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

No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893-0.895) in all patients' data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors' loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes

A Framework for Artificial Intelligence Applications in the Healthcare Revenue Management Cycle

There is a lack of understanding of specific risks and benefits associated with AI/RPA implementations in healthcare revenue cycle settings. Healthcare companies are confronted with stricter regulations and billing requirements, underpayments, and more significant delays in receiving payments. Despite the continued interest of practitioners, revenue cycle management has not received much attention in research. Revenue cycle management is defined as the process of identifying, collecting, and managing the practice’s revenue from payers based on the services provided.This dissertation provided contributions to both areas, as mentioned above. To accomplish this, a semi-structured interview was distributed to healthcare executives. The semi-structured interview data obtained from each participant underwent a triangulation process to determine the validity of responses aligned with the extant literature. Data triangulation ensured further that significant themes found in the interview data answered the central research questions. The study focused on how the broader issues related to AI/RPA integration into revenue cycle management will affect individual organizations. These findings also presented multiple views of the technology’s potential benefits, limitations, and risk management strategies to address its associative threats. The triangulation of the responses and current literature helped develop a theoretical framework that may be applied to a healthcare organization in an effort to migrate from their current revenue management technique to one that includes the use of AI/ML/RPA as a means of future cost control and revenue boost

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches

Chronic Risk and Disease Management Model Using Structured Query Language and Predictive Analysis

Individuals with chronic conditions are the ones who use health care most frequently and more than 50% of top ten causes of death are chronic diseases in United States and these members always have health high risk scores. In the field of population health management, identifying high risk members is very important in terms of patient health care, disease management and cost management. Disease management program is very effective way of monitoring and preventing chronic disease and health related complications and risk management allows physicians and healthcare companies to reduce patient’s health risk, help identifying members for care/disease management along with help in managing financial risk. The main objective of this research is to introduce efficient and accurate risk assessment model maintaining the accuracy of risk scores compared to existing model and based on calculated risk scores identify the members for disease management programs using structured query language. For the experimental purpose we have used data and information from different sources like CMS, NCQA, existing models and Healthcare Insurance Industry. In this approach, base principle is used from chronic and disability payment system (CDPS), based on this model weight of chronic disease is defined to calculate risk of each patient. Also to be more focused, three chronic diseases have been selected as a part of analysis. They are breast cancer, diabetes and congestive heart failure. Different sets of diagnosis, electronic medical records, and member pharmacy information are key source. Industry standard database system have been in taken in consideration while implementing the logic, which makes implementation of model more efficient since data is warehoused where model is built. We obtained experimental result from total 4761 relevant medical records taken from Molina Healthcare’s data warehouse. We tested proposed model using risk score data from State of Illinois using multiple linear regression method and implemented proposed logic in health plan data, based on which we calculated p-value and confidence level of our variables and also as second validation process we ran same data source through original risk model. In next step we showed that risk scores of members are highly contributing in member selection process for disease management program. To validate member selection criteria we used fast and frugal decision tree algorithm and confusion matrix result is used to measure the performance of member selection process for disease management program. The results show that the proposed model achieved overall risk assessment confidence level of 99%, with R-squared value of 98% and on disease management member identification we achieved 99% of sensitivity, 89% of accuracy and 74% of specificity. The experimental result from proposed model shows that if risk assessment model is taken one step further not only risk of member can be determined but it can help in disease management approach by identifying and prioritizing members for disease management. The resulting chronic risk and disease management method is very promising method for any patient, insurance companies, provider groups, claims processing organizations and physician groups to more accurately and effectively manage their members in terms of member health risk and enrolling them under required care management programs. Methods and design used in this research contributes to business analytics approach, overall member risk and disease management approach using predictive analytics based on member’s medical diagnosis, pharmacy utilization and member demographics

Data Mining Techniques for Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models. Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matri

Critical success factors for preventing E-banking fraud

E-Banking fraud is an issue being experienced globally and is continuing to prove costly to both banks and customers. Frauds in e-banking services occur as a result of various compromises in security ranging from weak authentication systems to insufficient internal controls. Lack of research in this area is problematic for practitioners so there is need to conduct research to help improve security and prevent stakeholders from losing confidence in the system. The purpose of this paper is to understand factors that could be critical in strengthening fraud prevention systems in electronic banking. The paper reviews relevant literatures to help identify potential critical success factors of frauds prevention in e-banking. Our findings show that beyond technology, there are other factors that need to be considered such as internal controls, customer education and staff education etc. These findings will help assist banks and regulators with information on specific areas that should be addressed to build on their existing fraud prevention systems