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Misdiagnosis in Healthcare: Implications and Challenges for Patient Care (Part 1)
Name
College of Nursing
Policy, Finance, and Quality in Nursing and Healthcare
Professor
Date
Misdiagnosis in Healthcare: Implications and Challenges for Patient Care
Misdiagnosis is one of the prominent problems that has characterized health care over time and, therefore, determining very many elements of the treatment process in a patient’s life. The purpose of this paper is to review peculiar features of misdiagnosis, consequences it delivers and preventive measures practiced in order to avoid such complications. This quality issue in a health care setting can be resolved using the Quality Benchmark Project to improve patient care levels and ensuring low occurrence of diagnostic error. People who read this paper should be informed on what misdiagnosis is and the different effects it has had towards patients’ health as well as solutions that have been adopted over time to solve its problem. One of the most enduring questions in the medical community is this, therefore this research considered crucially important to doctors, legislators and patients.
Description of the Quality Issue
The quality problem of misdiagnosis is rather widespread in the field of human’s health care, which may entail unnecessary treatment, slow rehabilitation procedures as well as non-rectifiable harm to health or even lethal outcome. But misdiagnosis is not the result of clinical error alone; this is also about system-wide problems, which are associated with deficient healthcare infrastructure innovation, a lack of appropriate diagnostic equipment and sufficient instruction to medical workers. Nonetheless, the outcomes of diagnostic mistakes are far from being unambiguous and concern patients’ health state, emotional balance as well as financial stability. The immediate effects and consequences of misdiagnosis include the provision of treatment that may not have been required or desired and ultimately subjecting the patient to unnecessary pain. In the end, however, it leads to long-term health complications besides a poor-quality life and escalated medical expenditure.
Quality Issue Background
In recent years, misdiagnoses have been identified as a significant issue in the health care industry. Research shown that diagnostic errors are estimated to impact an astounding number of some 12 million adults in the United States annually in the outpatient setting, indicating how widespread this practice is (Newman-Toker et al., 2023). A number of issues underlie diagnostic accuracy in practice, and they include a patient’s complex medical condition, atypical presentations associated with chronic conditions and misunderstandings between the patient-physician interactions. In addition, the fast pace of medical knowledge and technology development that is to benefit in various dimensions also brings challenges regarding updating health care professionals with the current diagnostic criterion and treatment plans.
Previous Attempts to Address the Quality Issue
The issue of misdiagnosis is tackled in several ways, such as enhancing diagnostic precision through better communication and training healthcare workers to work on the same. An important strategy that has been used during the reduction of such errors is a tactic with the use of Artificial intelligence and machine learning systems for diagnoses due to its complex decision-making mechanisms (Jimma, 2023). The second is the change in the focus of treatment to patient-centered care because proper communication and participating in diagnostic activities are defined. This does not only the creation of a complete clinical picture, but also to ensure that patient concerns and symptoms are properly intervened (Gusmano et al., 2019).
Training of health care professionals has also been reformatted to focus on accuracy in diagnosis and prevention of cognitive biases that result to wrong prognosis. A lot of these programs depend on the use of case studies, simulation-like assignments and online short courses to ensure that healthcare providers do not lose touch with recent methodologies and most efficient treatments methods (Meyer & Singh, 2019).
In addition, policy based interventions have been suggested to create an enabling environment for correct diagnosis. This starts with encouraging a culture where diagnostic errors and their consequences may be openly discussed without any bias. There is also an effort to apply better recording systems and data analysis that can be utilized to monitor diagnostic error prevalence, as well as use those elements of the study in terms of what leads them and how they can be mitigated (Al Khafaji et al., 2023).
References
Al-Khafaji, J., Lee, M., & Mossburg, S. (2023). Improving diagnostic safety and quality . Agency for Healthcare Research and Quality. https://psnet.ahrq.gov/perspective/improving-diagnostic-safety-and-quality
Gusmano, M. K., Maschke, K. J., & Solomon, M. Z. (2019). Patient-centered care, yes; patients as consumers, no. Health affairs, 38(3), 368-373.
Jimma, B. L. (2023). Artificial intelligence in healthcare: A bibliometric analysis. Telematics and Informatics Reports, 100041.
Meyer, A. N., & Singh, H. (2019). The path to diagnostic excellence includes feedback to calibrate how clinicians think. Jama, 321(8), 737-738.
Newman-Toker, D. E., Nassery, N., Schaffer, A. C., Yu-Moe, C. W., Clemens, G. D., Wang, Z., Zhu, Y., Saber Tehrani, A. S., Fanai, M., Hassoon, A., & Siegal, D. (2023). Burden of serious harms from diagnostic error in the USA. BMJ quality & safety, bmjqs-2021-014130. Advance online publication. https://doi.org/10.1136/bmjqs-2021-014130
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