A couple weeks ago, Kabeer donated blood for the first time at the Canadian Red Cross, which got both (Aahaan, Kabeer) of us thinking about diving deeper into the problem of securing access to safe blood. While in a developed country like Canada the problem is rare, a perfect example of the problem was visible in India.
The main criteria we used to scope down to India was evaluating the existing infrastructure, overall culture (hard to change), availability of data, ease of deployment and finally the scale of the problem.
According to the WHO, all countries should maintain a 1% blood reserve at all times. In a country of 1.38 billion people, India needs 13.8 million units, however only 11.45 million donations were collected between 2017–2018. A shortage of almost 2.5 million units, without factoring into account wastage, contamination and misuse.
In order to develop a better understanding, the first step was to perform value stream mapping to follow the exact process a red blood cells takes from the point of donation to transfusion.
Donation → Processing into Components → Testing → Storage (Optional) → Hospital for Transfusion
We then dove super deep into each phase to understand what were all of the problem statements at each step of the process, which was then followed by performing root cause analysis on each problem to uncover the lowest-level problems to solve.
Designing the Optimal System
Reflecting on next steps, Kabeer and I realized that we were optimizing for a “silver bullet” solution when in reality there were several steps that needed to be taken to fix the overall broken system. We went back over all of our problem statements and grouped them into 1 of 3 major categories. These are the 3 major pillars that need to be established to implement a perfect system in India:
- Ensuring an adequate supply of blood in the system. This is where the problem starts, making sure there are enough donors to satisfy the population’s demand for blood. You can’t have a functioning system without ensuring the 1% blood reserve is always available for use.
2. Coordination between different stakeholders. India’s blood system is incredibly disorganized with numerous different types of blood banks and regulator bodies pictured in the diagram below. Developing a platform for coordination is vital to increase transparency about the supply and demand of blood at each location, and it enables the sharing of blood between institutions.
3. Developing infrastructure for blood transportation. 25% of all blood banks in India collected 66% of the total volume. What’s even worse is that there are some areas which experience an oversupply leading to wastage of blood due to its short shelf life and others where patients are refused transfusions due to lack of blood. There needs to be a process for transporting blood between blood banks to ensure every location can meet the 1% reserve according to its population.
After discussion, feedback, outreach and extensive research, we came to the conclusion that we are trying to solve the problem of deaths due to the blood supply, and not the supply itself. Why?
For this geographic, transportation is the needle mover in solving the overall shortage problem → solving #1 and #2 (above) will get nowhere without proper transportation of blood + adequate supply at blood facilities. We found that in the current status quo, solving #3 will create the biggest impact + drive system closer to eliminating shortage problems with the rural-urban divide that most states in India have. Plus, thinking for the long run, our solution will be able to be easier scalable if other countries are facing a similar problem.
Scoping down to Infrastructure for Blood Transportation
For Circulate, we decided to focus on building the infrastructure for blood transportation for a couple of reasons. Primarily, the blood transportation is the needle-moving solution to the overall broken system because even if there is an adequate blood supply and coordination between stakeholders, there will still be a severe shortage in certain locations (especially rural areas). Inversely, even in the current broken system, enabling blood transportation can cause the most impact. A couple of other reasons were that the solution is easily scalable to other countries as well plus it tackles the rural-urban disparity in India.
Referring back to the initial process diagram, Circulate is focusing on the transportation between the last three steps from testing → storage → hospitals or storage → hospitals.
Second-Order Problems
The lack of infrastructure leads is the root cause for several other issues as well. Currently when the hospital doesn’t have enough blood for the patient, the burden of responsibility to secure the blood falls upon the patient.
One common way to do this is the patient attendant at the hospital must physically travel to the next closest facility and bring back the blood. However in 40 degree Indian temperature without proper cold storage mechanisms, this often leads to more wastage. Alternatively, the patient also often pays regular people to donate, which then leads to them lying about their medical history and an increased risk of infection.
An alternate scenario when the hospital doesn’t have enough blood on hand, often in rural areas, is they defer the patient to the next closest facility without providing any method of transportation. The patient then ends up in a far worse condition or even passes away before reaching the hospital.
