SDO Opinion - How To Make Leaders Pay Attention to Your Next Data Initiative
Case study on a failed AI initiative.
The data market is changing drastically in 2023, and business fundamentals are more important than ever in a world where capital is no longer cheap. Though data initiatives have the potential to generate millions in revenue, many C-suite executives are beginning to question the viability of expensive data teams that are often burdened with high upfront costs and long-tail ROI. It’s not enough to have analytics and ship ML models– data teams must show their impact on the business’s strategy and bottom line. But how can data teams communicate this to leadership?
I recently came across a viral post on Twitter by Julia MacDonald that aligned with the processes I’ve used to successfully deliver high-impact data projects within various startups. Specifically, she shared her experience as a McKinsey consultant utilizing the Hypothesis-Driven Framework, in which leaders paid over $400k for their presentations. I highly encourage going through the original post on Twitter, but to quickly summarize the process, she detailed seven steps:
1. Gather the Facts
2. Formulate an Initial Hypothesis
3. Build an Issue Tree
4. Understand the Big Picture
5. Set the Stage with SPQA
6. Persuade with the Pyramid Principle
7. Make the Impact Clear
I had to learn more from her, so I reached out via DMs… and she responded. Our conversation resulted in the following case study that dives into how a data team could support leadership, via the Hypothesis-Driven Framework, after a major company mistake. Specifically, Epic System’s (electronic health record software company) recent blunder where their deployed ML classification model for sepsis performed worse than standard care within many hospitals.
Case Study — Epic’s Sepsis AI Blunder
Through my graduate studies at Stanford Medicine and work in the real-world evidence space in healthcare, one thing has become abundantly clear: the potential of AI in healthcare is massive… but the difficulty of safely deploying such models is even greater.
The Epic Sepsis Model is an excellent example of the challenges of such endeavors. According to a 2021 JAMA research paper:
The Epic Sepsis Model predicted the onset of sepsis with an area under the curve of 0.63, which is substantially worse than the performance reported by its developer.
This huge mistake resulted in Epic Systems pulling the product feature from hospitals, losing substantial money, and increasing scrutiny from regulators.
Using the Hypothesis-Driven Framework, how could a data team assist executives at Epic Systems in 1) recognizing the scope of impact, 2) the root cause of the issue, and 3) developing a path forward to reimplement the model to improve patient outcomes safely and recover a revenue stream?
1. Gather the Facts
According to the above 2021 JAMA research paper on the poor performance of the Epic Sepsis Model (ESM), the potential root cause of the failed model can be summed up into four points:
ESM missed 7% of Sepsis cases leading to late administration of antibiotics — this is worse than standard care.
ESM also caused “alert fatigue” via false positives for 18% of patients.
The algorithm behind ESM is proprietary and thus lacks peer-review performance documents, and there is minimal regulation for these models.
Epic is one of the largest electronic health record providers, leading to quick mass adoption in hospitals.
2. Formulate an Initial Hypothesis
From the above-gathered facts, a data team could generate the following hypothesis:
“The Epic Sepsis Model is performing poorly as it is trained on national-level data but can’t be retrained for local-level differences within hospitals due to the model being proprietary and thus opaque.”
3. Build an Issue Tree
This massively complex problem can go in numerous directions for causes. Rather than get stuck in analysis paralysis, Julia recommended in her original post to break a large problem into multiple smaller problems that are much easier to manage.
Below is the issue tree that a data team could create detailing the potential problems of the Epic Sepsis Model:
4. Understand the Big Picture
Now the data team can take the individual issues and craft a story of the big-picture problem being experienced by Epic and provide a recommendation:
“Despite Epic having one of the largest electronic health record datasets available, the Epic Sepsis Model model built on this data is generalized on a national level but not to a specific hospital level.
Coupled with Epic being a proprietary software with one of the largest market shares, this model was adopted quickly without being validated in the respective hospitals it was deployed in.
This led to 7% of false negatives and 18% of false positives, resulting in below-standard care that did not align with initial study results released by Epic — leading to further scrutiny from regulators, Epic pulling the product feature, and lost trust among healthcare providers.
We recommend that the Epic Sepsis Model be retrained on data specific to a respective hospital and only if the hospital meets the inclusion criteria to retrain the model successfully.”
