Authored by Eileen Tanghal
Last week, I had the opportunity to celebrate the 25th anniversary of In-Q-Tel (IQT) with former colleagues and alumni. IQT is non-profit strategic investor servicing the CIA and broader defense and intelligence community in the US, UK, and Australia.
I spent five and a half years at IQT, serving as a Senior Partner in the Menlo Park, CA office and then Managing Director for IQT International in London. The 20+ investments I made at IQT were in support of these agencies' missions of collecting information, analyzing, and turning that into insightful and actionable intelligence.
Over the years technology, often from startups, has empowered intelligence analysts to successfully collect and integrate diverse, disparate, fragmented, and regulated data sources and perform real-time sophisticated analysis on that data using machine learning and artificial intelligence as a force multiplier for everyday tasks.
Healthcare today is marked by unsustainable and rising costs and poor quality underlined by a massive data problem. 30% of the world's data is healthcare data that comes from a variety of sources like Electronic Health Records (EHRs), medical claims, clinical trials, imaging, and increasingly from newer sources such as at remote monitoring devices. Clinicians and hospital workers are overwhelmed with having to handle their patient loads and get actionable insights from this data. Researchers at pharmaceutical companies face challenges in getting and using the data they need to reduce the time and spend necessary to find and get approval for a life-changing therapeutic. These clinicians and researchers, just like CIA agents, do not have the time to learn Python or become data scientists themselves.
As a result, this data is siloed and largely unusable in its current state. These bring us to 3 major problems.
Much of healthcare data is incomplete. Building datasets and training models with this data has the potential to exacerbate problems with health equity (Source).
There is a lack of data scientists and capabilities in healthcare, making it difficult to effectively produce, manage, and utilize the immense data that exists.
Despite best efforts, the healthcare industry lacks adequate data security. The introduction of AI driven applications into the workplace creates an additional attack layer for bad actors to disrupt normal hospital operations. Fortified Health Security reported that “more than 19 million records were implicated in healthcare data breaches” in the first half of 2022 alone and the cost of these breaches are immense ($10M average) and rising (Source).
As we move into a future where edge compute and 6G connectivity can cause another 10-100x increase in data sources for healthcare, I believe healthcare practitioners can garner a few lessons from the defense and intelligence agencies. Having supported members of several agencies, ranging from homeland security to the CIA, I can tell you that their work is highly complex, relying often on perceptions and experience alongside data. These dynamics are not unlike the doctor-patient relationship in healthcare.
Lesson 1: Siloed and unstructured data limits interoperability and contributes to high cost and poor quality of healthcare. With much of healthcare data being unstructured, there is also the reality that much of the usable data will be incomplete, contributing to inequities and inability to diagnose earlier and deliver better care. It is important to (1) get this data stored in one place that allows for scale or to adapt solutions that allow for (2) true federated access of siloed data sets. There is power in unlocking data, particularly when it provides actionable insights. For example, Black Opal Ventures portfolio company, TigerGraph is a graph analytics software company that specializes in connecting data assets to deliver enterprise-level knowledge and insights.
Lesson 2: It is crucially important to empower healthcare practitioners AND do NOT require them to become data scientists nor programmers. We need practitioners to hone their healthcare skills and enable them to use the latest technological tools to better collect and analyze data and even create simple applications that can significantly improve their workflows ultimately leading to better health outcomes. Black Opal Ventures portfolio company, Blaze.Tech offers HIPPA compliant prebuilt integrations, security and reliability which enables customers to make sophisticated and complex applications without writing a single line of code.
Lesson 3: To secure data being generated, it is important to monitor both the generation and usage of data on a real time basis. Legacy solutions record and monitor access to applications from a compliance point of view rather than a threat detection basis, allowing attackers to compromise them. Black Opal Ventures portfolio company, Authmind is a pioneering Identity Security provider that can arm healthcare security teams with end-to-end real time identity security posture management and threat detection.
At Black Opal Ventures, we are bringing together technology and health. Our fund focuses on companies that are revolutionizing diagnostics, delivery of care, and drug discovery all underpinned by data analytics, AI, and compute infrastructure. In the future, we hope to look towards companies with impact on other major determinants of health. We are just getting started.
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