Why do clinical trials take so much time and cost so much money? In part, the answer lies in the sheer complexity of the challenge at hand. As a physician, I am very familiar with the enormous challenges of medical research. I often point out that clinical trials are the most complex form of healthcare. Unlike traditional medical care, in which healthcare is delivered and measured at the individual patient level, clinical trials aim to deliver standard or protocol-based healthcare to many patients in many clinics and often in many countries. Additionally, the critical outcomes must be measured precisely across diverse groups of patients and over considerable lengths of time. The “team” in a clinical trial expands far beyond the standard healthcare delivery model of doctor and patient to include clinical research coordinators, site and medical monitors, clinical research organizations, institutional review boards, and sponsors and regulators, to name a few. Simple modeling reveals that the clinical trial process is considerably more complex than traditional healthcare.
Given this, bending the time-cost curve will come down to solutions that reduce complexity and introduce radical process efficiency into the clinical trial process. A favorite recent book recommendation by business process thought leader, Thomas H. Davenport, Competing on Analytics, highlights the power of enterprise analytics to unlock massive value. Looking at other industries, real-time enterprise analytics across complex processes collapse timelines and empowers stakeholders with the ability to act sooner. Enabling early and informed decision making empowers individuals and generates massive operational efficiency. According to Davenport, “These heralded—and coveted—applications amassed and applied data in ways that upended customer expectations and optimized operations to unprecedented degrees.” Additionally, with adoption, these technologies unlock a tremendous number of secondary benefits due to previously unrecognized bottlenecks and inefficiencies.
I would argue that there is a critical new horizon, one beyond the enterprise analytics Davenport outlines. Unlike typical business processes, clinical trials involve individuals and processes that exist in separate enterprises. We must go beyond enterprise analytics to metaprise analytics—patient-centric systems that create a data driven trial team with real-time connections between all stakeholders.
Wall Street is a particularly compelling model for the improvements we could unlock in clinical trials with metaprise analytics. Stock trading involves many individuals taking different actions that are highly interrelated and leverages a connected data ecosystem to empower these individuals to make real-time, high stakes decisions. Today’s Wall Street leverages several principles that are essential to reducing time and cost within complex systems: 1) data never stagnates, meaning that all data are analyzed in near real-time and 2) data is routed as either human essential and the actor is notified to make a data-driven decision or data is routed as human non-essential and processed by workflow automation. Algorithmic trading takes this a step further and removes the human decision making completely. Wall Street is merely one example of a metaprise data ecosystem.
Where is the industry today? The answer is revealed by answering the simple question, does data stagnate? Yes, and to an astonishing degree. Today, the majority of clinical trial data is captured on paper or entered into siloed databases. Where we have shifted from analog to digital, the mainstream technologies represent a form factor iteration rather than true transformation. To achieve metaprise analytics, we must move to intelligent systems that connect participants, clinicians, clinical monitors, data managers, CROs and sponsors so that data never stagnates and data undergoes evolving analysis to enable automation and highest quality decision making by both algorithms and humans in real-time.
Metaprise analytics opens the door for quantitative analysis of data. Quantitative analysis leverages probabilistic modeling of data to enable high throughput transactions via automation and data driven decision making. In stock trading this is leveraged in “Quant Trading” to enable high throughput trading and quantitative risk analysis for buying and selling stocks. Similarly, one can imagine a similar process in trials. Such Quant Trials represent a next generation adaptive clinical trial where the trial is not only adapting based on data but the entire development process leverages quantitative analysis to remove all gut based decision making and to enable unprecedented optimization.
The emergence of the “patient enterprise” is also driving the need for metaprise analytics. Increasingly, patients are insisting on direct data ownership. This phenomenon combined with the rising utilization of consumer data and consumer devices in healthcare is creating a true patient enterprise where the patient is a distinct entity with direct governance over an increasing number of data streams and data assets.
Fundamental Principles of an Intelligent Clinical Trial Metaprise Platform
- Harness Patient Enterprise
- Connect all stakeholders
- Automate data analytics - quantitative and qualitative
- Automate workflows to optimize human capital
- System-level transparency
- Role based access
- Zero data stagnation
Beyond time and cost, a connected and intelligent data ecosystem will improve the quality of care delivered to patients. Enabling clinical teams to identify and respond to clinical events faster via analysis can improve trial safety for individual participants as well as entire study cohorts.
Anticipated Benefits of Metaprise Analytics in Trials
- Higher quality of patient care
- Improvements in Safety
- Higher quality data
- Optimization of human capital
- Accountability across stakeholders
- Less time
- Less money
- Effective therapies to patients faster
- Sertkaya A, Wong HH, Jessup A, Beleche T. Key cost drivers of pharmaceutical clinical trials in the United States. Clin Trials. 2016 Apr;13(2):117-26.
- Thomas H Davenport; Jeanne G Harris. Competing on analytics : the new science of winning. Boston, Massachusetts : Harvard Business Review Press, ©2017
Michelle Longmire MD, is Co-founder and Chief Executive Officer of Medable. Dr. Longmire is a Stanford-trained physician-entrepreneur dedicated to transforming healthcare through innovation. Dr. Longmire has a background in epigenetics, novel diagnostics, and imaging. She has published over 20 peer reviewed articles and holds patents in machine learning as applied to mobile health applications. Dr. Longmire founded Medable in 2014 to enable healthcare technology to be as seamless, integrated, and adaptive as the human body. Medable is transforming healthcare by enabling patient generated data to drive healthcare delivery, clinical research, and personalized and predictive medicine. www.medable.com