Transition to ICD-11 for admitted patient data reporting: opportunities and challenges

Abstract

Keywords

international classification of diseases clinical coding health classification health information management ICD-11

The 11th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD) was officially released by the World Health Organization (WHO) in 2022 (WHO, 2022) and represents a major advance in the classification. Since then, several countries around the world, including Australia have been working towards a transition to the updated classification.

Currently Australia uses the tenth revision of the ICD with Australian modifications (ICD-10-AM), to report admitted patient morbidity statistics. ICD-10-AM is a book-based classification containing approximately 14,000 clinical codes. While the eleventh revision of ICD can be printed, if necessary, it is primarily designed for integration with electronic health records and health information systems and WHO’s long-term goal is that all users will use ICD-11 in an electronic environment (WHO, n.d.a, p.36). ICD-11 contains thousands more clinical codes than ICD-10 or ICD-10-AM in its Mortality and Morbidity Statistics “derived classification” known as ICD-11 MMS ((WHO, n.d.a, p.34). This is what will replace the ICD-10-AM, referred to hereafter as ICD-11.

An Australian ICD-11 Task Force (AITF) was created by the Australian Institute of Health and Welfare (AIHW) to develop a “broad, multi-year roadmap of activities required to make a decision regarding implementation of the ICD-11” (AIHW 2022). A roadmap was endorsed in November 2023 and work continues on identifying ICD-11 cost and benefit analysis, evaluating the broader IT system enhancements required to support ICD-11, mappings and dual coding studies, identifying and assessing the impacts on IT systems and infrastructure, and development of a Workforce Strategy (AIHW 2025).

Both opportunities and challenges exist for Australian adoption of ICD-11.

With a primary focus on Australia, the Virtual Special Issue of the Health Information Management Journal on the transition to ICD-11, guest edited by Jennie Shepheard, highlights recent articles from different countries, published in the Journal, which demonstrate some of these opportunities and challenges all countries will encounter in preparing for this transition (see Box 1).

Box 1.

Articles selected for inclusion in the virtual special issue on the transition to ICD-11 for admitted patient data reporting: opportunities and challenges.

How are ICD data used?

• Duke et al. (2024)

• Duke et al. (2022)

• Kibret et al. (2025)

• Lau et al. (2021)

• Riley et al. (2022)

• Riley et al. (2023)

• Sonneveld et al. (2026)

• Stoneham et al. (2024)

• Valentine et al. (2022)

Opportunities in transitioning to ICD-11

• Campbell and Giadresco (2019)

• Duke et al. (2024)

• Ibrahim et al. (2025)

• Ibrahim et al. (2024)

• Riley et al. (2023)

Challenges in the transition to ICD-11

• Eastwood et al. (2021)

• Fuad et al. (2025)

• Ibrahim et al. (2024)

• Lee and Kim (2022)

• Lee and Lee (2025)

• Lyall et al. (2025)

• Ooi et al. (2024)

• Otero Varela et al. (2022)

• Riley et al. (2022)

• Santos et al. (2021)

• Santos Martins et al. (2023)

• Zarei et al. (2023)

How are ICD data used?

The ICD is recognised as the global standard classification for mortality and morbidity ((WHO, n.d.d): global standard) and is used widely throughout the world. In Australia, admitted patient administrative databases are created by every state and territory government with statistics reported to the AIHW and from there to the WHO. These databases contain clinical data in the form of clinical codes, demographic data such as age, gender and local government area of the patient and administrative data such as health insurance status, care type, admission date and discharge date.

