Risk management and empirical study of the doctor-patient relationship: based on 1790 litigation cases of medical damage liability disputes in China.

BACKGROUND: Compensation for medical damage liability disputes (CMDLD) seriously hinders the healthy development of hospitals and undermines the harmony of the doctor-patient relationships (DPR). Risk management in the DPR has become an urgent issue of the day. The study aims to provide a comprehensive description of CMDLD in China and explore its influencing factors, and make corresponding recommendations for the management of risks in the DPR. METHODS: This study extracted data from the China Judgment Online - the official judicial search website with the most comprehensive coverage. Statistical analysis of 1,790 litigation cases of medical damage liability disputes (COMDLD) available from 2015 to 2021. RESULTS: COMDLD generally tended to increase with the year and was unevenly distributed by regions; the compensation rate was 52.46%, the median compensation was 134,900 yuan and the maximum was 2,234,666 yuan; the results of the single factor analysis showed that there were statistically significant differences between the compensation for different years, regions, treatment attributes, and trial procedures (P < 0.05); the correlation analysis showed that types of hospitals were significantly negatively associated with regions (R=-0.082, P < 0.05); trial procedures were significantly negatively correlated with years (R=-0.484, P < 0.001); compensat- ion was significantly positively correlated with years, regions, and treatment attributes (R = 0.098-0.294, P < 0.001) and negatively correlated with trial procedures (R=-0.090, P < 0.01); regression analysis showed that years, treatment attributes, and regions were the main factors affecting the CMDLD (P < 0.05). CONCLUSIONS: Years, regions, treatment attributes, and trial procedures affect the outcome of CMDLD. This paper further puts forward relevant suggestions and countermeasures for the governance of doctor-patient risks based on the empirical results. Including rational allocation of medical resources to narrow the differences between regions; promoting the expansion and sinking of high-quality resources to improve the level of medical services in hospitals at all levels; and developing a third-party negotiation mechanism for medical disputes to reduce the cost of medical litigation.

Development of a scoring system to quantify errors from semantic characteristics in incident reports.

OBJECTIVES: Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques. METHODS: We retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed 'report error score'. RESULTS: Overall, 114 013 reports were included. We calculated 36 131 'term error scores' from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001, d=0.73 (large)) and other incidents. The accuracy, recall, precision and F-score values were 0.8, 0.82, 0.85 and 0.84, respectively. Group error scores were positively associated with expert ratings (correlation coefficient, 0.66; 95% CI 0.54 to 0.75, p<0.001) for all departments. CONCLUSION: Our error scoring system could provide insights to improve patient safety using aggregated incident report data.

Bibliometric and visualization analysis of risk management in the doctor-patient relationship: A systematic quantitative literature review.

OBJECTIVES: This paper analyzed the research on risk management in the doctor-patient relationship (DPR) based on a systematic quantitative literature review approach using bibliometric software. It aims to uncover potential information about current research and predict future research hotspots and trends. METHODS: We conducted a comprehensive search for relevant publications in the Scopus database and the Web of Science Core Collection database from January 1, 2000 to December 31, 2023. We analyzed the data using CiteSpace 6.2.R2 and VOSviewer 1.6.19 software to examine the annual number of publications, countries/regions, journals, citations, authors, and keywords in the field. RESULTS: A total of 553 articles and reviews that met the criteria were included in this study. There is an overall upward trend in the number of publications issued; in terms of countries/regions, the United States and the United Kingdom are the largest contributors; Patient Education and Counseling is the most productive journal (17); Physician communication and patient adherence to treatment: a meta-analysis is the most cited article (1637); the field has not yet to form a stable and obvious core team; the analysis of high-frequency keywords revealed four main research directions: the causes of DPR risks, coping strategies, measurement tools, and research related to people prone to doctor-patient risk characteristics; the causes of DPR risks, coping strategies, measurement tools, and research related to people prone to doctor-patient risk characteristics; the keyword burst analysis revealed several shifts in the research hotspots for risk management in the DPR, suggesting that chronic disease management, is a future research direction for the continued development of risk management in the DPR. CONCLUSIONS: The visualization analysis of risk management literature in the DPR using CiteSpace and VOSviewer software provides insights into the current research status and highlights future research directions.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine.

