Establishing Trust with the BATHE Technique — How AI Can Nurture Alternative Forms of Care

Establishing Patient Trust with the BATHE Technique

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We’ve discussed at length how a patients’ perception of the quality of care they receive is directly related to how they feel about the interaction and trust between them and their care provider. Numerous studies show that the more a clinician is engaged and present and shows genuine interest and concern, the better patients’ feel about the care they receive, regardless of the actual result of the visit.

We know that building that rapport and trust is critical to the patient experience, but research also suggests that the most meaningful trust is built within the first few, critical moments of a patient encounter. The moment a clinician walks into an exam room, the pressure is on to establish that relationship.

The BATHE Technique

The BATHE technique refers to a psychosocial checklist that a clinician moves through in the first few minutes of a visit with a patient. It is used to gauge the patient's symptoms and attitudes by asking engaging, open-ended questions. BATHE stands for Background, Affect, Trouble, Handling, and Empathy. 

First, a clinician will ask background questions (reason for visit), then how they feel about the symptoms, how the symptoms trouble them, how they are handling it, and finally, offer an empathetic response that explains that the patients’ reactions to the symptoms are reasonable.

This psychotherapeutic process helps patients’ come to terms with their symptoms and positively affects their view of their own reality. These more intimate conversations also help physicians assess the mental health of their patient. That natural, engaged conversation immediately builds a patients’ trust with a clinician and often means the patient will be more likely to disclose other symptoms throughout the rest of the visit. The BATHE technique is a great way for a physician to start off on the right foot.

Logging BATHE Responses

More and more, clinicians are being asked to log patient BATHE responses into the “subjective” field of their SOAP notes and eventually into their EHR. This process gives the clinician valuable information to review later and creates a usable record for a patients’ symptom outlook, rationalization, and mental health.

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DeepScribe, BATHE, and Natural Language Processing

One of the DeepScribe’s most valuable features lies in its ability to listen to and decipher natural human conversation through an AI system called natural language processing (NLP). Not only does this mean that our AI can make sense of human conversation and arrive at medically relevant conclusions, but due to the nature of DeepScribe also means that this natural conversation will be automatically noted and recorded in the appropriate EHR fields. Logging patient conversations (like the ones that occur as a result of the BATHE method) suddenly becomes incredibly less burdensome for the clinician.

Additionally, data collected from these conversations becomes much easier to decode and use to arrive at medically relevant conclusions thanks to advancing artificial intelligence. There is emerging research that suggests that AI is capable of using vocal markers to help diagnose diseases such as Parkinson's, coronary artery disease, and depression by tracking pitch variability, pauses, and speech hesitancy. While the research in this specific field is limited, the possibilities are promising. DeepScribe not only allows clinicians to adopt more connecting forms of care (like the BATHE method), but also use them, with the help of AI, to provide more preventative, encompassing care — all without adding to their documentation workload.