Healthcare analytics has evolved during Covid19, enabling us to better understand what patients express on telehealth consultations, live feeds, and social media channels. The sudden onset of the pandemic has resulted in a paradigm shift in the way healthcare has been traditionally managed and delivered. Governments and healthcare organizations have realized that AI-driven technologies can be optimized for patient care and patient voice data in newer and better ways.

2. Understanding
Healthcare Data Analytics
With Practical Use Cases

3. Table of content:
1. AI-driven Healthcare Analytics
2. What are the new sources of healthcare data?
3. Use Cases - How Healthcare Analytics is Improving
Patient Care

4. Overview
Healthcare analytics has evolved during Covid19, enabling us to better
understand what patients express on telehealth consultations, live feeds,
and social media channels. The sudden onset of the pandemic has resulted
in a paradigm shift in the way healthcare has been traditionally managed
and delivered. Governments and healthcare organizations have realized
that AI-driven technologies can be optimized for patient care and patient
voice data in newer and better ways. This has also lead to a better quality of
healthcare data that is being used to improve care through sentiment
analysis of the gathered information..

5. AI-driven Healthcare Analytics

6. Why do we Need VoC Tools?

7. What are the new sources of healthcare
data?

8. Why do we Need VoC Tools?

9. Use Cases - How Healthcare Analytics is
Improving Patient Care
1. Hospital healthcare aspects: Text analytics and sentiment analysis allow us to
extract and classify vital insights from aspects like patient admissions,
hospital administration and hygiene, medical staff, the ER, and more.
Healthcare analytics from surveys and internal management systems can tell
how a hospital’s location features on the satisfaction scale, and even how
facilities like the cafeteria, washrooms, parking issues, are being managed.
2. Doctors office healthcare aspects: Repustate’s text analytics solution
examined and processed doctor notes recorded in the EHRs. This helped the
doctors understand compare and analyse aspects like the effectiveness of
medications and dosage combinations, the effectiveness of primary
physicians, lab results, medical imaging, and more.

10. 3. Healthcare worker satisfaction: Patient care is dependent as much on a
conducive work environment for the medical staff as it is on medicine. Important
elements scrutinized for sentiment through healthcare data analytics include
remunerations, working hours, benefits, the quality of medical equipment,
effective leadership, and staff morale.
4. Patient voice for the public sector: Government bodies and public healthcare
organizations depend heavily on healthcare analytics to understand the progress
or the lacuna of their policies. Studies from millions of annual surveys and feedback
forums help government agencies equate the distribution of medical resources
and specialty care to ensure that all members of the society receive equal quality
care. Read the Use Case.

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