Over the past few years, adoption of clinical decision support systems (CDSS) has tremendously gained popularity in various medical fields. Adoption and implementation of the CDSS have greatly transformed provision of healthcare and health-related services during patients’ encounters. CDSS tools are useful in provision of evidence-based outcomes and assisting clinicians with medical management procedures. Artificial Neuron Networks (ANNs) are important clinical decision support tools. As clinical support tools, the ANNs are based on nonlinear application, hence providing efficient classifications and predictions, especially in renal-failure related cases. Implementation of the ANNs has considerably improved quality of acute healthcare and efficiency in workflow. Implementation of Diagnosis-Related Group (DRGs) vocabularies that support the ANNs as clinical tools is important. The following research paper explores application of the ANNs in the emergency room for dialysis patients and availability of the DRGs vocabularies that support clinical tools.
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The ANNs are extensively applied as predictive models in different medical fields. Adoption of Artificial Neuron Networks is a result of their ability to tackle complex and non-linear relationship between dependent and independent variables. Thus, due to the ANNs’ ability to include a relatively large number of variables, the clinical tool is widely applied in the diagnosis and treatment of kidney failure. The emergency room is one of the most crucial segments in a hospital setting. The ability to determine renal failure and provide accurate diagnosis is of paramount importance since misdiagnosis can delay treatment of a patient and subsequently increase the mobility rate (Das et al., 2003). The use of Artificial Neuron Networks classifications are based on three important elements that include accuracy, sensitivity, and specificity of each patient’s categorization. Through this detailed classification, clinicians are able to make informed diagnosis in emergency rooms and estimate the length of stay of a patient (Kellum, Levin, Bouman & Lameire, 2002). Due to the predictive nature of the ANNs, the clinical tol is able to provide valid estimations on the length of hospital stay and cost of treatment, following admission of in-patients. Therefore, with the ability to make independent and accurate predictions, the DGRs vocabularies are frequently implemented to support the ANNs as clinical support tools.
Generally, the DRGs codes are implemented for the purpose of reimbursing hospitals for costs incurred and as a billing methodology. The DRGs are efficient in the classification of similar hospital cases into one category as a means of attaining price binding for all ‘products’ that patients receive in hospitals. Over time, the DRGs have been modified and upgraded to expand their original objectivity and meet an increasing demand of precision witnessed in emergency rooms today. As a result, the scope of the DGRs has been expanded and the DRGs codes aim to provide an approximately similar relationship between the cost of treatment incurred and the patient’s condition or illness.
As cost-based systems, the DRGs are implemented at various levels of patient encounter as an indicator of a diagnosis for the purposes of costs reimbursements. The number of patients visiting hospitals with renal failure is on the rise at an alarming rate. Costs involved in the diagnosis and treatment of kidney failure and related complications are relatively high for most patients. Thus, patients coming into the emergency room portraying signs of a potential dialysis without prior kidney failure diagnosis require an effective clinical decision support tool such as the ANNs, which provide reliable diagnosis for renal failure in the shortest time possible. Nevertheless, there are DRG vocabularies that extensively support Artificial Neuron Networks, hence providing quality and improved healthcare. Available DRG vocabularies support the ANNs through the implementation of coding systems such as the Case Mixer Index and the mixed distribution model. The DRGs codes are essentially important in the analysis of cost heterogeneity and estimating the length of stay for a patient in need of dialysis in the emergency room (Chertow, Burdick, Honour, Bonventre & Bates, 2005). Once proper diagnosis hhas been carried out using the ANNs, the DRGs vocabularies are applied from a national database to determine the cost of treatment the hospital will need for reimbursement. For instance, implementation of the mixed distribution model entails application of abstracts from the Health Care Financing Administration 3 DRG no. 316 “Renal failure” as a means of identifying similar cost of treatment and evaluating possible length of stay for patients in need of dialysis in the emergency room (Wilchesky, Tamblyn, & Huang, 2004).
Additionally, the implementation of DRG vocabularies significantly assists clinicians in understanding and identifying a specific choice of treatment that translates to the hospital costs incurred. The ANNs create subgroups that indicate similar outcomes with the exception of the age factor. Subgroups indicate a particular cost in the mode of treatment and high cost subgroups are inclusive of intensive care in renal failure and complications, which require specialized treatment that attracts higher costs (Schrier, Wang, Poole, & Mitra, 2004). Clinicians at this point can predict cost of treatment with the implementation of Diagnosis-Related Groups codes. However, the DRGs do not necessary reflect the cost of caring for patients in the emergency room that require dialysis, thus leaving out the acute need for supplementary services required by patients suffering from renal failure.
In conclusion, Artificial Neuron Networks (ANNs) are systematic models that can be applied in the emergency room encounters as flexible and valuable predictive models for clinical support purposes. Besides, Diagnosis-Related groups (DRGs) are functional coding systems that significantly aid in the payment-based systems and reimbursement method for hospitals. The implementation of the DRGs essentially boosts effectiveness of Artificial Neuron Networks application in emergency rooms among patients requiring dialysis treatment. Therefore, implementation of the DRGs vocabularies greatly improves cost prediction for renal failure and helps clinicians in understanding the most effective treatment to choose in regard to cost that may be incurred by the hospital.
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