BACKGROUND: The nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks. PURPOSE: This study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment. METHODS: Failure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine. FINDINGS: Results prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data). CONCLUSIONS: Despite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

Research on risk management incentive strategy based on the green financial ecosystem.

Taking the green financial ecosystem composed of innovators, green financial institutions and regulators as the object of research, it explores the issue of how to improve the level of efforts of the three types of subjects and the benefits of risk management in the green financial ecosystem. The optimal level of effort, optimal level of return, and optimal level of return on risk management of green financial ecosystems for innovators, green financial institutions, and regulators under the three modes of No-incentive Contract, Cost-sharing Contract, and Synergistic Cooperation Contract are investigated and analyzed respectively, and verified by numerical simulation analysis. The results show: (1) Compared to the No-incentive Contract, the Cost-sharing Contract and the Synergy Cooperation Contract generate more significant incentives, and returns increase over time in both models. (2) The effort level of the participating subjects under the Synergistic Cooperation Contract is the highest, which can realize the Pareto optimization of the participating subjects and the green financial ecosystem at the same time. The study's findings contribute to a deeper understanding of cooperation among innovators, green financial institutions and regulators in facilitating risk management in green financial ecosystems and provide a realistic reference for risk managers in green financial ecosystems.

Developing an Ontology Representing Fall Risk Management Domain Knowledge.

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults.

What We Do, What We Say, What We Don't Say: Confidentiality In The Publication Of Clinical Writing.

Questions concerning analysts' publication of material from the analyses of their patients have troubled the field of psychoanalysis since its inception. Disguise inevitably distorts the clinical material and is often insufficient to protect the patient from recognition. Asking the patient's consent for publication intrudes upon and alters the analytic process. While analysts have largely reached a consensus about the need for anonymity in published material, there is still considerable debate about the necessity for obtaining patients' consent when using their material for publication. In this paper, I will trace the evolving meanings of disguise, and particularly of consent, in the analytic literature. I will place a particular emphasis upon the differing theoretical belief systems that underlie the analyst's decision to ask consent from her patient or not to do so, and I will argue that, although decisions on asking consent remain a complex matter, such coherent belief systems should play an important part in analysts' decisions regarding consent. I will illustrate my thought processes and some clinical situations with brief examples, and I will conclude with some practical recommendations, with the hope that these will stimulate further discussion in the analytic community.

Automatic identification of incidents involving potential serious injuries and fatalities (PSIF).

Safety incidents have always been a crucial risk in work spaces, especially industrial sites. In the last few decades, significant efforts have been dedicated to incident control measures to reduce the rate of safety incidents. Despite all these efforts, the rate of decline in serious injuries and fatalities (SIFs) has been considerably lower than the rate of decline for non-critical incidents. This observation has led to a change of risk reduction paradigm for safety incidents. Under the new paradigm, more focus has been allocated to reducing the rate of critical/SIF incidents, as opposed to reducing the count of all incidents. One of the challenges in reducing the number of SIF incidents is the proper identification of the risk prior to materialization. One of the reasons for risk identification being a challenge is that companies usually only focus on incidents where SIF did occur reactively, and incidents that did not cause SIF but had the potential to do so go unnoticed. Identifying these potentially significant incidents, referred to as potential serious injuries and fatalities (PSIF), would enable companies to work on identifying critical risk and taking steps to prevent them preemptively. However, flagging PSIF incidents requires all incident reports to be analyzed individually by experts and hence significant investment, which is often not affordable, especially for small and medium sized companies. This study is aimed at addressing this problem through machine learning powered automation. We propose a novel approach based on binary classification for the identification of such incidents involving PSIF (potential serious injuries and fatalities). This is the first work towards automatic risk identification from incident reports. Our approach combines a pre-trained transformer model with XGBoost. We utilize advanced natural language processing techniques to encode an incident record comprising heterogeneous fields into a vector representation fed to XGBoost for classification. Moreover, given the scarcity of manually labeled incident records available for training, we leverage weak labeling to augment the label coverage of the training data. We utilize the F2 metric for hyperparameter tuning using Tree-structured Parzen Estimator to prioritize the detection of PSIF records over the avoidance of non-PSIF records being mis-classified as PSIF. The proposed methods outperform several baselines from other studies on a significantly large test dataset.

The price continuity, return and volatility spillover effects of regular and after-hours trading.

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange's regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation.

Clinical Guideline for Preimplantation Genetic Testing in Inherited Cardiac Diseases.

