Innovator CEO profile: AKASA’s Malinka Walaliyadde

In this Innovation Lab interview series, Walaliyadde discusses what working on health care investments at Andreessen Horowitz taught him about building balance with his own health care tech company.

Health Evolution | August 20, 2021

Malinka Walaliyadde, a former partner at Andreessen Horowitz, co-founded AKASA on a mission to remedy the financial complexity crippling U.S. healthcare. The company pioneered a new approach called Unified Automation. It provides hospitals and health care systems with a single solution that uses the same artificial intelligence (AI) and machine learning (ML) technology employed by driverless cars to improve the efficiency, accuracy, and scalability of healthcare operations, starting with revenue cycle management (RCM).

Health Evolution interviewed Walaliyadde to learn how AKASA is enabling health systems to reduce their cost of care and be better stewards of the health care dollar, along with his journey as founder of a rapidly growing Silicon Valley health care tech company that recently raised $60million in its series B round of funding.

What is the inspiration fueling AKASA or its origin story?
Prior to starting AKASA, I was a partner at Andreessen Horowitz (a16z), a venture capital firm in Silicon Valley with $16.5 billion in assets under management. I helped start their health care investment team, and worked on almost $250 million of investments across about 20 health care companies, ranging from new providers, payers, biotech companies, and health technology companies.

During my time at a16z, we invested in and evaluated a lot of great clinical-facing health care companies. But when they needed to commercialize and plug into the financial infrastructure of health care, these businesses realized how difficult it was. I saw it happen time and time again.

No one was solving systemic issues in the back end of health care with modern technology approaches. There was a lack of new technology in the field and nothing being purpose-built. It became clear to me that you could use technology like AI and ML to solve these operational challenges in an innovative way.

My co-founders and I looked at this issue as a team of complimentary experts: individuals with health care expertise and with PhDs in AI and ML. We understood that the constant change in the revenue cycle and other core health care operations required a dynamic, comprehensive approach to address the inherent inefficiencies. The patchwork of currently available solutions could never accomplish that. We knew we had our foundational concept for a startup.

How does your technology help healthcare organizations?
In the U.S., the complexity of health care reimbursement drives up hidden costs that affect what every consumer pays and erodes the trust people have in the health care system. It’s a massive, deeply embedded problem. At AKASA, our mission is to fix it.

How? With Unified Automation. United Automation is a flexible AI-based solution that operates within a health care system’s existing electronic health record (EHR) and revenue cycle infrastructure, automating complex administrative tasks, reducing errors, and improving efficiencies. It can be deployed completely remotely, and our expert-in-the-loop technology works with AKASA’s team of in-house revenue cycle and operations experts to ensure that exceptions and edge cases are resolved, and the system learns from those actions in real-time.

Consequently, this solution can help address the biggest challenges in health care, which, in my view, fall into two main categories:

The first pertains to the traditional hurdles health care organizations have faced, including how to manage an efficient operational back end and how to provide a positive patient financial experience, while also navigating an overly complex reimbursement environment.

The other category relates to the trials and tribulations that have emerged due to the pandemic. Health systems across the country have seen significant decreases in the number of elective procedures and patient volumes. That volume is now returning in waves. Health system leaders must be prepared with the right amount of staffing and resources to accommodate this unpredictability.

How should prospective clients plan AI and automation rollouts?
My recommendation is to be practical in the short term and ambitious in the long term. Many health care leaders have had their vision capped by negative experiences with technology like basic robotic processing automation (RPA) that doesn’t scale or deal well with complex environments.

It’s important to start with automation of critical tasks in the short term, so you can generate near-term value quickly for customers and build a foundation on which you can deploy generational long-term investment.

At AKASA, we’re also developing AI-based predictive solutions, such as denials prediction. Not just the automation of workflows, but preventing unnecessary workflows from happening in the first place. Being able to predict these types of inefficiencies with a high level of accuracy is a significantly harder problem than automation. We’re already doing it with some customers, and believe it will really come to bear for the entire industry in the coming years.

