Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Refer to the Discussion Forum Grading Rubric under the Settings icon above for guidance on how your discussion will be evaluated.
|Healthcare Analytics; Regulations, Clinical Quality, and Patient Safety|
As many healthcare facilities seek to implement analytical patient quality and clinical value in collaboration with electronic health record management. Automated algorithms are capable of sifting through thousands of patient records to identify potential clinical errors and systematically measure patient safety in ways never before anticipated (Davenport, 2014). Discuss how social media can impact the present and future outlook on health care analytics.
Guided Response: Review your peers’ posts and provide a substantive response to at least two of your classmates’ posts by Day 7. A substantive response is a respectful, professional, and unique response that is at least five sentences in length and incorporates the following:
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- Highlights the key points of what you have learned from your peer’s post.
- Adds your content knowledge.
- Compares and contrasts.
- Provides further research.
- Is topic-related.
Monitor the forum through Day 7 to allow for robust dialogue.
MHA Week 3 – Discussion: Healthcare Analytics; Regulations, Clinical Quality, and Patient Safety
Week 3 Discussion:
Social media has become a unique tool for many industries, businesses, and healthcare as well. Many businesses use social media as means to target specific consumer populations and market their products to these target populations to drive in sales and consumers. Social media is a large platform that reaches billions of consumers and using it for business purposes is a smart business move for many companies. Data derived from social networks can be used for biomedical research and clinical trials (Deloitte, 2018). Big Data is the information derived from EMR and EHR systems to help health care organizations make decisions about patient care, reduce costs, and increase revenue. Big data can also be derived from social media platforms to help healthcare organizations and health entities make decisions as well. Social media is used by its users for many things; users often post about their lifestyle choices, hobbies, eating habits, etc. Health entities can use this information from social media to understand the decisions that patients are making and why these decisions are being made (Deloitte, 2018). Additionally, the government may use social media to see what people are eating, their patterns, and their health status (Deloitte, 2018).
While social media may seem like a great place to collect patient data because of its ability to share information; there is no way to determine fact from fiction. Users of social media oftentimes post information that is “fluffed up” or not completely accurate. If social media is to be used for healthcare analytical purposes, the information should be scrubbed for accuracy. The question is, how would one be able to determine what is actually fact and what is fiction? The entities using social media for health analytics would need to find an error-proof mechanism to determine what information can actually be used for data analytical purposes.
Deloitte Development, LLC. (2018). Social Media Analytics in Healthcare. Retrieved from http://government-2020.dupress.com/trend/social-media-analytics-health-care/