Burden of serious harms from diagnostic error in the USA.

BACKGROUND: Diagnostic errors cause substantial preventable harms worldwide, but rigorous estimates for total burden are lacking. We previously estimated diagnostic error and serious harm rates for key dangerous diseases in major disease categories and validated plausible ranges using clinical experts. OBJECTIVE: We sought to estimate the annual US burden of serious misdiagnosis-related harms (permanent morbidity, mortality) by combining prior results with rigorous estimates of disease incidence. METHODS: Cross-sectional analysis of US-based nationally representative observational data. We estimated annual incident vascular events and infections from 21.5 million (M) sampled US hospital discharges (2012-2014). Annual new cancers were taken from US-based registries (2014). Years were selected for coding consistency with prior literature. Disease-specific incidences for 15 major vascular events, infections and cancers ('Big Three' categories) were multiplied by literature-based rates to derive diagnostic errors and serious harms. We calculated uncertainty estimates using Monte Carlo simulations. Validity checks included sensitivity analyses and comparison with prior published estimates. RESULTS: Annual US incidence was 6.0 M vascular events, 6.2 M infections and 1.5 M cancers. Per 'Big Three' dangerous disease case, weighted mean error and serious harm rates were 11.1% and 4.4%, respectively. Extrapolating to all diseases (including non-'Big Three' dangerous disease categories), we estimated total serious harms annually in the USA to be 795 000 (plausible range 598 000-1 023 000). Sensitivity analyses using more conservative assumptions estimated 549 000 serious harms. Results were compatible with setting-specific serious harm estimates from inpatient, emergency department and ambulatory care. The 15 dangerous diseases accounted for 50.7% of total serious harms and the top 5 (stroke, sepsis, pneumonia, venous thromboembolism and lung cancer) accounted for 38.7%. CONCLUSION: An estimated 795 000 Americans become permanently disabled or die annually across care settings because dangerous diseases are misdiagnosed. Just 15 diseases account for about half of all serious harms, so the problem may be more tractable than previously imagined.

Oncologic Errors in Diagnostic Radiology: A 10-Year Analysis Based on Medical Malpractice Claims.

PURPOSE: To retrospectively analyze the nature and extent of oncology-related errors accounting for malpractice allegations in diagnostic radiology. METHODS: The Comparative Benchmarking System of the Controlled Risk Insurance Company, a database containing roughly 30% of medical malpractice claims in the United States, was searched retrospectively for the period 2008 to 2017. Claims naming radiology as a primary service were identified and were stratified and compared by oncologic versus nononcologic status, allegation type (diagnostic versus nondiagnostic), and imaging modality. RESULTS: Over the 10-year period, radiology was the primary responsible service for 3.9% of all malpractice claims (2,582 of 66,061) and 12.8% of claims with diagnostic allegations (1,756 of 13,695). Oncology (neoplasms) accounted for 44.0% of radiology cases with diagnostic allegations, a larger share than any other category of medical condition. Among radiology cases with diagnostic allegations, high-severity harm occurred in 79% of oncologic but just 42% of nononcologic cases. Of all oncologic radiology cases, 97.4% had diagnostic allegations, and just 55.0% of nononcologic radiology cases had diagnostic allegations. Imaging misinterpretation was a contributing factor for a large majority (80.7% [623 of 772]) of oncologic radiology cases with diagnostic allegations. The modalities most commonly used in oncologic radiology cases with diagnostic allegations involving misinterpretation were mammography and CT. CONCLUSION: Oncology represents the largest source of radiology malpractice cases with diagnostic allegations. Oncologic radiology malpractice cases are more likely than nononcologic radiology cases to be due to diagnostic errors. Furthermore, compared with those that are nononcologic, oncologic radiology cases with diagnostic allegations are more likely to be associated with high-severity harm. Efforts are warranted to reduce misinterpretations of oncologic imaging.

Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the "Big Three".

