Missed Diagnosis of Cardiovascular Disease in Outpatient General Medicine: Insights from Malpractice Claims Data

The Big Three diagnostic errors through reflections of Japanese internists

OBJECTIVES

To analyze the Big Three diagnostic errors (malignant neoplasms, cardiovascular diseases, and infectious diseases) through internists' self-reflection on their most memorable diagnostic errors.

METHODS

This secondary analysis study, based on a web-based cross-sectional survey, recruited participants from January 21 to 31, 2019. The participants were asked to recall the most memorable diagnostic error cases in which they were primarily involved. We gathered data on internists' demographics, time to error recognition, and error location. Factors causing diagnostic errors included environmental conditions, information processing, and cognitive bias. Participants scored the significance of each contributing factor on a Likert scale (0, unimportant; 10, extremely important).

RESULTS

The Big Three comprised 54.1 % (n=372) of the 687 cases reviewed. The median physician age was 51.5 years (interquartile range, 42-58 years); 65.6 % of physicians worked in hospital settings. Delayed diagnoses were the most common among malignancies (n=64, 46 %). Diagnostic errors related to malignancy were frequent in general outpatient settings on weekdays and in the mornings and were not identified for several months following the event. Environmental factors often contributed to cardiovascular disease-related errors, which were typically identified within days in emergency departments, during night shifts, and on holidays. Information gathering and interpretation significantly impacted infectious disease diagnoses.

CONCLUSIONS

The Big Three accounted for the majority of cases recalled by Japanese internists. The most relevant contributing factors were different for each of the three categories. Addressing these errors may require a unique approach based on the disease associations.

A data-efficient zero-shot and few-shot Siamese approach for automated diagnosis of left ventricular hypertrophy

Left ventricular hypertrophy (LVH) is a life-threatening condition in which the muscle of the left ventricle thickens and enlarges. Echocardiography is a test performed by cardiologists and echocardiographers to diagnose this condition. The manual interpretation of echocardiography tests is time-consuming and prone to errors. To address this issue, we have developed an automated LVH diagnosis technique using deep learning. However, the availability of medical data is a significant challenge due to varying industry standards, privacy laws, and legal constraints. To overcome this challenge, we have proposed a data-efficient technique for automated LVH classification using echocardiography. Firstly, we collected our own dataset of normal and LVH echocardiograms from 70 patients in collaboration with a clinical facility. Secondly, we introduced novel zero-shot and few-shot algorithms based on a modified Siamese network to classify LVH and normal images. Unlike traditional zero-shot learning approaches, our proposed method does not require text vectors, and classification is based on a cutoff distance. Our model demonstrates superior performance compared to state-of-the-art techniques, achieving up to 8% precision improvement for zero-shot learning and up to 11% precision improvement for few-shot learning approaches. Additionally, we assessed the inter-observer and intra-observer reliability scores of our proposed approach against two expert echocardiographers. The results revealed that our approach achieved better inter-observer and intra-observer reliability scores compared to the experts.

Malpractice Claims of Internal Medicine Involving Diagnostic and System Errors in Japan

Objectives Medical litigation resulting from diagnostic errors leads to lawsuits that are time-consuming, expensive, and psychologically burdensome. Few studies have focused on internists, who are more likely to make diagnostic errors than others, with assessments of litigation in terms of system and diagnostic errors. This study explored factors contributing to internists losing lawsuits and examined whether system or diagnostic errors were more important on the outcome. Methods Data regarding 419 lawsuits against internists closed between 1961 and 2017 were extracted from a public Japanese database. Factors affecting litigation outcomes were identified by comparative analysis focusing on system and diagnostic errors, environmental factors, and differences in initial diagnoses. Results Overall, 419 malpractice claims against internists were analyzed. The rate of lawsuits being decided against internists was high (50.1%). The primary cause of litigation was diagnostic errors (213, 54%), followed by system errors (188, 45%). The foremost initial diagnostic error was "no abnormality" (17.2%) followed by ischemic heart disease (9.6%) and malignant neoplasm (8.1%). Following cause-adjustment for loss, system errors were 21.37 times more likely to lead to a loss. Losses were 6.26 times higher for diagnostic error cases, 2.49 times higher for errors occurring at night, and 3.44 times higher when "malignant neoplasm" was the first diagnosis. Conclusions This study found that system errors strongly contributed to internists' losses. Diagnostic errors, night shifts, and initial diagnoses of malignant neoplasms also significantly affected trial outcomes. Administrators must focus on both system errors and diagnostic errors to enhance the safety of patients and reduce internists' risk exposure.

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

A description of medical malpractice claims involving advanced practice providers

BACKGROUND

The number of physician assistants (PAs) and advanced practice registered nurses (APRNs), together known as advanced practice providers (APPs), has risen dramatically. The goal is identifying characteristics of paid medical malpractice claims, in which APPs are defendants.

METHODS

Retrospective cohort study using Harvard's malpractice insurer's national database. Closed claims (2007-2016) with PAs, APRNs, or physicians as defendants. The primary analysis compared claims by role group by patient-, provider-, and claim-level characteristics. Supplemental analyses compared claims naming APPs with and without physicians. Multivariable logistic regression identified variables associated with claim payment.

