Implementing Ambient Clinical Intelligence
Don’t fret if you are short of the $19.7 Billion that Microsoft is paying to acquire Nuance. There are over 850 libraries available for you to implement Ambient Clinical Intelligence use cases.
Ambient Clinical Intelligence encompasses leveraging AI across the provider lifecycle, enabling automated clinical documentation, improving patient data, and increasing the time spent on patient care activities. Use cases across telehealth, radiology, medical events prediction, and payer adjudication can be enhanced with Ambient Clinical Intelligence.
To help jumpstart your Ambient Clinical Intelligence applications, we have assembled a diverse set of software components.
- stanza by stanfordnlp, Medico by pranayjoshi, PrescAI by 090max are components that help you experiment on speech to text and NLP with specialization on healthcare.
- If you would like to compare the speech to text capabilities of the hyperscale cloud providers, try Cloud Speech-to-Text API by Google, Train-Custom-Speech-Model by IBM, and amazon-comprehend-medical-fhir-integrationby aws-samples.
- If you further want to experiment on medical event prediction and automated clinical actions, try ehr-rwe by som-shahlab, SequentialPhenotypePredictor by wael34218, MetaPred by sheryl-ai, CogStack-SemEHR by CogStack, clicr by clips.
- To support these applications and experiments, you would need an EHR/ EMR to connect to, such as openemr by openemr, and tons of patient data that you can synthesize from components such as synthea by synthetichealth.
kandi Collection: Implementing Ambient Clinical Intelligence
Discover more exciting components to jumpstart your application development on kandi.
Happy Reuse!