Beyond their primary use of reporting health statistics, these data are “extensively used to estimate disease burden and inform public health policy” (Sonneveld et al., 2026), being easily accessible, standardised, inexpensive to access and are a rich data source. These “secondary” users use the data for purposes different to that for which they were created and there is sometimes concern about their “fitness for purpose” in the secondary use (Riley et al., 2022). Researchers will at times seek to derive a measure of confidence in the clinical data by comparing the clinical codes to the clinical documentation held in the patient’s medical record. For example, Kibret et al. (2025) investigated the concordance of ICD-10-AM clinical coding with COVID polymerase chain reaction laboratory-confirmed COVID-19 test results reported in the patient record, while Lau et al. (2021) compared ICD-10-AM clinical coding for alcohol use with routine blood alcohol tests documented in patient records. Valentine et al. (2022) used the ICD-10-AM coding of pneumonia and compared that to a standardised case definition of pneumonia finding that the Independent Health and Aged Care Pricing Authority’s (IHACPA) Hospital Acquired Complication list for pneumonia (IHACPA, 2021), used in the national public hospital funding model, was not sensitive enough to describe hospital acquired pneumonia in a haematology-oncology casemix. Duke et al. (2022) investigated the clinical coding of sepsis for those patients with community acquired sepsis and found that determining an accurate picture of sepsis was difficult due to limitations with the available clinical codes.

Others use the clinical codes in the administrative database to support their public health policies. For example, Stoneham et al. (2024) created a list of ICD-10-AM codes that could be used in Australia’s environmental health clinical referral process, a public health policy to trigger an “healthy home” intervention in Indigenous (capital I) populations. Other policy developers, who found the number of clinical codes difficult to work with, developed clinically meaningful groups of clinical codes “to identify a population of interest, to stratify case-mix or function as independent covariates in either explanatory or prediction models” (Duke et al., 2024). In their scoping review, Riley et al. (2023), while referencing both ICD-10-AM morbidity and ICD-10 mortality codes specifically, drew attention to the importance of the increasing use of, and growing need for accurate and timely clinically coded data for research purposes.

Governments at state and federal level use the data to develop funding models and clinical programs.

Opportunities in transitioning to ICD-11

Post-coordination and clustering

“Post-coordination” enables clinical codes, representing diagnostic concepts, such as injury cause and effect or infection and infective agent to be explicitly linked creating a “cluster” (WHO, n.d.a, p. 43). Currently, admitted patient datasets cannot provide these explicit relationships between clinical codes. Duke et al. (2024) stated that “reverse-translation of each ICD-10 code back into a meaningful clinical category that reflects the patient’s actual disease is complicated by many-to-one relationships, non-linear sequencing, miscellaneous and ambiguous ‘residual’ definitions. Post-coordination and clustering allow for one long cluster to express related diagnostic information, therefore representing an opportunity for medical researchers and policymakers to make better sense of the data and make better decisions based on these data” (Riley et al., 2023).

Annual updates by WHO

ICD-11 is updated annually, and the updating process is supported by a proposals system whereby users of ICD-11 can propose new or updated clinical codes, deletion of existing clinical codes or updates to definitions (Ibrahim et al., 2025). Accepted proposals are included in each annual update. Country-specific modifications should not be needed as their required modifications can be included in the annual updates. In effect, the work of updating the classification to reflect changing clinical landscapes in Australia may be largely handed off to the WHO processes. It may also mean that a twelfth revision of the ICD may not be needed, which will be determined by the WHO in 2032 (Ibrahim et al., 2025). Government departments currently responsible for maintaining and developing ICD-10-AM may be able to redirect their classification experts towards less resource-intensive activities that still makes significant use of their specialised knowledge.

Increased clinical codes and new chapters

ICD-11 contains a significantly increased number of clinical codes enabling a much finer granularity in the clinical data. This opportunity to drill down to finer detail will benefit all secondary users of the data as well as providing more detailed health statistics.

ICD-11 includes three new chapters: Sexual Health (conditions previously classified in the mental health chapter), Traditional Medicine (conditions previously not included) and Extension Codes providing qualifying information in the clusters such as the distinction of diagnoses present at admission from diagnoses arising after hospital stay began (WHO, n.d.a, p. 56). These additions will greatly enhance the usability of admitted patient data for medical researchers and policymakers. The addition of the sexual health chapter also destigmatises these conditions in keeping with modern ethical values.

Digital tool

ICD-11 is a digital tool and as such can be fully integrated with electronic health records (Ibrahim et al., 2024). As the roll-out of electronic health records continues across Australia, this advantage will become more obvious. Mapping of ICD-11 to the Systematized Nomenclature of Medicine – Clinical Terms, which is integrated into many clinical information systems including electronic health records, may have a profound effect on how inpatient clinical coding is conducted in the future enabling it to become even more “computer assisted.” This is an opportunity rather than a threat for clinical coders who will be able to further develop their clinical coding skill for future career progression into new roles such as clinical coding editors or clinical coding analysts (Campbell and Giadresco, 2019).

