A New Neuroinformatics Approach to Optimize Diagnosis Cost in Neurology: An Operational Research Tool
| Autor: | Mohammad Rashid Hussain, Mohammad Equebal Hussain |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Neuroinformatics
Operations research Computer science business.industry Random Tests (RT’s) media_common.quotation_subject Computer applications to medicine. Medical informatics General Engineering Modified distribution method R858-859.7 Context (language use) stepping stone method Cost optimization Health administration Reduction (complexity) linear programming problem (LPP) Initial Basic Feasible Solution (IBFS) Health care Predictive power Quality (business) business media_common |
| Zdroj: | International Journal of Online and Biomedical Engineering, Vol 15, Iss 06, Pp 31-52 (2019) |
| ISSN: | 2626-8493 |
| Popis: | Cost optimization approach of operational research is a predictive power and economy of compactness that is applied to solve specific clinical needs relevant to healthcare cost reduction. Technology helps the healthcare management, decision making, and policy that we have implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The treatment cost of brain tumor is high. Sometimes, cost becomes a problem for individuals to get their complete treatment, which makes their health at risk and may lead to higher cost in future. Here we address neuroinformatics approach to optimize diagnosis cost in neurology through an operational research tool (optimization) on how the diagnosis cost of neuro-patient can optimize. In this context, we introduce a new and unique optimization approach in healthcare, yet what we are clearly lacking for applying applications of operational tools to translate this understanding to the different level to apply the concept in healthcare. The costs of treatment achieved by three standard initial basic feasible solutions (IBFS) methods (North-west corner method, Minimum cost method, Vogel’s approximation method) are 763, 763, and 779. The optimal solution is 761, and three random tests (RT’s) are 826, 783, and 788. Optimal solution provided an overall difference in treatment cost with IBFS 2, 2, 18 and with RT’s 65, 22, and 27. These results establish the basis for a deliberate integration of operational research tools and neuroscience into diagnosis of cost optimization mechanisms for neuro- patient. |
| Databáze: | OpenAIRE |
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