Big data computing poses challenges to the privacy and security of patient information. In fact, the rapid growth in the volume of health-related information increases the risk of privacy violations particularly when data sets are combined. Explain the role of data and information governance in making organizational improvements and higher quality decision-making.

In two different paragraph  give your personal opinion to  Leeann Forsythe and  Tyler Blevins 

 Tyler Blevins 

Data governance is the practice of managing data assets throughout their lifecycle to ensure that they meet organizational quality and integrity standards.  Data governance is geared towards making sure that users can trust their data, which is especially important when making patient care decisions.

AHIMA defines information governance as “an organization-wide framework for managing information throughout its lifecycle, and for supporting the organization’s strategy, operations, regulatory, legal, risk, and environmental requirements.” 

With these two terms you can see why it is so important to ensure this is protected, up-to-date, and managed correctly. When you look at something like this you have to put yourself in the patient’s shoes. If I were the patient I would want to ensure that my information is well protected at all times, not only for my privacy, but also for peace of mind. Ensuring that data and information is governed properly only leads to a bigger and better organization as a whole. 

A strong governance program not only makes clinical care easier and more reliable, but will allow organizations to prepare for future analytics projects in an increasingly complex environment, said Deborah Green, MBA, RHIA, Executive Vice President and Chief Operating Officer of AHIMA.

“Sometimes we feel like we’re drowning in data, but the challenge isn’t just the volume.  It’s the integrity and the quality of that data,” she stated at the organization’s 2015 convention.

“We need information governance to enable trust. What will that buy us?  Each organization will be able to trust their data for key decision making and be confident enough to leverage that information to promote themselves.”

Data, information, and technology are ever growing. Being prepared and mindful of this is key to a successful organization. Adapt to the changes, overcome the obstacles, and stay up-to-date with the way the healthcare field is evolving. 

Healthcare IT Analytics. (July 26, 2016). The Role of Healthcare Data Governance in Big Data Analytics. Retrieved from: https://healthitanalytics.com/features/the-role-of-healthcare-data-governance-in-big-data-analytics

 

Leeann Forsythe

     The healthcare industry relies heavily on data collection. Some of the data collected is patient demographics, health histories, medication records, surgical records, treatment records, insurance, billing ,and payment information. Before electronic health records (EHR) information sharing was difficult.  With the implementation of EHRs, data collection, analysis, and improved health care delivery is possible. Many organizations struggle with making data governance a priority and therefore have not completely realized the  many benefits of EHRs. Data governance in healthcare is developed and managed by health information management professionals. It is the process of managing data assets throughout their lifecycle to ensure they meet organizational quality and integrity standards. In addition, it is regulated to ensure that users can trust their data which is important when making patient care decisions. The overall goal of data governance is to improve staff efficiently, create an environment of accountability, and create standardized data sets that can be used for organizational improvements and improved clinical decision making (HealthITSecurity, 2019).

References

HealthITSecurity. (2019, December 18). The role of healthcare data governance in big data analytics. HealthITAnalytics. Retrieved March 30, 2020, from https://healthitanalytics.com/features/the-role-of-healthcare-data-governance-in-big-data-analytics