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

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).

To explore a user-friendly approach to make text entry easier and faster for Chinese physicians.

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.

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.

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.