Journal Title Here DOI HERE Advance Access Publication Date: Day Month Year
Paper

Falis et al.

[$\ast$]Corresponding author: Matúš Falis, School of Informatics, The
University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, United
Kingdom<EMAIL_ADDRESS>
# Can GPT-3.5 Generate and Code Discharge Summaries?

Matúš Falis (MScR)  Aryo Pradipta Gema0009-0007-1163-3531 (MScR)  Hang
Dong0000-0001-6828-6891 (PhD)  Luke Daines0000-0003-0564-4000 (PhD)  Siddharth
Basetti0000-0002-7639-1859 (MBSS)  Michael Holder0000-0001-9828-9153 (MMedSci)
Rose S Penfold0000-0001-7023-7108 (BMBCh)  Alexandra Birch0000-0002-9022-3405
(PhD)  Beatrice Alex0000-0002-7279-1476 (PhD)  School of Informatics, The
University of Edinburgh, Edinburgh, United Kingdom  Department of Computer
Science, University of Oxford, Oxford, United Kingdom  Centre for Population
Health Sciences, Usher Institute, The University of Edinburgh, Edinburgh,
United Kingdom  Department of Research, Development and Innovation, National
Health Service Highland, Inverness, United Kingdom  Ageing and Health, Usher
Institute, The University of Edinburgh, Edinburgh, United Kingdom  Advanced
Care Research Centre, The University of Edinburgh, Edinburgh, United Kingdom
Edinburgh Futures Institute, The University of Edinburgh, Edinburgh, United
Kingdom  School of Literatures, Languages and Cultures, The University of
Edinburgh, Edinburgh, United Kingdom

(2023; 2023)

# Can GPT-3.5 Generate and Code Discharge Summaries?

Matúš Falis (MScR)  Aryo Pradipta Gema0009-0007-1163-3531 (MScR)  Hang
Dong0000-0001-6828-6891 (PhD)  Luke Daines0000-0003-0564-4000 (PhD)  Siddharth
Basetti0000-0002-7639-1859 (MBSS)  Michael Holder0000-0001-9828-9153 (MMedSci)
Rose S Penfold0000-0001-7023-7108 (BMBCh)  Alexandra Birch0000-0002-9022-3405
(PhD)  Beatrice Alex0000-0002-7279-1476 (PhD)  School of Informatics, The
University of Edinburgh, Edinburgh, United Kingdom  Department of Computer
Science, University of Oxford, Oxford, United Kingdom  Centre for Population
Health Sciences, Usher Institute, The University of Edinburgh, Edinburgh,
United Kingdom  Department of Research, Development and Innovation, National
Health Service Highland, Inverness, United Kingdom  Ageing and Health, Usher
Institute, The University of Edinburgh, Edinburgh, United Kingdom  Advanced
Care Research Centre, The University of Edinburgh, Edinburgh, United Kingdom
Edinburgh Futures Institute, The University of Edinburgh, Edinburgh, United
Kingdom  School of Literatures, Languages and Cultures, The University of
Edinburgh, Edinburgh, United Kingdom

(2023; 2023)

###### keywords:

ICD Coding, Data Augmentation, Large Language Model, Clinical Text Generation,
Evaluation by Clinicians

250 words in abstract; 3,929 words in main body; 2 figures (0 black and white,
2 colour); 4 tables; 34 references.

##  1 Abstract

Objective: To investigate GPT-3.5 in generating and coding medical documents
with ICD-10 codes for data augmentation on low-resources labels.
Materials and Methods: Employing GPT-3.5 we generated and coded 9,606
discharge summaries based on lists of ICD-10 code descriptions of patients
with infrequent (generation) codes within the MIMIC-IV dataset. Combined with
the baseline training set, this formed an augmented training set. Neural
coding models were trained on baseline and augmented data and evaluated on a
MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset,
generation codes, and their families. Weak Hierarchical Confusion Matrices
were employed to determine within-family and outside-of-family coding errors
in the latter codesets. The coding performance of GPT-3.5 was evaluated both
on prompt-guided self-generated data and real MIMIC-IV data. Clinical
professionals evaluated the clinical acceptability of the generated documents.
Results: Augmentation slightly hinders the overall performance of the models
but improves performance for the generation candidate codes and their
families, including one unseen in the baseline training data. Augmented models
display lower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by
the prompted descriptions, but performs poorly on real data. Evaluators note
the correctness of generated concepts while suffering in variety, supporting
information, and narrative.
Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding.
Augmentation positively affects generation code families but mainly benefits
codes with existing examples. Augmentation reduces out-of-family errors.
Discharge summaries generated by GPT-3.5 state prompted concepts correctly but
lack variety, and authenticity in narratives. They are unsuitable for clinical
practice.

