What do we do?
AlemHealth’s hardware and software platform connects hospitals in developing countries to a global network of specialist consultation services. Its doctor network raises the quality of care available to patients in the least developed countries, at prices they can afford.
More specifically, we link developing country hospitals and imaging centres to a proprietary telemedicine network through a health IT platform tailored to the limitations of developing countries. Technologically, we have developed a cloud-based platform that spreads the infrastructure costs among all its customer hospitals, and low-cost hardware that replaces the need for expensive legacy IT servers and PACS systems. We’re also developing tools to automatically analyse radiology and pathology images and put that information in the hands of its doctors and patients. We’re building healthcare the way it should be in the mobile era.
Why do we do it?
Developing country healthcare systems cannot train enough medical specialists to address their rapidly growing needs, so patients either go without care or spend billions per year on medical tourism. As the largest health issues for the next billion people in emerging and frontier markets are changing from communicable to non-communicable diseases, they’re going to need a lot of capacity to diagnose and treat strokes, cardiac disease, cancers, and other health issues. We’re seeing this shift already, and are redesigning how healthcare is delivered in these countries to be more cost-efficient and higher quality for the patients, physicians, and facilities.
More personally, it’s a real bonus to have found a business model that couples saving lives on a daily basis in places where good healthcare is hard or impossible to find, with strong revenue sources.
Why are we the ones to do it?
The cofounders Sajjad and Aschkan both combine western education and experience with developing country practicality that makes them uniquely qualified to execute this business. Aschkan pairs experience as a healthcare investment banker and management consultant with 5 years spent in Afghanistan, Iraq, and other developing countries managing projects and strategy consulting for large multinational companies and aid organisations. Sajjad has worked at numerous startups and other companies including Apple and Blackberry, but also prototyped internet-enabled remote Health care kiosks to streamline last mile care in Bangladesh.
On top of our management team, we’re able to attract top tier talent from other startups and organisations because frankly, people love what we do and want to be a part of it. The CVs we get for every open position astound us, and are the best indicator that we will have the best talent solving this problem as we grow.
How are we doing at it?
Thus far, we launched our MVP in our pilot office in Afghanistan, and have seen consistently growing revenues thus far. We’ve served about 300 patients, and diagnosed strokes, cancers, birth defects, and other urgent issues in as little as 90 minutes, saving a number of patients lives through emergency diagnoses or early detection.
We’ve established that people will pay for our service, and that we can turn around high-quality diagnoses in as little as 90 minutes. We’ve attracted vendors like Apollo Hospitals in India and OnRad in the US to provide services on our network, and have a quality control programme that’s first rate. Finally, we’ve developed our prototype hardware that costs as little as $200 and replaces systems costing 10s of thousands of dollars, and links existing CT, X-Ray, MRI, Ultrasound, and Mammography machines to our platform.
What do we have planned for the future?
We’re looking to grow geographically into multiple new countries that have similarly low levels of health IT adoption and radiologist availability, scale our operations to be able to manage thousands of consultations per day, and add to our product set in adjacent verticals like pathology, pharmacy prescriptions, and video-consultations. We’re also collecting proprietary data assets on each patient, and will be using that to build our machine learning capabilities for preliminary diagnosis and decision support.