In 2023, Israel-based AI health care company Aidoc evaluated its future. The company, founded in 2016, had grown from commercializing a single AI product for radiologists to a software platform that could detect 20 conditions and immediately notify care teams of critical results for priority patients. Aidoc's products detected over 75% of common acute pathologies visible on CT scans. The company had 20 algorithms used by over 900 global hospitals and had 17 FDA clearances-the highest number of any clinical AI company at the time. The company, which had initially produced point solutions, had evolved to manage a multisided network platform that hosted its own as well as its partners' algorithms. Aidoc had a rich history of producing highly accurate AI products, but partnership allowed it to bring more innovative products to market. The company needed to determine how to continue to grow. Should it focus more on developing highly technical AI health care solutions or on developing and managing a platform to bring other company's products to its growing customer base?

In mid-January 2022, Nadine Hachach-Haram, founder and CEO of Proximie, was thinking about the company's growth plans. Launched in 2016, Proximie was a platform that enabled clinicians, proctors, and medical device company personnel to be virtually present in operating rooms (OR) where they would use mixed reality and a suite of digital audio and visual tools to communicate with, mentor, assist, and observe those performing procedures. The goal was to improve patient outcomes. The company had grown quickly, opening offices in Beirut, London, and Boston, and had 135 employees. Proximie's technology had been used in tens of thousands of procedures in over 50 countries and 500 hospitals. It had raised close to $50 million in equity financing and was now entering strategic partnerships to broaden its reach. In deploying its platform, Proximie also digitized and structured formerly uncaptured data from ORs, making it possible to build a novel database of rich surgical data and prepare an infrastructure for both analytics and for curating insights from users. Hachach-Haram aspired for Proximie to become a platform that powered every OR in the world. To achieve these goals, Hachach-Haram had to navigate several challenges as she scaled the company. She needed to carefully think about the company's partnership and data strategies. Which formula would position the company best for the next stage of growth?

Set in early 2020, this (B) case provides an update to the (A) case (no. 622-009) and provides additional context regarding the challenges facing Somatus.

Hello Heart, a hypertension management app debated whether to go deep and cover other heart conditions, or to expand its solution to other chronic conditions.

When Dr. Ikenna Okezie founded Somatus, a value-based kidney care provider, his goal had been nothing short of transforming kidney care delivery in the United States. Rather than relying on dialysis, a costly and intensive treatment for late-stage kidney disease, the Somatus model called for early identification of patients at high risk along with active management of the underlying conditions that accelerated kidney damage. But he had struggled to find health plans willing to take a chance on his unproven startup, especially in the highly concentrated dialysis treatment market. Thus, in 2017, Somatus agreed to manage traditional dialysis services for a group of hospitals in Virginia, focusing on improving the quality of care. In 2018, Somatus won its first contract with a health plan, finally allowing the startup to implement its full care model. But the contract generated just 10% of Somatus's revenues. Now, in December 2019, Okezie must decide whether to prioritize improving traditional dialysis services or continue to chase health plans willing to implement Somatus's innovative kidney care model.

This (C) case provides an update on the work of the Global Coalition for Adaptive Research (GCAR) and also illustrates how adaptive platform trials can nimbly respond to a global pandemic.

In 2019, the co-founders of the Swedish medical start-up 1928 Diagnostics, CEO Dr. Kristina Lagerstedt and COO Dr. Susanne Staaf, had to pick the right business model to commercialize their novel technology to hospitals and health care providers. Developed in partnership with research hospitals to help fight the global antibiotic resistance crisis, the firm's cloud-based technology platform helped partners identify resistant genes and mutations in bacteria more quickly and accurately, allowing for easier outbreak cluster tracking in support of hospital infection control management, as well as better diagnostics and antibiotic selection. By 2019, they had raised $5 million, employed 16 people, and had their tool deployed at 24 partner sites in 10 different countries. Their decisions on which markets to focus on and with which business model would crucially impact the young firm's chances at successfully converting existing users and attracting new clients.