BACKGROUND: Missed vascular events, infections, and cancers account for ~75% of serious harms from diagnostic errors. Just 15 diseases from these "Big Three" categories account for nearly half of all serious misdiagnosis-related harms in malpractice claims. As part of a larger project estimating total US burden of serious misdiagnosis-related harms, we performed a focused literature review to measure diagnostic error and harm rates for these 15 conditions. METHODS: We searched PubMed, Google, and cited references. For errors, we selected high-quality, modern, US-based studies, if available, and best available evidence otherwise. For harms, we used literature-based estimates of the generic (disease-agnostic) rate of serious harms (morbidity/mortality) per diagnostic error and applied claims-based severity weights to construct disease-specific rates. Results were validated via expert review and comparison to prior literature that used different methods. We used Monte Carlo analysis to construct probabilistic plausible ranges (PPRs) around estimates. RESULTS: Rates for the 15 diseases were drawn from 28 published studies representing 91,755 patients. Diagnostic error (false negative) rates ranged from 2.2% (myocardial infarction) to 62.1% (spinal abscess), with a median of 13.6% [interquartile range (IQR) 9.2-24.7] and an aggregate mean of 9.7% (PPR 8.2-12.3). Serious misdiagnosis-related harm rates per incident disease case ranged from 1.2% (myocardial infarction) to 35.6% (spinal abscess), with a median of 5.5% (IQR 4.6-13.6) and an aggregate mean of 5.2% (PPR 4.5-6.7). Rates were considered face valid by domain experts and consistent with prior literature reports. CONCLUSIONS: Diagnostic improvement initiatives should focus on dangerous conditions with higher diagnostic error and misdiagnosis-related harm rates.

Harnessing the power of medical malpractice data to improve patient care.

Intelligence gleaned from medical malpractice cases helps health care institutions analyze their litigation practices, trend financial outcomes, and even identify clinical services needing attention. But when examined more deeply, medical malpractice data can also be a powerful patient safety tool by revealing clinical patterns that contribute to medical errors and by enabling leadership to more accurately plan investments in patient safety and risk management. This case study describes how one organization, UMass Memorial Health Care in Worcester, Massachusetts, harnesses its deeply coded medical malpractice data and benchmarks its performance against national peers to catalyze clinical improvements. This strategy has proven successful in yielding positive change in such areas as emergency department ultrasound coverage, obstetrics communication, and airway management training. UMass Memorial's ability to embed claims data use into its culture and to share learning across clinical services offers lessons for health care organizations of any size.

Breakdowns in the initial patient-provider encounter are a frequent source of diagnostic error among ischemic stroke cases included in a large medical malpractice claims database.

Background Misdiagnosis of dangerous cerebrovascular disease is a substantial public health problem. We sought to identify and describe breakdowns in the diagnostic process among patients with ischemic stroke to facilitate future improvements in diagnostic accuracy. Methods We performed a retrospective, descriptive study of medical malpractice claims housed in the Controlled Risk Insurance Company (CRICO) Strategies Comparative Benchmarking System (CBS) database from 1/1/2006 to 1/1/2016 involving ischemic stroke patients. Baseline claimant demographics, clinical setting, primary allegation category, and outcomes were abstracted. Among cases with a primary diagnosis-related allegation, we detail presenting symptoms and diagnostic breakdowns using CRICO's proprietary taxonomy. Results A total of 478 claims met inclusion criteria; 235 (49.2%) with diagnostic error. Diagnostic errors originated in the emergency department (ED) in 46.4% (n = 109) of cases, outpatient clinic in 27.7% (n = 65), and inpatient setting in 25.1% (n = 59). Across care-settings, the most frequent process breakdown was in the initial patient-provider encounter [76.2% (n = 179 cases)]. Failure to assess, communicate, and respond to ongoing symptoms was the component of the patient-provider encounter most frequently identified as a source of misdiagnosis in the ED. Exclusively non-traditional presenting symptoms occurred in 35.7% (n = 84), mixed traditional and non-traditional symptoms in 30.6% (n = 72), and exclusively traditional in 23.8% (n = 56) of diagnostic error cases. Conclusions Among ischemic stroke patients, breakdowns in the initial patient-provider encounter were the most frequent source of diagnostic error. Targeted interventions should focus on the initial diagnostic encounter, particularly for ischemic stroke patients with atypical symptoms.

Electronic Health Record-Related Events in Medical Malpractice Claims.

BACKGROUND: There is widespread agreement that the full potential of health information technology (health IT) has not yet been realized and of particular concern are the examples of unintended consequences of health IT that detract from the safety of health care or from the use of health IT itself. The goal of this project was to obtain additional information on these health IT-related problems, using a mixed methods (qualitative and quantitative) analysis of electronic health record-related harm in cases submitted to a large database of malpractice suits and claims. METHODS: Cases submitted to the CRICO claims database and coded during 2012 and 2013 were analyzed. A total of 248 cases (<1%) involving health IT were identified and coded using a proprietary taxonomy that identifies user- and system-related sociotechnical factors. Ambulatory care accounted for most of the cases (146 cases). Cases were most typically filed as a result of an error involving medications (31%), diagnosis (28%), or a complication of treatment (31%). More than 80% of cases involved moderate or severe harm, although lethal cases were less likely in cases from ambulatory settings. Etiologic factors spanned all of the sociotechnical dimensions, and many recurring patterns of error were identified. CONCLUSIONS: Adverse events associated with health IT vulnerabilities can cause extensive harm and are encountered across the continuum of health care settings and sociotechnical factors. The recurring patterns provide valuable lessons that both practicing clinicians and health IT developers could use to reduce the risk of harm in the future. The likelihood of harm seems to relate more to a patient's particular situation than to any one class of error.

