SUMMARY
With its AI-powered platform, Delhi-based healthtech startup MedML claimed to help solve hospital and insurance administration problems, seamlessly
MedML’s platform is said to increase hospital’s revenue by at least 20% per annum and reduce their operational cost by at least 1%
Currently, the company is in the middle of raising a seed round and has managed to raise an undisclosed amount of funding from angel investors and counting
India always had a legal backlog with thousands of cases pending in courts, but with Covid-19, there’s also expected to be a healthcare backlog. According to healthcare experts and professionals, a lot of hospitals have either cancelled or postponed operations and surgeries during the pandemic times, creating a backlog which is expected to take the best part of a year to clear for both hospitals and insurance providers.
In these stressful times, it is only Covid-19 patients, outpatient department (OPD) and critical cases that are being addressed by a large majority of hospitals. Facilities are expected to remain jam-packed with patients flocking towards hospitals in the pandemic as well as in the post-Covid-19 times, and it becomes crucial for them to proactively manage inventory, beds and resources so that it doesn’t burn deep holes in their pockets.
Coming to the rescue is Delhi-based artificial intelligence (AI)-powered product MedML which claims to solve hospital and insurance administration problems, seamlessly. Founded by Niti Jain in June this year, an actuarial science and data science professional with over seven years of experience in the healthcare space, the company claims to provide upfront predictions and real-time actionable triggers assisting the healthcare administrators to solve for various operational challenges with its state of the art AI-technology products Provider 360 (hospitals) and Payer 360 (insurance providers), respectively
MedML’s solution is currently priced at INR 3.5 Lakh, with the dashboard allowing management of bed occupancy, medical cost, revenue, member readmission, consumable utilisation, with monthly and annual renewal plans for healthcare providers. Similarly, for insurance providers, the company offers unlimited access to features such as actuarial and underwriting, claim adjudication, member and provider engagement.
MedML competes with a host of players in the fast-burgeoning hospital management space, particularly with Covid-19 highlighting the various gaps in management of healthcare facilities as well as the need to automate certain processes since human resources are stretched.
“Healthcare is very complex in itself, and there is no one solution, fits all,” said the founder, and indeed there are various startups looking to tap this space. Startups such as Innovaccer, in particular, have been able to make inroads in the insurance and healthcare management space in the Western markets and are now turning their attention to the Indian market. Besides this, the likes of LiveHealth, MocDoc, Suvarna Technosoft, which acquired Mumbai-based laboratory information management systems (LIMS) startup MedPrecinct in May last year and others.
Besides this, there are specialised solutions such as Indegene Omnipresence, which raised $20 Mn in January this year, providing healthcare customer relationship management (CRM), omnichannel engagement, advanced analytics, and AI capabilities.
Some of the existing hospital management systems and hospital ERP software that are currently being used by multiple hospitals in the country include SoftClinic, MocDoc, Attune, Insta, ITDose Infosystem and Suvarna HIS among others which helps hospitals manage their day-to-day operations across departments.
Focused on hospital and insurance administration, MedML claims to provide customised solutions according to the hospital’s needs based on healthcare and actuarial data. “Unlike consultancy firms which take six to seven months to develop the product and solution on premises and leave it at that, and keep insurance players hanging. We, on the other hand, are not only offering a particular product, which is customised to our clients in-house data, but also provide what, why and how aspects of predictive output,” said the founder.
At present, MedML is backed by healthcare industry veterans and insurance tech leaders, including Narayana Health’s Commander Navneet Bali; ASSCOM chairman Mr Arun Seth; Chubb’s C-Executive Gaurav D Garg; and Mphasis CFO Manish Dugar. Having launched just four months ago, MedML is currently working with two healthcare providers in India, which have a total capacity of close to 800 beds, and one of the largest insurance providers in the Middle East, which it could not name due to non-disclosure agreements.
The Pitfalls Of AI Adoption In Healthcare
According to the latest market estimates, it is predicted that the applications of artificial intelligence in the healthcare space is expected to touch INR 431.97 Bn by 2021, growing at a compound annual growth rate of 40%. Also, the doctor-patient ratio in India is expected to reach approximately 6.9:1000 by 2023, from its 2017 ratio of about 4.8:1000. The adoption of AI applications is further said to help in tackling challenges such as uneven doctor-patient ratio, and effective utilisation of beds, consumables and medicines, etc.
But, in reality, setting up the AI infrastructure is not only overwhelming but it pinches the budget as it involves an expensive process of onboarding healthcare consultants, data scientists, partnering with tech startups and major corporations. Only a handful of hospital chains today such as Manipal, Fortis, Aster DM Healthcare and Apollo are able to bear this cost and deploy resources towards AI-based hospital management.
“Currently, we are able to increase our clients revenue by at least 20% per annum, and reduce their operational cost by at least 1%,” Jain claimed. The key operational metrics considered are average revenue per occupied bed (ARPOB) and average length of stay (ALOS). Mathematically, the average revenue per occupied bed for hospitals is inversely proportional to the length of stay.
Jain said that MedML offers a user-friendly dashboard and actionable triggers for either the hospital’s internal management systems or external patient-facing app. The real-time insights and triggers help hospital administrators manage their resources effectively and thus optimise cost. Similarly, for insurance providers, MedML’s solutions help assess claims, based on hospital data and help in achieving more efficient underwriting.
In addition to this, the startup also claims to reduce resource wastage by at least 1% each year which will further add to the cost-savings. While low-occupancy hospitals will be able to utilise their resources more effectively and save on operational cost, high occupancy hospitals need to free up room for more patients. A reduction in ALOS results in extra bed days for a fully occupied hospital — and Jain states that even a 0.25 day reduction in per patient length of stay can result in approx. additional 6 bed days for a fully occupied 100-bedded hospital.
In other words, when the length of stay reduces, the average revenue per occupied bed increases. “Since most of the revenue comes during the initial days of patients being admitted at the hospital. If hospitals are able to reduce the length of stay, they can bring in more patients, and hence generate more revenue” remarked Jain, justifying how it is able to help hospitals earn more revenue.
Integrating With Government Systems
With the National Digital Health Mission also in the picture, MedML is looking to strengthen its data-play by on-boarding more and more hospitals and insurance companies in the coming times. Once that happens, the platform would be able to predict industry average cost as well, which will not only change the game for the company, but hopefully, also help the government, hospitals and insurance to predict outcomes and track risks.
In fact, MedML has recently presented a case study to the Karnataka State government in relation to Ayushman Bharat PM-JAY programme, which will help them to track risk and stop fraud from occurring. Jain didn’t reveal any more details about the project as it is still under discussion stage.
“Currently, we are in the service cum product stage. However, in the coming months we will be moving to an easy plug and play model, where we will be able to integrate with any hospital management system and insurance management system as an AI power extension,” Jain added.