In late 2017, Kate Ryder, the founder and CEO of digital women's health telemedicine company Maven Clinic, faced an important decision. Maven offered both a direct to consumer (D2C) product that anyone could use to book virtual appointments with health practitioners across a range of services and specialties for a fee, and an enterprise product-the Maven Family Benefits platform-which it sold to large employers as an employee benefit. Since founding Maven in 2014, Ryder had believed that the way to achieve true impact and scale is to work within that system, but the most effective strategy for pursuing enterprise customers was an open question. She considered her options.

This Technical Note provides an introduction to Bayes' Rule and the statistical intuition that stems from it. In this note, we review the concepts that underlie Bayesian statistics, and we offer several simple mathematical examples to illustrate applications of Bayes' Rule. Instructors can assign this note either as a standalone reading to familiarize students with Bayesian statistics or as a supplement to accompany "Adaptive Platform Trials: The Clinical Trial of the Future?" (HBS Case No. 618-025). This case introduces the concept of an adaptive platform clinical trial, in which Bayesian statistical methods are used to expedite the investigation and regulatory approval of promising new therapies, such as cancer treatments.

This case provides an update to the (A) case, which introduces students to adaptive platform trials, an ambitious, more efficient type of clinical trial that increases access to therapies. The A case centers on Dr. Brian Alexander's efforts to launch an adaptive platform trial for studying potential therapies for glioblastoma (GBM), an aggressive form of brain cancer. The (B) case provides an update and explores the strategies that Alexander is considering for sustainably financing this trial.

In September 2018, the executive team at Ariadne Labs (Ariadne), a Boston-based organization dedicated to improving health systems through the discovery and implementation of simple tools, faced a number of strategic decisions. Chief among them, the seven-year-old organization needed to determine how it might establish and shepherd successful partnerships with a wide range of diverse entities-ranging from major international public health organizations like the World Bank to small non-profit implementing organizations.

In July 2017, Dr. Brian M. Alexander, president and CEO of the AGILE Research Foundation, was preparing to launch a new type of clinical trial-an adaptive platform trial-to study potential therapies for glioblastoma (GBM), an aggressive form of brain cancer. Alexander believed that the standard way in which new cancer drugs were tested-the traditional randomized controlled trial (RCT)-was limited in many ways. While statistically rigorous and still considered the "gold standard" in clinical research, traditional RCTs were time-consuming, costly, and limited to testing just one new drug at a time. Adaptive platform trials, by contrast, facilitated simultaneously studying multiple therapies for a given disease and promised a number of efficiency improvements. They also used statistical techniques to allow more patients to access promising therapies. As such, they had the potential to fundamentally change the clinical research process, making clinical trials for new cancer drugs more efficient, more accessible to patients, and more ambitious in scope. For the past three years, Alexander had been working closely with a group of like-minded oncologists, statisticians, and clinical trial strategists to design an adaptive platform trial for GBM in the hopes of identifying effective therapies more quickly. By mid-2017, Alexander and his colleagues had completed a master protocol for the trial. But now the research team faced several design and operational challenges as they prepared for the trial's launch. Most pressing, how should Alexander and his colleagues finance the trial?

In late 2014, Dr. John Moore (CEO), Frank Moss (chairman), and Scott Gilroy (CTO) of Twine Health (Twine) had to resolve several challenges that threatened to restrict the widespread dissemination of its sole product, Twine. Twine was a cloud-based platform that enabled patients to create and manage chronic disease treatment plans in conjunction with their primary care providers and specialized coaches. Twine had already enjoyed impressive successes in early clinical trials and among early adopters. The issues Twine's leadership team had to address included identifying clinical care providers willing to pay for Twine, ensuring adoption and effective use by both patients and health care providers, adding capabilities to support the management of additional chronic diseases, and seamlessly integrating Twine with a client organization's electronic medical record (EMR) system and information technology (IT) infrastructure. The need to solve these problems had become more pressing since Twine was named a finalist in the Health Acceleration Challenge (HAC) sponsored by Harvard Business School and Harvard Medical School.