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In Their Own Words

Warning: Spoilers for the Pitt S2 ahead.
LDI Fellows use The Pitt‘s second season to show how fictional storylines often mirror real-world challenges—from a violent assault on an ER nurse to a hospice patient whose end-of-life preferences shift amid great pain, and a near-miss caused by a generative AI charting error. While The Pitt is fiction, the policy implications are very real.
Hear from LDI experts below. More perspectives will be published here next week.

In Episode 10, Roxie, 42, a hospice patient with advanced lung cancer, debates whether to return home to die as her husband wishes. Dr. Robby asserts supporting her choices is the best thing they can do for her. She ends up dying in the hospital with her family by her side.
Katie Auriemma, MD, MSHP: Dying at home is often touted as a central part of a “good death”. Dying at home is cited as the majority preference of Americans. Hospital deaths, particularly for patients enrolled in home hospice, are often considered a system failure. However, preferences for end-of-life care are not always stable, and the reality of caring for someone at the end of life in the home setting, even with supportive services, can be incredibly challenging for patients and families. The importance of eliciting and honoring shifting preferences over time or with changes in health status is central to the provision of goal-concordant care. On a system level, we need better methods to identify and measure delivery of goal-concordant care so that “place of death” is not the only marker of a “good death.”

In Episode 6, a generative AI charting tool, championed by Dr. Al-Hashimi, makes a critical mistake by hallucinating a patient’s history, including a false appendicitis record and incorrect medications, nearly causing a dangerous medical error. This highlights risks of over-dependence on AI and the need for human oversight.
Eric Bressman, MD, MSHP: Though the scene is fictional, the risks it highlights are real. Generative AI documentation tools can hallucinate, inventing details or misstating key facts about a patient’s history. Likewise, automation bias–the tendency to over-rely on the information that AI generates–is a well-documented challenge. In medicine, these kinds of errors can propagate from a charting error into a genuine patient safety problem. The solution is both simple and complex. At the local level, we need to create the conditions for more consistent and reliable review of AI-generated information, both by human providers and, potentially, secondary AI systems designed to flag errors. More broadly, as adoption accelerates, the larger challenge is building rules for clinical AI that emphasize validation, transparency, and accountability before convenience outruns patient safety.

In Episode 11, Emma checks on a sleeping patient who was taken to the hospital for combative behavior and intoxication. The patient wakes up, confused and still combative, and places Emma in a chokehold. She cannot call for help because the room’s door is closed.
Karen Lasater, PhD, RN: Nurses in emergency departments (ED) are assaulted on the job more than nearly any other professional in any industry. In the absence of national standards to prevent workplace violence against health care workers, legislative action is progressing piecemeal across states, or at the discretion of hospital administration, making protection for health care workers uneven. Nurse understaffing in hospitals has been associated with greater incidence of workplace violence and can be addressed through policy mandates to ensure safe care environments for clinicians and patients alike. Understaffing of nurses in inpatient units contributes to bottlenecks in the ED, with patients boarding in hallway beds and leaving before care is finished due to frustrating care delays in the chaotic and high-stress environment. For these reasons, ED clinicians endorse safe nurse staffing standards (among others) as a workplace violence prevention strategy.
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