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The focus of the Final Paper is an evaluation of how data analysis is changing the health care industry.
In your paper,
· Discuss how your Reducing Costs is changing the health care industry.
· Evaluate a minimum of three barriers of data analysis related to the topic.
· Describe any national initiatives related to the topic.
· Explain any financial incentives related to topic.
· Describe any accreditation expectations related to the selected topic.
· Compare two software options for data analysis.
· Summarize an example of a study related to your topic. For example, the use of data analysis for multiple sclerosis patients.
· Explain any liability considerations related to the selected topic.
Reducing costs is an ongoing challenge in the health care industry, and data analysis can be a powerful tool for achieving this goal. Data analysis helps clinicians understand patterns of use, identify areas of excess spending and waste, measure outcomes to determine effectiveness, track patients’ progress over time, improve safety and quality of care, reduce errors and misdiagnoses, increase patient satisfaction with their healthcare experience and more. This type of data-driven decision making has become increasingly important in shaping the future direction of health care delivery systems.
Despite its potential value in reducing costs within the health care system there are several barriers that must be considered when implementing data analysis solutions. First among these is cost itself: collecting and storing large amounts of data requires significant investments in hardware infrastructure as well as skilled personnel who know how to interpret it correctly. Additionally, privacy issues come into play when working with sensitive patient information; ensuring that all necessary security measures are taken not only protects individual privacy but also prevents costly breaches from occurring which could damage a provider’s reputation. Finally cultural factors can prevent some providers from embracing data-driven decisions due to lack of trust or understanding about what it really entails – overcoming such challenges requires clear communication about the benefits available through using this type of technology as well as strong leadership support for implementation efforts.
The federal government has been at the forefront in recognizing the need for increased use of analytics within health care systems with initiatives like The ONC’s Health IT Dashboard which aims to provide practitioners with real-time access to key performance metrics such as utilization rate trends or top procedures by facility so they can better manage operations while meeting accreditation standards set out by regulators like CMS (Centers for Medicare & Medicaid Services). Additionally under The Affordable Care Act financial incentives were created through programs like Meaningful Use or Advanced Clinical Quality Improvement Activities (ACQIAs) where eligible providers receive bonus payments when they demonstrate successful adoption and meaningful use eHealth technologies such as EHRs (Electronic Health Records) or CPOE (Computerized Physician Order Entry). Furthermore specific accreditation bodies have established criteria around how different types of organizations should collect analyze store report on clinical outcome measures in order to maintain compliance including those outlined by The Joint Commission on Accreditation Healthcare Organizations (JCAHO).
When selecting software for analyzing large datasets two options stand out: SAS Enterprise Miner Suite offers comprehensive tools designed specifically for predictive modeling along with integrated platforms featuring advanced analytics text mining capabilities while SPSS Statistics provides more generalized solutions designed more broadly across research industries both offer robust features suitable for complex analyses involving multiple variables .
As an example consider a recent study conducted at Massachusetts General Hospital utilizing SAS Enterprise Miner Suite which used medical records from 214 MS patients between 2005–2012 alongside other demographic information such as age gender race etc.,to look into predictors associated with higher rates hospitalization due to exacerbations symptoms; results revealed that certain prognostic characteristics were linked significantly greater probability admission including younger age having longer disease duration & presence optic neuritis sensory disturbances among others providing insight into potential target interventions reduce emergency visits thus resulting savings both financially terms continuity treatment & improved quality life outcomes being experienced frontline staff level respectively .
Finally liability considerations must be taken account during development deployment execution processes since improper handling protected health information could lead legal action being brought against respective parties involved namely those responsible conducting analysis safely securely eternally especially if deemed negligent activity led breach occurred potential damages awarded plaintiffs include reimbursement incurred events caused prerogatives actions classified punitive means punish offenders meet ends justice . To mitigate risks must ensure adequate training employee oversight accurate documentation secure storage encrypted transmission policies adherence applicable regulations legislation governing area expected ethical behavior professionals operating system another pertinent factor bear consideration periodic review existing protocols assesses whether objectives aligned business goals changing nature environment affects efficacy risk management strategies employed moving forward .