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What is the technology?<\/h2>\n\n\n\n
To achieve this, DeepScribe uses a combination of voice recognition and AI. Voice recognition is distinguishable from speech recognition in that it doesn\u2019t simply interprets what is being spoken \u2013 it pays attention to who<\/em> is speaking. This difference is key in patient interactions; discerning what is spoken by the physician as opposed to the patient allows DeepScribe to accurately categorize a patient\u2019s symptoms as understood by the patient versus a medical professional. It also receives information on what procedures are being performed during the session by listening specifically to the physician\u2019s cues to the patient (see heart examination example above). As the voice recognition component transcribes the audio input word-for-word, the AI scribe component simultaneously uses natural language processing (NLP) algorithms to parse out medical information from the conversation by removing small talk and filler words and subsequently summarizes the information.<\/p>\n\n\n\n <\/p>\n\n\n\n In doing this, DeepScribe completely relieves doctors from the burden of note taking. This allows doctors to focus on providing face-to-face care to their patients and build their client relationships while also reducing the risk of human error in medical documentation and giving back physicians 3 hours per day<\/em> to combat fatigue, improve their work-life balance, or even see more patients to increase their revenues. The notes generated are also HIPAA-compliant and incorporate ICD-10 codes that are needed to meet insurance and medical billing standards, drastically reducing administrative labor dedicated to interpreting and categorizing the large amount of medical information collected by doctors on a day-to-day basis.<\/p>\n\n\n\n The value created for doctors through improved financial performance (i.e. opportunity to expand revenue in addition to significant cost reductions), better client relationships, greater job satisfaction (e.g. reduced fatigue, focus on delivering care rather than administrative tasks), and peace of mind with regard to potential human error in the lengthy process of collecting client information and entering the information accurately in the EHR system is reflected in the doctor testimonials below.<\/p>\n\n\n\n \u201cDeepScribe has allowed us to do what led us to medicine in the first place which is empathetic listening and caring for the patient.\u201d \u2013 Dr. Ravi Mallavarapu<\/p> \u201cDeepScribe has not only allowed me to become more connected with my patients, but also increased my revenue by allowing me to see more patients.\u201d \u2013 Dr. Pinkal Patel<\/p> \u201cDeepScribe has feed up so much time for us. We actually have time to meet and do business development, get home, make dinner, and spend time with family.\u201d \u2013 Dr. Navin Reddy<\/p><\/blockquote><\/figure>\n\n\n\n These benefits directly carry over to the patient as well. Because physicians are more rejuvenated physically and emotionally, the quality of interactions with their patients will improve (e.g. more attentive, more compassionate, more present etc.). As such, patients feel more connected and listened to by their physicians throughout what could be an emotionally taxing conversation about their pain and symptoms. Patients can also rest assured that their medical information is being captured completely and accurately.<\/p>\n\n\n\n Despite the fact that they deliver such huge value, DeepScribe ensures that their offering stays affordable to physicians, at a cost that is 1\/6 that of a human scribe or transcription service. Given the increasing number of direct competitors in the space, I believe the low price point is a wise strategy to capture as many users as possible and collect valuable data that will feed and improve DeepScribe\u2019s AI algorithm.<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n DeepScribe\u2019s largest challenge is around user adoption. It faces the traditional distrust and hesitations around AI from potential customers, exacerbated due to the fact that it is involved in a medical setting. When they do successfully acquire new customers, the physicians initially struggle with a learning curve, which is described to be \u201csimilar to having a new medical student or an intern\u201d where the quality of notes only improve after sufficient usage (approximately 3 weeks). Additionally, though they support integration with a large number of widely-used, out-of-the box EHRs, they must perform custom integrations for EHR software that they have not currently developed.<\/p>\n\n\n\n The DeepScribe team should actively address these issues by building out a strong engineering team to (1) iterate on the quality of their product to reduce the ramp-up time for new customers and (2) support more out-of-the-box integrations. This will make the purchasing decision significantly easier for potential customers. The team should also consider new opportunities to expand their product capabilities, the most powerful (and most risky) option being the use of AI to suggest diagnoses based on the notes collected.<\/p>\n\n\n\n With respect to user attitudes toward AI, as younger, more tech-savvy doctors begin to form a greater proportion of practicing physicians and AI continues to permeate new parts of our day-to-day lives, I am optimistic that DeepScribe\u2019s prospective customer base will increasingly perceive AI in a favorable light, resulting in higher rates of adoption.<\/p>\n","protected":false},"excerpt":{"rendered":" With over 75% of physicians using documentation tools in the space, they are still spending nearly half their day writing notes — DeepScribe is changing this<\/p>\n","protected":false},"author":19334,"featured_media":17455,"comment_status":"open","ping_status":"closed","template":"","categories":[],"class_list":["post-17453","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/machine-learning-3\/","yoast_head":"\nBringing back the \u201cjoy of care\u201d<\/h2>\n\n\n\n
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DeepScribe named as Outperformer<\/strong> in the NLP voice documentation market among 6 other companies, including Suki<\/a>, Rad AI<\/a>, and Notable<\/a>.<\/figcaption><\/figure>\n\n\n\nChallenges and opportunities<\/h2>\n\n\n\n