Ananda Impact Ventures

There is a branch of venture capital, which I practice, that embraces “Impact Investment” and includes a particular lens on healthcare. One of our themes at Ananda Ventures is “Redefining Healthcare”, which is focused on novel technologies to personalise, improve access, and disrupt systemic structural inefficiencies. We seek a social purpose in what our investee companies do, to improve outcomes, particularly for the underserved in our communities, but without compromising on profit.

I am not purely focused on healthcare and so must thank all the specialist partitioners that have educated and coached me along the way, like Dr. Andy Blackwell CSO at Ieso Digital Health, Dr. Paul Goldsmith, and Dr. Hakim Yadi at Closed Loop Medicine, alongside other healthcare Angels and VCs that have been great co-investors, such as Dr. Vishal Gulati, Dr. Mike Murphy and many more. With this approach, we have funded technologies and ideas that can potentially change how we positively do things. I have invested in a number of healthcare start-ups that are leveraging technology, AI, and data to make a significant social and clinical impact.

Health system data availability has radically increased recently, with telehealth and digital therapeutics generating significant data sets after increased engagement during the pandemic. As we all know, the rapid adoption of digital devices, social media, and the internet has permeated most areas of our lives, leading to increasing amounts of data collected about ourselves, including our bodies and our overall health.

There is huge potential to accelerate our understanding based on real-time data. How can we translate that data and learning into impactful changes in clinical practice using medical devices? How can founders focused on “tech for good” use this to build successful businesses that deliver genuine value to patients and help society as a whole? What are the opportunities and barriers ahead?

Personalised treatment improves outcomes and reduces costs

By providing integrated and personalised treatment solutions that support patients to monitor, track and improve their health, we can not only improve outcomes but also reduce the cost of care. By using technology and data to increase accessibility, optimise therapies, and reduce healthcare delivery costs, we can create value-based care and value-based products.

Closed Loop Medicine, a Tech[1]Bio company I invest in is developing personalised digital healthcare solutions to improve outcomes for patients and clinicians. Their technology platform allows the creation of drug plus software combination products with precision dosing and additionally “titration” — by introducing or removing a drug from the patient. This enables them to provide patients and clinicians with the tools and information they need to personalise dosages, optimise different treatment options, minimise unwanted side effects and increase patient compliance.

This is the next generation of patient-centric pharmaceuticals - ensuring individuals receive better health outcomes and quality of life.

Harnessing the power of machine learning to unlock actionable insights

Ieso Digital Health, a digital mental healthcare company I invest in is using machine learning and natural language processing to identify patterns and correlations during online therapy sessions to analyse what exactly makes it effective.

The language-based nature of therapy means data can be captured via digital communication and then analysed by natural language processing for a multitude of variables to assess what makes therapy work. What words or phrases are being used by the therapist, in what order, and at what frequency, have the most impact on outcomes and recovery?

“By providing integrated and personalised treatment solutions that support patients to monitor, track and improve their health, we can not only improve outcomes but also reduce the cost of care”

What’s fascinating is all these data and insights add up quickly. As these datasets continue to grow, individual mental health treatment will become more personalized and precise over time, beyond a “one size fits all” approach. This will lead to faster diagnosis and treatment, leading to increased recovery rates and times.

Current hurdles preventing the adoption of digital technology

All this sounds great, but what are the hurdles preventing the adoption of digital healthcare technologies to drive better outcomes and recovery rates? One of the main benefits of precision treatment is that it enables early intervention. However, the current healthcare system fails to incentivise this.

In economics, we often think purely in terms of money saved or gained. However, this isn’t always obvious when evaluating “healthcare economics.” Pharmaceutical companies can spend years, maybe sometimes over a decade developing a drug for a rare disease that only affects less than 10,000 patients in the world.

But the pharma company may charge up to half a million dollars annually for this therapy, citing R&D costs. Is that price tag worth the lives the drug will save? What about how long the patient will live? How do you calculate the price of cancer therapy, that may extend survival for a mother just for a few weeks to watch their child graduate from college? Can insurance companies put a price tag on that?

Healthcare economics seeks to understand the role individuals, health care providers, insurers, government agencies, and public and private organizations play in driving these costs. Healthcare economics must be distinguished from the economics of healthcare. The former is focused on cost-effectiveness and cost-benefits of drugs or therapies, while the latter relates to the allocation of resources.

But I’m optimistic that the gradual shifts I see in both UK and US health systems to a more holistic, value-based care approach are changing this and will see investment in data-driven healthcare companies pay off.

Data interoperability challenges across geographies

Any company working in this space wants to build solutions that will work globally. But there are challenges to consider in the transfer of data and associated learning across geographies.

First, data security and privacy. Highly sensitive patient data has to be protected. Cybersecurity is an increasing problem, but people also have concerns about potentially sensitive data being used or shared for commercial purposes. Transparency, consent, and responsible innovation must be our watchwords.

Secondly, language and culture. We can now transfer data around the world easily, but will it be just as useful everywhere? When machine learning and natural language processing are used to analyse therapy talk or patient descriptions of drug side-effects, how transferable is this to other countries? If the data analysed was in English, how translatable is it to other languages, if at all? Will cultural differences play a part?

Finally, regulation plays a key role. Although there are promising signs of collaboration between geographies, currently any company with a new product must seek regulatory approval in every new country. Each country has its own set of regulatory requirements for approval. A global regulatory framework could enable a far more rapid spread of effective new technologies. For example, the MHRA (UK) and FDA (US) now have regulatory alignment in key areas, such as confidentiality.

This may mean that different business models are needed for different geographies, impeding scalability. However, I believe these challenges can be overcome and that technology can play a huge role in improving access to healthcare globally, as well as improving outcomes in meaningful ways. Patients deserve a more scalable, data[1]driven, and personalised approach to their healthcare – and this is worth investing in.