Healthcare

Consulting-led, Solution-focused

KANINI being an Industry-led organization, Healthcare is a critical and significant segment for us. Adopting a consultative approach, backed by our domain expertise, we have delivered engagements and have developed several accelerators and artifacts across the spectrum of the patient-centric journey map.

Transforming Healthcare - One Solution at a Time

Deep-rooted Partnerships

We believe in establishing deep-rooted partnerships with our clients and deliver solutions to address business problems in an ever-evolving technical landscape and business domain.

Strong Delivery Track Record

We have 150+ digital implementation success stories to demonstrate our strong technical acumen and excellent delivery track record.

Solutions and Accelerators

We support our Healthcare customers with digital engineering solutions and accelerators, including Telehealth Platform, Intelligent Automation, and Connected Hospital.

Modern Engineering

We capitalize on our proven expertise in Cloud Computing, Agile Product Engineering, Data Analytics & AI, Automation, IoT, and custom solutions.

Healthcare Segments

Providers

Payers

Pharmacies

Intermediaries

healthcare consulting solutions

Our approach is to view Healthcare business challenges through the prism of a patient-centric journey and consult with our clients and alliance partners to provide effective solutions.

healthcare consulting solutions

By solving business problems and keeping the patient at the core of everything we do, we bring uniqueness to our consulting, solutioning, and delivery model.

healthcare consulting solutions

We have developed an efficient operating model that traces a patient’s journey for various persona-episode combinations.

Our Solutions

Medical Code Extraction for SDOH

Map medical codes with the identified key phrases of EHR accurately

Patient Recommendations

Help patients with suitable hospital and doctor recommendations

Appointment Booking and Cancellation

Streamline appointments and resolve patient queries with a health chatbot

AI-driven No-show Prediction

Manage healthcare services better and improve patient experience

Denial Management

Save costs by reducing the time and effort in claims processing

HIPAA Compliance Solutions

HIPAA Compliance Solutions

KANINI’s HIPAA solutions helps you get successfully compliant with accurate assessment and efficient implementation support.

Words We Love. Testimonials.

What Clients have to say About Us

Insights. Alcove Of Assembled Resources.

CASE STUDY

Application Modernization

CASE STUDY

AI-powered Automated Medical Coding Platform

WHITE PAPER

The Rise of Value-based Healthcare

BLOG

The Role of Technology in Addressing Public Health Emergencies and Policy Changes

CASE STUDY

An EHR Solution to Automate Doctor's Notes and Medical Records Summarization

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.

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.

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.