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An empirical reality is that, with the exception of the study52, the current BERT-based systems still do not outperform CNN-based techniques for multi-label classification applied to clinical coding44,60,61. The inefficiency of BERT in modelling concept-level information (often contained in a few keywords or phrases instead of complicated relationships of tokens in the context) and lengthy documents may be the cause of its limitations60.Managing the billing process accurately is not easy as providers might face hurdles in revenue cycle management. Moreover, Net Collection Rate below 95% shows that your practice is facing troubles in the billing process. To eliminate all these hurdles and maintain your NCR up to 96%, MedsIT Nexus Medical Coding Services are around the corner for you so that your practice does not have to face a loss.

In addition, as we previously mentioned, manual coding is primarily based on a standard and implied process with rules that are applied to the healthcare system, such as the priority of particular codes, hypothetical mentions, code definitions, mutual exclusion, etc. To produce outcomes that are better and easier to understand, future deep learning-based systems must incorporate knowledge reasoning along with rules and ontologies.

The technological difficulties we encountered while working in clinical coding are listed here, along with pertinent references. A recent, concurrent assessment by Teng et al.13 also presents some of the difficulties in a different approach. The multi-label classification strategy faces explainability, few- and zero-shot learning issues more frequently; nevertheless, the NER + L approach may help.

The existing, widely used benchmark data set MIMIC-III may have been severely under-coded62. Creating gold standard coding data sets. Large, publicly accessible, and expert-labeled data sets from electronic health records are lacking in this field, and models trained on MIMIC-III may not generalise to other data sets because of the length, style, and language differences (for example, clinical notes in China, Spain, or even the UK). For different uses of clinical codes (for decision-making, diagnosis, epidemiology, etc.), different expert-labelled coding data sets are also required. For instance, epidemiology studies that identify deep phenotypes from multimodal and multi-source clinical data may link to nuanced terminologies like SNOMED CT. The clinical NLP community will be better served by ensuring accurate and openly accessible data sets from more healthcare systems for a variety of objectives.

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Coding from diverse, erratic, and noisy sources

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As mentioned in Alonso et al.14, clinical coding should be based on all of a patient's pertinent documents rather than just discharge summaries as in the bulk of recent research. The difficulties presented by lengthy documents are now present. Additionally, structured data can be used as a source for coding48, such as lab findings. Radiographs can also be helpful for coding. Furthermore, even for the same type of document (such as a discharge summary), there is no guarantee that it will be available for all cases and provided in a consistent style. Real-world data for clinical coders are typically sparse and noisy (i.e. can be hand-written or typed, with various levels of completeness).

Clinical coding must be explicable; coders must comprehend how the system makes decisions. The difficulty mostly relates to the multi-label categorization strategy based on deep learning. Work in this field to date highlights important n-grams19, words, and sentences61,63 using label-wise attention mechanisms. The highlighted texts, however, mostly show connections rather than causality. Additional research is required to assess the value of highlights for clinical coders as well as to combine more naturally explicable techniques, such as combining deep learning with symbolic representations of the coding phases.

To properly implement an automated coding tool into practise, it is crucial to incorporate coders' feedback into the system9. This is known as "human-in-the-loop learning with coders' feedback." Different types of feedback are possible, including human corrections, highlights, and rules. A deep learning system for coding may need to take the feedback into account. The system may go through several iterations of updating based on coders' suggestions. Examples of NER + L tools that have not yet been used for clinical coding include MedCATTrainer64, which has a dedicated interface for users to add new concepts, synonyms, and abbreviations, correct concepts (of samples chosen using active learning), and binary annotations of temporality and phenotyping. SemEHR65 also has a designed interface for users to add labels for mentions, which is used to categorise mentions. Active learning, which is implemented in NER + L in MedCATTrainer64 and evaluated in automated coding to potentially reduce human annotations68, is a relevant area for human-in-the-loop learning. Active learning is about choosing the smallest set of the most crucial data for humans to provide annotation feedback.

Few-shot and zero-shot learning—a major issue for multi-label classification with many labels is that many codes appear in the training data with a low frequency or even never at all (or are "unseen") (e.g., 68,000 codes in ICD-10)

37. The best systems to date are still below or about 40% recall at K (or the percentage of correct codes in the top-K predictions, K = 10 or 15), which is the MIMIC-III data set's low-frequency (5 times) code threshold. 37,46,47. The overall performance and use of the coding will be enhanced by better support for few-shot and zero-shot learning. As discussed in the general domain69, knowledge (such as descriptions, attributes, relations from various linked sources, and coding rules) can close the gap between the seen and unseen codes.

How can a trained model be updated to fit new coding standards or an entirely other ontology (such as going from ICD-10 to ICD-1124)? ICD-11 is semantically more complex than ICD-10 thanks to its poly-hierarchical backbone structure and post-coordination of codes, as we previously discussed. Terminology change may call for cutting-edge deep learning paradigms (such as self-supervised learning, transfer learning, and meta-learning), precise ontology matching, concept drift handling, and the aforementioned robust few-shot and zero-shot learning for new codes with no or little training data.

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Finally, and perhaps most importantly, several of the technical directions above recommend integrating knowledge or semantic information in coding classification systems and ontologies. In recent studies, ICD code descriptions (19, 55) and hierarchies (41) have been taken into account (and see the blog about hierarchical evaluation70 for ref. 41). To attain cutting-edge performance, other ontologies, such CCS71 and code synonyms in UMLS, have lately been adopted45,51. Additionally, manual coding is primarily based on a standard procedure and coding guidelines, which may be formalised as a set of rules and terminologies used in the healthcare system, such as the priority of specific codes, the number of codes required for each case, the mutual exclusion of specific codes, the rules for coding hypothetical cases (like possible and probable), locally defined specific codes, etc. The study29 presents an illustration of formalising and combining rules for the mutual exclusion of codes and hypothetical instances. These rules must be properly expressed in a machine-readable manner and progressively included into the automatic coding system powered by deep learning.