Back to Home
Healthcare Data Engineering Survey
Healthcare Data Engineering Survey
1.
What is your profession?
Healthcare
Tech profession
Data Scientist
Data Engineer
Researcher
Other
Please select at least one option.
2.
How familiar are you with potential data usecases in healthcare?
Not familiar
Slightly familiar
Moderately familiar
Very familiar
Extremely familiar
Please select at least one option.
3.
How many years of experience do you have in your field?
Less than 1 year
1-3 years
4-6 years
7-10 years
More than 10 years
Please select at least one option.
4.
What is your gender?
Male
Female
Non-binary
Prefer not to say
Please select at least one option.
5.
What is your age group?
Under 18
18-24
25-34
35-44
45-54
55-64
65 or older
Please select at least one option.
6.
How important is it to improve data integration and interoperability between healthcare systems?
Not important
Slightly important
Moderately important
Very important
Extremely important
Please select at least one option.
7.
What challenges have you encountered in healthcare data integration?
Lack of standardization
Data privacy concerns
Technical difficulties
Cost
Other
Please select at least one option.
8.
How critical is data security and privacy in healthcare, especially in the context of sensitive patient data?
Not critical
Slightly critical
Moderately critical
Very critical
Extremely critical
Please select at least one option.
9.
Which of the following methods could enhance data privacy in healthcare?
Encryption
Access controls
Data anonymization
Regular audits
Other
Please select at least one option.
10.
How impactful do you believe big data analytics is for healthcare in improving patient outcomes or operational efficiency?
Not impactful
Slightly impactful
Moderately impactful
Very impactful
Extremely impactful
Please select at least one option.
11.
Which specific use cases in healthcare could benefit the most from big data analytics?
Patient monitoring
Predictive analytics
Operational optimization
Drug development
Other
Please select at least one option.
12.
How important is the development of real-time data pipelines for applications like patient monitoring and emergency response?
Not important
Slightly important
Moderately important
Very important
Extremely important
Please select at least one option.
13.
What are the biggest obstacles to implementing real-time healthcare data pipelines?
Data quality issues
Integration with existing systems
Cost
Scalability
Other
Please select at least one option.
14.
How important is the role of AI and machine learning in transforming healthcare through predictive analytics and decision support systems?
Not important
Slightly important
Moderately important
Very important
Extremely important
Please select at least one option.
15.
Which areas of healthcare would benefit the most from AI and machine learning?
Diagnostics
Treatment recommendations
Operational efficiency
Patient engagement
Other
Please select at least one option.
16.
How significant is data quality and governance in ensuring accurate, reliable, and consistent healthcare data?
Not significant
Slightly significant
Moderately significant
Very significant
Extremely significant
Please select at least one option.
17.
Which of the following measures can improve data quality in healthcare?
Data validation
Data standardization
Regular updates
Data cleansing
Other
Please select at least one option.
18.
How important is effective data visualization in communicating healthcare insights to decision-makers?
Not important
Slightly important
Moderately important
Very important
Extremely important
Please select at least one option.
19.
Which improvements could be made to current data visualization practices in healthcare?
More interactive dashboards
Better integration with data sources
Enhanced graphical representations
User training
Other
Please select at least one option.
20.
Are there any other aspects of data engineering in healthcare that you believe deserve more attention or research (Optional)?
Submit Survey