##  2 BACKGROUND AND SIGNIFICANCE

Large-Scale Multi-Labelled Text Classification (LMTC) tasks in Natural
Language Processing (NLP) associate input documents with a set of output
labels from a large label space, often hierarchically with a big-head long-
tail distribution and data sparsity issues.

Medical document coding is the task of assigning structured codes from a
medical ontology – _e.g._ , the International Classification of Diseases
(ICD)111https://www.who.int/standards/classifications/classification-of-
diseases – to clinical documents, a task performed by specially trained
hospital staff. Coding consumes human resources that could be allocated to
patient care. To ease this burden, research in machine learning and NLP cast
medical document coding as an LMTC task [1].

In automatic ICD Coding, discharge summaries serve as input, yielding codes
from a specified ICD version (_e.g._ ,
ICD-10-CM)222https://www.cdc.gov/nchs/icd/icd-10-cm.htm. ICD coding faces
distribution challenges mirroring other LMTC tasks. Few common conditions
(_e.g._ , hypertension), contrast with many underrepresented or absent in
corpora, such as MIMIC-IV [2]. Moreover, limited real-world data availability,
often restricted for privacy reasons, compounds these challenges. However,
modern deep learning ICD coding approaches (_e.g._ , CAML [3], HLAN [4], RAC
[5]) are data-driven, and adversely affected by data sparsity unless
explicitly designed to handle label under-representation. Techniques such as
auxiliary information [6, 7, 8, 9], or data augmentation and synthesis [10,
11, 12] attempt to mitigate these issues. ICD coding models with pre-trained
encoders at best match the current state-of-the-art – usually involving
domain-specific versions of BERT [13]. Large Language Models (LLMs) such as
GPT-3 and its newer variants [14] (_e.g._ , GPT-3.5) or Large Language Model
Meta AI [LLaMA; 15] have recently displayed state-of-the-art performance on
several tasks with emerging capabilities [16]. In the medical domain, notably
Med-PaLM 2 [17] matching human performance in multiple-choice medical schools’
exams. While using models such as GPT-3.5 is problematic with real discharge
summaries due to privacy issues, these models have the potential to aid in
generating synthetic discharge summaries for training local models.

Recently, Large Language Models (LLMs), notably GPT-3.5, have become a new
standard for advanced NLP tasks, especially ones reliant on understanding
natural language. These models retain and apply background knowledge observed
during training, yet can also produce fluent but inaccurate information (known
as hallucination) [18]. Writing and coding discharge summaries requires
extensive background knowledge making it of interest to explore an LLM’s
capability of reading, coding, and generating discharge summaries.

An LLM capable of handling medical text could address the data sparsity issue
by synthesising new data. This study aims to investigate the viability of
GPT-3.5-generated medical documents for data augmentation in training local
neural models and their credibility in clinical settings. We investigate
GPT-3.5 given its performance in natural language understanding tasks
requiring background knowledge, within an ethical experimental setting for
clinical note data (by disabling content monitoring by the service provider).
Furthermore, we explore GPT-3.5’s ability to code real discharge summaries and
self-generated text.

Recent studies have prompted discussions on GPT’s utility in medicine,
including applications in medical chatbots [19], or radiology [20]. Yeung et
al. [21] compared the ChatGPT with a clinical GPT model [22] on generating
patient vignettes. While research exists in generating data in low-resource
settings in similar domains (_e.g._ , law [23]), to the best of our knowledge,
GPT’s performance in generating discharge summaries based on input conditions
and its ability to perform ICD coding has not yet been reported.

## OBJECTIVE

This study aims to assess GPT-3.5’s efficacy in the context of automated
ICD-10 coding and investigate its viability as:

  * •

A data generator for ICD-10 coding, enhancing the training of local neural
models by including GPT-3.5-generated discharge summaries, especially for rare
labels;

  * •

An automated ICD-coding classifier, either using synthetic text with explicit
code descriptions or real data as prompts;

  * •

A discharge summary generator focusing on producing clinically accurate and
plausible synthetic data from expert perspectives.

## MATERIALS AND METHODS

We queried GPT-3.5
(gpt-3.5-turbo)333https://platform.openai.com/docs/models/gpt-3-5, from hereon
referred to as _G_ , through the OpenAI Python
API444https://platform.openai.com/docs/api-reference?lang=python to generate a
patient discharge summary based on specific conditions and procedures
represented by ICD-10-CM and ICD-10-PCS code descriptions from gold standard
labels associated with MIMIC-IV discharge summaries (from the table
hosp/d_icd_diagnoses.csv.gz). These produced label descriptions chosen to
represent realistic combinations of conditions and procedures are not
considered a part of the MIMIC-IV dataset. Note that sharing access to MIMIC
data via any online API is
prohibited555https://physionet.org/news/post/415666Our method was consulted
with and approved by PhysioNet, as we merely use the descriptions of attached
codes.

While various dataset splits have been proposed since the release of coded
discharge summaries in MIMIC-IV, we specifically chose not to follow the one
proposed by Edin et al. [24] due to its exclusion of the long tail, which
contrasts with our aim of addressing this aspect through generation
techniques. Instead, we adopted the dataset split proposed by Nguyen et al.
[25] that preserved the long-tail, aligning better with our focus.
Nonetheless, we utilised the implementation of common ICD coding models
produced by Edin et al. [24] for our analysis.

###  2.1 Label Selection

Candidate source documents for generation were selected from MIMIC-IV based on
label populations in our selected split. We identified codes common across
training, validation, and test sets, choosing those appearing up to 5 times in
the training set, resulting in 195 unique codes (compared to 15,353 unique
few-shot codes in training).

For these 195 codes we produced a list of codes belonging to their families
(identified by the head of the code). Families with at least one relatively
frequent code (population $>$ 100 in training) and at least one code exclusive
to the test set (zero-shot) were retained leaving 16 families. We randomly
selected 10 of these to generate from. For each family, we gathered its
descendant codes appearing in the training set (139) and the zero-shot codes
(16) totalling 155 codes. Of these, 114 codes had a population lower than 100
and are henceforth termed _generation codes_ (list of codes available in
Supplementary Material).

###  2.2 Preparation of Samples for Generation

In the Nguyen’s MIMIC-IV training set, we found documents with the 98 relevant
few-shot codes (the 16 zero-shot were by-definition absent). Some documents
contained multiple relevant codes. We have collected documents for each of the
relevant codes and cloned them to bring their population up to 100. To reduce
duplication, we have randomly dropped up to 5 of the assigned non-relevant
labels within clones to create the new set of labels for generation (referred
to as the silver standard).

We identified documents containing the siblings of the 16 zero-shot labels. A
silver standard set was created for each of these documents substituting the
sibling code with the zero-shot code (similar to the zero-shot approach in
[10]). If multiple siblings were present, a random one was replaced with the
zero-shot code.

This resulted in 9,606 input sets of labels – 6,779 unique and 2,827
duplicated.

###  2.3 Generation

Natural Language Generation is the task of producing natural language text
based on a set of input data. We used the model “gpt-3.5-turbo-0613” given its
wide recognition in the field of LLMs, relative cost-effectiveness, and time
efficiency (compared to gpt-4). For distinct label sets, we utilised a
temperature (parameter in the 0-1 range controlling randomness) of 0 to
produce deterministic outputs. A temperature of 0.1 was employed for
duplicates, allowing output variation.

Within the prompt (available in Supplementary Material), we state the task of
writing a discharge summary for a patient with a list of standard descriptions
of their conditions and procedures based on our silver standard. We also add
further specifications:

  * •

Length of up to 4,000 words (following the maximum cutoff point in previous
work [24]). The overall input and output token restriction of GPT-3.5 is 4,096
(inclusive of the prompt);

  * •

Inclusion of social and family history;

  * •

Anonymisation was required for personal and location data (due to uncertainty
of the anonymity of the data used in training of GPT-3.5), maintaining numeric
information when relevant despite potential removal in preprocessing;

  * •

Explicit ICD code mentions within the main text were to be avoided to prevent
model association or potential errors;

  * •

Clear numeric values were preferred rather than ranges, especially for time-
related codes;

  * •

Providing a specific concept for codes involving the umbrella term “other”
encompassing a range of conditions;

  * •

Omission of the keyword “unspecified” present in standard descriptions opting
for a more natural means of expression;

  * •

Coding of the discharge summary was to be positioned at the end in a regular
pattern (codes in square brackets) for model coding assessment.

The generated documents were processed to find the discharge diagnoses and
extract the assigned codes with a regular expression.

In total, we have generated 9,606 synthetic training documents. We removed all
mentions of ICD-10 codes and pre-processed the documents the same as the
baseline data. Then we merged these documents with the _baseline_ training set
(110,442 MIMIC-IV documents), forming the _augmented_ training set (120,048
documents). The baseline and augmented settings used the same validation and
test sets with 4,017 and 7,851 real documents, respectively.

Analysis between the generated text, and MIMIC-IV ICD-10-coded discharge
summaries (the entire dataset, and the subset used for source label sets used
in generation) showed differences in word count (per label and overall –
Figures 1a and 1b) between synthetic and real data while retaining similar
distributions of label counts (Figure 1c). The synthetic discharge summaries
tend to be shorter which could be the result of GPT-3.5’s 4,096 token limit.
Upon publication, we intend to make the synthetic discharge available.

(a)

(b)

(c)

Figure 1: A comparison between the discharge summary data in MIMIC-IV, the
source data, and the generated discharge summaries. Subfigures 1a and 1b focus
on the number of words in documents, indicating that the GPT-generated data
generally contains fewer words overall and per assigned label compared to the
real data from MIMIC-IV. Subfigure 1c demonstrates that, although there’s a
variance in document size, the distribution of the number of labels per
document remains relatively similar across the datasets.

###  2.4 Local Neural Models

Most recent LMTC neural architectures are encoder-decoder models whose encoder
processes the input text to generate a latent representation.

Architectures using a non-BERT-like encoder (_e.g._ , in CAML [3], LAAT [26],
or Multi-Res CNN [27]) utilise non-contextual (_e.g._ , Word2Vec [28]) word
embeddings, while BERT([29])-like encoders (_e.g._ , in PLM-ICD[30]) enable
contextual token representation. The decoder determines a probability for each
label based on the latent representation. A probability threshold determines
positive predictions.

###  2.5 Evaluation

We have conducted four evaluation rounds:

  * •

Local Neural Model Evaluation: Assessing CAML, LAAT, and Multi-Res CNN models’
performance on Nguyen’s test set. Models were trained either solely on
Nguyen’s training set or enhanced with data generated by _G_ (the augmented
training set).

  * •

GPT’s coding on real data: Evaluation of _G_ ’s coding ability on MIMIC-IV
using Nguyen’s test set;

  * •

GPT’s coding on GPT-generated data: Evaluation of _G_ ’s coding ability on
generated documents (with provided code descriptions in the prompt)

  * •

Acceptability of Generated Data in Clinical Practice: Reviewing _G_ -generated
discharge summaries by clinical professionals to gauge their suitability in
clinical settings.

####  2.5.1 Local Neural Model Evaluation

We used the codebase and training procedure of Edin et al. [24] to train and
evaluate CAML, LAAT, and Multi-Res CNN on the ICD coding task using Nguyen’s
split. Each model is trained with 20 epochs on the training set. Throughout
the training, we evaluated the model on the validation set, selecting the
model with the highest mean average precision (MAP) from each run as the final
model. Subsequently, this final model underwent evaluation on the test set.
Additionally, we explored PLM-ICD, considered a state-of-the-art model for
this task. However, its performance on the baseline and augmented data was
notably lower than previously reported. This model has been reported to be
unstable during training, which depends on random seeds. We decided against
tuning the random seed, and only report the performance for CAML, LAAT, and
Multi-Res CNN.

The models’ test performance was evaluated using standard information
retrieval metrics commonly used in LMTC tasks – micro- and macro-averaged
Precision, Recall, and F1 scores

Micro-averaging assigns equal weight to each prediction, favouring high-
population classes (_e.g._ , hypertension). Macro-averaging, in contrast,
computes the performance for each unique label and averages across the label
space, giving each label’s average result equal weight regardless of their
population. This highlights poor performance in less common classes. Our
primary evaluation metrics common with the majority of previous work are
micro-F1 and macro-F1 scores. Metrics are further explained in Supplementary
Material.

####  2.5.2 GPT’s coding on real clinical notes

We used the Azure AI Services API777https://azure.microsoft.com/en-
gb/products/ai-services to test GPT’s ability to assign diagnosis codes based
on real clinical notes. The API returns a free text response which we further
processed to retrieve the predicted ICD-10 codes. In postprocessing, we verify
the response format. For correctly structured arrays of JSON objects we simply
extract the predictions. For incorrectly structured outputs, we employ a
regular expression pattern to extract all diagnoses and ICD code pairs. The
result is a list of predicted diagnoses and corresponding ICD-10 codes for
each clinical note. Figure 2 illustrates the API call workflow.

Figure 2: The workflow of the GPT-3.5 prediction. We used Azure AI Services
API to query GPT-3.5 and we employed a postprocessing step to extract the
predicted diagnoses and ICD-10 codes for each clinical note.

For reproducibility, we specified the model version and the API version as
“gpt-3.5-turbo-0613” and “2023-03-15-preview”, respectively. All parameters
were set to zero for deterministic responses from _G_ , including temperature,
top P, frequency penalty, and presence penalty. The system prompt directed _G_
to act as a clinician assigning ICD-10 diagnosis codes to clinical notes,
specifying the expected output format as JSON objects with keys “diagnosis”
and “icd_code”. Table 1 presents all hyperparameter details. Our code
implementation can be found in our Github repository
888https://github.com/EdinburghClinicalNLP/chatgpt_icd_coding.

Parameter |  Value
---|---
Model Name |  gpt-3.5-turbo-0613
API Version |  2023-03-15-preview
Temperature |  0
Top P |  0
Frequency Penalty |  0
Presence Penalty |  0
System Prompt |  You are a clinician and your task is to assign diagnoses and the associated ICD-10 clinical codes to a given clinical note. You should output an array of JSON objects containing the diagnosis name and the ICD-10 code, structured as diagnosis and icd_code.
Table 1: Azure API call hyperparameters.

We opted out of human review of the data for two reasons. Firstly, the terms
of the data use agreement of MIMIC-IV 999https://physionet.org/news/post/415
did not grant us the authority to permit a third party to process the data for
abuse detection. Secondly, we assessed the likelihood of harmful misuse to be
low given the sensitive nature of the clinical notes.

In our evaluation of GPT’s performance, we have also employed hierarchical
evaluation techniques – set-based hierarchical evaluation [31] and Count-
Preserving Hierarchical Evaluation (CoPHE) [32]. These metrics award partial
credit to mispredicted labels by extending prediction and gold standard sets
with their ancestor labels. While set-based evaluation ancestor labels track
only presence of descendants, in CoPHE, ancestor labels link to the count of
descendants, penalising over- and under-predictions within code families.

Comparing set-based and CoPHE results helps to evaluate the model’s tendency
to over-/under-predict. A lower CoPHE score indicates this phenomenon. See
Supplementary Materials for details on calculating hierarchical scores.

We utilise macro-averaged metrics from Weak Hierarchical Confusion Matrices
(WHCM) [10] to summarise in-family versus out-of-family (OOF) prediction
errors. These metrics are chosen to explore how expanding the population of
codes within code families to a minimum of 100 instances impacts within-family
performance. Within-family errors involve false positives that align with
false negatives within the same family in the gold standard. On the other
hand, an OOF error for a false negative in the gold standard lacks a false
positive within the prediction set from the same family to match. Our primary
goal in generating synthetic data is to reduce OOF errors, enhancing true
positive predictions or ensuring mispredictions occur within-family.

####  2.5.3 GPT-3.5’s coding on Synthetic data

The prompt asked _G_ to code the conditions and procedures mentioned in the
document it generated. This experiment tested _G_ ’s ability to assign ICD-10
codes to concepts presented in their standard descriptions. Alongside this,
the prompt required creating a patient’s social and family history, which
might have led to the model introducing new conditions like substance abuse
and potentially coding them, despite not being part of the initial prompt.

####  2.5.4 Acceptability of Generated Data in Clinical Practice

Four clinical professionals (co-authors SB, LD, MH, and RSP) assessed the
quality of the generated data. As the data was generated based on labels
associated with MIMIC-IV discharge summaries, this evaluation included both
synthetic discharge summaries generated by _G_ and discharge summaries from
MIMIC-IV. The clinicians were presented with 20 discharge summaries – 10
synthetic and 10 real (based on whose adjusted gold standard the synthetic
ones were generated).

Each discharge summary was assessed for:

  * •

Correctness – accuracy in describing patient conditions and procedures;

  * •

Informativeness – clarity and sense in supporting information (_e.g._ , test
results, medication suggestions);

  * •

Authenticity (patient) – whether such a patient could exist;

  * •

Authenticity (clinical scenario) – whether the hospital course was plausible
as reported;

  * •

Acceptability – suitability of the document for clinical use.

Additionally, they separately evaluated correctness and informativeness for
both non-low-resource and low-resource labels to gauge _G_ ’s ability to
generate low-resource data. Scores from 1 (failure to perform) to 5 (perfect
performance) were assigned to each metric, with accompanying comments
justifying the score.

##  3 RESULTS

###  3.1 Local Neural Model Evaluation

We assessed performance across three code subsets: the entire codeset in
MIMIC-IV ($overall$), a restricted set encompassing our candidate generation
labels and their related codes ($f$), and a further subset ($f(gen)$)
containing only the 114 low-population candidate generation labels. Results
are shown in Table 2. Baseline results for CAML and LAAT align closely with
prior findings by Nguyen et al. [25] for $overall$ metrics. While Nguyen et
al. [25] did not report on Multi-Res CNN, its performance trends were similar
to CAML and LAAT in comparison to Edin et al. [24] on non-filtered codesets.
LAAT excels in micro-F1, Multi-Res CNN leads in macro-F1, and CAML generally
lags behind. Baseline models outperform augmented ones in $overall$ micro-F1,
a common observation when enhancing lower-resource label performance.
Nonetheless, macro-F1 scores improved for two out of three models within the
$overall$ codeset and for all models on $f$ and $f(gen)$. Multi-Res CNN and
CAML macro-F1 scores display sizable relative improvement (26% and 78%
respectively) in $f(gen)$.

Augmented models performed on par with or outperformed baseline models in
micro-F1 scores for $f$ and $f(gen)$. Augmented Multi-Res CNN outperforms its
baseline in micro-F1 for both $f$ and $f(gen)$, indicating benefits for the
code family from augmenting less-populous members. Augmented LAAT shows
improvement in macro-F1 in both $f$ and $f(gen)$ but lags in micro-F1

Comparing within-family and out-of-family errors (Table 3), augmented models
generally exhibit fewer out-of-family errors on $f$ and $f(gen)$. An exception
to this is augmented Multi-Res CNN in $f(gen)$, whose slight increase in OOF
came with a sizable reduction in within-family error.

Unlike baseline models, augmented ones occasionally predicted codes absent
from the original data, although incorrectly, except for one correctly
predicted code (S02.63XA) by a Multi-Res CNN model trained on augmented data.
While consistent enhancement in zero-shot code performance was not achieved
through augmentation, the potential for improvement is evident.

Experiment | $mi_{overall}$ | $ma_{overall}$ | $mi_{f}$ | $ma_{f}$ | $mi_{f(gen)}$ | $ma_{f(gen)}$
---|---|---|---|---|---|---
CAML baseline | 53.651 | 3.866 | 38.431 | 3.027 | 17.413 | 6.635
CAML augmented | 53.544 | 3.899 | 38.410 | 3.780 | 20.680 | 11.858
LAAT baseline | 57.285 | 6.181 | 43.586 | 4.959 | 26.790 | 14.478
LAAT augmented | 57.176 | 6.091 | 43.364 | 5.381 | 25.696 | 14.978
Multi-Res CNN baseline | 55.663 | 6.402 | 40.159 | 5.039 | 26.801 | 13.923
Multi-Res CNN augmented | 54.694 | 6.464 | 42.688 | 5.846 | 30.390 | 17.681
Table 2: A comparison between local neural network models trained on baseline and augmented training sets is evaluated using micro- and macro-averaged $F1$ scores ($mi$ and $ma$ respectively) on three codesets – $overall$ on all codes present in MIMIC-IV; $f$ comprising all codes within the families we chose for generation; and $f(gen)$ corresponding to candidate codes used in generation with a population of at most 100 in the training set. The highest score in each metric for each model pair (baseline versus augmented) is highlighted in bold. Experiment | OOFf | In-Familyf | OOFf(gen) | In-Familyf(gen)
---|---|---|---|---
CAML baseline | 66.525 | 25.045 | 83.809 | 9.831
CAML augmented | 65.983 | 23.768 | 79.785 | 9.169
LAAT baseline | 58.568 | 28.351 | 74.033 | 12.197
LAAT augmented | 55.932 | 29.783 | 73.650 | 11.981
Multi-Res CNN baseline | 52.720 | 32.406 | 69.676 | 15.242
Multi-Res CNN augmented | 49.653 | 32.359 | 70.413 | 10.216
Table 3: Weak Hierarchical Confusion Matrix (WHCM) error rates of local neural
network models trained on the original (baseline) training set versus on the
training set augmented with synthetic data (augmented). Performance on the
common test set is reported using the macro-averaged proportion of errors that
were Out-of-Family (OOF) and within-family (In-Family). Errors are analysed
for two codesets – $f$ on the codes within the families of codes used for
generation; and $f(gen)$ corresponding to codes in $f$ with a population of at
most 100 in the baseline training set. The best (lowest) error rate for each
error type for each model pair (baseline versus augmented) is presented in
bold.

###  3.2 GPT’s coding ability on real and synthetic data

We examined _G_ ’s ability to code real MIMIC-IV documents and generate coded
documents with explicit code descriptions in the prompt. The results (Table 4)
show that the performance on prompt-guided self-generated (synthetic) data
resembles that of local models on the MIMIC-IV test set, not surpassing it.
Hierarchical metrics show higher precision, recall, and consequently, F1-score
in CoPHE compared to set-based hierarchical evaluation indicating errors
coming from within-family misprediction, rather than incorrectly estimating
the number of expected labels.

However, the performance on the MIMIC-IV test set is notably low, especially
in precision. The improvement in the precision from leaf-only results to
hierarchical is minimal. This implies that incorrect predictions were more
likely to be out-of-family. Moreover, results on CoPHE are lower than on the
set-based hierarchical evaluation indicating a tendency of the model to
over-/under-predict within the scope of the family – an issue previously
reported in local ICD coding models [32].

These results demonstrate that _G_ can identify ICD-10 codes based on provided
descriptions if presented within the prompts. Its performance when tasked with
standard ICD coding without explicitly identified concepts or non-standard
surface forms of the concepts significantly deteriorates.

Evaluation Set | Leaf-Only | Set-Based | CoPHE
---|---|---|---
| P | R | F1 | P | R | F1 | P | R | F1
Real | 9.46 | 33.511 | 14.755 | 10.585 | 44.873 | 17.129 | 10.3 | 44.33 | 16.715
Synthetic | 59.064 | 40.715 | 48.203 | 66.461 | 41.319 | 50.958 | 67.198 | 41.545 | 51.346
Table 4: Results of GPT’s coding ability on real and self-generated data.

###  3.3 Acceptability of Generated Data in Clinical Practice

We calculated the inter-evaluator agreement for the 7 metrics using Fleiss’
kappa ($\kappa$) [33]. As $\kappa$ is designed for categorical variables and
does not fully capture ordinal scores, we present it alongside the mean scores
for each metric ($\mu$). The evaluators’ agreement was poor ($\kappa<0$) in
examples from MIMIC-IV for Correctness and Informativeness of non-low-resource
codes, and the Acceptability of the discharge summaries. For the other
metrics, a $\kappa>0$ was reached but never exceeded 0.4 (lower than moderate
agreement). All mean scores are higher than 4. Hence, while the clinicians
disagreed on the exact scores, they rated real discharge summaries positively.
The disagreement may be due to clinicians being UK-based with significant
differences in reporting style within the UK and the US (where MIMIC-IV is
from).

For GPT-generated summaries, slight agreement was seen in acceptability, and
fair agreement in the correctness of low-resource labels. All other metrics
had poor agreement. Both correctness metrics scored above 4, with low-resource
correctness surpassing 4.5—an encouraging outcome for our primary goal of
generating low-resource code data. Mean informativeness in the low-resource
scenario and authenticity of scores were at least 3. Once again, performance
on the low-resource codes exceeded non-low-resource codes. Other metrics had
$\mu$ scores above 2. Informativeness and authenticity for non-low-resource
codes had poor agreement ($\kappa<0$), while acceptability had some agreement
with the lowest mean score of 2.225. The full table of $\kappa$ and $\mu$
scores for the evaluation can be found in Supplementary Material.

While _G_ exhibits a good level of correctness as affirmed by the clinicians,
several challenges persist in the generation of natural-looking clinical
notes:

####  3.3.1 GPT-3.5 tends to do verbatim reproductions of the prompted
diagnoses list

_G_ tends to generate clinical notes which list all concepts mentioned in the
prompt. While an LLM following instructions is desirable behaviour, it is also
important to exclude non-crucial details when tasked to generate a natural-
looking clinical note. Real clinical notes often omit irrelevant normal
findings and certain less-critical diagnoses within the context of an
admission inferrable from supporting information such as medications and
treatments for brevity. For instance, _G_ unnecessarily noted a BMI within a
normal range.

####  3.3.2 GPT-3.5 may phrase diagnoses in an unnatural manner

_G_ may also express diagnoses in technical and verbatim manners, not typical
in clinical text. For instance, _G_ mentioned “anaemia, which was
unspecified.” in the generated clinical note as it was prompted with “D64.9:
Anemia, unspecified”. _G_ also occasionally introduced vague phrases such as
“geriatric team provided supportive care, including behavioural interventions
and medication management” without further detail. This unnatural style easily
differentiates synthetic clinical notes from real ones.

####  3.3.3 GPT-3.5 misses necessary supporting information

_G_ tends to produce clinical notes lacking crucial supporting information.
For instance, _G_ mentioned “Following a traumatic event” without specifying
the event, which is unacceptable in the clinical setting. _G_ also mentioned
the introduction of _pleural intervention_ without additional detail or
rationale. This omission limits the overall informativeness of the patient’s
medical context, potentially hindering the notes’ usability for a
comprehensive view.

####  3.3.4 GPT-3.5 may introduce spurious supporting information

The clinicians also observed that _G_ sometimes introduces improbable but
possible details. For instance, _G_ overemphasised the significance of a
patient’s anxiety disorder regarding an episode of syncope and a subsequent
facial fracture, which the clinicians consider unlikely.