BACKGROUND: Preimplantation genetic testing (PGT) is a reproductive technology that selects embryos without (familial) genetic variants. PGT has been applied in inherited cardiac disease and is included in the latest American Heart Association/American College of Cardiology guidelines. However, guidelines selecting eligible couples who will have the strongest risk reduction most from PGT are lacking. We developed an objective decision model to select eligibility for PGT and compared its results with those from a multidisciplinary team. METHODS: All couples with an inherited cardiac disease referred to the national PGT center were included. A multidisciplinary team approved or rejected the indication based on clinical and genetic information. We developed a decision model based on published risk prediction models and literature, to evaluate the severity of the cardiac phenotype and the penetrance of the familial variant in referred patients. The outcomes of the model and the multidisciplinary team were compared in a blinded fashion. RESULTS: Eighty-three couples were referred for PGT (1997-2022), comprising 19 different genes for 8 different inherited cardiac diseases (cardiomyopathies and arrhythmias). Using our model and proposed cutoff values, a definitive decision was reached for 76 (92%) couples, aligning with 95% of the multidisciplinary team decisions. In a prospective cohort of 11 couples, we showed the clinical applicability of the model to select couples most eligible for PGT. CONCLUSIONS: The number of PGT requests for inherited cardiac diseases increases rapidly, without the availability of specific guidelines. We propose a 2-step decision model that helps select couples with the highest risk reduction for cardiac disease in their offspring after PGT.

Science- and Risk-Based Stability Strategies to Support Product Lifecycle Changes.

ICH Q12 asserts that science- and risk-based approaches are applicable to stability studies supporting Chemistry, Manufacturing and Controls (CMC) post-approval changes (PAC) to enable more timely implementation; however, no guidance or specific examples are provided to demonstrate how prior knowledge of the product can inform the risk assessment for the proposed change(s). Ten diverse case studies are presented in this manuscript to demonstrate how science- and risk-based stability strategies were used to support drug substance and product CMC PAC and lifecycle management activities. The accumulated stability knowledge held by original manufacturers of marketed products is substantial, and different elements of this knowledge base were used to assess the risks and impact of the proposed changes for confident change management. This paper provides ways to leverage science- and risk-based stability strategies as part of the post-approval change-management risk-mitigation strategy, which may enable a reduced stability data commitment and/or a reduced reporting category for change implementation.

Risk assessment and classification prediction for water environment treatment PPP projects.

Water treatment public-private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies.

Insights from Turkey's big data: unraveling the preventability, pathogenesis, and risk management of Alzheimer's disease (AD).

Extensive research into dementia has more recently honed in on several key areas. These areas include the advancement of techniques such as the accumulation of amyloid-ß and tau proteins, the monitoring of cerebral hypometabolism rates etc. The primary objective of this study is to explore the intricate interplay between Alzheimer's disease (AD)-other dementias (D) and various chronic illnesses in terms of time, intensity, and connectivity. In this context, we retrospectively examined data of 149,786 individuals aged 65 and above who received diagnoses of AD and D in the year 2020. At first, logistic regression (LR) analysis has been made with "sex", "age" and "foreigner" (citizenship status) independent variables for AD and D. The LR models shows that while "sex" and "age" variables have a small rate on the risk of developing AD/D, it is detected that being a foreigner increase the risk of AD and D as 69.8% and 88.5% respectively. Besides, the LR models have middle-level success prediction rate for both of the two dependent variables. Additionally, we used the parallel coordinates graphs method within the R Studio to visualize their relationships and connections. The findings of this investigation strongly suggest that AD/D don't stand as isolated conditions, but rather stem from intricate interactions and progressive processes involving diverse chronic diseases over time. Notably, ailments including hypertension, coronary artery disease, diabetes, hyperlipidemia, and psychological disorders, contribute substantially to the emergence of both AD and D. This study highlights that the fight against AD/D can only be possible with next-generation prophylactic interventions that can predict and manage risks. Such an approach holds the potential to potentially lower AD and dementia to levels that are amenable to treatment.

[THE IMPACT OF REGULATION AND CENTRAL MANAGEMENT ON PATIENT SAFETY IN ISRAEL].

