Exclusive Interview with Pinaki Dasgupta, Founder and CEO of Hindsait

With each industry thriving in the era of cutting-edge technology, optimization, accuracy, and efficiency become more vital than ever. Especially in the healthcare sector, the clinical and hospital equipment and other medical procedures are becoming even productive and flexible with the merger of Artificial Intelligence and medical facilities.

While the healthcare industry generates a huge gauge of data regularly, its management becomes a matter of serious consideration for industry professionals. Hindsait is one such company with an AI Platform that enables improved quality of care and prevention of unnecessary services, errors, and fraud.

In an exclusive interview with Analytics Insight, Hindsait Founder and CEO Pinaki Dasgupta hails the AI SaaS platform that applies AI and predictive analytics to large healthcare datasets, helping payors and providers improve patient health at a much lower cost.

Kindly brief us about the company, its specialization and the services that your company offers.

Hindsait is headquartered in New Jersey. Established in 2013 by a multi-disciplinary team consisting of a visionary CEO, a renowned Astrophysicist as CTO and a pool of expert data scientists, doctors and nurses. The company's Artificial Intelligence technology that includes a sophisticated Clinical Natural Language Processing platform and Robotic Process Automation (RPA) toolset, enables Payors to reduce unnecessary health services, human errors, biases, wastage, and fraud.

Hindsait's capabilities start with its ability to ingest and translate "unstructured free text" inpatient charts within the context of clinical guidelines and business rules. The AI platform's RPA toolsets along with its CNLP enabled extractions to drive machine learning and predictive analytics. Hindsait's AI algorithms can quickly sift through thousands of pages of medical records, evaluating, scoring, and accurately predicting medical necessity of diagnostic tests, procedures or medications during pre-service as well as post-service medical necessity reviews and recoupment audits, saving the health plans several tens of millions of dollars cost of care savings, besides administrative review costs.

The company has pioneered the application of an AI-enabled MEDICAL NECESSITY REVIEW solution that has been tested and proven with 5+ years of experience covering 30+ million Commercial, Exchange, Medicare, and Medicaid health plans. Its achievements are industry leading and will be the basis of continued company success.

Please brief us about the products/services/solutions you provide to your customers and how do they get value out of it.

We offer efficient clinical cost management for health insurance companies and benefits managers during pre or post service medical necessity reviews like "Pre-Certification", 'Prior-Authorization' and "Payment Integrity Audits".

To illustrate the point, let's review how Health Insurance Companies or Benefits Managers, collectively called 'Payors" eliminate unnecessary healthcare, wastages, errors, and frauds. Payors often run Pre or Post Service clinical necessity reviews and audit to manage the overall cost of care.

Such reviews and audits are often performed by the expensive and 'hard to recruit' clinical staff like board-certified Physicians, Registered Nurse (RN) and/or Pharmacists. During such clinical reviews, clinical staff, appointed by Payors, typically hand-comb Patient Charts or 'free-text' responses received from Providers, to validate clinical necessities of Diagnostics Tests, Procedures, Medications, DMEs and so on.

To perform tasks such as 'Prior-authorizations reviews', 'Payment Integrity Audits' a human reviewer may need anywhere from 10 minutes all the way to 180+ minutes to hand-comb often a lengthy Patient chart to find relevant clinical evidence buried in that document. Now the same task of finding evidence of medical necessities, based on established clinical guidelines, could be performed by an AI algorithm almost in near real-time, often without any human touch and often more reliably than humans!

That's not all. Even the best-trained clinical staff has her own decision biases and or error rates, but decisions from AI Algorithm are often more consistent and reliable than humans and accuracy of continuously learning AI algorithm gets better with time, learning from newer dataset and information.

Hindsait's AI technology has generated a significant incremental cost of care savings by automating and streamlining such an error-prone manual review process and has demonstrated reliability in recommending continuously improving decision-consistencies.

What is your leadership mantra?

At Hindsait, we believe in a participatory leadership style in a non-hierarchical organizational setting, where each individual in the team is valued for her/his contribution and the subject-matter expertise s/he brings to the table. Given that the US healthcare system is very complex and for us to deliver a quality AI-led 'clinical decision support' platform to our clients, we adopt 'all-hands-on-the-deck' approach to execute on our mission. On a typical day, Hindsait's Data Scientists work closely with the clinical team consisting of Doctors and Nurses, along with the industry experts and business leaders to deliver a high-quality product – a product that enables efficient and consistent clinical decision-making, based on established clinical guidelines and protocols.

All Hindsait team members enjoy flexible work hours and may choose to work either from home and or from fixed workplace based on his/her personal preference and work-life balance. At Hindsait, there is no fixed work hours, no upper limits on the number of holidays one can avail, no sign-up sheets to start or end the workday. We use cloud-based collaborative tools to communicate, share and learn and execute in real time. All our strategy sessions take place in faraway retreats, where we unwind, go together for a team hike, light a campfire under the open night sky to revisit and redefine our short term and long-term goals and strategies, every 6 months.

What is your biggest USP that differentiates the company from competitors?

At Hindsait, we are client and industry focused. We listen to our clients, we empathize with the enormity of the challenges our clients face every day to bring in high-quality healthcare that eliminates unnecessary spend, wastage, and inefficiencies while improving the patient experience and outcomes.

At Hindsait, we believe that we will be miles ahead of our competitions, if we help solve our client's key challenges and concerns elegantly each and every day and enable our clients to go the extra mile in delivering high-quality healthcare by using our clinical decision support platform that improves clinical cost management.

Kindly mention some of the major challenges the company has faced until now.

Healthcare being one of the most regulated industries, structurally it is risk-averse and slow to adopt newer innovative technologies. So, if you happen to be one of the tech start-ups touting a brand new approach to clinical decision-making for medical necessities, using innovative technology such as CNLP, RPA, ML, predictive analytics, etc., collectively known as AI technology and you need access to Protected Health Information (PHI) -the un-redacted actual patients' medical records, congratulations! You have just created an insurmountable barrier to enter your core market.

That's how it felt @Hindsait when we had started our journey in 2013. In the last 5.5 years, we have learned a lot and we were fortunate to have come across quite a few amazing healthcare industry leaders, professionals, who had believed in our technology and us. They are the real unsung heroes behind Hindsait's success. They are the early adopters of Hindsait's groundbreaking technology and have reaped the rewards too. Their active support and sponsorship with a healthy dose of skepticism made us architect one of the most secure architecture and technology platforms that can process any unstructured free texts be it a fax or a PDF document and extract all meaningful clinical extractions of context-sensitive key concepts, like diagnoses, chief complaints, quantities, associated dates of services, lab values, medications, procedures performed and so on to reach  predicted medical necessity outcomes based on established clinical guidelines.