Current Difficulties in Medical Diagnostics

Medical diagnostics allow medical professionals to chart medical symptoms to other data and produce diagnoses and outcomes. However, difficulties in this medical field can result in diagnostic errors, causing detrimental costs to patients and healthcare systems.


Medical diagnostics allow medical professionals to chart medical symptoms to other data and produce diagnoses and outcomes. However, difficulties in this medical field can result in diagnostic errors, causing detrimental costs to patients and healthcare systems. 


A significant source of mortalities and unnecessary costs is due to diagnostic errors in healthcare. The Institute of Medicine defines a diagnostic error as:

‘Failure of a planned action to be completed as intended (i.e., error of execution) and the use of a wrong plan to achieve an aim (i.e., error of planning)’

It is recognised that medical diagnostic errors are predominantly attributable to four main challenges within clinical settings – diagnostic uncertainty with time constraints, population trends and health disparities, limited resources, and cognitive biases. These challenges limit the ability to provide accurate medical diagnoses. These are particularly evident within Accident and Emergency Departments, where physicians must assess patients rapidly and either admit them to the inpatient wards or send them home. If the patient is diagnosed incorrectly and is not admitted into the hospital, this could result in the patient’s death after discharge. Alternatively, if the patient is diagnosed incorrectly and is admitted into the hospital, this would deplete medical testing and treatment resources as well as available staff, which could reduce the quantity and quality of care received by those who are critically ill, affecting the diagnosis/prognosis of such patients. With roughly 40,000 patients in the UK visiting an Accident and Emergency Department in the National Health Service (NHS) every day, the population at risk is extensive.

Diagnostic Uncertainty and Time 

Most diseases evolve over time so there is often a delay between the onset of the disease and the patient’s symptoms. During onset, it is difficult to determine which diagnosis is indicated by each unique combination of symptoms, especially if symptoms are non-specific, such as fatigue or loss of appetite. This raises the question of whether the harms of initiating immediate treatment to lesson symptoms exceed the harms of further diagnostic testing, including the impact of delaying treatment. Diagnoses can be delayed not only due to clinician uncertainty but due to patients delaying presenting to the clinicians for medical help – The generic symptoms of illness such as fever and fatigue may not be a cause for concern by the patient during early onset. A common situation where misdiagnosis often occurs due to atypical symptoms is the diagnosis of heart attack in women. Women are more likely than men to experience a missed diagnosis of a heart attack, a situation that has been partly attributed to gender biases, but which may also be the result of physiologic difference as women have a higher likelihood of presenting with atypical symptoms, including abdominal pain, shortness of breath and congestive heart failure. 

Furthermore, since diseases evolve, clinicians gain additional information over time, where available information during initial disease stages may support a wrong disease, and only later additional information allows the clinicians to diagnose correctly. Clinician delay also occurs when immediate diagnostic testing to obtain a definite diagnosis may be more harmful to the patient – invasive and harmful diagnostic testing may be detrimental to the patient’s diagnosis, or definite diagnosis may not alter the treatment received by the patient.

Population Trends 

Population trends, including increasing overall populations and the ageing of the populations add significant complexity to diagnostic processes, requiring clinicians to consider other factors in diagnosis as comorbidity and polypharmacy can result in ambiguous symptoms and provide greater differential diagnoses. Aging populations will likely result in more less common representations of diseases – for example, acute myocardial infarctions may onset will commonly with fatigue and confusion rather than more common chest or arm pains. Multiple comorbidities, medications, or cognitive and functional impairments, which are becoming increasingly prevalent, particularly in older patients, are more likely to have atypical disease presentations which can increase the risk of diagnostic errors.  

Frequent comorbidities often occurs when patients, typically ones with a compromised immune system such as elderly patients, suffer from multiple illnesses. Alongside atypical manifestations of disease, diagnostics are further complicated as diagnostic testing do not produce accurate results. Diagnostics tests can detect each of the underlying medial symptoms incorrectly. This can lead to an incorrect diagnosis and treatment due to the commonness of certain symptoms and the perceived overlapping of unconnected symptoms. Even if several machine testts are used, this would strain the supply of medical resources and the availability of medical testing for other patients.  

Increasing overall populations can put healthcare systems under strain, with numerous health issues arising from high population densities and the pollutant existing in such places. Squatter settlements in some of the world’s largest countries such as India and Brazil have significant effects on local healthcare systems. People who need to receive life-saving healthcare are deprived access to sufficient resources for diagnostic testing. Diagnostics are typically resource intensive processes, requiring both expert physicians and highly expensive and sensitive medical imaging/testing technology, which is not practical for developing world and industrialised nations. Furthermore, poor sanitation and deficient access to healthcare has increases the number of serious infections such as cholera and diarrhoea. In China, high population densities result in dangerous levels of air populations causing serious respiratory issues. As these infections and diseases increase, people with underlying chronic illnesses may not receive the care they need and may be left undiagnosed, increasing the risk of further complications. 

However, strain of resources, due to population trends, also affect high income countries. The National Health Service in the UK deals with over 1 million patients every 36 hours, and is evidently already under strain. With rising populations, this issue of lack of sufficient resources and infrastructures will increase along with it. Lack of staff can result in overworked medical staff who can make diagnostic decisions that could further jeopardise health. 

