{"id":884,"date":"2023-03-22T13:07:36","date_gmt":"2023-03-22T13:07:36","guid":{"rendered":"https:\/\/iijpl.com\/en\/?page_id=884"},"modified":"2023-03-22T13:09:44","modified_gmt":"2023-03-22T13:09:44","slug":"health-bot","status":"publish","type":"page","link":"https:\/\/iijpl.com\/en\/health-bot\/","title":{"rendered":"Health Bot"},"content":{"rendered":"\t\t
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Health Bot<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The democratization of Artificial Intelligence and Machine Learning technologies, along with the ubiquity of smartphones, IoT devices and other smart appliances provide us with an enormous opportunity to make healthcare more efficient.<\/p>

It’s a big challenge. But we believe technology – specifically cloud, AI, and Learnings along with collaboration and business optimization tools will be central to health care transformation.<\/p>

Praja Vaidya<\/span>\u00a0, a new initiative from theDigitalworkers to transform health care, will deeply integrate greenfield research and health technology product development, as well as establish a model for strategic health industry partnerships. Through these collaborations between healthcare partners and BOT AI ML (theDigitalworkers’s AI and research organization), our goal is to enable a new wave of innovation and impact using HealthBOTs that leverage Artificial Intelligence, Machine Learning, IoTs and global-scale cloud.<\/p>

Our Digital Health Assistants will enable both individuals and healthcare professionals to better manage outcomes and also allow personal health records to be maintained in a secure way, while also being easily accessible and shareable.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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OUR DIGITAL HEALTH ASSISTANTS<\/h2><\/div><\/div><\/div>
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PRE CONSULTATION DIGITAL ASSISTANT<\/h3>

The end user describes a symptom to the HealthBOT; for example, “I have a headache”, and the bot will engage in a conversation that helps the user to understand the symptoms and suggests how to react. Loaded with general information about conditions, symptoms, causes, complications, and more, the digital assistant can provide information about medical conditions, symptoms, causes, and complications; for example, “information about diabetes,” “what are the causes of malaria,” “tell me about the complications of arthritis.”<\/p>

This conversation can be further escalated to finding relevant doctors, scheduling appointments or receiving guidance on whether immediate medical attention is required etc.<\/p><\/div><\/div><\/div>

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PERSONAL HEALTH RECORDS MANAGER<\/h3>

The end user can upload health records including clinical and\/or lab reports, images such as X-Ray or MRI etc. The user can even take a picture from their phone and annotate on the image, in case a digitized version of the document is not available. These records are then stored on the cloud and available on demand. The health records can be shared with healthcare professionals with the user’s consent. This data will also be used by our machine learning algorithms to learn and deliver personalized health tips.<\/p><\/div><\/div><\/div>

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POST CONSULTATION DIGITAL ASSISTANT<\/h3>

The Post Consultation Digital Assistant acts as a digital nurse, that can provide individuals with:<\/p>

  • Timely reminders to follow the prescription, including but not limited to ingestion of medicines, exercise, diet etc.<\/p><\/li>

  • Personalized, simple and short health tips based on the person’s health conditions.<\/p><\/li><\/ul><\/div><\/div><\/div>

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    DIAGNOSIS DIGITAL ASSISTANT<\/h3>

    The end user provides the findings of a lab test and\/or consultation along with all the related documents to the diagnosis bot. The bot will then look at all the data about conditions, symptoms, causes, complications, and more, to provide further guidance including diagnosis, next steps etc. For example, if a user has just been diagnosed with arthritis, our diagnosis assistant can help validate it based on the clinical data provided and provide suggestions and tips.<\/p><\/div><\/div><\/div>

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    FRONT DOOR KIOSK SYSTEMS<\/h3>

    The kiosk system acts as a digital concierge, adding to patient convenience, allowing them to identify themselves upon arrival and perform the following functions:<\/p>

    • Appointment check-in<\/li>
    • Record symptoms along with vitals such as weight, height, BP etc.<\/li>
    • Facility directions<\/li>
    • Prescription refill ordering<\/li><\/ul>

      The kiosks use voice and touch based interfaces that are easy to interact with and employ clinical grade IoT devices to collect the vitals.<\/p><\/div><\/div><\/div>

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      CONSULTATION MACHINE LEARNING SYSTEMS<\/h3>

      Consultation machine learning systems employ an array of modern devices to record and observe the interaction between the doctor and patient. It will create a digital record of the conversations and by applying machine learning, we will also provide quality metrics regarding the care and guidance provided.<\/p><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      Health Bot The democratization of Artificial Intelligence and Machine Learning technologies, along with the ubiquity of smartphones, IoT devices and other smart appliances provide us with an enormous opportunity to make healthcare more efficient. It’s a big challenge. But we believe technology – specifically cloud, AI, and Learnings along with collaboration and business optimization tools […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-884","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/pages\/884","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/comments?post=884"}],"version-history":[{"count":7,"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/pages\/884\/revisions"}],"predecessor-version":[{"id":892,"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/pages\/884\/revisions\/892"}],"wp:attachment":[{"href":"https:\/\/iijpl.com\/en\/wp-json\/wp\/v2\/media?parent=884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}