Baicheng Normal University,白城师范学院贴吧,高校强制学生实习,美国航空空姐爆料,司机冲进晨练队伍,举报学校被劝退,项俊波被双开

banking 北京打击地铁色狼 徐州幼儿园爆炸

Posted on September 29, 2017 by hanson

Health The healthcare industry has been trailing its peers in the retail, banking, technology and other sectors in the adoption and usage of data to improve efficiencies and predict outcomes. As the Obama government moves towards increased healthcare adoption, payers today are keen to implement advanced technologies, adopt healthcare analytics framework and build interoperable platforms, with a view on increasing care quality and reducing healthcare costs. Additionally, having access to clinical data, key stakeholders i.e.provider, payers and other care givers would be able to address disease management challenges such as readmissions, risk adjustments, etc.. Let us look at a few key areas where advanced healthcare analytics can play a pivotal role in addressing disease management challenges: A recent AHRQ statistic states that chronic conditions constitute nearly 70 percent of all healthcare expenses. A majority of these expenses can be averted by having access to clinical data. In its absence, care givers often end up overlooking many highly preventable conditions that has a potential to turn chronic. For example: a person who is diagnosed with marginal levels of blood sugar and pressure levels is often let off without immediate treatment. However, these act as breeding grounds for debilitating, life-shortening and costly chronic diseases. Unified healthcare analytics tools, in the above scenario could have helped in collating and reporting of clinical and claims data thereby reducing the healthcare risk. The aggregation of data also helps in identification of patients who are at the highest risk of readmission. Using advanced predictive analytics software, health systems can precisely understand which patient is at the highest risk of cardiac arrest or epileptic strokes. A Texas based health system was able to reduce its readmission rate by more than half by using predictive analytics software to understand which patients are at the highest risk of heart failure. Additionally, payers can also get involved in the care giving process at an early stage to optimize treatment options based on patient medical records. This can help in treating chronic diseases like diabetes which can work as a significant cost saver while at the same time ensuring better quality of care to the patient through early detection. The area of population health is moving towards value based payments and accountable care making it a necessity to have tools to measure it. Predictive and prescriptive analytics through its visual graphical trends and dashboards can assist caregivers in recognizing and acting on gaps in care and if required prescribe direction, interpretation and recommended action. Predictive analytics presents the evidence to which incremental prescriptive analytics can be applied. Such level of forecasting would not have been possible without the powerful capabilities of healthcare analytics. Over the years, the awareness and adoption of healthcare analytics and related business intelligence platforms have become more pronounced for the healthcare providers, payers and other essential stakeholders. Healthcare Analytics frameworks, through its varied offerings in the clinical, regulatory, financial, operational and predictive analytics space have definitely caught the eye of the key healthcare stakeholders as efficient tools in optimizing patient care while at the same time ensuring the corresponding healthcare costs are kept low and efficiencies are at the maximum. About the Author: 相关的主题文章: