Strategy – Kaivalya Powale

a bit heavy, takes 6 mins

Having worked on a project aimed towards improving health and wellness across hospitals in the US, I grew closer to understanding the issues faced by healthcare systems. Automation of operational tasks and proper technical infrastructure can eliminate the vast majority of these issues. With this in mind, I explored the advancement of Artificial Intelligence in Healthcare, the problems it can solve, and a few roadblocks along the way.

Machine Learning is transforming the healthcare industry by changing the outlook on care delivery, operational optimization, and disease detection.

The Problems

Enterprises in healthcare have long been troubled by problems like maintenance of health records, identification of care programs, early disease diagnostic, insurance fraud, waste, and abuse (FWA), time spent on medical imaging, and outbreak prediction for diseases. They are now looking towards machine learning (ML) techniques and artificial intelligence (AI) systems for the solution.

The US Healthcare system alone generates approximately 1 trillion gigabytes of data annually. ^[1]

The ecosystem map for healthcare data by Datavant

This data is both structured and unstructured, creating the need for a complex set of algorithms to make sense from it. Typical statistical methods work on confined problems making use of structured data to drive insights. ML is required to learn from highly complex datasets and derive a relation between multiple parameters informing experts of the multimodal facets of the issue.

How to make sense of this data?

Various ML techniques apply to the healthcare industry based on the specific problem and the intended outcome.

1. Disease detection and prediction uses supervised learning techniques where the model is trained on a set of target outcomes. This technique is implemented with expert opinions and structured datasets to predict the occurrence of diseases or ailments.

2. Medical image analysis is implemented using Computer Vision and deep neural networks (like CNN) as they include highly complex and undescribed variables in the dataset.

3. Unsupervised learning algorithms find their application in outbreak prediction as they can be used for clustering and anomaly detection.

4. Insurance fraud and patient record maintenance use techniques like Natural Language Processing (NLP) and deep learning to make sense from unstructured patient records and missing data.

Where is the value for AI?

AI in Healthcare is expected to grow at a CAGR of 50.2% from 2018 to 2026 and reach a market size of $150 billion. ^[2]

ML is projected to deliver 63% of that value. ^[3]

The specific use cases of ML that can be used to solve the problems in healthcare include Automation of Patient Health Records, Disease Outbreak Prevention, Early Detection of Chronic Diseases, Patient Journey Monitoring, Equipment Maintenance, Operational Scheduling, Fraud Detection, and Medical Imaging Diagnosis.

Top 2 Use Cases

1. Early Detection of Chronic Diseases

The total annual cost of chronic diseases in the US is $3.7 trillion which is close to one-fifth of the entire US economy.

This cost is expected to increase as the US population is aging. ^[4] Using ML and predictive analytics can help identify high-risk patients and develop tailored monitoring or care programs that can prevent a total cost of $30.8 billion related to chronic diseases. ^[5]

2. Operations Scheduling

Deep neural networks can be used for the optimization of operational scheduling by leveraging electronic health record (EHR) data and resource utilization patterns.

This alone can save USD 500k per Operation Room per year. ^[6]

Operational Scheduling will allow for better patient care and will ensure the availability of resources including nurses and staff.

Necessary Infrastructure

ML and Deep Neural Networks are expected to reach the plateau of productivity in the next 2-5 years. To maximize the value we can create through them, we need to have the infrastructure and human capabilities in place. The technological infrastructure as it pertains to these two use cases includes proper data collection and storage techniques, hardware capacity to train and deploy ML models, and an interconnected system of equipment (IoT).
An ML model is only as good as the data provided to it. The sophistication of data collection techniques is important to realize a sound and scalable AI system. The lack of high-quality data and ineffective privacy protection are hindering the growth of AI in healthcare. ^[7]

Readying the Human Capital

Not a black box. Interpretabe and usable by doctors.