Case Study
Take a look at the image below, and let us assume we are focusing in Rajasthan, which met 78% of its total required amount of blood. It’s a good average for what the situation looks like in India.
Now imagine we have 3 cities: A, B, and C.
City A has a population of 125k, meaning an ideal 1% reserve would equal 1250 units, assuming the 78% supply-demand ratio is consistent throughout the state, it only has 975 units.
City B has a population of 234k, producing an ideal reserve of 2340 units, but it only collects 1825 units (assuming 78% once again)
Finally, City C has a population of 68k, requiring 680 units, but only collecting 530 units.
How do we distribute the blood to reduce the number of shortage deaths in these 3 cities?
Primary Component: Load-Balancing
The first step in getting the blood to the right place at the right time is figuring out where the blood needs to go.
Circulate needs to build an algorithm to output specifically how much blood needs to be transported from which hospitals in city A/B/C to which other hospitals in the corresponding cities.
Below are all the different variables that need to be included in the algorithm:
i. Data About the Blood
India currently uses a centralized software that tracks the stock of whole blood and all of its components at every hospital (except Armed Forces). The following are a few examples of factors that we need insight on:
- Stock (how many units at each hospital)
- Shelf-life: super important because blood wastage is a massive problem in India. Red cells are stored in refrigerators at 6ºC for up to 42 days. Platelets are stored at room temperature in agitators for up to 5 days. Plasma and cryoprecipitate are frozen and stored in freezers for up to one year. After defrosted, have a shelf life of 5 days
- Blood type
- Hemoglobin count
- Blood collection data
ii. Scheduled Surgeries (Planned Demand for Blood)
A large percentage of blood transfusions in India are predictable occurrences or regular transfusions for conditions such as sickle cell anemia, cancer and hemophilia.
The model must be able to analyze each hospitals scheduled surgeries to determine its scheduled demand for the appropriate time period.
iii. Forecasting Demand (Unplanned Demand for Blood)
The other reason for blood transfusions are immediate or unplanned diseases or injuries that require transfusions.
Although these are inherently random, there are trends visible in historical data which correlates to the demand for blood based on:
Demographics of the population
- Age
- Sex
- Medical History
- Timing (day of the week, seasons, etc.)
- Geographical location
- Prevalence of certain illnesses
iv. Optimizing for 1% Reserve (WHO)
Sometimes, large demands for blood will be completely unplanned and therefore the final variable fed into the algorithm, which also has the least weightage, is how can we get each city as close to the 1% reserve as possible.
Predicted Output: Map of all the blood that needs to be transported from one point to another and how much in all 3 cities
Secondary Component: Transportation
Once we have the map of where, when and how much blood needs to be transported, the next step is physically moving the blood.
Similar to above, we are assuming a per-state distribution of blood.
The way the blood is transported depends on the volume and timing at which is the blood is needed.
On-demand Ola Cabs
Ola Cabs is the leading ride-sharing platform in India, available in over 110 cities with 1 million drivers serving 125 million customers.
Our hypothesis is to partner with Ola Cabs and develop a network of designated ‘Blood Drivers’ that can transport blood as an additional source of revenue. These drivers will be equipped with a passive blood refrigerator (doesn’t require electricity) in their car and will be able to transport blood in the same way as a customer, but with additional priority.
Criteria: if the transportation of blood is up to 12 units (typical capacity of passive blood refrigerator)
Ola Cabs includes flexibility for on-demand or emergency requests for blood because it can be delivered safely and extremely quickly. On top of that, they are much better for smaller volume within the cities and suburbs.
Predicted Output: Blood is available whenever a transfusion is required at all 3 cities (where shortage exists)
Talking to Experts
Throughout the last few weeks, we’ve met with several experts working in the blood supply chain in India to get their perspective and validate the assumptions we’ve been making.
Currently, we have mentors working at blood banks, Narayana Health, and the WHO Advisor on Blood Safety!
An interesting insight that was uncovered was that the scale of problems are often hard to distinguish in research, but talking to people helped to provide clarity on what were the needle-movers behind the problem and what was insignificant.