####  3.3.5 GPT-3.5 failed to present diagnoses as interconnected events

_G_ also failed to link diagnoses as interconnected events, crucial for
coherent patient summaries. The clinicians described _G_ -generated clinical
notes as collections of unrelated facts. For example, _G_ listed multiple
complications of Type 1 diabetes mellitus (_i.e._ , “E10.43”, “E10.621”,
“E10.628”, “E10.21”, “E10.319”) separately, failing to acknowledge their
connectedness. Within the same note _G_ failed to recognise the connection
between vascular complications and diabetes, presenting them as independent
facts. The lack of coherence between diagnoses may impede the plausibility of
the clinical note and undermine the overall acceptability and usefulness of
synthetic notes.

####  3.3.6 GPT-3.5 failed to prioritise and emphasise critical diagnoses

Furthermore, _G_ failed at prioritising critical diagnoses, which undermined
the authenticity of the portrayed scenario. For example, _G_ presented
multiple fractures alongside lesser issues, such as impacted ear wax,
cataracts, and conjunctival haemorrhage. Hence, we concluded that _G_
struggles to effectively convey the relative clinical significance of certain
diagnoses.

##  4 DISCUSSION, CONCLUSION AND FUTURE STUDIES

In this work, we have investigated the capability of GPT-3.5’s potential in
augmenting ICD-10 coding for local neural models in low-resource scenarios.
While overall performance dipped with synthetic data augmentation, filtered
codeset evaluation showed improvements, especially in advanced models like
LAAT and Multi-Res CNN. Error analysis indicated augmented models made fewer
out-of-family predictions, with some shift to within-family errors (closer to
the correct answer). Augmentation showed promise in improving the prediction
of generated codes and their siblings. Zero-shot labels did not consistently
benefit from the augmentation, emphasising the need for real data in
augmentation success. However, a zero-shot code learned from the synthetic
data was predicted correctly.

In guided synthetic settings with ICD-10 descriptions, GPT-3.5 showed partial
code identification ability displaying lesser over-/under-prediction
tendencies than previously reported local models. It, however, struggled in
the realistic scenario without in-prompt aid, performing below locally-trained
models. Hence, it is unsuitable for deployment in a clinical setting.

Clinician-evaluated synthetic discharge summaries showed correctness in
individual codes, yet lacked naturalness and coherence compared to real data,
resulting in lower informativeness, authenticity, and acceptability scores.
Synthetic summaries failed to represent holistic patient narratives or
prioritise critical diagnoses.

One potential solution to generating synthetic discharge summaries involves
restructuring the prompt to order diagnoses chronologically, providing their
corresponding timestamps. This could guide LLMs in creating synthetic notes
mirroring chronological progression of a patient’s medical journey, enhancing
coherence and prioritisation.

Another promising solution is to retrieve real clinical notes as in-context
learning examples to help guide the generation process [34] to aid LLMs in
generating more realistic and coherent content. As this study focuses on
evaluating LLMs’ existing capability, we opted to evaluate it in a zero-shot
framework. Future work may explore this idea’s potential for generating more
realistic-looking clinical notes.

##  5 Funding

This work is supported by the United Kingdom Research and Innovation (grant
EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the
University of Edinburgh, School of Informatics. HD is supported by the
Engineering and Physical Sciences Research Council (EPSRC, grant
EP/V050869/1), Concur: Knowledge Base Construction and Curation. RSP is a
fellow on the Multimorbidity Doctoral Training Programme for Health
Professionals, which is supported by the Wellcome Trust [223499/Z/21/Z]. BA is
supported by the Advanced Care Research Centre at the University of Edinburgh.

##  6 Competing Interests

We have identified no competing interests.

##  7 Author Contributions

MF proposed exploring data augmentation for ICD coding with GPT-3.5. and
developed the main ideas of the publication with AG, HD, AB, and BA. MF, AG,
HD, AB, and BA contributed to the overall experimental design. MF designed and
performed the synthesis of new discharge summaries, and the evaluation of
GPT’s ICD-10 coding performance on GPT-generated data. AG evaluated GPT’s
ICD-10 coding performance on MIMIC-IV data. MF, AG, HD, and LD designed the
evaluation of real versus generated discharge summaries by clinical staff. LD,
SB, MH, RSP performed the clinical expert evaluation of the data. MF and AG
analysed the results of the evaluation by clinicians. MF, AG, HD wrote the
manuscript. All authors participated in editing the manuscript. BA and AB
supervised the project and provided feedback and input on the experiments and
analyses.

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  *[LMTC]: Large-Scale Multi-Labelled Text Classification
  *[NLP]: Natural Language Processing
  *[ICD]: International Classification of Diseases