Healthcare is different from other industries in the sense that there is an intricate and intimate relationship between healthcare providers and patients. The ML systems and algorithms in healthcare need to be transparent where the various metrics and decision criteria are clearly understood by the doctors or providers. This means the human capital must be data literate and trained about the impact of ML models on their decision chain. This will be a key factor in the acceptance of ML in healthcare. ^[1]

Photo by Stephen Dawson on Unsplash

Delivering value through AI

Current players in the market offer a comprehensive set of products that can be implemented in the healthcare industry to deliver value for these use cases. They should look to serve the industry using the Three Horizon Model i.e. through their current capabilities, emerging technologies, and future ventures.

1. Growing the current business
ML techniques that have applications like Predictive Maintenance of equipment, Sensor Health monitoring, and Continuous Analytics can deliver value for Operations Scheduling. These applications help monitor the systems and predict asset failure that results in lower downtimes. Machine Learning services and AI modeling platforms will empower organizations to deploy models that help optimal predictions for chronic diseases.

2. Emerging Opportunities
The second horizon for healthcare AI business is its emerging opportunities that will create value shortly and will require considerable investment. This includes investing in Natural Language Processing, Computer Vision, and Speech Recognition to deliver use cases like Medical Imaging Diagnostics, Patient Journey Monitoring, and Improved Patient Record Maintenance.

3. Imagining the Future
The third horizon contains planning for profitable growth down the line including partnerships with upcoming tech labs, diversification of resources and focus areas. The future techniques will go beyond the focus on resolving operational issues to delivering value to the healthcare industry through Personalized Care.

Anticipating the future needs of the industry and preparing for acceptance of ML will empower us to benefit from the advent of AI in Healthcare.

References:
[1] https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/machine-learning-and-therapeutics-2-0-avoiding-hype-realizing-potential
[2] https://www.accenture.com/fi-en/insight-artificial-intelligence-healthcare
[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/
[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429690/
[5] https://hbr.org/2017/05/how-machine-learning-is-helping-us-predict-heart-disease-and-diabetes
[6] https://leantaas.com/wp-content/uploads/2019/01/OR-case-study-booklet-27-01_online.pdf
[7] https://www.cbinsights.com/research/report/ai-trends-healthcare/

Scenario (excerpt from audio above)

You are going to San Francisco for a conference and you need a place to stay
All the hotels on your radar are booked out and the ones that are available are either very high-end or a bit too shady!
In this situation, you’d either have to shell out extra cash or compromise on your safety.
Well, thankfully, you don’t have to do that. You could just get an Airbnb, an airbed and breakfast.
Airbnb lets you choose from hundreds of local houses for your stay.
This gives you a local experience of the region at cheap prices. So you live like a local; where you feel comfortable!

How does Airbnb do this? In an industry with cut-throat margins where competitors are striving to get profits, how does Airbnb continue to grow? How are they so innovative?

Our team analyzed Airbnb’s innovation strategy to understand how the company sustains its ability to capture the market and create a strong pull from customers.

I have tried to keep the content lightweight and easy-to-read. Let me know if you’d like to read the detailed version.

Let’s take a look at Airbnb’s foundations and current strategies.

The Past

Airbnb was conceived in 2007 when two roommates started renting out their living room to conference visitors in San Francisco. They provided guests with an airbed and breakfast (roll credits) for $80 per night.

At that time, the concept of renting out someone’s house was a thing that the industry had not seen before. It created a new section within the accommodation industry. It was something refreshing

… something like …

The Blue Ocean

According to the blue ocean strategy, a company needs to Eliminate, Reduce, Raise, and Create factors compared to the industry standards. And that’s what Airbnb did!

While reducing factors like prices & safety, Airbnb created a ubiquitous availability of home-like experiences.

Airbnb’s business model was developed on being a low-cost solution for a low-value customer. And using the concept of ‘Sharing Economy’, they created a newfound supply of income for the hosts. This led to a simultaneous rise in demand and supply of accommodation.

Disrupting the industry

They provided a service that was lower than the market standards and over time, they improved the quality-of-service which the mature market could not cope up with.

A typical disruptive innovation starts with a low-par service with respect to the industry

In this asset heavy industry where competitors were bogged down with owning and maintaining real estate, Airbnb owned just the intellectual property and leveraged the assets of their hosts to gain market share.

