Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models

Yamamoto, Naokatsu; Sukegawa, Shintaro; Watari, Takashi

doi:10.3390/healthcare10050892

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Surgical specialties can, of course, cause diagnostic or medication related incidents as well. Nevertheless, a Japanese study [ 17 ] of machine learning-based prediction models for litigation outcomes found that 41.67% of all reported incidents occurred in an operation room. Procedures or surgeries were the most common reasons for litigation, with also the highest acceptance rate (56.1%).…”

Section: Discussionmentioning

confidence: 99%

Trends and Developments in Medical Liability Claims in The Netherlands

2022

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Recent data on number of claims, final judgement of claims and their costs are scarce. This study analyzes 15 years of malpractice claims in the Netherlands. All claims filed, and all claims closed by two insurance companies (which insure approximately 95% of all hospitals in the Netherlands) between 2007–2021 are included. Trends in number of claims, medical specialties involved, final judgements and costs from malpractice claims are analyzed, as well as the impact of COVID-19 on malpractice claims. In total, 20,726 claims were filed and 21,826 claims were closed. Since 2013, the number of claims filed decreased. Of all claims filed, 64% were aimed at surgical specialties and 18% at contemplative specialties. Of all claims closed, 24.49% were accepted, 19.26% were settled and 48.94% got rejected. The financial burden of all claims closed quadrupled between 2007 and 2021; this increase was caused by rare cases with excessively high costs. Since the COVID-19 pandemic, we observed a decrease in the number of claims filed, and the number of incidents reported. This study provides valuable insights into trends and developments in the number and costs of liability claims, which is the first step towards improving patient safety and preventing incidents and malpractice claims.

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Section: Discussionmentioning

confidence: 99%

Trends and Developments in Medical Liability Claims in The Netherlands

2022

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“…We summarized the previous literature reports [14,[25][26][27][28][29][30][31], after fully understanding those common factors and considering the reality of medical malpractice cases in China, and divided the influencing factors on the degree of causality into medical factors (91 technical faults and 29 non-technical faults) and 10 patient factors (S1 Table ). Regarding the attribution of medical malpractice, it is generally observed that an increased number of affirmative responses in medical factors correlates with a higher degree of hospital liability in the case.…”

Section: Plos Onementioning

confidence: 99%

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

Yang,

Luo,

Lin

et al. 2024

PLoS ONE

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Purpose To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions. Methods We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models. Results We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%. Conclusions The optimal model of this study is expected to predict the causality accurately.

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“…Machine learning plays a crucial part in the battle against various life-threatening diseases [ 5 , 6 , 7 ]. It also contributes significantly to educational and clinical studies [ 8 , 9 , 10 , 11 ]. Machine learning has shown significant potential in medical, engineering, psychology, multi-disciplinary science, earth sciences, analytical practices, healthcare, and other domains.…”

Section: Introductionmentioning

confidence: 99%

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

et al. 2022

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Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.

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Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models

Cited by 4 publications

References 34 publications

Trends and Developments in Medical Liability Claims in The Netherlands

Trends and Developments in Medical Liability Claims in The Netherlands

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

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