INTRODUCTION: An adverse event is defined as an unwanted and unexpected occurrence in a medical process that may end in harm to the patient. In the USA the number of deaths due to failures reaches 253,000 per year. In Israel, over 10,000 deaths occur per year due to errors in the medical treatment of hospitalized patients, the third most common cause of death after heart disease and cancer. The main cause of failures in medical diagnosis and treatment is the complexity of the medical profession. A large number of caregivers in different medical disciplines are needed to treat one patient, therefore there are many errors, especially regarding communication between therapists. The Israeli health system has been operating with a budget deficit for many years and an addition of at least NIS 20 billion is needed to bring it to optimal functioning. The number of doctors, nurses, and hospital beds per 1000 inhabitants is significantly less than the average of the OECD countries. When there was a 30% increase in the population of Israel it was necessary to enhance the existing situation, with the addition of 7700 hospital beds, but only 1400 were added. This caused a decrease from 2.1 beds per 1000 residents to 1.8 beds per 1000 residents. There is an urgent need to change the elements of treatment safety in the Ministry of Health's strategic plan. An administration for quality, treatment safety, risk management in medicine, and accreditation should be established which, in addition to the care quality division, will include a safety division with investigation and monitoring units and will prepare strategic improvement plans, and a university-level research institute with researchers, computing, statistics, and information gathering units. The institute will receive all reports of adverse events, results of investigations, inspection committees, control and quality committees, relevant verdicts, and updated literature reviews, for research and systemic learning. Strategic plans will be prepared to prevent failures in diagnosis and medical treatment, leading to a decrease in mortality due to adverse events and the significant expenses involved.

An Analysis of Incident Reports Related to Electronic Medication Management: How They Change Over Time.

OBJECTIVE: Electronic medication management (EMM) systems have been shown to introduce new patient safety risks that were not possible, or unlikely to occur, with the use of paper charts. Our aim was to examine the factors that contribute to EMM-related incidents and how these incidents change over time with ongoing EMM use. METHODS: Incidents reported at 3 hospitals between January 1, 2010, and December 31, 2019, were extracted using a keyword search and then screened to identify EMM-related reports. Data contained in EMM-related incident reports were then classified as unsafe acts made by users and the latent conditions contributing to each incident. RESULTS: In our sample, 444 incident reports were determined to be EMM related. Commission errors were the most frequent unsafe act reported by users (n = 298), whereas workarounds were reported in only 13 reports. User latent conditions (n = 207) were described in the highest number of incident reports, followed by conditions related to the organization (n = 200) and EMM design (n = 184). Over time, user unfamiliarity with the system remained a key contributor to reported incidents. Although fewer articles to electronic transfer errors were reported over time, incident reports related to the transfer of information between different computerized systems increased as hospitals adopted more clinical information systems. CONCLUSIONS: Electronic medication management-related incidents continue to occur years after EMM implementation and are driven by design, user, and organizational conditions. Although factors contribute to reported incidents in varying degrees over time, some factors are persistent and highlight the importance of continuously improving the EMM system and its use.

How Inpatient Psychiatric Units Can Be Both Safe and Therapeutic.

Inpatient psychiatric units should be therapeutic environments that support dignity and recovery. When adverse outcomes (eg, self-harm, violence) happen in these settings, clinicians and administrators can face litigation and other pressures to prioritize risk management over supporting patients' access to personal belongings, exercise equipment, and private spaces. This article describes these downward pressures toward sparser, controlling environments in inpatient psychiatric settings as a safety funnel and suggests strategies for balancing safety, humanity, and recovery in these contexts.

Social vulnerability assessment under different extreme precipitation scenarios: A case study in Henan Province, China.

Extreme precipitation usually cause grievous losses&casualties, which varies greatly under different scenarios. This paper took Henan province as an example, it innovatively constructed three different extreme precipitation scenarios and built indicators system of social vulnerability from exposure, sensitivity and resilience based on MOVE framework. Social Vulnerability Indexs(SoVI) were then calculated by mathematical models under three different reoccurrence intervals. The results show that SoVI was low in the west and high in the north. High SoVI areas expanded to the middle and south as recurrence intervals increased. SoVI in each area of Henan province increased along with the recurrence intervals at different growth rates. The larger the recurrence interval was, the faster the SoVI increased. The results indicate SoVI is greatly affected by disaster levels, which need to be incorporated into social vulnerability. This study provides not only a new thought for social vulnerability assessment, but also a reference for the policymakers to formulate related risk management policies.