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.

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.

Studying near-miss errors is essential to preventing errors from reaching patients. When an error is committed, it may be intercepted (near-miss) or it will reach the patient; estimates of the proportion that reach the patient vary widely. To better understand this relationship, we conducted a retrospective cohort study using two objective measures to identify wrong-patient imaging order errors involving radiation, estimating the proportion of errors that are intercepted and those that reach the patient. This study was conducted at a large integrated healthcare system using data from 1 January to 31 December 2019. The study used two outcome measures of wrong-patient orders: (1) wrong-patient orders that led to misadministration of radiation reported to the New York Patient Occurrence Reporting and Tracking System (NYPORTS) (misadministration events); and (2) wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder (RAR) measure, a measure identifying orders placed for a patient, retracted and rapidly reordered by the same clinician on a different patient (near-miss events). All imaging orders that involved radiation were extracted retrospectively from the healthcare system data warehouse. Among 293 039 total eligible orders, 151 were wrong-patient orders (3 misadministration events, 148 near-miss events), for an overall rate of 51.5 per 100 000 imaging orders involving radiation placed on the wrong patient. Of all wrong-patient imaging order errors, 2% reached the patient, translating to 50 near-miss events for every 1 error that reached the patient. This proportion provides a more accurate and reliable estimate and reinforces the utility of systematic measure of near-miss errors as an outcome for preventative interventions.

Healthcare quality and safety span multiple topics across the spectrum of academic and clinical disciplines. Keeping abreast of the rapidly growing body of work can be challenging. In this series, we provide succinct summaries of selected relevant studies published in the last several months. Some articles will focus on a particular theme, whereas others will highlight unique publications from high-impact medical journals.

Key points

A randomised controlled trial showed that a communication coach improved cardiologists’ ability to respond to patients with empathy, elicit questions and facilitate enhanced conversational flow. Cardiologists reported that a communication coach helped their clinical practice. JAMA Intern Med; 1 June 2023

In a randomised controlled trial conducted across multiple hospital sites, a written communication tool provided to clinicians significantly improved documentation of goals-of-care discussions in the electronic medical record, with a more substantial impact on patients in racial or...

Recalled masks have magnets that can cause injuries or death when the mask is near certain implanted metallic medical devices or metallic objects in the body.

Equinoxe Shoulder Systems were packaged in defective bags. Potential risks including premature implant wear or failure, which may lead to additional surgery.

ROi CPS, LLC recalls Regard Operative Lap P&S Surgical Kit due to sterility concerns. Kits contain a Cardinal Health item linked to Nurse Assist, LLC's recall.

Fresenius Kabi is recalling the Ivenix Large Volume Pump (LVP) for mechanical issues causing the internal Fluid Valve Pins not to move correctly.

The FDA has been evaluating reports of suicidal thoughts or actions in patients treated with a class of medicines called glucagon-like peptide-1 receptor agonists. FDA's preliminary evaluation has not found evidence that use of these medicines causes suicidal thoughts or actions.

ResMed Ltd. recalls AirFit & AirTouch masks due to possible magnetic interference with certain medical devices. ResMed is updating the labels for safer use.

Busse Hospital Disposables is recalling their Tracheostomy Care Trays and sets in response to Nurse Assist’s November saline recall due to sterility concerns.

The FDA posts letters to health care providers about the safe use of medical devices.

FDA issues a letter to health care providers on potential exposure to certain chemicals with use of GE HealthCare EVair and EVair03 compressors with certain ventilators.

Megadyne is updating the instructions and product labeling to reflect that the Mega Soft Universal pads should not be used in patients under 12 years old.

January 5, 2024 – Englewood, Colorado, Leiters Health is voluntarily recalling 33 lots of products listed below to the user level. The recalled batches of vancomycin IV bags, phenylephrine IV bags, and fentanyl IV bags are being recalled due to the potential for superpotency because they may contain

Omnipod 5 App recalled due to a software error which occurs when users input bolus amounts below 1 unit without adding a zero before the decimal point.

2024 Medical Device Recalls