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Misdiagnosis in Healthcare: Implications and Challenges for Patient Care (Part2)
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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).
Jurisdiction Over Quality Issues
Misdiagnosis as a quality issue in healthcare is regulated on different levels such as governmental health agencies, healthcare institutions, and professional boards. First, regulatory agencies including the U.S. Department of Health and Human Services (HHS) and its divisions, such as the Centers for Medicare & Medicaid Services (CMS) and the Agency for Healthcare Research and Quality (AHRQ), possess considerable power to implement policies and standards aimed at reducing diagnostic mistakes (Shi et al., 2022). These groups can drive health policy by instituting national policies, conducting quality research about healthcare, and requiring reporting and performance standards for healthcare providers.
At the organizational level, hospitals and healthcare systems are fully in a position to take control of changes that can reduce misdiagnoses. These should include embracing new technologies, better delivery processes, and ensuring tough rules are in place regarding the conduct of diagnostic tests. Professional bodies and associations, including the AMA and specialty respective organizations, also contribute significantly to clinical guideline development, education, and policy change aimed at reducing misdiagnosis (Shi et al., 2022).
Stakeholders and Decision-Makers
In the case of misdiagnosis, relevant stakeholders are patients, healthcare providers (doctors, nurses, diagnostic specialists), healthcare institutions, insurance companies, policymakers and technology developers. Different groups of stakeholders influence, and are influenced by, the quality of diagnostic practices in unique ways.
Patients themselves, representing the affected group, are interested in correct diagnoses because this issue influences them as its users through patient advocacy and feedback. Healthcare providers, who are the first to diagnose a patient and provide treatment, influence the issue by their clinical practice and professional progress. As to the management of diagnoses, an indirect organization environment is created by healthcare institutions through the allocation of resources and the establishment of internal policies. With their reimbursement policies, insurance companies can motivate practices to be instituted that will reduce the rate of such diagnostic errors.
The legal and regulatory framework that governs the rules concerning the diagnostics standards and patient safety are determined by policymakers, which can include government officials as well as regulatory agencies. By designing and perfecting diagnostic instruments and health information technologies, technology developers offer essential inputs that could improve diagnostic accuracy.
Powerbases and Resources Needed for Change
To help foster change and improvement, in dealing with misdiagnosis, a multifaceted approach involving different powerbases and resources needs to be utilized. The biggest resource is financial as the implementation of innovative diagnostic technologies, regular professional training and effective health information systems have a great capacity to reduce errors. Legislative and policy-making power is critical in establishing a healthcare setting that focuses on patient safety and quality care, policies and laws that promote reporting diagnostic error as well as transparency as would be the case if this study were implemented (World Health Organization, 2021).
Knowledge and information are critical tools in the fight against misdiagnosis. Increased attention and awareness in the health professionals and even patients to errors in diagnosis may bring transformation. This includes publishing research findings, sharing good practices, and creating an environment of openness and learning from misjudgment (Ranji et al., 2022).
Moreover, technological tools as artificial intelligence and machine learning, electronic health records and decision support systems assess will be necessary to ensure correctness of the diagnosis (Senbekov et al., 2020). Such tools allow therefore medical practitioners to process great volumes of medical information, recognize the emerging patterns and give better conclusions on diagnosis.
Lastly, there are multiple human resources that one can think about in this connection including leadership and an empowered workforce, whose presence serves as sources to lead to enhanced quality. Safety and quality improvement initiatives must be continually promoted in all ranks of leadership. Effective changes come with a highly skilled workforce implementing the latest diagnostic techniques and technologies and practicing patient-focused care (Olson et al., 2019).
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 2023; 33 82-85. doi: 10.1136/bmjqs-2023-016496
Olson, A., Rencic, J., Cosby, K., Rusz, D., Papa, F., Croskerry, P., … & Graber, M. L. (2019). Competencies for improving diagnosis: an interprofessional framework for education and training in health care. Diagnosis, vol. 6, no. 4, 2019, pp. 335-341. https://doi.org/10.1515/dx-2018-0107
Ranji, S. R., & Thomas, E. J. (2022). Research to improve diagnosis: time to study the real world. BMJ Quality & Safety. 13. 2044-5415. doi: 10.1136/bmjqs-2021-014071
Senbekov, M., Saliev, T., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Aitenova, N., … & Fakhradiyev, I. (2020). The recent progress and applications of digital technologies in healthcare: a review. International journal of telemedicine and applications, 2020. 1687-6415. https://doi.org/10.1155/2020/8830200
Shi, L., & Singh, D. A. (2022). Essentials of the US health care system. Jones & Bartlett Learning.
World Health Organization. (2021). Global patient safety action plan 2021-2030: towards eliminating avoidable harm in health care. World Health Organization.
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