Want to deliver exceptional patient care and increase your revenues?

Leverage Data Analytics & AI to achieve a single view of the patient, gain patient intelligence and build a patient recommendation engine for improving overall healthcare delivery.

Data Discovery is the first step to analyzing and extracting actionable insights from data that help enhance decision-making and deliver better customer service. Are you ready to tap the full potential of your data and leverage it for your business growth?

We are conducting a personalized workshop where our experts will answer your questions related to your data initiatives and help you develop a three-pronged approach to efficient data discovery. We can also enable you to deploy Single view of patient, Patient Intelligence Framework, or Recommendation Engines that helps you grow your business.

Free Personalized Workshop

To Improve Patient Experience and Engagement

AI Transforming Healthcare

Using AI algorithms capable of learning and drawing inferences from complex data, the healthcare sector today is delivering highly personalized services to patients through meaningful and actionable insights.

Our Solution Approach for Patient-centric Journey Map

Single View of Patient

  • Enables the provider to track the patient history across all the services they have solicited.
  • Allows the provider to get additional patient intelligence in terms of improving patient engagements
  • Helps the provider to use patient feedback to proactively address issues for other patients.
  • Improves operational efficiency within the provider organization
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AI-driven Patient Intelligence

AI-powered patient Intelligence framework can help healthcare providers achieve:

  • In precise early diagnosis and prevention of diseases
  • Rapid image recognition, symptom checking, and risk stratification to personalize health screening in the field of diagnostics
  • Generation of new medicines by algorithms that can compare thousands of drug compounds in a matter of weeks instead of years
  • Administrative workflow support
  • Virtual nursing assistants
  • Clinical use cases
  • Fraud detection and many more

AI-driven Patient Recommendation Engine

KANINI’s Patient Recommendation Engine framework can significantly improve customer experience, revenues, and engagement. It can help you: 

  • Establish a Long-term Provider-Patient Relationship
  • Make Accurate Healthcare Decisions
  • Get Comprehensive Patient Summaries
  • Predict Patient No-shows
  • Use Patient Feedback to Deliver Quality Care
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Register for a Personalized Workshop

To achieve these results, you need a robust data platform built on a world-class data discovery process that involves gathering data from disparate systems and locations as the preliminary step to deriving meaningful and actionable insights.

You can also download this Data Discovery whitepaper that our solution team has put together. 

transformations@kanini.com
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Patient Recommendation

Problem Statement
  • The Client required a feature where existing/new patients can log in to the provider’s website to book an appointment.
  • Most of the times, patients are not sure of their symptoms and where should they go – which hospital and doctor – for treatment.
  • On the other hand, hospitals are not sure about the probability of patient no-shows while booking an appointment.
Patient Recommendation
Patient Recommendation engine
Techniques

Django WEB UI

  • Users can select one or multiple symptoms from the list given
  • Symptoms can be sent to the ML engine through integration layer APIs
  • User can book the appointment

Integration Layer

  • Based on the user’s input invokes the required recommendation engine 
  • Built on microservices architecture 

ML Processing

  • Recommendation engine to identify the suitable hospitals for the symptoms
  • Recommendation engine to identify the suitable doctors from the selected hospital
Outcome

Based on the symptoms selected by the user, the ML recommendation engine recommends the top three hospitals and upon selecting one of those three hospitals, it recommends the top three doctors, and after selecting one of those three recommended doctors, the engine recommends the time slots that have the least probability of patient no-shows.