  LLMs are revolutionizing NLP tasks. However, the use of the most advanced
LLMs, such as GPT-4, is often prohibitively expensive for most specialized
fields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM
that is purpose-built for medical conversations and measured on automated
scribing. Our results demonstrate that HEAL outperforms GPT-4 and PMC-LLaMA in
PubMedQA, with an accuracy of 78.4\%. It also achieves parity with GPT-4 in
generating medical notes. Remarkably, HEAL surpasses GPT-4 and Med-PaLM 2 in
identifying more correct medical concepts and exceeds the performance of human
scribes and other comparable models in correctness and completeness.
