Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study.

BACKGROUND: In emergency departments (EDs), triage nurses are under tremendous daily pressure to rapidly assess the acuity level of patients and log the collected information into computers. With self-service technologies, patients could complete data entry on their own, allowing nurses to focus on higher-order tasks. Kiosks are a popular working example of such self-service technologies; however, placing a sufficient number of unwieldy and fixed machines demands a spatial change in the greeting area and affects pretriage flow. Mobile technologies could offer a solution to these issues. OBJECTIVE: The aim of this study was to investigate the use of mobile technologies to improve pretriage flow in EDs. METHODS: The proposed stack of mobile technologies includes patient-carried smartphones and QR technology. The web address of the self-registration app is encoded into a QR code, which was posted directly outside the walk-in entrance to be seen by every ambulatory arrival. Registration is initiated immediately after patients or their proxies scan the code using their smartphones. Patients could complete data entry at any site on the way to the triage area. Upon completion, the result is saved locally on smartphones. At the triage area, the result is automatically decoded by a portable code reader and then loaded into the triage computer. This system was implemented in three busy metropolitan EDs in Shanghai, China. Both kiosks and smartphones were evaluated randomly while being used to direct pretriage patient flow. Data were collected during a 20-day period in each center. Timeliness and usability of medical students simulating ED arrivals were assessed with the After-Scenario Questionnaire. Usability was assessed by triage nurses with the Net Promoter Score (NPS). Observations made during system implementation were subject to qualitative thematic analysis. RESULTS: Overall, 5928 of 8575 patients performed self-registration on kiosks, and 7330 of 8532 patients checked in on their smartphones. Referring effort was significantly reduced (43.7% vs 8.8%; P<.001) and mean pretriage waiting times were significantly reduced (4.4, SD 1.7 vs 2.9, SD 1.0 minutes; P<.001) with the use of smartphones compared to kiosks. There was a significant difference in mean usability scores for "ease of task completion" (4.4, SD 1.5 vs 6.7, SD 0.7; P<.001), "satisfaction with completion time" (4.5, SD 1.4 vs 6.8, SD 0.6; P<.001), and "satisfaction with support" (4.9, SD 1.9 vs 6.6, SD 1.2; P<.001). Triage nurses provided a higher NPS after implementation of mobile self-registration compared to the use of kiosks (13.3% vs 93.3%; P<.001). A modified queueing model was identified and qualitative findings were grouped by sequential steps. CONCLUSIONS: This study suggests patient-carried smartphones as a useful tool for ED self-registration. With increased usability and a tailored queueing model, the proposed system is expected to minimize pretriage waiting for patients in the ED.

Using less keystrokes to achieve high top-1 accuracy in Chinese clinical text entry.

Background: As a routine task, physicians spend substantial time and keystrokes on text entry. Documentation burden is increasingly associated with physician burnout. Predicting at top-1 with less keystrokes (TLKs) is a hot topic for smart text entry. In Western countries, contextual autocomplete is deployed to alleviate the burden. Chinese text entry is intercepted by input method engines (IMEs), which cut off suggestions from electronic health records (EHRs). Objective: To explore a user-friendly approach to make text entry easier and faster for Chinese physicians. Methods: Physicians were shadowed to uncover the real-word input behaviors. System logs were collected for behavior validation and then used for context-based learning. An in-line web-based popup layer was proposed to hold the best suggestion from EHRs. Keystrokes per character and TLK rate were evaluated quantitatively. Questionnaires were used for qualitative assessment. Nine hundred fifty-two physicians were enrolled in a field testing. Results: 14 facilitators and 17 barriers related to IMEs were identified after shadowing. With system logs, physicians tended to split long words into short units, which were 1-4 in length. 81.7% of these units were disyllables. Compared to the control group, the intervention group improved TLK rate by 40.3% (p < .0001), and reduced keystrokes per character by 48.3% (p < .0001). Survey results also promised positive feedback from physicians. Conclusions: Keystroke burden and frequent choice reaction time challenge Chinese physicians for text entry. The proposed system demonstrates an approach to alleviate the burden. Contextual information is easily retrieved and it further helps improve the top-1 accuracy, with a smaller number of keystrokes. While positive feedback is received, it promises a benefit to protect patient privacy.

Towards an effective framework for integrating patient-reported outcomes in electronic health records.

Background: In the past decade, electronic modalities are increasingly deployed to integrate patient-reported outcomes into electronic health records. Most popularly, patient portals are used for remote questionnaires, and tablets are provided to patients in-office in case they need help. They are both useful. But some barriers are still in the way, which place burdens on patients and clinicians in the process of routine data collection. Objective: This study aims to describe a portable and scalable framework which can simplify the patient-reported outcome integration by mitigating the related burdens. Methods: A framework was proposed to use a modular approach to replace the tethered approach. The framework was open-sourced on GitHub. After development and testing, it was evaluated on an instrument with 24 questions in a real clinical setting. Patients were randomly selected in every modality-based group. For objective analysis, completion time and response rate were collected. No-show data was collected and analyzed. For subjective analysis, the NASA Task Load Index was used to measure workload, and the Net Promoter Score was used to assess user satisfaction. Results: The model could contain 46,656 questions. A quick response code could store 1120 encoded items. For remote visits, the response rate was improved compared to the portal group (76.6% vs. 61.1%). The completion time was reduced by 37.5% when compared to the tablet group and was reduced by 43.4% when compared to the portal group. The workload for clinicians and patients was both reduced significantly (p < 0.001). A higher Net Promoter Score was rated by both clinicians (89.3%) and patients (86.5%). Compared to the portal group, the no-show rate was reduced (11.7% vs. 8.6%). Conclusions: Collecting patient-reported outcomes over a quick response code appears to be an alternative modality to enable a simplified integration. This study provides new insights to collect patient-reported outcomes with interoperability and substitutability in mind.