Want to improve patient experience by delivering exceptional care?

Leverage Data Analytics & AI for your Healthcare business to meet patient-centric care delivery goals.
Advanced AI technologies and tools are enabling healthcare enterprises worldwide to achieve a single view of the patient, gain patient intelligence, take informed decisions driven by a  recommendation engine, and improve overall health care delivery.

If you are planning to embark on this journey of leveraging data analytics and AI in your healthcare business or have already started and need help along the way, look no further. Reach out to us!

Build a Successful Data & AI Strategy

To Improve Patient Experience and Engagement

How AI is Transforming Healthcare

Unlock the full potential of your patient data with us! 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 team of experts can guide you by bringing the right use cases of AI that perfectly fit into your business model.
Analyzing and deriving actionable insights from your healthcare data through Data Analytics & AI can help you –
  • Improve decision-making
  • Achieve positive patient outcomes
  • Improve the performance of your enterprise
  • Enhance operational efficiencies
  • Manage costs
  • Interpret industry trends and be better prepared for the future
  • And much more.
Our Solution Approach for Patient-centric Journey Map

What We Offer

Our consulting-led approach in leveraging data analytics and AI has been enabling healthcare organizations to utilize the right AI solutions for Single view of patients, Patient Intelligence, Patient Sentiment Analysis, Appointments and Patient No-shows Management, Patient Payment Experience, Claims Denial Management, Document Intelligence, Medical Coding, and more, and stay relevant in this evolving healthcare landscape.

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
Talk to us

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
Know More

Medical Code Extraction for SDOH

Map medical codes with the identified key phrases of EHR accurately

Know More

Patient Recommendations

Help patients with suitable hospital and doctor recommendations

Know More

Appointment Booking and Cancellation

Streamline appointments and resolve patient queries with a health chatbot

Know More

AI-driven No-show Prediction

Manage healthcare services better and improve patient experience

Know More

Denial Management

Save costs by reducing the time and effort in claims processing

Know More

Speak To Us Today

KANINI is a trusted IT partner for several Healthcare organizations, guiding them in the right direction to data maturity using the most appropriate data analytics & AI tools and technologies.
Get in touch with us today for healthcare industry-specific advice around leveraging data analytics & AI in your healthcare business.
transformations@kanini.com
Contact us
© 2024 KANINI Software Solutions | All Rights Reserved | Privacy Policy

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.

Intelligent Medical Code Extraction

Problem Statement
  • Existing healthcare EHR documents are often very unstructured and may contain notes on patients’ social and behavioral aspects and their family history.
  • The current process involves going through the entire EHR document and then mapping the notes with respective medical code. This process is tedious and time-consuming.
Medical Code Extraction
Medical Code Extraction
Techniques and Solutions

Django WEB UI

  • Uploads EHR documents
  • Supports multiple types of docs (pdf, doc, txt, out)
  • Displays the extracted text against the respective medical codes

ML Pre-processing 

  • Extracts the text from the document
  • Stop Word Removal
  • Lemmatization
  • Sentence Detection
  • Removed Numbers | Special Characters | Duplicates | Email | Name

ML Processing

  • Multiple custom-trained models to check context similarity
  • Uses medical code descriptions and EHR documents as input to the model
  • Calculates the matching confidence score for extracted text and matched medical code
Build a solution that can
  1. Identify the ICD/medical codes
  2. Identify the key phrases in the EHR document against the ICD/medical code descriptions
  3. Use NLP to identify and contextually match the key phrases with content
    1. Extract portions/phrases from a document that match the key phrases along with the matching score
    2. Create a top N recommended set of content from the document that has the highest matching score
    3. Display matching content with the highest score with mapped medical code in a UI
Outcome
Based on the context similarity model built, any new EHR document can be processed, and the outcome will be the list of extracted phrases from the document along with the mapped medical code and confidence score.

Appointment Booking and Cancellation

Problem Statement

In the client’s current system, call center agents were answering all user queries over the phone. The client was looking for an agent-based chat system to handle patients’ queries efficiently. They were ready to implement a health bot.

Appointment Booking and Cancellation
Appointment Booking and Cancellation
Techniques

Azure Health Bot

  • Bot can be integrated with any front-end UI.
  • Can easily initiate the chat with agents directly
  • Easy to configure the welcome message, chat keywords, etc.
Outcome

Azure health bot provides a framework that is easy to integrate, with minimal configuration and code, with the client’s existing systems. 

Patient No-show Predictions

Problem Statement
  • Patient no-shows cost healthcare providers nearly $150 Billion annually. (MedBridge Transport Report)
  • 3.6 Million patients miss appointments citing transportation issues
  • No-shows cost a single physician medical practice $150k annually
  • One physician practice with multiple physicians can have 14,000 timeslots go unfilled each year
Patient No-show Predictions
AI-driven No-show Prediction
Techniques
  • AI model predicts the occurrence of patient no-shows based on various parameters by learning from past data
  • It recommends the provider’s operations team about the no-show probability
  • Operations team works towards identifying a better slot that works for both the patients and the providers
Outcome
  • Costs saved by arresting no-shows
  • Improved patient experience
  • Better management of physician services

Denial Management

Problem Statement

Denials Index

  • Claim denials increased by 23%; 11.1% on initial submission
  • Denials have risen by 11% since the onset of the pandemic
  • 86% of denials are avoidable
  • Current denials systems are very rules-based and do not help in reducing the denials
Denial Management
Denial Management
Techniques
  • AI can help in understanding top denial patterns and help providers fix them at the submission stage itself
  • AI can classify denials based on root causes like registration/missing invalid data/service not covered, etc.
  • AI can also help in providing the probability of denial, thereby helping providers to better handle this with patients.
Outcome

Saved costs by cutting down claims processing time and efforts.