Physicians generally rely on radiographs to diagnose a wide range of medical conditions including cancer and broken/cracked bones. Unfortunately, radiography is not an exact science meaning that it is prone to errors that could negatively influence treatment outcomes. Luckily, radiologists can take several measures to improve their ability to interpret radiographs more accurately. Below is more information on how to improve diagnostic accuracy in radiology.
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According to the American Journal of Roentgenology (AJR), failure to diagnose is one of the leading reasons cited in medical malpractice lawsuits. In fact, up to 75% of all medical malpractice claims filed against radiologists involve diagnostic errors while radiology exam readings have a daily error rate of about 3 to 5%. Besides this, physicians miss at least 30% of abnormal radiography studies. The common causes of diagnostic errors include cognitive biases, rising workloads (among physicians and radiologists), poor system factors, and quality expectations among patients. In general, diagnostic errors cause approximately 40,000 to 80,000 fatalities in the US every year. Although this figure is tiny compared to the entire American population, it represents systemic failures that should be addressed urgently.
Diagnostic errors usually stem from biases, failed heuristics, and erroneous perceptions. Out of these, cognitive errors are the most prevalent accounting for up to 74% of all diagnostic errors. A cognitive bias is simply an illogical interpretation, perceptual distortion, misguided view, or wrong judgment.
Common Diagnostic Biases
This type of bias is based on preconceptions that tend to cloud one’s ability to make an informed decision. As a result, health practitioners look for results/information that validates their preconceptions, which in turn leads to faulty diagnoses. This puts the health of patients at risk because most trust their physicians unequivocally. The downside is it is not easy to determine whether doctors hold preconceptions that constitute confirmation biases unless they are thoroughly evaluated by behavioral experts.
Framing bias refers to the tendency to generalize incomplete information. For instance, the lack of a patient’s health history may cause a radiologist to have a bias when interpreting the patient’s radiographs, thereby leading to a misdiagnosis.
Availability bias simply refers to the tendency to consider diagnoses that one may have encountered recently or that pop in the mind. It is worth noting that this type of bias is not unique to the healthcare industry.
Search satisfaction bias
After coming up with a satisfactory diagnostic explanation, some radiologists fail to explore other possibilities or leads. The problem with this approach is many diseases exhibit similar symptoms, which means they can only be differentiated via exhaustive probing and evaluation of patients and lab results.
Premature closure bias
Premature closure bias occurs due to the inclination to turn premature observations into a diagnostic conclusion. Radiologists may develop premature closure bias to cope with huge workloads since most health care facilities tend to have understaffed radiography departments. Sadly, this leads to unsatisfactory treatment outcomes that put the health sector under more pressure over time.
In some cases, radiologists develop initial impressions that also form the basis for their diagnostic interpretations. The acceptable practice is to discard pre-conceived notions/impressions before evaluating radiographs.
Although the human element is the most common factor in diagnostic errors, system-related errors are problematic and feature in at least 65% of all diagnostic error cases. Common causes of these errors include technical issues and equipment failures, inefficient processes, poor teamwork and coordination, system bottlenecks, communication breakdowns, as well as policy and procedural challenges. Possible solutions for such errors include:
For starters, decision makers should develop and implement a feedback channel designed to detect abnormal radiography interpretations, track disease rates, and monitor key metrics. This channel should be set up in accordance with the relevant oversight/legal frameworks as well as widely acceptable best practices. In addition, it should be evaluated and updated regularly to reflect changes in the workplace dynamics whenever necessary.
Medical facilities should consider subjecting themselves and their staff to continuous reviews. Doing so would enable health institutions to identify problem areas and plug them before they compromise the health of innocent patients. It is also worth noting that medical experts and scientists trust peer-reviewed processes/undertakings because they can be evaluated or challenged based on concrete evidence.
Leveraging the power of IT
Another strategy that healthcare facilities can deploy to lower diagnostic errors is investing in IT tools/technologies that can enhance staff training initiatives. For instance, data collection, storage, and analysis can help hospital administrators quantify and monitor diverse performance metrics. This is in addition to making data-driven decisions. Since health records are subject to strict data privacy and security regulations such as the HIPAA, healthcare facilities must comply with the applicable regulatory requirements.
The AJR recommends implementing education programs that focus on real-world diagnostic situations, improve analytical and intuitive thinking among learners, cover diverse possibilities, and equip learners with multidimensional evaluation skills. Moreover, learners should be encouraged to use digital resources to find information, improve the rate of accurate diagnoses, and improve their clinical judgment.
Traditional reporting methodologies are typically prone to errors. Due to this, 95% of radiologists and 92% of clinicians believe that structured reporting should become a mandatory requirement during residency training.
Workloads should be distributed in a manner that allows hospital administrators to measure the productivity of staff members with a high degree of accuracy. This could be achieved by using the Relative Value Unit (RVU) metric, which is widely used to measure the productivity of radiologists.
Health care facilities should invest in computer algorithms that are optimized to detect anomalies in radiography diagnoses. This approach also reduces the risk of diagnostic-related medical lawsuits.
Radiography has revolutionized medical diagnosis greatly because they enable physicians to develop more accurate treatment regimens. However, they are prone to errors resulting from preconceptions, biases, and operational routines/habits that develop over time. These issues can be resolved via educational initiatives, productivity benchmarking, structured reporting and computer-aided detection.