Cognitive Biases

The causes of diagnostic error can also be examined at the individual clinician level, where clinicians can provide misdiagnosis because of incorrect application of heuristics. A prevalent cognitive bias is the availability heuristic where the diagnosis of the current patient is biased by experience with past case – the clinician refers to what comes to mind most easily. Anchoring heuristic (also referred to premature closure) is where clinicians dismiss subsequent information (symptoms or diagnostic tests), relying on the initial diagnostic impression. An example of this bias is where repeated positive blood cultures can be dismissed as contaminants, resulting a misdiagnosis and the patient developing severe complication after an untreated infection. Framing bias often occurs when clinicians do not elicit different perspective by broadening the history, and make a diagnostic decision unduly biased by subtle cues and collateral information. This bias is often seen in Emergency Departments where quick diagnoses are made, despite the lack of through diagnostic tests, to increase the rate of clinical workflow. Misdiagnoses where medical history and demographic data are predominantly used as evidence are typically because of framing bias. For example, a patient with a previous history of heroin addiction displaying abdominal pain may be treated for opiate withdrawal but later may be identified to have a bowel perforation. 

Disparities in healthcare access and outcomes are well documented and persistent, often arising from cognitive bias – disparities by race, sex, gender, geographic location, and socioeconomic status are most common. The COVID-19 pandemic has also further unveiled these increasing disparities in health and healthcare. Before the COVID-19 pandemic, in 2011, diagnostic errors due to healthcare disparities in United States of America cost over USD$309 billion annually. A study in 2011 also revealed that racial and ethnic disparities in healthcare, particularly imposed costs on both direct medical costs, such as treating illness and complications, and indirect costs such as loss of productivity. Though there are very few studies that examine the relationships between diagnostic errors and healthcare disparities. 

Future Outlook and Conclusion

There is a dire need to reduce diagnostic error in medicine and diagnosis-related harm. Preventable diagnostic errors that lead to patient harm (mortality or morbidity) should be given priority in research. But, it is not clear how to determine preventability and which specific factors to intervene on first. 

However, technological advancements seem to be one of the most rapidly advancing solutions to aid medical diagnostics. The World Health Organisation suggests that worldwide populations numbering in the billions lack access to even basic healthcare, and this pent-up escalating demand has the potential  motivate advances in telemedicine and other technological advancements – these advancements may alleviate the issue with higher risks of diagnostic errors as a result of poor access to healthcare. Progress in AI imaging technology is also being driven by the rapidly expanding processing power of machines, the availability of large electronic health records and significant financial input from private technology corporations and government industries. 

Nevertheless, difficulties in medical diagnostics still exist and technological advancements may not be integrated into healthcare systems in the near future. Cognitive factors and systematic factors are still closely linked and untangling contributing factors is another challenge by itself. Other contextual information such as the certainty of the final diagnosis and the delay for diagnosis are also relevant considerations in understanding the complex interplay of factors in medical diagnostics. 

Image 2- 

Swetha Babu, Youth Medical Journal 2022


Baker, C. (2021). NHS Key Statistics: England, October 2021. [online] House of Commons Library. Available at: [Accessed 30 Jan. 2022].

Balogh, E.P., et al. (2015). Overview of Diagnostic Error in Health Care. [online] National Institute of Health. Available at: [Accessed 30 Jan. 2022].

Fletcher, D. (2017). How Will the Growing Population Affect Healthcare in the Future? [online] Socialist Health Association. Available at: [Accessed 30 Jan. 2022].

Ibrahim, S.A. and Pronovost, P.J. (2021). Diagnostic Errors, Health Disparities, and Artificial Intelligence. JAMA Health Forum, [online] 2(9), p.e212430. Available at:[Accessed 30 Jan. 2022].

Image 1 – United Nations (2017). World Population Prospects Data Booklet 2017 REVISION. [online] Available at: [Accessed 30 Jan. 2022].

Image 2 – Itri, J.N., et al. (2018). Fundamentals of Diagnostic Error in Imaging. RadioGraphics, [online] 38(6), pp.1845–1865. Available at: [Accessed 30 Jan. 2022].

Institute of Medicine (US) Committee on Data Standards for Patient Safety, et al. (2004). Patient Safety: Achieving a New Standard for Care. [online] PubMed. Washington (DC): National Academies Press (US). Available at: [Accessed 29 Jan. 2022].

 J. Flowers Health Institute (n.d.). Medical Diagnosis before Treatment Is Vital | Learn Why | J.Flowers Health. [online] J. Flowers Health Institute. Available at: [Accessed 28 Feb. 2021].

LaVeist, T.A., Gaskin, D. and Richard, P. (2011). Estimating the Economic Burden of Racial Health Inequalities in the United States. International Journal of Health Services, [online] 41(2), pp.231–238. Available at: [Accessed 30 Jan. 2022].

Zwaan, L. and Singh, H. (2015). The Challenges in Defining and Measuring Diagnostic Error. Diagnosis, [online] 2(2), pp.97–103. Available at: [Accessed 30 Jan. 2022].

By Swetha Babu

Swetha Babu is a student at Wycombe High School in Buckinghamshire, UK. She is an aspiring medic, studying Biology, Chemistry and Mathematics at A Level.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s