Challenges in the transition to ICD-11

Enormous resources are needed to transition a country to a new edition of the ICD. In Australia, this will be the responsibility of the AIHW with input and practical assistance from organisations such as IHACPA.

Transitioning the clinical coding workforce

Initial preparation for the implementation of ICD-11 must include the training of clinical coders “based on their needs” (Zarei et al., 2023). Eastwood et al. (2021), citing Hazelwood (2003) and Paoin et al (2018), suggested that 6 months are needed for clinical coders to reach proficiency post-implementation of a new revision of the ICD. This represents a significant challenge for the clinical coder workforce. Ibrahim et al. (2024) suggested the content of training modules could be modified in recognition of the digital nature of ICD-11, which differs dramatically from the alphabetical index and tabular books of ICD-10. Learning to use the coding tool, which is included with the ICD-11 browser (WHO, n.d.b) takes only a few moments (Ibrahim et al., 2024) and may be very similar to the clinical coding electronic tools currently in use in many hospitals. Ibrahim et al. (2024) claimed it would not be necessary to understand how ICD-11 works in order to find the correct code and, therefore, training should focus on the “additional functions of the coding tool, such as the tools ability to automatically generate post-coordinated clusters from diagnostic statements.”

Regardless of how the education is delivered, the transition to ICD-11 will reduce the productivity of the clinical coding teams in the short term, and possibly in the long term, due to the increased complexity of the classification. Lee and Kim (2022) opined that greater clinical knowledge was needed for clinical coders in Korea to select the stem code and to apply post-coordination in ICD-11. Santos Martins et al. (2023) reported on Portugal’s transition from ICD-9-CM (clinically modified version of ICD-9) to ICD-10-CM (clinically modified version of ICD-10) and reported that ICD-10-CM “appeared to achieve higher quality coded data but also increased the effort.” Similar experiences occurred in Australia’s transition from ICD-9-CM to ICD-10-AM and, given the even greater complexity of ICD-11, we should expect to experience the same challenges again; and bearing in mind that the “quality of the data is one of the major priorities that must be ensured” (Santos Martins et al., 2023) it is imperative that clinical coder education is comprehensive, appropriate to the newly developed classification and easily accessible by all clinical coders.

During the development of ICD-11, many countries, including Australia, assisted with field trials or conducted ICD-10 versus ICD-11 comparison studies, thus exposing some of their clinical coding workforce, in advance, to the new concepts, chapters and clinical code formats, while assisting with identifying areas that needed further clarification or expansion, as well as facilitating a “stable transition” to ICD-11. In Korea, Lee and Kim (2022) found that clinical coders struggled with the assignment of clinical codes in the cluster coding process, with the change of terms used in ICD-11 and with the removal from ICD-11 of clinical codes, they were familiar with in ICD-10. They concluded that “more detailed reference guidelines and efficient training from the WHO was needed to enable ICD-11 to be an effective classification.” A further study reporting on a second field trial in Korea by Lee and Lee (2025) identified similar issues with the cluster coding in spite of “clinical coding rules” being developed based on the results of the first field trial. This reinforced the need for clear clinical coding rules, guidelines or standards to support clinical coders in using this new concept. The preservation of quality clinical coding may depend on how comprehensive the education of the workforce is in this respect.

Both Eastwood et al. (2021) and Fuad et al. (2025) have reported on the development of training material for Canadian and Malaysian clinical coders, respectively, which meet with high levels of approval from the clinical coders. Feedback to the WHO led to improvements in the browser and the reference guide and both studies highlighted the importance of effective clinical coder training before the implementation of ICD-11.