Barriers to Innovation

Barrier: Safety – how could guests be safe in strangers’ homes?
Solution: Risk scoring of properties, watchlist and background checks, safety workshops with hosts; secure payments and account protection.

Barrier: Trust – how to know we are getting a good place? what if renters damage the property?
Solution: Used professional photos to inspire trust – to show that places weren’t dumps and had verified addresses; user profile verification, insurance options.

Barrier: Regulations – cities imposing bans on short term rentals.
Solution: positioned as a ‘platform to connect’ hosts with visitors, agreement putting host responsible for tax payments.

Barrier: Cultural Shift – how to get people comfortable with living in someone’s house?
Solution: intensive marketing campaigns branding as a local experience and ‘Belong Anywhere’.

Belong Anywhere
That’s how Airbnb is getting people to associate with the cultural shift

We saw how Airbnb started out, a robust strategy with a good market fit. It takes a lot more, though, to continue budding this innovative spirit. You need the right people.

The Present

If you have the right people, they will do great things for you. Right? Well, it is not so easy. What matters more than the people is your culture. You need to provide your organization with the right culture to grow and share.

Innovation, in itself never comes from one person or one idea. Innovation comes from the collision of diverse ideas. It is not thinking outside the box. It is more like thinking in multiple boxes at the same time.

This kind of mindset buds ideas. Ideas that are formed with knowledge. Knowledge from people working across divisions or departments. And the main reason why most “innovative” companies have open and shared workspaces. And that’s how Airbnb works.

Airbnb is not about booking or traveling, but about bringing people together and giving them a sense of belonging

Organizational Structure

Airbnb has created a strong culture of community and collaboration. They have setup an organizational structure to maximize networking within the organization. The managers exist not to lead but to facilitate information and act to remove obstacles for the team.

‘Ground Control’
An employee experience team that looks after the workplace environment, communication, and celebration.

Transnational Innovation

As they grew and moved into countries globally, Airbnb adopted the culture in those countries and adapted their strategy accordingly. Thus, they locally leveraged their innovation strategy and spread the learnings across all markets. That is truly transnational.

Airbnb has kept their information and knowledge free-flowing. They host community meetups to learn from their hosts and also use it as a repository to share this accrued knowledge.

Research Culture

‘Bulldozing‘ is when a feedback from researchers seem to decimate whatever product has been built. When researchers point out what is not working, it is definitely a good feedback, but how is it helping the product teams?

Instead, Airbnb makes the researchers work directly with product team throughout development and not just for testing. That is when it is easier to build something and test if it works. Researchers still tell what isn’t working and ‘bulldozing’, but now it is at the right time.

When it comes at the right time “bulldozing” is a good thing. It doesn’t flatten the building, it flattens the ground on which we build.
Airbnb’s Judd Antin, From the Ground Up

Airbnb has maintained its growth stats and quite recently surpassed Hilton in US consumer spending after expanding to boutique hotel rentals in 2018.

As Airbnb is prepping to go public with a much anticipated IPO, what would its future strategy look like?

The Future

Being a prime example of how to organize your culture and keep employees as close as family, Airbnb needs to increase the transparency between guests and hosts. At this moment, there are still a lot of issues with respect to broken promises and broken ~~hearts~~ apartments. Customers are facing problems from the likes of incorrect pictures to hiked prices.

If it hits an IPO, it will need to keep its trust ratings high and have a ton of tricks up its innovation pipeline.

Sustaining Innovation

With their extreme growth, Airbnb’s company size has also grown a lot. When new managers come in, they are not imbued into Airbnb’s culture and this adds a layer of bureaucratic barrier to innovation. The ‘managers’ need to be trained from grassroots to be ‘facilitators’ instead.

As with every new innovation, the first-mover advantage only lasts as long. Competitors (both hotels and new tech companies) are nibbling off market share from Airbnb. And that’s not even the problem! This competition is not based on quality. It is based on cost, which is never good for the market! (as it results into a race to the bottom)

Airbnb might soon have to come out with new product line offerings to maintain their lead. And for this they have to do what Airbnb does best; that’s not by striving for growth but constantly learning from the market. That’s when they will truly be able to “Belong Anywhere.“