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In an Oct. 20 MedPage Today opinion essay, nurse and cancer survivor Davis Austria describes how clinical cancer guidelines failed to answer practical questions during treatment. Austria argues that health systems should test patient-facing materials with patients, especially as they use AI to create or rewrite instructions.
Davis Austria, an emergency and critical care nurse who has had head and neck cancer, argues that health systems should test patient-facing medical materials with patients, after clinical guidelines left him without answers to practical questions during treatment. In an Oct. 20 MedPage Today opinion essay, he says the need is growing as hospitals use artificial intelligence to draft or rewrite patient information.
Austria writes that when he was diagnosed four years ago, his radiation oncologist showed him the National Comprehensive Cancer Network treatment recommendations, including a plan for 10 chemotherapy rounds and 35 radiation sessions. The document was accurate and evidence-based, he says, but written for clinicians. He was handed it and sent home without the patient-oriented guidance he needed.
His questions were about what to expect: whether painful swallowing was normal, whether his hearing would recover, and how he would learn to eat after his feeding tube was removed. Austria says the patient-facing NCCN material he later downloaded advised him to ask a provider about treating a radiation burn. He was using Aquaphor on his neck before radiation, he writes, and only later found another document that explained he had been using it at the wrong time.
Without answers in the documents or a way to ask questions through the patient portal, Austria says he turned to a Facebook group for people with the same cancer. He argues that medical instructions should explain what patients may experience and what they can do, rather than relying on technical terms or directing them elsewhere for basic information.
Patient Instructions Shape Care
Patient instructions can affect how well people understand and manage care after leaving a clinic. Austria’s account highlights a gap between information that is clinically accurate and information that is clear and useful to the person receiving treatment. Terms such as “ototoxicity” may communicate a risk to clinicians while failing to tell a patient in everyday language that a drug could damage hearing or cause ringing in the ears.
The issue takes on added importance as health systems adopt AI tools that can rewrite or generate materials at scale. Austria notes that AI may help people get explanations at times when clinicians are unavailable, but can also provide information that is wrong, incomplete or confidently misleading. He says AI should not replace a patient’s care team, and that its outputs need testing for clarity and practical usefulness as well as accuracy.
Austria cites a randomized study of 128 patients in Germany that compared standard physician-written discharge summaries with AI-generated versions rewritten for patients. The patient-oriented group scored 9.6 points higher on patient activation, a measure of how prepared people are to manage their care. That result is evidence from one study, not proof that every AI-generated instruction improves outcomes.
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From Clinical Guidelines to AI
Austria writes from two perspectives: as a patient treated for head and neck cancer and as a nurse who has built hundreds of treatment order sets in electronic health records. He says his clinicians knew him as a medical professional, which may have shaped how they communicated. He also cites a 2024 study in BMC Primary Care on doctors treating fellow doctors; it found that clinicians changed their communication and sometimes assumed their colleagues already knew more.
His essay describes several forms of patient information: clinical guidelines used to inform treatment decisions, patient-facing guideline versions, discharge paperwork and after-visit summaries. Austria says these materials can include abbreviations and clinical language that make sense to staff but may not help patients understand instructions. He points to Epic’s patient-facing virtual assistant, Emmie, which includes a feature that rewrites after-visit summaries in plainer language.
Austria’s central recommendation is that health systems ask whether materials work for the patients they serve across differences in language, age and social circumstances; whether review goes beyond medical accuracy; and whether actual patients have tested their ability to understand and act on the instructions.
“What I did not know was what was normal.”
— Davis Austria, in his MedPage Today opinion essay
easy-to-understand cancer care instructions
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Evidence and Implementation Gaps
The essay does not report a new study or policy change; it presents Austria’s experience and recommendations. The account does not establish how often patient materials leave people with unanswered questions or whether his experience reflects cancer patients generally. The German trial’s reported 9.6-point difference concerns patient activation; the essay does not provide details here about longer-term health outcomes or whether the result applies across health systems and patient groups.
It is also unclear how widely hospitals use AI to write patient instructions, what review and testing procedures they apply, or how well tools such as Epic’s Emmie perform for different patients. Austria’s personal account of Aquaphor use is not a treatment recommendation. Patients should ask their care team about instructions specific to their treatment.
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Testing Materials With Patients
Austria urges health systems to involve patients before instructions are put into routine use. His proposed checks are whether the material works for the people served, whether it has been reviewed for more than accuracy, and whether patients have tried to understand and act on it. The essay does not announce a deadline, new program or specific next milestone.
For health systems using AI to rewrite materials, the next step in Austria’s argument is to evaluate those outputs with patients rather than assume that simpler wording is automatically more helpful. The article leaves open how institutions will measure that usefulness and how they will address errors. Austria says the standard should apply to all patient instructions, whether written by people or generated with AI.
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Key Questions
What is the news development?
Nurse and cancer survivor Davis Austria published an opinion essay calling for health systems to test patient-facing instructions with patients, particularly as AI is used to write or rewrite them.
What was missing from the information Austria received?
Austria says the clinical materials described treatment but did not answer practical questions about what symptoms were normal, how side effects might affect him, or what to expect next.
What evidence does the essay cite about AI-written summaries?
Austria cites a randomized trial involving 128 patients in Germany. Patients who received AI-generated, patient-oriented discharge summaries scored 9.6 points higher on patient activation than those given standard physician-written summaries. The essay does not claim that this finding proves better health outcomes.
Does Austria say AI should replace clinicians?
No. He says AI can quickly explain health information but can also be wrong or incomplete, and is not a substitute for the clinicians caring for a patient.
What does Austria recommend health systems do?
He recommends checking whether materials work for the patients a system serves, reviewing them for more than medical accuracy, and asking patients to test whether they can understand and act on the instructions.
Source: rss
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