Technical considerations

WHO has examined the considerable technical issues and has developed mapping tables between ICD-10 and ICD-11 as well as an Application Programming Interface for accessing and interacting with ICD data (WHO, n.d.c.: application programming interface). However, Australia will need forward and backward mapping between ICD-10-AM and ICD-11, work that is being undertaken by IHACPA (IHACPA, 2025a). Although the long-term use of mapping tables is not recommended (Ooi et al., 2024), it will be essential to enable comparison of data in time periods crossing the implementation date, and also to initially ensure there is no disruption to the secondary uses of the data (Riley et al., 2022), including funding programs using Diagnosis Related Groups (DRGs) that are based on ICD clinical codes (IHACPA, 2025b). Lyall et al. (2025) have reported on a project to develop backward mapping from ICD-11 to Canada’s version of ICD-10 (ICD-10-CA), in addition to forward mapping already developed, finding that the benefits of ICD-11’s increased specificity was obvious. However, these researchers also noted that forward mapping from ICD-10-CA to ICD-11 created a loss of specificity. Detailed analysis of mapping tables will be required to protect not only the integrity of funding models in particular but also the various secondary uses of the admitted patient data.

The ability of post-coordination and clustering of ICD-11 clinical codes has the potential to create very long strings of clinical codes, presenting challenges for information systems and their ability to accommodate the new codes (Ooi et al., 2024), and delays may be experienced as a consequence (Santos et al., 2021). DRGs will need to be updated with the new clinical codes, medical researchers will need to update their search algorithms, and policymakers and funders will need to monitor the impact of the mapping tables and greater granularity of the clinical codes on their programs.

Clinical documentation training

Lee and Kim (2022) reported that clinical coders need greater clinical knowledge and that clinical documentation needs improvement. This contention was supported by Zarei et al. (2023), who reported that in addition to clinical coder training “interventions to enhance the documentation of medical records according to ICD-11” will also be needed. While ICD-11 provides much finer granularity of clinical codes, if clinical documentation does not support the assignment of these very specific codes, then much of the benefit will be lost. Comprehensive guidelines will be needed to standardise both clinical coding and clinical documentation to ensure high-quality clinical coding.

Private sector

The private sector will have its own specific set of challenges when it comes to transitioning to ICD-11. Private hospitals have contracts with health insurance funds, who typically provide funds based on DRGs. However, rather than using the DRGs currently in use in the public sector, Australian hospitals in the private sector often continue to use older versions of DRGs This may create a problem in the private sector, as the new versions of DRGs will be created using ICD-11, and older versions may not continue to be supported by IHACPA. Versions that are supported will have to use backward mapping tables to convert ICD-11 clinical codes to ICD-10-AM clinical codes and, over time, this will impact on the integrity of the funding models. Vendors of computer-assisted clinical coding products may find it financially prohibitive to update to ICD-11 and, having done so, may be reluctant to maintain a dual product to satisfy the needs of private hospitals.

Conclusion

ICD-11 represents a revolution in the development of the ICD and may also lead to both a revolution in the development of the clinical coding workforce and in the development of health information systems. Fortunately, Australia is well placed to effectively and efficiently transition to ICD-11. Around the world, clinical coding training varies considerably and in some countries, clinical coding qualifications may not even be mandated (Otero Varela et al., 2022). However, Australia’s clinical coders are formally trained and accustomed to undertaking standardised training for each new edition of ICD-10-AM and in many cases, in-hospital training for between editions updates. This will be a great advantage in transitioning to ICD-11.

Recent years have also seen the development of roles for clinical documentation improvement specialists (CDIS) in many hospitals. These established relationships between CDIS and clinical staff will be invaluable to ensure that clinical documentation is detailed enough for Australia to get maximum value from ICD-11. The AIHW continues to work on the transition through its AITF, and IHACPA is working on a business casework to consider the replacement of ICD-10-AM with ICD-11 (AIHW, 2025). IHACPA has also introduced the concept of clinical coding clusters in its latest edition of ICD-10-AM (IHACPA, 2025c). Thus, by the time ICD-11 is introduced, potentially in the early 2030s, Australia’s clinical coders will be well versed in the principles of clustering and this should make the transition to post-coordination and clustering in ICD-11 relatively painless.

Australian health information managers are working across the spectrum of health information services, in electronic health record development and implementation, in government departments, clinical registries and private companies providing products to the health information services industry. They and the clinical coders are in a prime position to contribute and should be included, in the plans to transition to ICD-11.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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