Voice, Medical Dictation App

During our initial opportunity analysis we researched the letter creation problem space to build an understanding of the high-level approaches that could explored, and if any looked viable from a business model perspective for for solving this known problem for our users.

The key question we were trying to answer at this early stage was if we should build vs. partner.

It was apparent quite early on that building ourselves was not viable.

Why?

Medical terminology is narrative bases, natural-language describing the care of medical conditions of a patient. This data is:

• Sensitivite

• Structured to semi-structured

• Inconsistent with high-cardinality

• Technical (and often medical discipline specific)

• Error prone input but low error threshold output

Solutions would require extremely highly degrees of accuracy to be usable, and our business was not specialised to compete with incumbent solutions in this space without big risk, investment and time, all of which we did not have appetite for.

Outcome:

• Defined user problems and test hypotheses

• Identified a Unique Selling Proposition vs incumbents

• Identified target partners

• Explored initial user solution concepts

Key Activities:

• Customer Problem: Identified clear signals in VOC that patient letters was a key problem but still lacked nuance understanding of motivators, customer pains etc.

• Market Research identifiying growing market of Automated Speech Recognition (ASR) & Transcription demand in user base & industry.

• Market Research identifying numerous integration partner requests in ASR & Transcription category.

• Partner Research exploring viability, desirability & feasibility of partnering options through technical and commercial studies.

• Value Prop exploration around how we might solve this problem better than incumbents in highly differentiated and better ways.