RESULTS

Of 54,772 claims, PAs were defendants without APRNs or physicians in 26 claims; APRNs were defendants without PAs or physicians in 63; physicians were defendants without PAs or APRNs in 37,354. Approximately 75% of claims naming APPs co-named physicians. More claims naming PAs and APRNs were paid on behalf of the hospital/practice (38% and 32%, respectively) compared with physicians (8%, P < 0.001). Payment was less likely for inpatient care (OR 0.89, 95% CI 0.85-0.93, P < 0.001) but higher when APRNs were defendants (1.82, 1.09-3.03), when procedure-related (1.31, 1.25-1.38, P < 0.001) or patients died (1.09, 1.03-1.16, P = 0.003).

CONCLUSIONS

These results can inform patient safety initiatives to prevent future harms. The target is outpatient airway procedures performed by APRNs.

Lessons Learned From Medical Malpractice Claims Involving Critical Care Nurses

BACKGROUND

Medical malpractice data can be used to improve patient safety.

OBJECTIVE

To describe the types of harm events involving nurses that lead to malpractice claims and to compare claims among intensive care units (ICUs), emergency departments, and operating rooms.

METHODS

Malpractice claims closed between 2007 and 2016 were extracted from a national database. Claims with a nurse as the primary provider were identified and then compared by location of the harm event: ICU, emergency department, or operating room. Multivariable regression was used to determine predictors of claims payment.

RESULTS

Of 54 699 claims, 314 involved ICU nurses as the primary provider. The majority (59%) of claims involving ICU nurses resulted in death or permanent injury. The most common allegation of claims involving ICU nurses was failure to monitor (47%), which was higher than among claims against nurses in the emergency department (9%) or the operating room (4%) (P < .001). The most common diagnosis in claims involving ICU nurses was decubitus ulcers (26%). Despite equivalent numbers of defendants per claim, the median indemnity for paid claims involving ICU nurses was higher ($125 000) than that paid for claims originating in the emergency department ($56 799) or operating room ($43 910) (P < .001). In multivariable regression, 2 variables increased the risk of claim payment: ICU location (odds ratio, 1.79 [95% CI, 1.29-2.48]) and permanent injury (odds ratio, 1.50 [95% CI, 1.07-2.09]).

CONCLUSIONS

Malpractice claims involving ICU nurses were distinct from claims in comparably fast-paced settings. Focusing harm-prevention efforts in the ICU on skin integrity and monitoring of patients would most likely mitigate many highly severe harms involving ICU nurses, which would benefit both patients and nurses.

Missed diagnosis of cancer in primary care: Insights from malpractice claims data

BACKGROUND

In the ambulatory setting, missed cancer diagnoses are leading contributors to patient harm and malpractice risk; however, there are limited data on the malpractice case characteristics for these cases.

OBJECTIVE

The aim of this study was to examine key features and factors identified in missed cancer diagnosis malpractice claims filed related to primary care and evaluate predictors of clinical and claim outcomes.

METHODS

We analyzed 2155 diagnostic error closed malpractice claims in outpatient general medicine. We created multivariate models to determine factors that predicted case outcomes.

RESULTS

Missed cancer diagnoses represented 980 (46%) cases of primary care diagnostic errors, most commonly from lung, colorectal, prostate, or breast cancer. The majority (76%) involved errors in clinical judgment, such as a failure or delay in ordering a diagnostic test (51%) or failure or delay in obtaining a consult or referral (37%). These factors were independently associated with higher-severity patient harm. The majority of these errors were of high severity (85%).

CONCLUSIONS

Malpractice claims involving missed diagnoses of cancer in primary care most often involve routine screening examinations or delays in testing or referral. Our findings suggest that more reliable closed-loop systems for diagnostic testing and referrals are crucial for preventing diagnostic errors in the ambulatory setting.

Medical Malpractice Involving Pulmonary/Critical Care Physicians

BACKGROUND

Medical malpractice data can be leveraged to understand specialty-specific risk.

METHODS

Malpractice claims were examined from the Comparative Benchmarking System (2007-2016), a national database containing > 30% of claims data in the United States. Claims were identified with either internal medicine or pulmonary/critical care (PCC) physicians as the primary provider involved in the harm. Claim characteristics were compared according to specialty and care setting (inpatient vs outpatient), and multiple regression analysis was performed to predict claim payment.

RESULTS

Claims involving PCC physicians differed from those involving internal medicine physicians in terms of harm severity, allegation, final diagnosis, procedure involvement, payment rate, and contributing factors. The majority of claims involving PCC physicians resulted from inpatient care (63%), of which only 26% occurred delivering intensive care. Eighty-one percent were from harm events that resulted in death/permanent injury. The most common diagnosis was laceration during a procedure for inpatient claims (6%) and lung cancer for outpatient claims (28%). Thirty-one percent of claims overall involved procedures. Although only 26% were paid, the median indemnity per paid claim of $285,769 ranked PCC as the twelfth highest of 69 specialties. The two variables associated with indemnity payment were outpatient care (OR, 1.70; 95% CI, 1.01-2.86) and temporary harm (OR, 0.36; 95% CI, 0.15-0.87).

CONCLUSIONS

Malpractice claims involving PCC physicians were distinct from claims involving internal medicine physicians. Although only one-quarter of claims was paid, the indemnity per claim was high among specialties. Specialty-specific prevention strategies must be developed to mitigate both patient harm and provider malpractice risk.