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Protocol for a qualitative study exploring perspectives on the INternational CLassification of Diseases (11th revision); Using lived experience to improve mental health Diagnosis in NHS England: INCLUDE study.

ABSTRACT: INTRODUCTION:Developed in dialogue with WHO, this research aims to incorporate lived experience and views in the refinement of the International Classification of Diseases Mental and Behavioural Disorders 11th Revision (ICD-11). The validity and clinical utility of psychiatric diagnostic systems has been questioned by both service users and clinicians, as not all aspects reflect their lived experience or are user friendly. This is critical as evidence suggests that diagnosis can impact service user experience, identity, service use and outcomes. Feedback and recommendations from service users and clinicians should help minimise the potential for unintended negative consequences and improve the accuracy, validity and clinical utility of the ICD-11. METHODS AND ANALYSIS:The name INCLUDE reflects the value of expertise by experience as all aspects of the proposed study are co-produced. Feedback on the planned criteria for the ICD-11 will be sought through focus groups with service users and clinicians. The data from these groups will be coded and inductively analysed using a thematic analysis approach. Findings from this will be used to form the basis of co-produced recommendations for the ICD-11. Two service user focus groups will be conducted for each of these diagnoses: Personality Disorder, Bipolar I Disorder, Schizophrenia, Depressive Disorder and Generalised Anxiety Disorder. There will be four focus groups with clinicians (psychiatrists, general practitioners and clinical psychologists). ETHICS AND DISSEMINATION:This study has received ethical approval from the Coventry and Warwickshire HRA Research Ethics Committee (16/WM/0479). The output for the project will be recommendations that reflect the views and experiences of experts by experience (service users and clinicians). The findings will be disseminated via conferences and peer-reviewed publications. As the ICD is an international tool, the aim is for the methodology to be internationally disseminated for replication by other groups. TRIAL REGISTRATION NUMBER:ClinicalTrials.gov: NCT03131505.

SUBMITTER: Hackmann C

PROVIDER: S-EPMC5588998 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Protocol for a qualitative study exploring perspectives on the INternational CLassification of Diseases (11th revision); Using lived experience to improve mental health Diagnosis in NHS England: INCLUDE study.

Hackmann Corinna C Green Amanda A Notley Caitlin C Perkins Amorette A Reed Geoffrey M GM Ridler Joseph J Wilson Jon J Shakespeare Tom T

BMJ open 20170903 9

<h4>Introduction</h4>Developed in dialogue with WHO, this research aims to incorporate lived experience and views in the refinement of the International Classification of Diseases Mental and Behavioural Disorders 11th Revision (ICD-11). The validity and clinical utility of psychiatric diagnostic systems has been questioned by both service users and clinicians, as not all aspects reflect their lived experience or are user friendly. This is critical as evidence suggests that diagnosis can impact s ...[more]

PMID: 28871029

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Project description:PURPOSE:We previously developed and validated informatic algorithms that used International Classification of Diseases 9th revision (ICD9)-based diagnostic and procedure codes to detect the presence and timing of cancer recurrence (the RECUR Algorithms). In 2015, ICD10 replaced ICD9 as the worldwide coding standard. To understand the impact of this transition, we evaluated the performance of the RECUR Algorithms after incorporating ICD10 codes. METHODS:Using publicly available translation tables along with clinician and other expertise, we updated the algorithms to include ICD10 codes as additional input variables. We evaluated the performance of the algorithms using gold standard recurrence measures associated with a contemporary cohort of patients with stage I to III breast, colorectal, and lung (excluding IIIB) cancer and derived performance measures, including the area under the receiver operating curve, average absolute prediction error, and correct classification rate. These values were compared with the performance measures derived from the validation of the original algorithms. RESULTS:A total of 659 colorectal, 280 lung, and 2,053 breast cancer cases were identified. Area under the receiver operating curve derived from the updated algorithms was 89.0% (95% CI, 82.3% to 95.7%), 88.9% (95% CI, 79.3% to 98.2%), and 80.5% (95% CI, 72.8% to 88.2%) for the colorectal, lung, and breast cancer algorithms, respectively. Average absolute prediction errors for recurrence timing were 2.7 (SE, 11.3%), 2.4 (SE, 10.4%), and 5.6 months (SE, 21.8%), respectively, and timing estimates were within 6 months of actual recurrence for more than 80% of colorectal, more than 90% of lung, and more than 50% of breast cancer cases using the updated algorithm. CONCLUSION:Performance measures derived from the updated and original algorithms had overlapping confidence intervals, suggesting that the ICD9 to ICD10 transition did not affect the RECUR Algorithm performance.

Project description:BackgroundThe automatic coding of clinical text documents by using the International Classification of Diseases, 10th Revision (ICD-10) can be performed for statistical analyses and reimbursements. With the development of natural language processing models, new transformer architectures with attention mechanisms have outperformed previous models. Although multicenter training may increase a model's performance and external validity, the privacy of clinical documents should be protected. We used federated learning to train a model with multicenter data, without sharing data per se.ObjectiveThis study aims to train a classification model via federated learning for ICD-10 multilabel classification.MethodsText data from discharge notes in electronic medical records were collected from the following three medical centers: Far Eastern Memorial Hospital, National Taiwan University Hospital, and Taipei Veterans General Hospital. After comparing the performance of different variants of bidirectional encoder representations from transformers (BERT), PubMedBERT was chosen for the word embeddings. With regard to preprocessing, the nonalphanumeric characters were retained because the model's performance decreased after the removal of these characters. To explain the outputs of our model, we added a label attention mechanism to the model architecture. The model was trained with data from each of the three hospitals separately and via federated learning. The models trained via federated learning and the models trained with local data were compared on a testing set that was composed of data from the three hospitals. The micro F1 score was used to evaluate model performance across all 3 centers.ResultsThe F1 scores of PubMedBERT, RoBERTa (Robustly Optimized BERT Pretraining Approach), ClinicalBERT, and BioBERT (BERT for Biomedical Text Mining) were 0.735, 0.692, 0.711, and 0.721, respectively. The F1 score of the model that retained nonalphanumeric characters was 0.8120, whereas the F1 score after removing these characters was 0.7875-a decrease of 0.0245 (3.11%). The F1 scores on the testing set were 0.6142, 0.4472, 0.5353, and 0.2522 for the federated learning, Far Eastern Memorial Hospital, National Taiwan University Hospital, and Taipei Veterans General Hospital models, respectively. The explainable predictions were displayed with highlighted input words via the label attention architecture.ConclusionsFederated learning was used to train the ICD-10 classification model on multicenter clinical text while protecting data privacy. The model's performance was better than that of models that were trained locally.

Dataset Information

Protocol for a qualitative study exploring perspectives on the INternational CLassification of Diseases (11th revision); Using lived experience to improve mental health Diagnosis in NHS England: INCLUDE study.

Publications

Protocol for a qualitative study exploring perspectives on the INternational CLassification of Diseases (11th revision); Using lived experience to improve mental health Diagnosis in NHS England: INCLUDE study.

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