The big problem of healthcare data in India is that most of it remains untapped and unstructured, and Pachar is working towards standardising and optimising this mammoth data before it can be used to improve scientific knowledge.
The current health system in India provides limited resources to patients. The result is that they remain disengaged, uninformed and frustrated, with long waiting times, fragmented care, and duplicate requests.
Well-informed patients, who are educated about their health condition, in comparison, have lower readmission rates, better outcomes, and lower out-of-pocket cost, says Rajesh Pachar, CTO, and Co-Founder of homegrown clinical intelligence firm THB.
According to Pachar, AI-backed digital health technologies encourage self-monitoring, increase engagement between patients and healthcare providers, promote behaviour change and improve patients’ understanding of diagnoses and treatment plans.
“As better-informed patients mean lower healthcare costs, THB facilitates patient empowerment by leveraging technology to generate personalized clinical recommendations, leading to positive health behaviours and improved health outcomes,” he says.
Using analytics to transform healthcare delivery
Indian patients are usually noncompliant when it comes to healthcare. For instance, THB found that diabetes patients have an average of up to 2 visits per year as against the recommended frequency of 3-4 visits in a year. They often miss comprehensive care. For instance, 2 out of 3 diabetic patients are detected with CKD (Chronic Kidney Disease) in the 2nd or 3rd stage.
For this, THB analysed data of more than 13,000 Indian patients using a mathematical model to predict the probability of chronic kidney disease.
Pachar asserts that there is a negligible tracking of health records for patient satisfaction rates and predictive analysis can be a powerful tool to help minimize delays, unnecessary treatments, and unexpected costs. Higher risk patients can be identified early, and preventative intervention can begin earlier in the process.
“Deaths related to misdiagnosis, treatment errors, and malpractice can also be reduced,” says Pachar.
Pachar is also working on AI-based platforms that track individual personas of clinicians and patients.
“Through these engines, we’ll be able to track clinicians’ preferences in their respective therapy areas to allow intelligent discussions on concerns related to diagnosis, patient journey, and treatment options,” he avers.
“We also keep a track of patients’ health data for risks and triggers and encourage them to go for early diagnosis, stay compliant, and motivate them for a comprehensive treatment,” Pachar adds.