SF Medic: Real-time Clinical Decision Support Interventions for Improved Patient Safety powered by AWS Machine Learning services

Product Overview

SF Medic uses machine learning services and data streaming technologies to enable doctor-patient text-(or asynchronous voice)-based interaction anywhere across the globe. It learns about a patient’s medications, allergies, and past records from an EHR integration and provides real-time clinical decision support interventions. This unique feature empowers the doctor with stronger and safer prescriptions, minimizing the chances of human error. It provides spontaneous language translations and the real-time messaging feature further enhances the doctor-patient interaction experience.

SF Medic in Action

Once a patient has booked an appointment with their doctor, they start a private text- (or asynchronous voice) -based interaction with the provider directly. Security & data privacy is at the core of SF Medic, creating a dedicated chat room for each doctor-patient interaction.

SF Medic advances the Journey to Innovation

The idea of telemedicine was to provide primary healthcare services to patients through a remote-based facility. This was a major breakthrough as patients struggled to access hospitals due to distance, commute, and financial constraints. However, telemedicine applications were heavily dependent on technology like connectivity issues, heavy bandwidth requirements, access to the internet, technology costs, and (lack of) tech-savviness of the user. As per Vaidant’s real-world telemedicine experience in India, 7 out of the 10 telemedicine consultations failed in simply establishing a connection between the doctor and the patient. Of the remaining three, two telemedicine consultations faced unsteady connections resulting in poor adherence to physician recommendations. Even when the applications worked on low data bandwidth, the results were not promising.

SourceFuse Medic Unique Capabilities

Deploying SF Medic

SourceFuse offers SF Medic as a standalone SaaS (Software-as-a-Service) solution for hospitals and specialty clinics. SF Medic is also available as a plugin that integrates into existing hospital management systems and EHRs.

The Tech Stack

Amazon Web Services (AWS) Cloud and PubNub are the primary technologies used by SF Medic. PubNub is a real-time data streaming service that can deliver messages over the Internet between two applications. PubNub has data-centers across the world. It uses these data-centers to guarantee a quarter of a second latency for chat messages between the Doctor and patient. Moreover, PubNub is also a HIPAA compliant service. These features make PubNub an ideal choice for building the communication backbone for SF Medic.

  1. Amazon Comprehend Medical: It is an NLP engine that can detect tonal and contextual features of text. Amazon Comprehend Medical can be used to identify medical terminology in text. SF Medic uses Comprehend Medical to extract medical terms from text messages. Extracted terms are processed and made ready for the Doctor’s reference.
  2. Amazon Translate: It is a neural language translation service. Its neural translation uses deep learning models to deliver more accurate and more natural-sounding translation than traditional statistical and rule-based translation algorithms.
  3. Amazon Transcribe Medical: Amazon Transcribe makes it easy for developers to add speech-to-text capability to their applications. Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately.
  4. Amazon Polly: Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products.
  1. Alerting the doctor and patient if the Doctor recommends a drug that the patient has a known allergy.
  2. Alerting the doctor and patient if the Doctor recommends a drug that has a known contraindication to a drug that patient is on.


Traditional telemedicine has tremendous potential to improve the continuum-of-care cycle by improving the quality of life, process efficiency, and cost-effectiveness of healthcare services. It is especially beneficial for underserved communities in rural or remote areas. However, is limited by the dependency on technology and high bandwidth internet which reduces the success probability by a big margin.



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