Big Data Analytics in Personalized Medicine Market

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Question ^*

What is your biggest challenge in delivering personalized medicine today?

1. Limited access to relevant patient data

2. Difficulty integrating diverse data types (genomics, imaging, EHR)

3. Lack of actionable insights from available data

4. High cost of personalized medicine approaches

5. Regulatory or ethical concerns

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Question ^*

Which data types are most crucial for your personalized medicine efforts?

1. Genomic data (DNA sequencing, gene expression)

2. Electronic Health Records (EHR)

3. Imaging data (MRI, CT scans)

4. Wearable sensor data (activity, sleep)

5. Patient-reported outcomes (PROs)

6. Other (Please specify)

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Question ^*

How would you rate your current ability to extract meaningful insights from large datasets?

1. Very Good

2. Good

3. Fair

4. Poor

5. Very Poor

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Question ^*

What specific analytical capabilities would most improve your personalized medicine practice?

1. Predictive modeling for treatment response

2. Identification of novel biomarkers

3. Patient stratification for clinical trials

4. Drug repurposing opportunities

5. Automated risk assessment

6. Other (Please specify)

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Question ^*

What level of investment are you prepared to make in big data analytics for personalized medicine?

1. None

2. Low (focus on open-source tools and in-house expertise)

3. Medium (cloud-based solutions and some external consulting)

4. High (comprehensive analytics platform and dedicated support)

5. Unsure

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