LHIF 5th Edition: Registration Form

Use Case Code : AI_POST_SURG

Use Case Description:

Expected Outcomes:

• Risk Prediction Model: An AI model to predict the likelihood of post-surgical complications based on patient data.
• Personalized Care Pathways: Tailored care plans for high-risk patients to minimize complications and reduce the length of stay.
• Remote Monitoring: Post-operative monitoring using wearable devices or mobile apps to detect early signs of complications.
• Early Intervention: Proactive interventions to address potential complications before they escalate.

Use Case Code : AI_Clm

Use Case Description:

1. Enhancing claim management processes by predicting claim rejections, analyzing disallowances, and performing time-based analyses of claim approvals. Prediction of the likelihood of a claim being rejected based on various factors such as claim amount, type of service, patient history, provider, and coding accuracy.
2. Flag high-risk claims, allowing for corrective actions before submission.
3. Identify common patterns and reasons for disallowances such as coding errors, lack of documentation, or policy non-compliance and provide detailed insights into the root causes of disallowances, helping providers address systemic issues.
4. Analyze historical approval times, identifying trends and bottlenecks in the process and predict the time required for future claim approvals based on past data and current conditions.

Expected Outcomes – Deliverables:

1. More revenue cycles predictable for the orgnaisation.
2. Improved accuracy in claim submissions.
3. Reduction in the number of disallowances.
4. Optimize the time taken for claim approvals
5. Reduce the number of rejected claims, leading to higher acceptance rates.
6. Data-driven insights from AI to improve claim outcomes.

Any other inputs:

1. The solution provider should have clients in the healthcare or insurance sectors, with case studies or testimonials that demonstrate successful implementations.
2. The solution should be scalable to handle increasing volumes of claims as the organization grows.
3. The provider should ensure seamless integration with the existing claim management systems, HIS, and other relevant platforms

Use Case Code : ONCO_DRG

Use Case Description:

Chemotherepeutic agents act against the rapidly dividing cancer cells. As collateral damage happens, the patients develop several side effects. Such side effects may vary from nausea, vomiting to a major cardiac event. Based upon the exisitng knowledge from published scientific literature, we try to pre-empt such toxicities. This may mean not giving a particular drug, or decreasing dosage or starting other medicines emperically to counter the effects. There is merit in being able to evaluate these effects, not only rare catastrophic ones but also routine ones with higher grades. We want to evaluate if AI can predict such toxicities to various chemotherepeutic drugs.

Expected Outcomes – Deliverables:

Prediction of grade 3/4 toxicities related to commonly used drugs to treat various cancers. Input variables will include clinical and demographic data, co-morbidites, various labarotary parameters

Any other inputs:

Before proceeding we can discuss about the drugs we would like to cover.

Use Case Code : DR_ArMD

Use Case Description:

The solution needs to clinically validate diabetic retinopathy and age related macular degeneration based on AI-driven software algorithm on fundus images for analysis for the detection and diagnosis . Special emphasis on fundus images from android phone with attachment

Expected Outcomes – Deliverables:

Diabetic retinopathy and Age related macular degeneration Grading as referable /non-referable.

Use Case Code : LLM_PC

Use Case Description:

In rural areas, conducting eye-checkup camps poses challenges due to the high costs associated with hiring patient counselors. Typically, only one human counselor is available, leading to inefficiencies when dealing with a large number of people. To address this issue, a patient counseling chatbot based on Large Language Models (LLMs), based on English and specifically crafted for the Tamil language and tailored to the domain of Ophthalmology is needed. It should capture spoken language inputs, comprehend user queries, retrieve relevant medical information, and synthesize natural-sounding speech for responses. The key innovation lies in the adaptability and accessibility of the chatbot. It must be able to get deployed on any device.

Expected Outcomes – Deliverables:

The solution must not only enhance the efficiency of counseling sessions but also aim to revolutionize patient counseling practices, ultimately improving healthcare accessibility and outcomes in rural communities.

