**Andrew Lo: Exploring the Relationship Between Finance and Healthcare**
**The Convergence of Biomedicine and Engineering**
**The Importance of Economics in Healthcare Research**
**Revolutionizing Healthcare Funding Models**
**Unlocking Potential Through a New Approach**
**The Risky Nature of Drug Development**
**Maximizing Payoff Potential Through Collective Investment**
**Improving Modeling with Machine Learning**
**The Power of Crowdsourcing and Collaborative Research**
**The Role of Finance in Achieving Success**
Andrew Lo, a Charles E. and Susan T. Harris Professor at the Massachusetts Institute of Technology, sees a significant relationship between finance and healthcare. In a thought-provoking video, he highlights the potential for finance to contribute to more prolific and improved drug research outcomes. By integrating data science, financial engineering, and clinical development successfully, Lo believes the healthcare industry can experience a positive transformation.
**The Convergence of Biomedicine and Engineering**
During his presentation, Lo emphasizes that the healthcare industry is currently at an inflection point – a “convergence” where life science and engineering can complement each other. He discusses the “omics” revolution, including fields like genomics, epigenomics, transcriptomics, and metabolomics, which have greatly informed research. However, Lo believes that one important aspect is often overlooked – economics.
**The Importance of Economics in Healthcare Research**
Lo emphasizes that advancements in healthcare require substantial funding. While various disciplines contribute to the progress of the industry, business models are slow to change. Lo suggests that incorporating new approaches could vastly improve results and outcomes.
**Revolutionizing Healthcare Funding Models**
To shed light on healthcare finance metrics, Lo presents an equation that considers profits and costs in the context of drug development. He explains that the expected value of a drug program is determined by the present value of profits if the drug gets approved, multiplied by the probability of approval, minus the cost of development. While economists can provide insights into profits and development costs, they are unable to determine the probability of success due to its scientific and engineering nature. Lo challenges the audience to consider the difficulty in developing a drug.
**Unlocking Potential Through a New Approach**
Lo introduces a new model that could revolutionize the investor outlook on clinical development and trial processes. He highlights that the average drug development project costs approximately $200 million over ten years, with a 5% probability of success. Recognizing the low success rate, Lo suggests an intriguing alternative.
**The Risky Nature of Drug Development**
While the potential payoff for a successful drug development project is estimated to be around $12.3 billion, Lo acknowledges the inherent risk. In terms of risk measurement, he explains that standard deviation serves as an indicator, with a volatility of 423.5%. Lo acknowledges that most investors are unlikely to invest in projects with such high risk over an extended period.
**Maximizing Payoff Potential Through Collective Investment**
Lo presents an alternative math model that combines collective investment and project risks. He demonstrates how a $30 billion investment could generate a series of development processes, increasing the chances of success. Lo emphasizes that with this approach, there is a 98% chance of producing $36.9 billion over a ten-year period – an incredibly attractive proposition.
**Improving Modeling with Machine Learning**
Lo highlights the use of machine learning and how his company, QLS Advisors, applies tools similar to those used in credit scoring and other analyses to enhance clinical trial outcomes. He demonstrates the targeting of specific results through tried-and-true statistical principles such as random forest and nearest neighbor models. Lo directs viewers to an article published in the Harvard Data Science Review, where they can find QR codes and access the code for some of these projects on GitHub.
**The Power of Crowdsourcing and Collaborative Research**
Lo discusses a partnership between QLS Advisors and Novartis, where MIT professionals put this theory into practical use. Through crowdsourcing, over 300 participants in 50 different teams submitted 3,000 different models in an attempt to improve upon initial models. The results were both useful and interesting, showcasing the potential of collaborative research and the enhancement it brings to modeling.
**The Role of Finance in Achieving Success**
In conclusion, Lo reflects on the purpose of finance and its potential to benefit individuals. He emphasizes that finance does not have to be a zero-sum game, but rather a means to an end. By adopting the right business models and securing adequate financing, the healthcare industry can achieve the desired outcomes.
Andrew Lo’s expertise in finance and his understanding of healthcare research make him a leading voice in considering the relationship between these two fields. By incorporating new funding models, utilizing machine learning, and embracing collaborative research, the healthcare industry can navigate the challenges of drug development and enhance its success rates.
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