Healthcare Analytics; Regulations, Clinical Quality, and Patient Safety



Healthcare Analytics; Regulations, Clinical Quality, and Patient Safety
Healthcare Analytics; Regulations, Clinical Quality, and Patient Safety
MHA 605 Week 3 – Discussion

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:

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 605 Week 3 – Discussion

MHA 605 Week 3 – Discussion
Social Media

Social media has the potential of transforming both the present and future outlook of healthcare analytics. Social networks provide a rich source of data for enhancing predictive analytics, thereby helping to improve planning and coordination of care. As more data becomes available for aggregation, predictive analytics will be more vibrant and accurate than ever before. Predictive systems are valuable in biomedical research, clinical trials, and personalized medicines (Mitra & Padman, 2014). For instance, predictive models that aggregate data from social media can help to identify and predict readmissions, high-cost patients, triage, adverse effect, decompensation, and adverse effects.

Social media also provide greater opportunities for personalized medicine by helping in the development of self-management tools. Through the aggregation of Social media data, it will be possible for the policymakers and hospitals to segment populations into various groups and develop personalized care (Mitra & Padman, 2014). For instance, it will be possible to identify populations that are likely to use mobile and social-based platforms for obtaining healthcare information. Segmentation is vital due to differences among the population as a result of factors such as age, ethnicity, gender, education, and socioeconomic level. Organizations use this data to identify patients that are most likely to benefit from intervention and the precise interventions that will bring the most significant impact on the patient (Mitra & Padman, 2014). For instance, insurance providers will be able to identify the right group to target for particular plans.

However, on the negative note, social media analytics are likely to increase concerns about guarding the privacy of patient’s healthcare information. This is because social networks mostly facilitate communication among many patients simultaneously. More importantly, social media data is user-generated and as the networks grow, the possibility of spreading false information or information that should have remained private increases (Hawn, 2009).

Therefore, social networks the potential to impact the outlook of healthcare analytics by enriching sources of data. They also make it easier for organizations to segment populations and create personalized care. They are also valuable tools for medical research and overall predictive analytics.

References

Mitra, S., & Padman, R. (2014). Engagement with Social Media Platforms via Mobile Apps for Improving Quality of Personal Health Management: A Healthcare Analytics Case Study. Journal of Cases on Information Technology (JCIT), 16(1), 73-89.

Hawn, C. (2009). Take two aspirin and tweet me in the morning: how Twitter, Facebook, and other social media are reshaping health care. Health affairs, 28(2), 361-368.


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