Are Medical Errors Deadlier than Most Diseases?

Are Medical Errors Deadlier than Most Diseases?

Alan Peng - 30 July 2021

Introduction

The natural image of a physician is someone capable of the right diagnosis and providing the suitable treatment. I aspire to be someone like that, but I also know I often make mistakes, and I cannot begin to imagine what it is like to make a mistake with permanent and harmful consequences, or even one that causes the death of another. So many of the decisions of healthcare workers, whether you’re a nurse or a neurosurgeon, are coupled with severe ramifications which affect the safety of the patients. Without a thorough examination, it is hard to know just how prevalent errors are in the medical field.

As a result, it was shocking to see a paper from the prestigious Johns Hopkins University estimating that medical errors are the third leading cause of death in America (only behind cancer and heart attacks), causing 250, 000 deaths per year (Makary & Daniel, 2016). This paper has been cited more than 2, 500 times and their numbers used in two TED talks on medical errors. I truly hope this is not true, or is at least an overestimation. Regardless, this matter is an important topic to consider as preventable deaths are disheartening and unnecessary tragedies.

To further understand the status quo regarding medical errors’ prevalence and implications, I examined the aforementioned paper along with other relevant studies on this matter.

Medical Errors – the Third Leading Cause of Death in the US...?

Previously, the pivotal book by the Institute of Medicine, To Err is Human: Building a Safer Health System, broke the silence on medical errors; they cited studies that suggested deaths caused by medical errors (preventable lethal adverse events) were 44, 000 to 98, 000 annually (Kohn et al., 2015). This range was considered an underestimation by Makary and Daniel's paper but even if the estimation was conservative, it is still too high to overlook.

In this short paper, Makary and Daniel revealed a certain incompleteness in the United States' medical system. When one passes away, the cause of his or her death cannot be classified as human or system errors because the US uses an International Classification of Disease (ICD) code which lacks labels associated with errors (since they are not “diseases”). And even though there is a code for overdose, the details of how medical errors led to an overdose would be neglected. The "physiological cause" hints at potential errors, but the desirable and detailed data is still not properly collected. Altogether, this lack of information hinders the reduction of errors.

As a result of the lack of such a database, Makary and Daniel produced an estimate of the number of deaths caused by medical errors using four previous data sources. For example, they considered how many iatrogenic deaths (deaths relating to diseases caused by medical examination or treatment) were preventable. Sometimes a treatment has its risks, chemotherapy for instance, and therefore, not all iatrogenic deaths mean medical errors. But if it was preventable, it would be an error.

The notion that deaths in the few hundreds of thousands were caused by medical errors was supported by reports from the Institute of Medicine (IOM), the US department of Health and Human Services, and the data company HealthGrades. By taking the estimates of the proportion of admissions that led to a preventable "lethal adverse event" or death, Makary and Daniel extrapolated the number of preventable deaths based on the total number of admissions in 2013, 35+ million. Hence, they reached a daunting number of 251, 454 deaths, making it the third leading cause of death in the United States.

Figure 1: Table 1 of Markary and Daniel’s analysis paper (2016)


Methodology Examination and Details of Interest

The sample sizes of three out of four estimates used in this summary analysis might be problematic: 3 percentages of preventable death was formulated based on 838, 795, and 2341 admissions. If a sample of 1000 patients found 1 patient that suffered “X (a hypothetical condition),” is it reasonable to estimate 0.001 of 35 million or 35 000 others suffered "X" in the US that year? How similar are the hospitals in terms of safety? How many hospitals must be sampled to represent the entire population of hospitals and their patients? And although the report from HealthGrades used 37 million patients, the actually report was less definitive than portrayed:

“Of the total of 323,993 deaths among patients who experienced one or more PSIs [patient safety incidents] from 2000 through 2002, 263,864, or 81%, of these deaths were potentially attributable to the patient safety incident(s).”

“[Extrapolating for the whole country, beyond those in Medicare] over 575,000 preventable deaths occurred, as a direct result of the 2.5 million patient safety incidents that occurred in U.S. hospitals from 2000 through 2002.”

I was confused while reviewing the report because I could not find the right estimations that resulted in the 389, 576 nor the 0.71% as seen in the table above but I assume it is I who missed something; the lack of clarity in the exact data points used does however reduce the ability to verify Makary and Daniel's analysis. A response to the paper was also troubled by how the "authors of the article do not provide any sort of formal methodology" and had "precarious" steps in their derivation of the final number (G. Shojania & Dixon-Woods, 2016).

