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Introduction
Evidence-based public health (EBHP) is the use of results from scientific studies and evaluations to inform policy and practice in public health. This includes using research evidence to determine where to target interventions, evaluate programs, evaluate outcomes, monitor success and make recommendations for change (Brownson et al., 2017). Evidence-based public health also emphasizes a systematic and transparent approach to identifying, evaluating, and applying the best available evidence to improve outcomes for individuals, families, and communities. EBHP combines the most current science with historical information to set priorities for action and guide interventions at multiple levels: from individual behaviors to policy-level changes.
The idea behind EBPH is simple: there is a need to make decisions about public health policy based on high-quality scientific evidence rather than intuition or opinion. Evidence can come from randomized controlled trials (RCTs) or other types of rigorous studies that test whether an intervention works or not, such as cohort studies or case-control studies (Zhang, Chen, and Zhao, 2020). Evidence can also come from systematic reviews of multiple studies combined into one analysis, meta-analyses, or even good old observational data (data collected without a specific experimental design).
ICD-10 Chapters and Codes
ICD-10 is the 10th revision of the International Classification of Diseases and Related Health Problems, published by the World Health Organization (WHO). It contains codes for diseases and conditions, which are not necessarily unique to a specific diagnosis but are frequently used in multiple diagnoses (Fung, Xu, Bodenreider, 2020). The ICD covers health problems diagnosed using objective clinical examination and laboratory tests, including chronic and communicable diseases. The ICD is the basis for health information systems in many countries. It allows clinicians to record their patients diagnoses, procedures, and therapies in a standardized format and to transmit this information electronically so it can be shared with other healthcare providers or public health agencies (Bagheri et al., 2020). The primary purpose of ICD-10 is to provide a uniform code for every condition that can be diagnosed in a person regardless of where they are treated or live (Wang et al., 2020). This means that one patient can have multiple codes during their treatment process as they move from hospital to hospital or region to region, depending on where they need treatment first. The ICD-10 contains twenty-two chapters, each containing a list of codes relating to specific conditions or categories of conditions (Bagheri et al., 2020). Each chapter contains a list of codes for different types of diseases or injuries. There are also several appendixes with additional information about topics such as psychiatric disorders and neonatal conditions.
The ICD-10 contains twenty chapters, provided in Table 1, each containing a list of specific codes relating to specific conditions or categories of conditions. The first chapter contains general terms and guidelines for coding as well as instructions for coding in other chapters. Each chapter contains a list of codes for different types of diseases or injuries (Fung, Xu, Bodenreider, 2020). There are also several appendixes with additional information about particular topics such as psychiatric disorders and neonatal conditions.
Table 1: ICD-10 blocks
Contemporary issues surrounding ICD-10
There are several contemporary issues surrounding ICD-10s implementation in the United States. One issue is the lack of training for physicians and nurses on how to use the new system (Fung, Xu, Bodenreider, 2020). Another issue is that there is no standard process for patients to receive care from a physician who uses ICD-10 codes, so it can be difficult for patients to know what kind of information their healthcare provider needs to treat them properly.
These issues have resulted in some confusion among healthcare providers and patients alike. The lack of training has caused some physicians to feel unprepared when using ICD-10 codes, leading to errors on patient charts or, even worsemissed diagnoses altogether (Melonie P, 2017). Patients unfamiliar with ICD-10 codes can also experience confusion about what information their healthcare provider needs from them to provide proper care, which could lead to unnecessary delays in treatment or misdiagnoses due to incorrect information being provided by the patient or family member.
When it comes to patient care, one of the biggest challenges with implementing ICD-10 codes is that there is no standard process for patients to receive care from a physician who uses these codes. Some physicians will have their own processes for requesting information from patients; others will not have any process at all, leaving it up to the patients knowledge about what information they need to get proper treatment (Fung, Xu, Bodenreider, 2020). In addition, many healthcare providers working with ICD-10 are still unfamiliar with how the new coding system works or how best to use it. This means that there could be some confusion as they learn more about ICD-10 and how best to implement it into their practices over time. The United States has been transitioning to ICD-10 codes for several years (Weiner,2018). This transition has been complicated because there is no standard process for patients to receive care from a physician who uses ICD-10 codes. Hence, it can be difficult for patients to know what information their healthcare provider needs to treat them properly (Weiner, 2018). With the introduction of ICD-11, the transition problem continues to surface, especially in underdeveloped countries with no systems and proper education.
Conclusion
In conclusion, ICD-10 is an effective coding system that enables healthcare providers with a standardized method of recording their diagnoses. Its implementation will ultimately help eliminate medical records ambiguity and reduce the risk of misdiagnoses. It is also simple enough that it can be used by all health care team members, including allied healthcare workers and self-reporting patients.
Reference List
Bagheri, A., Sammani, A., Van der Heijden, P.G., Asselbergs, F.W. and Oberski, D.L., (2020) The automatic ICD-10 classification of diseases from Dutch discharge letters, BIOINFORMATICS 2020-11th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, 13, pp. 281-289.
Brownson, R.C., Baker, E.A., Deshpande, A.D. and Gillespie, K.N., (2017) Evidence-based public health. Oxford: Oxford university press.
Fung, K.W., Xu, J. and Bodenreider, O., (2020) The new International Classification of Diseases 11th edition: a comparative analysis with ICD-10 and ICD-10-CM, Journal of the American Medical Informatics Association, 27(5), pp. 738-746.
Melonie P., H. (2017) Deaths: leading causes for 2015. Web.
Wang, S.M., Chang, Y.H., Kuo, L.C., Lai, F., Chen, Y.N., Yu, F.Y., Chen, C.W., Li, Z.W. and Chung, Y., (2020) Using deep learning for automatic ICD-10 classification from free-text data. European Journal of Biomedical Informatics, 16(1).
Weiner, M.G., (2018) POINT: Is ICD-10 diagnosis coding important in the era of big data? Yes, Chest, 153(5), pp. 1093-1095.
Zhang, L., Chen, K. and Zhao, J., (2020) Evidence-based decision-making for a public health emergency in China: Easier said than done, The American Review of Public Administration, 50(6-7), pp. 720-724.
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