly increased compared to before system introduction (4.8% vs. 12.9%, P < 0.0001). On the whole, the probability of multiple casualties increased more than 2 times after system introduction 6.2% vs. 13.6%, P < 0.0001).

Discussion: After introducing the system, probability of multiple casualties increased. Thus the system may contribute to improvement in the ability to respond to sudden excessive patient needs in multiple causalities.

Conclusions: A novel system using ICT successfully secured immediate responses from needed physicians outside the hospital without increasing user workload, and increased the ability to respond to excessive patient needs. The system appears to be able to compensate for a shortage of physician in the ED due to excessive patient transfers, particularly during off-hours.

Keywords: Cloud server; Critical care; Information sharing; Life threatening; Mail; Mass casualty; Mobile phone; Night; Trauma.

Fig. 1

Schematic diagram of data flow in the newly developed system. A requester in the hospital activates the system by filling in a request form. The request is securely transferred to a cloud server via Internet line. The cloud-based software automatically creates an e-mail based on the content of a request, and sends the e-mail to physicians’ personal mobile phones. Responses from physicians outside the hospital are transferred to the cloud server, where a list of current response status is created and updated at all times, and shared in real-time among physicians both inside and outside the hospital

Fig. 2

Input screen for request. The requester selects 4 input items by button input including categories of request, rationales for request, and needed type and number of physicians. To enable inclusion of key information in the request, two text input fields in the section (rationale and note) were provided

Fig. 3

a. Received mail text in mobile phone of physician outside hospital. b. List of current response status in mobile phone

Fig. 4

Probability of multiple casualties with a high likelihood of a life-threatening condition before and after system introduction. The probability of multiple casualties with a high likelihood of life-threatening conditions before and after system introduction was not significantly different during daytime weekday hours (before vs. after, 10.0% vs. 15.1%, P = 0.062) (a). However, during nights or weekends, the probability of multiple casualties with a high likelihood of life-threatening conditions after system introduction was significantly higher compared to before system introduction (before vs. after [nights or weekends], 4.8% vs. 12.9%, P < 0.0001) (b), as well as in the overall time period (before vs. after, 6.2% vs. 13.6%, P < 0.0001) (c). P value was calculated using a chi-square test