The HealthGrades report also used computer programming to evaluate which the authors themselves admitted may have its limitations. In short, I am not very confident in the number as it may not be a very accurate estimation but all the estimates so far have been nothing but dismal (44, 000 to 251, 454). Any evaluations of the overall credibility of the paper and the validity of the numbers here are unjustified due to my lack of expertise and limited knowledge of the methodology and data. I am simply pointing out what caused uncertainties for me.

The accuracy of the data aside, the paper is pointing out a serious flaw of the medical system where the data collected is insufficient to represent the relevant causes of deaths, including those from medical errors. What's more frightening is that the number of deaths alone does not encapsulate the totality of medical errors because death is merely one of the possible consequences. It is important to also consider the group of patients that had their organs, mental health, and financial stability damaged to name a few.

“Although the assumptions made in extrapolating study data to the broader US population may limit the accuracy of our figure, the absence of national data highlights the need for systematic measurement of the problem (Makary & Daniel, 2016)”

Meta-findings and Implication

A meta-analysis of more studies would paint a clearer image of reality. A recent meta-analysis published in the British Medical Journal may provide the appropriate precision and accuracy. Examining 70 studies which comprised of a total of 337 025 patients, Panagioti et al. found the prevalence of preventable patient harm to be 6% with a 95% confidence interval from 5% to 7%—meaning they are 95% confident that the true percentage of patients harmed is in that range (2019). Additionally, among these preventable patient harm, 12% (95% confidence interval from 9% to 15%) resulted in severe harm or death (Panagioti et al., 2019). In other words, 5%-7% patients suffer harm from medical errors and 9-15% of those errors are severe or lethal errors.

To put into perspective the overall percentage of severe or deadly errors: 12% of the 6% is 0.72% of patients. If I were to extrapolate the figure based on the 37 million admissions above, then around 266, 400 patients were severely harmed or killed. Note that the percentages could actually be 9% and 5% which would produce a smaller number of patients severely harmed or killed.

Figure 2: Table 1 of Panagioti et al’s meta-analysis

With this meta-analysis, it revealed not only the prevalence of this issue internationally but also provides the figures (with confidence intervals) for the severities and the sources of harms. The 6% of patients that suffered mild to severe harm translates to over 2 million patients per year in the US based on the total admissions, 37 million, used by the analysis in the above section.

“At least one in 20 patients are affected by preventable patient harm in medical care settings. (Panagioti et al., 2019).”

“Total national costs (lost income, lost household production, disability and health care costs) of preventable adverse events (medical errors resulting in injury) are estimated to be between $17 billion and $29 billion, of which health care costs represent over one-half (Panagioti et al., 2019).”


Behind the Errors

Medical errors can range from acts of commission and acts of omission – such as a false diagnosis or delayed intervention. (1) Commissions errors: Panagioti et al. concluded that most preventable patient harm came from drugs/therapeutic treatments, and invasive/surgical procedures (2019). However, consistent with their title, the authors of To Err is Human remind us that to blame the individual is not the most productive solution and it is best to instead focus on "designing safety into the system." On the individual levels, we should expect the appropriate degree of accountability but we should focus more on working alongside these physicians, specialists, and especially intensive care providers, considering their first-hand experiences. What are the best adjustments to effectively treat this problem on a systemic scale is a whole other investigation. (2) omission errors: the errors that arise from the inaction are equally, if not more, devastating. Occasionally, one hears stories of how a patient sought advice from their doctors but was dismissed or referred repeatedly between specialists without any resolution. In fact, they occurred in the stories from the aforementioned Ted Talks by Dr. Carol Gunn and Theresa Sabo respectively. Personally, I find specialization to be a double-edged sword: I have had my medical concerns relieved by a capable specialist whose specialization was reassuring but it usually took months to see them (sometimes I think these visits weren't worth the wait and money). I believe many others can relate. As well, too narrow of a focus may hinder an inclusive interpretation for the individual patient.

“The decentralized and fragmented nature of the health care delivery system (some would say "nonsystem") also contributes to unsafe conditions for patients, and serves as an impediment to efforts to improve safety. Even within hospitals and large medical groups, there are rigidly-defined areas of specialization and influence. For example, when patients see multiple providers in different settings, none of whom have access to complete information, it is easier for something to go wrong than when care is better coordinated. (Kohn et al., 2015)”

Change in Perspective

And while the reduction of the errors via policy changes is important, it seems that the perception of errors may require adjustments too. Rodziewicz & Hipskind highlighted that the critical nature of the medical field fosters hesitancy towards reporting errors, fearing disciplinary actions, a stained reputation, or the loss of their job. Furthermore, healthcare workers suffer from medical errors too due to self-criticism or guilt which may not only endanger their psychological health, but also their physical health (Rodziewicz & Hipskind, 2020). Such a healthcare worker may cause more errors due to their suboptimal state, creating an undesirable cycle.