For generations, we have advanced the standard of care based on peer reviewed research. But we don’t evaluate technology with the same rigor. That’s part of why the U.S. has the best health care in the world, but not the best health care system.

Malinka Walaliyadde , AKASA

How are your clients evolving?
We expect health care executives to get more sophisticated in their understanding of AI technologies. More leaders are envisioning what AI can do — not just in terms of patient care and tools for care delivery — but in running health care organizations.

Last summer, we commissioned a survey of almost 600 CFOs and revenue cycle leaders at health systems across the country. We found that more than 60 percent that don’t currently use revenue cycle automation solutions plan to do so by the end of 2021.

Automation is becoming core to every health system. Some hospitals are already hiring a Head of Automation or Head of AI, where it’s a person’s or team’s responsibility to think about how they will deploy automation across an organization. That’s setting the table for agile, productive collaboration with these health systems.

What is the most difficult challenge you have overcome on the road to success?
One of my primary early apprehensions about starting AKASA was attributable to the incredibly complex process of starting a business as an immigrant in the U.S. That complexity and uncertainty really challenged my risk tolerance as an individual and as a leader.

Fortunately, I was able to draw from my earlier experience as an immigrant. I had never been to the U.S. before arriving for college and didn’t know a single person here. It’s very hard to build a network and understand how things work in this country with no knowledge base.

That personal experience gave me a framework for how to think about starting from scratch and developing a meaningful path forward in the face of uncertainty, ambiguity, and risk. It serves me well even now as the company is growing. We’re doing a lot of things that have never been done before. As a result, I’ve built a mental resilience to deal with uncertainty and still make the best possible decisions. That was extremely helpful training early in my life.

At AKASA, our customer base has now grown to over $100 billion in NPR across 48 states. Our rapid growth is due to our incredible team and the invaluable mentors I had when we started the company. Now I actively try to pay it forward by helping other early-stage founders.

Which accomplishments are you as a CEO and founder proudest of?
I’m incredibly proud of the talented and uniquely “bilingual” team that we’ve built. Our machine learning experts deeply understand health care operations and revenue cycle concepts; they talk about CPT codes in their daily conversations. And, on the other side, we have health care operations experts on our team talking about core machine learning concepts like training datasets and test datasets. These pairings are unusual and incredibly powerful when building transformational health care technology

We’ve recruited the best of both worlds, while maintaining a healthy degree of respect between them. A company can become biased toward the health care side or the tech side, with the opposite team feeling like they don’t have an equal presence at the table.

What advice would you give to other CEOs and founders?
Health care technology companies specifically need to think about balance. Building a balanced team of both technology and health care experience — from the beginning — is crucial. If you start with only one side, it’s hard to bring in the other side later and have that group play an equal role. Your company, your product, and your people will ultimately suffer.

As a VC, I saw numerous companies struggle with this. They indexed on the tech side and then brought health care teams in as an afterthought. To succeed, you have to bring the right people and teams in from the start. That balance is what allows you to build best-in-class products that are an order of magnitude better than what already existed.

What can leaders do to encourage and accelerate innovation?
For generations, we have advanced the standard of care in the U.S. based on the rigor of peer reviewed research. But when adopting new technology on the administrative side of health care, we often don’t evaluate that technology with the same level of rigor. I think that’s part of why the U.S. has the best health care in the world, but not the best health care system.

I think we should hold ourselves to equally high standards when evaluating innovation on both the clinical and administrative sides of health care. At AKASA, we are committed to publishing rigorous validation of our AI technology at top peer-reviewed AI conferences. For example, our research has been presented at conferences, such as the International Conference on Machine Learning (ICML), NLP Summit, Machine Learning for Healthcare conference (MLHC), among others.

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About the Author

Health Evolution, Staff Writer