Trial of Converting Nursing Records into Simplified and Structured Information Utilizing ChatGPT-3.5.

In Japan, the excessive length of time required for nursing records has become a social problem. A shift to concise "bulleted" records is needed to apply speech recognition and to work with foreign caregivers. Therefore, using 96,000 descriptively described anonymized nursing records, we identified typical situations for each information source and attempted to convert them to "bulleted" records using ChatGPT-3.

Terminology Gap in Continuous Care Between Acute and Long-Term Care Hospitals.

This study aimed to elucidate the gap in terminology between acute and long-term care (LTC) hospitals. Fifty-seven hospital documents were analyzed using text mining. Each document contained a mean 194.