VOX made a video journal which pointed out the discrepancies in the societal reaction to unfortunate deaths where deaths from plane crashes are seen as horrendous events worthy of in-depth investigation but deaths on the road are merely unfortunate tragedies (Harris et al., 2015). On Statista, you find that the number of air traffic fatalities worldwide ranges in the hundreds (289 in 2019, 137 in 2020), and the number of traffic fatalities in the United States is about 30, 000 annually. The lack of outcry and demand for reducing lethal adverse events reveal something about the selectiveness of the modern media and of human emotions.

For various reasons, I find the video by Harris & Kliff meaningful as they show how having the proper attitude towards these errors affected the response they took to make sure it does not happen again. Certainly, humans are flawed and thus err, but a constant effort would reduce the frequencies of these errors–indeed, akin to the investigation and addition of safety measures that follow after a plane crash.

Conclusion

Overall, it does seem that severe or lethal harm affects hundreds of thousands of patients each year in the United States alone. Medical errors may not be the third leading cause of death but these deaths are preventable nonetheless, meaning they must be reduced in the future. As well, the culture around "medical error" is shifting: hopefully, it shifts towards an environment that can acknowledge and deal with errors productively: that is, without the dreaded punishments preventing discussions.

One can condemn the medical system based on the eye-opening results but I think we must take a moment to consider the proportions with some cold objectiveness. If some 30+ million patients are admitted into hospitals each year and 6% suffer preventable harm of some sort with varying severity, the remaining 94% still receive neutral care or beneficial treatments.

When I think about medicine as a possible career for myself, the heavy responsibilities of having to maintain constant vigil and of ensuring one's knowledge is up-to-date are daunting. But consequently, I am also motivated to ensure I work and strive towards becoming someone who is able to "do no harm."

References

Aghapour, Z., Gholizadeh, P., Ganbarov, K., Bialvaei, A. Z., Mahmood, S. S., Tanomand, A., Yousefi, M., Asgharzadeh, M., Yousefi, B., & Kafil, H. S. (2019). Molecular mechanisms related to colistin resistance in enterobacteriaceae. Infection and Drug Resistance, 12, 965–975. https://doi.org/10.2147/IDR.S199844

Andersson, D. I., & Hughes, D. (2010). Antibiotic resistance and its cost: Is it possible to reverse resistance? Nature Reviews Microbiology, 8(4), 260–271. https://doi.org/10.1038/nrmicro2319

Cosgrove, S. E., Sakoulas, G., Perencevich, E. N., Schwaber, M. J., Karchmer, A. W., & Carmeli, Y. (2003). Comparison of mortality associated with methicillin‐resistant and methicillin‐susceptible Staphylococcus aureus bacteremia: a meta‐analysis. Clinical Infectious Diseases, 36(1), 53–59. https://doi.org/10.1086/345476

Davies, J., & Davies, D. (2010). Origins and evolution of antibiotic resistance. Microbiology and Molecular Biology Reviews, 74(3), 417–433. https://doi.org/10.1128/mmbr.00016-10

Ghosh, C., Sarkar, P., Issa, R., & Haldar, J. (2019). Alternatives to conventional antibiotics in the era of antimicrobial resistance. Trends in Microbiology, 27(4), 323–338. https://doi.org/10.1016/j.tim.2018.12.010

Haaber, J., Penadés, J. R., & Ingmer, H. (2017). Transfer of antibiotic resistance in Staphylococcus aureus. Trends in Microbiology, 25(11), 893–905. https://doi.org/10.1016/j.tim.2017.05.011

Hawkey, P. M. (1998). The origins and molecular basis of antibiotic resistance. BMJ, 317(7159), 657–660. https://doi.org/10.1136/bmj.317.7159.657

Lobanovska, M., & Pilla, G. (2017). Penicillin’s discovery and antibiotic resistance: Lessons for the future? The Yale Journal of Biology and Medicine, 90(1), 135–145. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5369031/

Lowy, F. D. (2003). Antimicrobial resistance: The example of staphylococcus aureus. Journal of Clinical Investigation, 111(9), 1265–1273. https://doi.org/10.1172/jci200318535

Ventola, C. L. (2015). The antibiotic resistance crisis: Part 1: Causes and threats. P & T : A Peer-Reviewed Journal for Formulary Management, 40(4), 277–283.

Zaman, S. B., Hussain, M. A., Nye, R., Mehta, V., Mamun, K. T., & Hossain, N. (2017). A Review on Antibiotic Resistance: Alarm Bells are Ringing. Cureus, 9(6). https://doi.org/10.7759/cureus.1403