AI Led 360-Degree Patient View

In India, there is a scarcity of skilled doctors to patients. According to the Indian Journal of Public Health(2017 edition), there are 4.8 doctors who had been practising per 10,000 of the population which is expected to grow 6.9 per 10,000 of the population by the year 2030.

But according to WORLD HEALTH ORGANISATION minimum doctor-patient ratio is 1:1000.

To tackle this challenge of uneven ratio of doctors per patient, AI is the solution.

This is how AI helps Indian Healthcare Industry to deal with many issues.

TOP APPLICATIONS OF AI IN HEALTHCARE INDUSTRY

1. Analysing Healthcare System-

In many countries 95-97% of health care invoices are digital.AI is used to highlight the error in treatment, workflow inefficiencies etc. AI will use to save more time, lower costs and increase accuracy.

2.Medical Records Management-

Managing Medical Health Records is the most widely used application of Artificial Intelligence as data compilation, data management is the first step in the healthcare industry so AI will give faster and more consistent access.

3.Virtual Care Assistants-

According to the study Virtual care(nursing), the assistant could save $20 Billion annually from interacting with patients to effective care setting.

Virtual care assistant is 24*7 available, they can communicate with the patient, answers them quickly and monitor the patient. AI is also used in giving voice assistant to the patient.

4. Analysing Image-

The human takes too much time to analyse an image but using machine learning that can analyse 3D scans 1000 times faster than a human.

AI LED 360-DEGREE PATIENT VIEW

Tools of AI can provide quick service, helps in diagnosing the issue and genetic disease can be identified as when saving a minute mean saving lives.

5. Doing Repetitive jobs-

X-Rays, CT scans and many another test can be done more accurately by Robots and with the help of AI a doctor can save time and precise report can be check avoiding mistakes.

6.Robotic-Assisted Surgery-

According to the research approximately there will be 21%of reductions in patient’s stay when a robot analyzes data from pre-op medical records to guide a surgeon’s instrument during surgery. Robotic surgery helps patient to heal from minor incisions as robotic surgery are “minimally invasive”.

7.AI Vital Sign System-

The AI Vital Signs System will automate the Health Data Monitoring sensed from the patients; enabling a Continuous time and Real-time Vital Signs Monitoring from and to Anywhere, Anytime connecting Medical Drs, Nursing staff, physicians, and Patients in a Sophisticated Healthcare Information System. When this smart wireless Mobile Vital Signs Monitoring System is developed and integrated with intelligent Adaptive-Statistical-AI-Machine Learning, the potential of such system is an amazing and sophisticated Health Information System that will improve current healthcare by reducing morbidity and mortality, and bring Clinical Care to a new advanced level of smart communication and interaction among Medical Drs, Clinicians,Nurses, and Patients. The main R&D objectives of the AI Vital Signs Monitoring System is to provide continuous, real-time, high-precision vital signs such as ECG, HR, PAR, SPO2, HBP-LBP-ABP, R-BR, and ST that will be a digital signal and statistically processed, analyzed, detect, correlate, prognosis, and diagnose abnormalities and critical events for cardiovascular, cancer, chronic diseases.

8.Fraud Detection-

In the healthcare industry, we see a number of cases of fraud like illegal medical billing practices, multiple claims are given by providers for the same patient, patient identities are stolen to claim reimbursement. All this results in an increase of 3-6% to annual healthcare cost. But using AI we can identify inconsistencies, detect and prevent improper payments.

Healthcare Future with AI (An Outlook)

IN the present days only small steps had made by AI but in coming future AI would be the game-changer for healthcare-industry. The best opportunity for AI over the next few years are hybrid models where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but retain ultimate responsibility for the patient’s care. This will result in quick adoption by healthcare providers by reducing perceived risk and start to deliver measurable improvements in patient results and operational efficiency at scale.