scholarly journals Does state malpractice environment affect outcomes following spinal fusions? A robust statistical and machine learning analysis of 549,775 discharges following spinal fusion surgery in the United States

2020 ◽  
Vol 49 (5) ◽  
pp. E18
Author(s):  
Andrew K. Chan ◽  
Michele Santacatterina ◽  
Brenton Pennicooke ◽  
Shane Shahrestani ◽  
Alexander M. Ballatori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Fusion ◽  
Medical Malpractice ◽  
Medical Liability ◽  
Learning Models ◽  
Malpractice Claim ◽  
Discharge Disposition ◽  
Medical Malpractice Claim ◽  
Malpractice Claims ◽  
Machine Learning Models

OBJECTIVESpine surgery is especially susceptible to malpractice claims. Critics of the US medical liability system argue that it drives up costs, whereas proponents argue it deters negligence. Here, the authors study the relationship between malpractice claim density and outcomes.METHODSThe following methods were used: 1) the National Practitioner Data Bank was used to determine the number of malpractice claims per 100 physicians, by state, between 2005 and 2010; 2) the Nationwide Inpatient Sample was queried for spinal fusion patients; and 3) the Area Resource File was queried to determine the density of physicians, by state. States were categorized into 4 quartiles regarding the frequency of malpractice claims per 100 physicians. To evaluate the association between malpractice claims and death, discharge disposition, length of stay (LOS), and total costs, an inverse-probability-weighted regression-adjustment estimator was used. The authors controlled for patient and hospital characteristics. Covariates were used to train machine learning models to predict death, discharge disposition not to home, LOS, and total costs.RESULTSOverall, 549,775 discharges following spinal fusions were identified, with 495,640 yielding state-level information about medical malpractice claim frequency per 100 physicians. Of these, 124,425 (25.1%), 132,613 (26.8%), 130,929 (26.4%), and 107,673 (21.7%) were from the lowest, second-lowest, second-highest, and highest quartile states, respectively, for malpractice claims per 100 physicians. Compared to the states with the fewest claims (lowest quartile), surgeries in states with the most claims (highest quartile) showed a statistically significantly higher odds of a nonhome discharge (OR 1.169, 95% CI 1.139–1.200), longer LOS (mean difference 0.304, 95% CI 0.256–0.352), and higher total charges (mean difference [log scale] 0.288, 95% CI 0.281–0.295) with no significant associations for mortality. For the machine learning models—which included medical malpractice claim density as a covariate—the areas under the curve for death and discharge disposition were 0.94 and 0.87, and the R2 values for LOS and total charge were 0.55 and 0.60, respectively.CONCLUSIONSSpinal fusion procedures from states with a higher frequency of malpractice claims were associated with an increased odds of nonhome discharge, longer LOS, and higher total charges. This suggests that medicolegal climate may potentially alter practice patterns for a given spine surgeon and may have important implications for medical liability reform. Machine learning models that included medical malpractice claim density as a feature were satisfactory in prediction and may be helpful for patients, surgeons, hospitals, and payers.

CREDIBILITY GAP BETWEEN MALPRACTICE INSURERS AND REGULATORS?

PEDIATRICS ◽  
1989 ◽  
Vol 84 (3) ◽  
pp. A62-A62
Keyword(s):  
South Dakota ◽  
Medical Malpractice ◽  
Malpractice Claim ◽  
Medical Malpractice Claim ◽  
Malpractice Insurance ◽  
Malpractice Claims ◽  
Commerce Department ◽  
Medical Malpractice Insurance ◽  
Medical Malpractice Claims ◽  
North And South

Minnesota insurance regulators said that a study of thousands of medical malpractice claims in three states shows that medical insurers overcharged the state's doctors for malpractice insurance while the number and severity of malpractice claims was actually dropping. In a report to the Minnesota legislature yesterday, state Commerce Department officials said they studied every medical malpractice claim filed in Minnesota and North and South Dakota from 1982 to 1987. Michael Hatch, Minnesota's commerce commissioner, said the study showed that malpractice premiums rose some 300% while the number of claims and the amount that insurers paid to claimants was falling. A spokeswoman for St. Paul Cos., the nation's largest underwriter of medical malpractice insurance and one of two companies that write such insurance in Minnesota, called the state's findings "inaccurate" and "meaningless." She said an outside actuary hired by the company had also found the state's study flawed. Mr. Hatch called the state's study "airtight." Minnesota said average payouts on claims fell from $22,906 in 1982 to $7,550 in 1987. St. Paul said its figures showed payouts rose between 1978 and 1987 from an average of $23,000 to $120,000, including litigation costs.

scholarly journals Medical malpractice claim risk in emergency departments

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Claudio Bianchin ◽  
Carolina Prevaldi ◽  
Matteo Corradin ◽  
Mario Saia
Keyword(s):  
Medical Malpractice ◽  
Emergency Physicians ◽  
Malpractice Claim ◽  
Medical Diagnoses ◽  
Medical Malpractice Claim ◽  
Malpractice Claims ◽  
Risk Areas ◽  
Pelvic Symptoms ◽  
Management Recommendations ◽  
Medical Malpractice Claims

Medical malpractice claims are a major problem for emergency physicians and for the health system which must be addressed in a rational and effective fashion: claim analysis seems the best way to identify risk factors and risk areas and to elaborate risk management recommendations. The Emergency Department (ED) is one of the areas at higher risk. Medical diagnoses associated with the highest number of claims are acute myocardial infarction, fractures, appendicitis, abdominal/pelvic symptoms, aortic aneurism and open wounds to fingers. The present paper emphasizes the necessity for ED emergency physicians to pay special attention when facing these health conditions and seeks to provide indications in order to reduce litigation.

The resolution process and the timing of settlement of medical malpractice claims

2019 ◽  
Vol 15 (4) ◽  
pp. 509-529 ◽  
Author(s):  
Samantha Bielen ◽  
Peter Grajzl ◽  
Wim Marneffe
Keyword(s):  
Medical Malpractice ◽  
Medical Liability ◽  
Factual Information ◽  
Malpractice Claim ◽  
Resolution Process ◽  
Level Data ◽  
Malpractice Claims ◽  
Regression Framework ◽  
Micro Level ◽  
Medical Malpractice Claims

AbstractWe draw on uniquely detailed micro-level data from a Belgian professional medical liability insurer to examine how different procedural and legal events that take place during the unfolding of a medical malpractice claim influence the timing of its settlement. Utilizing the competing risks regression framework, we find that settlement hazard is all else equal statistically significantly positively associated with the completion of those procedural and legal events that most effectively reveal factual information about the underlying medical malpractice case. Consistent with theory, settlement hazard is either unassociated or even negatively associated with the completion of other procedural and legal events. Our analysis, therefore, provides policy insights into which aspects of the resolution process could be emphasized, and which de-emphasized, in order to reduce the often excessive duration of medical malpractice claims and its adverse effects on the healthcare system.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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