Use Case Code : EMS_Disp

Use Case Description:

EMS / Ambulance service dispatch automation, a solution through which the customer can have interaction based on given input data and the AI triages the patient requirments to decide on the the type of ambulance and resources required, provides information interms of cost and TAT, pushes structured information interms of demgraphic details, address, clinical issue to the allow end user to take further action

Expected Outcomes – Deliverables:

Automated, fast and accurate discharge summary generation in a format as advised by the organisation

Any other inputs:

The solution needs to integrate with the exisiting EHR

Use Case Code : DSA

Use Case Description:

A robust and accurate automated solution which enables hospitals to prepare the final discharge summary of the patient within a matter of minutes once the discharge intimation is given. The solution must be capable of assimilating, analyzing and putting it in desirable final format consisiting of the clinical data, ward notes, cross consultation, investigation and so on for the enitre duration of patientb stay in the hospital.

Expected Outcomes – Deliverables:

Automated, fast and accurate discharge summary generation in a format as advised by the organisation

Any other inputs:

The solution needs to integrate with the exisiting EHR

Use Case Code : EMS_CDSS

Use Case Description:

Solution which can provide support to the paramedics with clinical decisions in out of hospital scenarios

Expected Outcomes – Deliverables:

Paramedics should be able to take timely actions in prehospital setup with help of CDS.

Use Case Code : AI_CC

Use Case Description:

1. The purpose of this use case is to enhance customer service operations by implementing an AI-based contact center. Main Flow:
2. Customer Initiates Contact: The customer contacts the center via a preferred channel (e.g., phone, chat, email).
3. Customer Engagement: The chatbot or virtual assistant engages with the customer to identify the issue or request.
4.  Speech Analytics: Speech analytics tools analyze the conversation in real-time to assess customer sentiment and intent.
5. Automated Call Routing: Based on the customer’s inputs and the analysis, the AI system routes the call to the appropriate department or CSR if needed.
6. Predictive Analytics: Predictive analytics tools provide insights and recommendations to the CSR or directly to the customer based on historical data.
7. Automated Resolution: The customer’s issue is resolved either through AI-powered tools or by a CSR, and the interaction is documented.
8. Post-Interaction Analysis: The system conducts a post-interaction analysis to evaluate the performance of AI tools and customer satisfaction.
9. Data from the interaction is stored and used for continuous improvement of AI tools and processes.

Expected Outcomes – Deliverables:

1. Enhanced Customer Experience: Faster Response time and personalised interactions with customers
2. Increased operational efficiency: Reduced workload for CSRs and Optimized call rounting
3. Improved Decision making: Data driven resource allocation and proactive redressal of customer needs
4. Cost Savings: Reduced employee cost and reduce call duration
5. Scalability of contact cemter
6. Auotmated Compliance Monitoring and Secure Data Management

Any other inputs:

Use Case Code : AI_USG

Use Case Description:

• Sonographer guidance solutions to aid in study acquisition
• Automated reporting solutions that compile measurements and findings into a standardized summary
• Automatic identification of suspect nodules
• Characterization of suspicious findings (e.g. BI-RADS)
• Follow-up examination: Identification of already known and new nodules
• Follow-up examination: Tracking nodule size changes over time
• Diagnosis of Cardio or Neuro vascular diseases

Expected Outcomes – Deliverables:

• FDA and CE clearance preferred but regulatory expertise is available to support in establishing clearances.
• NMPA (China) and KFDA (Korea) cleared startups may also apply
• Clinical evaluation/studies
• Prefer startups with academic or institutional established relationships to acquire clinical data

Any other inputs:

• Must demonstrate financial backing to sustain operations for at least 3 years.
• The company needs to give us confidence in their ability to stay in business until the solution is commercially released.

Use Case Code : ONCO_THYR

Use Case Description:

Thyroid nodules may be present in 30-40% of the general population. Majority of these nodules are benign, but in 5-10% cases these may be malignant. Generally, benign nodules don’t need any intervention. We generally judge whether a nodule is benign or malignant based upon USG and FNAC findings. Molecular tests have also come up in this domain. Still, we end up operating upon a large number of cases who eventually turn out to be benign. These are the patients who could have potentially avoided surgery. There is a need to be able to find out which nodules are at higher risk of malignancy. We want an AI based tool to predict risk of malignancy in thyroid nodules using clinical details (age, gender, etc), fine needle aspiration cytology (FNAC) and ultrasounf (USG) features

Expected Outcomes – Deliverables:

Better risk prediction of malignancy in thyroid nodules. This will help avoid surgery in many benign cases.

Any other inputs:

FNAC slides will need digitalization