Comment
ChatGPT: the future of discharge summaries?
ChatGPT (Open AI, San Francisco, CA, USA) has
taken the world by storm.1 Released to the public
in November, 2022, ChatGPT is based on artificial
intelligence (AI) technology and trained on data from
the internet written by humans, including conver­
sations. This AI-powered chatbot has vast capabilities
ranging from poem composition, essay writing, solving
coding issues, and explanation of complex concepts
including “how can we fix the UK National Health
Service?” Some consider that ChatGPT has advanced
the online search to the next level;2 the program offers
rapid and in-depth understanding of complex matters
and generates custom responses in a conversational
manner to the exact question asked, recalling its own
previous responses. Although still only a research
preview, ChatGPT has shown impressive performance
across multiple use cases. One potential area of
ChatGPT’s application could be to generate discharge
summaries. Composition of high-quality discharge
summaries containing the requisite information can
be time consuming, with the burden usually falling on
junior doctors. Therefore, when balanced against clinical
commitments, discharge summaries are often left underprioritised resulting in delays in patients’ discharges or
insufficient discharge summaries, which not only places
pressure on an already stretched junior doctor workforce,
but also presents potential patient safety issues in the
transition of care from secondary to primary care.3
ChatGPT allows doctors to input a brief of the specific
information to include, concepts to elaborate on, and
guidance to explain, outputting a formal discharge
summary in a matter of seconds (panel). Discharge
summaries are an obvious choice for this technology
because of their largely standardised format; this
standardisation has previously been recognised through
the creation of templates—eg, from the Royal College of
Physicians.4 Automation of this process could therefore
ease the work burden upon junior doctors allowing
more time for the delivery of patient care and the
seeking of training opportunities, often in roles centred
on service provision. ChatGPT could also improve the
quality of the discharge summaries themselves, with
previous literature showing discharge summaries that
are written through traditional means are often missing
details.5
www.thelancet.com/digital-health Vol 5 March 2023
Although ChatGPT offers significant promise,
deploy­ment into clinical practice will require multiple
barriers to be overcome. First, ChatGPT depends on
the data it receives and therefore still necessitates
salient information to be manually inputted. Future
iterations might allow automatic scraping of data
from the patient’s electronic health record without
the need for manual inputting of a brief. This practice,
however, presents significant issues with respect to data
governance. Previous use of AI within health care has
been associated with failings that significantly damage
patients’ trust6 and therefore care must be taken around
data storage and access.
Second, acceptability of this technology, from
patients in particular, must be considered. The concern
that automation might lead to depersonalisation of
care could lead to resistance to this technology. Pilot
Published Online
February 6, 2023
https://doi.org/10.1016/
S2589-7500(23)00021-3
Panel: ChatGPT’s response to a simple discharge summary request
Prompt
“Write me a discharge summary for John Doe, a 70-year-old man who has had a total hip
replacement 5 days ago and now can leave hospital to go home.”
Response*
“Patient: John Doe
Age: 70 years old
Date of admission: [date]
Date of discharge: [date]
Reason for Admission: Total hip replacement surgery
Summary of Hospital Course:
Mr Doe was admitted to the hospital on [date] for a total hip replacement surgery.
The surgery was successful and Mr Doe has made good progress in his recovery. He has
been compliant with his physical therapy and pain management regimen, and has
demonstrated good mobility and strength in his affected leg. He is able to perform
activities of daily living independently and has demonstrated good understanding of his
post-operative care instructions.
Recommendations for follow-up care:
Mr Doe is advised to continue with his physical therapy as prescribed by his physical
therapist. He should also continue to take his pain medication as directed and follow a
healthy diet and exercise routine to promote healing and maintain good overall health.
He should follow up with his orthopaedic surgeon in 1–2 weeks for a post-operative
check-up.
Overall, Mr Doe is in good condition and is ready for discharge from the hospital.
Signed,
[physician’s name]”
*Although this summary is likely to be accurate for most patients, the additional information that was not included in the
prompt was automatically generated by ChatGPT, highlighting the need for manual checking by clinical staff before
finalisation.
e107
Comment
trial data is therefore necessary to gather stakeholder
views and ultimately show improvements in efficiency
and quality of processes. Finally, the consequences of
technology failure must be considered. Although asking
trivial questions to a chatbot at home might have few
consequences, incorrect or inadequate information
concerning medication changes or follow-up can greatly
affect patient care. Therefore, although ChatGPT can be
considered as a first step in the writing process, manual
checking of its output by a doctor will be required before
completion.
ChatGPT demonstrates the power of this next gen­
eration of chatbots powered by large language models.
Although the use of chatbots within health care is by no
means novel, having previously been applied to areas
such as triage,7 this next generation aims to advance
the field significantly through its generative capacities.
Health care is an industry that has significant scope
for automation, especially in areas of documentation.
We should be reactive to these advances in technology
and explore potential use cases and the feasibility of
using chatbots such as ChatGPT across diverse areas in
health care. Proactive early adoption of this technology
at a central level might also reduce the risk of issues in
the future; parallels can be drawn with interoperability
issues due to differing electronic health records adopted
e108
at a local level.8 The question for the future will be how,
not if, we adopt this technology. We declare no competing interests. Funding and infrastructural support was
provided by the UK National Institute for Health and Care Research (NIHR)
Imperial Biomedical Research Centre. KL is supported by a NIHR Academic
Clinical Fellowship.
Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open
Access article under the CC BY-NC-ND 4.0 license.
Sajan B Patel, *Kyle Lam
k.lam@imperial.ac.uk
Department of Surgery and Cancer, St Mary’s Hospital, London W2 1NY, UK
(SBP, KL)
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www.thelancet.com/digital-health Vol 5 March 2023