5. Set the Stage with SPQA
SPQA stands for situation, problem, question, and answer. Something data professionals, including myself, struggle with is going way too deep into details. This is one of the quickest ways to lose the interest of executive leadership. Thus, SPQA is an excellent tool to quickly provide them with the needed information. If an effective data team were providing details to Epic’s leadership, I would imagine they would share the following:
Situation:
Epic released a product feature utilizing AI to classify if users are at risk for sepsis. This resulted in below standard of care for patients, the product feature being pulled, and regulator scrutiny.
Problem(s):
The released proprietary model was trained on national-level data but performed poorly when faced with local-level data within hospitals.
Coupled with a large market share, the propriety model was quickly adopted through Epic’s distribution channels without hospitals being able to validate for their respective populations under the false security of national-level results.
Question:
Can Epic release the sepsis model again while addressing the poor performance at a local hospital level?
Answer:
Yes, by making the product feature less opaque and allowing hospitals to retrain the sepsis model on data that are representative of their respective hospital population.
6. Persuade with the Pyramid Principle
Describing the problem to executive leadership is not enough for them to take action; a data team needs to persuade leadership that the issue and solution they are providing must be prioritized now. The Pyramid Principle is highly effective at doing such.
7. Make the Impact Clear
Now that the data team has determined the root cause and has buy-in from executive leadership that the problem needs to be addressed, the data team has to sell a vision of how they can alleviate the pain of the business. This is one of the most crucial steps in illustrating the data team’s impact on the business’s strategy and bottom line. I can imagine the data team suggesting the following to Epic’s executive team:
Work with regulators to fully understand the problem and repair trust with the medical community.
Identify CTOs of major hospital systems using Epic EHR software to build partnerships with key influencers of the healthcare market to retry the Epic Sepsis Model.
Build a software platform that allows hospital data teams to securely access and retrain the sepsis model with data representative of their respective hospital.
Create stronger inclusion and exclusion criteria for hospitals eligible to use the sepsis model in which they have the infrastructure to retrain the model to their specific hospital.
Utilize a neutral third party, such as a university healthcare system not using Epic, to validate the new model while accounting for the heterogeneity of different hospital locations.
Real-World Result
Though the above is just a thought exercise, it was rooted in real-life events that impacted patients' lives. In October 2022, Stat News released an article with the following:
… Epic is now recommending that its model be trained on a hospital’s own data before clinical use, a major shift aimed at ensuring its predictions are relevant to the actual patient population a hospital treats.
Conclusion
I hope this case study on utilizing the Hypothesis-Driven Framework can help you drive more value in your organizations and highlight to leadership how your data initiatives impact the business’s strategy and bottom line. Again, I want to thank Julia MacDonald for allowing me to leverage her original Twitter thread and providing feedback on this blog post. I highly encourage following her on LinkedIn and Twitter to see more of her helpful content.
Referenced Sources:
Commissioner, O. of the. (n.d.). Real-world evidence. U.S. Food and Drug Administration. Retrieved May 5, 2023, from https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence#:~:text=What%20is%20RWE%3F,derived%20from%20analysis%20of%20RWD.
Habib, A. R., Lin, A. L., & Grant, R. W. (2021). The epic sepsis model falls short—the importance of external validation. JAMA Internal Medicine, 181(8), 1040. https://doi.org/10.1001/jamainternmed.2021.3333
MacDonald, J. (2022, December 16). At McKinsey, we charged $400K+ per presentation.here's the simple 7-step framework we used (steal it for free)🧵: Twitter. Retrieved May 5, 2023, from https://twitter.com/julia_m_mac/status/1603727229053173767
Ranadive, A. (2013, June 21). The pyramid principle. Medium. Retrieved May 5, 2023, from https://medium.com/lessons-from-mckinsey/the-pyramid-principle-f0885dd3c5c7
Ross, C. (2022, September 30). Epic overhauls popular sepsis algorithm criticized for faulty alarms. STAT. Retrieved May 5, 2023, from https://www.statnews.com/2022/10/03/epic-sepsis-algorithm-revamp-training/
Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., Ghous, M., & Singh, K. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine. https://doi.org/10.1001/jamainternmed.2021.2626
About On the Mark Data:
On the Mark Data helps brands connect to data professionals through captivating content, such as this newsletter and other featured content! Please feel free to check out my website to learn how I can support your data brand via influencer marketing or content and go-to-market strategy consulting.