Serious misdiagnosis-related harms in malpractice claims: The "Big Three" - vascular events, infections, and cancers.

Background Diagnostic errors cause substantial preventable harm, but national estimates vary widely from 40,000 to 4 million annually. This cross-sectional analysis of a large medical malpractice claims database was the first phase of a three-phase project to estimate the US burden of serious misdiagnosis-related harms. Methods We sought to identify diseases accounting for the majority of serious misdiagnosis-related harms (morbidity/mortality). Diagnostic error cases were identified from Controlled Risk Insurance Company (CRICO)'s Comparative Benchmarking System (CBS) database (2006-2015), representing 28.7% of all US malpractice claims. Diseases were grouped according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS) that aggregates the International Classification of Diseases diagnostic codes into clinically sensible groupings. We analyzed vascular events, infections, and cancers (the "Big Three"), including frequency, severity, and settings. High-severity (serious) harms were defined by scores of 6-9 (serious, permanent disability, or death) on the National Association of Insurance Commissioners (NAIC) Severity of Injury Scale. Results From 55,377 closed claims, we analyzed 11,592 diagnostic error cases [median age 49, interquartile range (IQR) 36-60; 51.7% female]. These included 7379 with high-severity harms (53.0% death). The Big Three diseases accounted for 74.1% of high-severity cases (vascular events 22.8%, infections 13.5%, and cancers 37.8%). In aggregate, the top five from each category (n = 15 diseases) accounted for 47.1% of high-severity cases. The most frequent disease in each category, respectively, was stroke, sepsis, and lung cancer. Causes were disproportionately clinical judgment factors (85.7%) across categories (range 82.0-88.8%). Conclusions The Big Three diseases account for about three-fourths of serious misdiagnosis-related harms. Initial efforts to improve diagnosis should focus on vascular events, infections, and cancers.

Identifying Electronic Health Record Contributions to Diagnostic Error in Ambulatory Settings Through Legal Claims Analysis.

This qualitative study analyzes closed legal claims data to determine whether problems with electronic health records are associated with diagnostic errors, in which part of the diagnostic process errors occur, and the specific types of errors that occur.

Developing risk management dashboards using risk and quality measures: A visual best practices approach.

Because quality measures are ubiquitous, health care risk management leaders often use them as a proxy for risk management measures. While certain quality measures adequately reflect some aspects of risk management, they are neither a perfect nor complete substitute for well-developed and comprehensive risk management measures. Using a comprehensive approach consisting of quality measures, risk measures, and measures that are less amenable to classification would be the best approach. Identifying the most powerful and informative measures, designing the most appropriate dashboards, and incorporating visual best practices are crucial steps required for evaluating the effectiveness and value of an enterprise risk management program. The authors explain the terms and concepts, review the measures available in the literature, propose new measures, discuss visual best practices, and provide sample dashboard components.

The role of radiology in diagnostic error: a medical malpractice claims review.

Just as radiologic studies allow us to see past the surface to the vulnerable and broken parts of the human body, medical malpractice claims help us see past the surface of medical errors to the deeper vulnerabilities and potentially broken aspects of our healthcare delivery system. And just as the insights we gain through radiologic studies provide focus for a treatment plan for healing, so too can the analysis of malpractice claims provide insights to improve the delivery of safe patient care. We review 1325 coded claims where Radiology was the primary service provider to better understand the problems leading to patient harm, and the opportunities most likely to improve diagnostic care in the future.

Data as a catalyst for change: stories from the frontlines.

The landmark 1999 Institute of Medicine report, "To Err Is Human," challenged us all to reduce the number of preventable medical errors. While vulnerabilities and patient harm continue at unacceptable rates, there are also many success stories. This article presents a series of case studies that illustrate how healthcare organizations have used data-quantitative, qualitative, and comparative-to address vulnerabilities and guide meaningful change to improve patient safety. These examples are drawn from the data-sharing community of CRICO Strategies, a division of the Risk Management Foundation of the Harvard Medical Institutions, Inc. (CRICO). CRICO's data-driven strategy uses intelligence from thousands of medical malpractice cases across the country to examine what has gone wrong and why, and to help members and clients manage their risk and provide better care.