The Clock of Diagnosis

At the intersection of medicine and time, there is a conflict between how illness is measured

versus how it’s actually meant to be lived out. In medical practice, doctors often assume that

diseases and illnesses will follow a specific timeline when in reality they are unpredictable. This

instance is called chronological bias, where time is a subconscious influence on medical

predictions, leading to patients with unclear symptoms having delayed or incorrect diagnoses.

Research and data confirms that the uncertainty about timelines in medicine are widespread and

affect many clinical outcomes. Defining Diagnostics states that diagnostic uncertainty is a

common part in clinical practice and that it “is dynamic and changes with time”(Springer). This

data shows how time directly influences medical judgement rather than staying neutral within the

process and it also reinforces the idea that relying on fixed timelines fails to understand how

illness develops unpredictably. Additionally, in Electronic Health Record Reviews, it was found

that clinicians experienced diagnostic uncertainty in 71.6% of outpatient visits, with 37.2% of

that uncertainty not reflected in diagnostic coding .When symptoms displayed by patients don’t

follow an expected timeline, medical accuracy decreases and uncertainty increases displaying a

problem between medicine and time.

To reduce chronological bias and improve patient care, healthcare systems should adopt tracking

each symptom and looking at the probability of what comes next. Rather than forcing every

patient into a timeline, clinicians would follow how symptoms change over repeated

observations and try to build a care plan for the patients based off of that. For example, using

mobile health monitoring and data analysis, can be useful to track a patient’s repeated symptoms

and analyze what comes next without basing them on a timeline. By doing this, it helps

normalize symptom fluctuation instead of stating that odd symptoms are just noncompliance and

putting the patient in a rough situation. It also supports a more accurate understanding of disease

progression rather than relying on assumed checkpoints.

However, implementing these solutions globally is challenging. In the study, Overdiagnosis in

Countries, it is shown that low and middle income countries face diagnostic overdiagnosis as a

widespread problem (PubMed). For example, overdiagnosis of conditions such as malaria has

been reported at very high rates in some lower income regions like in Africa, and diagnostic tests

are often overused even though resources are very limited. Without addressing health

infrastructure, training, and resource allocation, longitudinal and adaptive models risk being

difficult to scale worldwide.

Ultimately, The Clock of Diagnosis shows that time is not neutral in medicine but intersects with

clinical practice, patient experiences, and social and global inequalities. Fixed timelines

advantage patients who have predictable symptoms while putting those with irregular illnesses at

a disadvantage, making them have delayed care, a misdiagnosis, or be under diagnosed for their

condition. Recognizing illness as variable and adopting flexible approaches to tracking

symptoms allows healthcare to better reflect how illness is actually experienced. Understanding

these intersections between medicine and time can help medical systems improve diagnostic

accuracy and move toward more adaptable care everywhere.

Works Cited

Albarqouni, Loai, et al. “Overdiagnosis and Overuse of Diagnostic and Screening Tests in

Low-Income and Middle-Income Countries: A Scoping Review.” BMJ Global Health,

vol. 7, no. 10, 1 Oct. 2022, p. e008696, gh.bmj.com/content/7/10/e008696,

https://doi.org/10.1136/bmjgh-2022-008696.

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Bhise, Viraj, et al. “Defining and Measuring Diagnostic Uncertainty in Medicine: A Systematic

Review.” Journal of General Internal Medicine, vol. 33, no. 1, 21 Sept. 2017, pp.

103–115, https://doi.org/10.1007/s11606-017-4164-1.

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