AI in Healthcare Industry Survey 2021

In this short survey, we want to find out how you and your peers in the healthcare and life science space are using data science and big data technologies. The survey should take 5 to 7 minutes, and your responses are completely anonymous.

Complete the survey and we'll send you the survey results and a free pass to the virtual Healthcare NLP Summit (Apr 6-7, 2021.)

If you have any questions about the survey, please email us: survey@gradientflow.com. We really appreciate your input!

Note: All questions in the survey require a response

Question Title

1. Who are the intended users of the AI applications that your organization builds? (select all that apply)

Clinicians (doctors, nurses, or other clinicians)

Healthcare providers (hospitals, clinics, or similar organizations)

Healthcare payers (includes insurance providers & ACO’s)

Patients (via digital health, mobile apps, or websites used directly by patients)

Drug development professionals (via - drug discovery, clinical trials, or regulatory processes)

Medical device creators (via new devices or algorithms that require regulatory approval)

Health IT companies (via software or data that other companies use to build AI systems)

Other (please specify)

Question Title

2. Where is your company located? (select one)

Question Title

3. What is the total size of your organization, including all locations where it operates? (select one)

Less than 100

101 to 500

501 - 1,000

1,001 - 5,000

5,001 - 10,000

Greater than 10,000

Question Title

4. Which of these job types most accurately describes your role? (select one)

Hobbyist or Student

Hands-on developer, engineer, data scientist

Architect

Director or Engineering Manager

VP or CxO

Researcher or Academic

Other (please specify)

Question Title

5. What is the stage of AI adoption in your organization? (select one)

Not actively considering AI as a business solution

Evaluating AI use cases

Experimenting & developing AI models, but not yet in production

Early stage adoption: AI models have been in production for less than 2 years

Mid-stage adoption AI models have been in production for 2-4 years

Sophisticated: AI models have been in production for 5+ years

If you have an AI system for healthcare or life science in production today, please answer all the following questions only with regard to that production system. Please do NOT include projects that are still in development or evaluation stages.

Question Title

6. On what types of data does your system apply AI models? (Select all that apply)

Structured data, from relational databases

Natural language text, from digital text or scanned documents

Medical images, such as radiology or pathology imagery

Audio or video data

Time series data, such as sensor feeds

N/A - we currently do not have models in production.

Other (please specify)

Question Title

7. On what types of data do you expect to train or apply models in the next 1-2 years? (Select all that apply)

Structured data, from relational databases

Natural language text, from digital text or scanned documents

Medical images, such as radiology or pathology imagery

Audio or video data

Time series data, such as sensor feeds

Other (please specify)

Question Title

8. How important are the following requirements when evaluating a machine learning, NLP, or computer vision solution? (1=Not Important, 5=Very Important)

	1	2	3	4	5
State-of-the-art Accuracy	State-of-the-art Accuracy 1	State-of-the-art Accuracy 2	State-of-the-art Accuracy 3	State-of-the-art Accuracy 4	State-of-the-art Accuracy 5
Scalability to large datasets	Scalability to large datasets 1	Scalability to large datasets 2	Scalability to large datasets 3	Scalability to large datasets 4	Scalability to large datasets 5
Speed of training or inference	Speed of training or inference 1	Speed of training or inference 2	Speed of training or inference 3	Speed of training or inference 4	Speed of training or inference 5
Production readiness	Production readiness 1	Production readiness 2	Production readiness 3	Production readiness 4	Production readiness 5
Fit with existing software stack	Fit with existing software stack 1	Fit with existing software stack 2	Fit with existing software stack 3	Fit with existing software stack 4	Fit with existing software stack 5
Healthcare-specific pre-trained models	Healthcare-specific pre-trained models 1	Healthcare-specific pre-trained models 2	Healthcare-specific pre-trained models 3	Healthcare-specific pre-trained models 4	Healthcare-specific pre-trained models 5
Ability to train your own models	Ability to train your own models 1	Ability to train your own models 2	Ability to train your own models 3	Ability to train your own models 4	Ability to train your own models 5
Open-source or open-core software	Open-source or open-core software 1	Open-source or open-core software 2	Open-source or open-core software 3	Open-source or open-core software 4	Open-source or open-core software 5
No data sharing with software vendor	No data sharing with software vendor 1	No data sharing with software vendor 2	No data sharing with software vendor 3	No data sharing with software vendor 4	No data sharing with software vendor 5

Question Title

9. Which of these are you using today to build your ML / DL / NLP / AI models? (check all that apply)

Open source software

Commercial software that’s installed on our infrastructure

Commercial SaaS services

Public cloud providers (like AWS, Azure, or GCP)

Commercial data providers

Consulting companies that build custom software, models, or data for us

N/A - we currently do not have models in production.

Other (please specify)

Question Title

10. Which of these do you plan to use in 1-2 years to build your ML / DL / NLP / AI models? (check all that apply)

Open source software

Commercial software that’s installed on our infrastructure

Commercial SaaS services

Public cloud providers (like AWS, Azure, or GCP)

Commercial data providers

Consulting companies that build custom software, models, or data for us

Other (please specify)

Question Title

11. How important are the following requirements, when evaluating a locally installed software library or SaaS on which your AI project depends? (1=Not Important, 5=Very Important)

	1	2	3	4	5
Implements state-of-the-art techniques (like deep learning, transfer learning, or reinforcement learning)	Implements state-of-the-art techniques (like deep learning, transfer learning, or reinforcement learning) 1	Implements state-of-the-art techniques (like deep learning, transfer learning, or reinforcement learning) 2	Implements state-of-the-art techniques (like deep learning, transfer learning, or reinforcement learning) 3	Implements state-of-the-art techniques (like deep learning, transfer learning, or reinforcement learning) 4	Implements state-of-the-art techniques (like deep learning, transfer learning, or reinforcement learning) 5
Implements healthcare-specific models & algorithms	Implements healthcare-specific models & algorithms 1	Implements healthcare-specific models & algorithms 2	Implements healthcare-specific models & algorithms 3	Implements healthcare-specific models & algorithms 4	Implements healthcare-specific models & algorithms 5
Production ready codebase	Production ready codebase 1	Production ready codebase 2	Production ready codebase 3	Production ready codebase 4	Production ready codebase 5
Large and active user community	Large and active user community 1	Large and active user community 2	Large and active user community 3	Large and active user community 4	Large and active user community 5
Regularly updated with new and improved functionality	Regularly updated with new and improved functionality 1	Regularly updated with new and improved functionality 2	Regularly updated with new and improved functionality 3	Regularly updated with new and improved functionality 4	Regularly updated with new and improved functionality 5
Optimized for modern hardware (like GPUs or TPUs)	Optimized for modern hardware (like GPUs or TPUs) 1	Optimized for modern hardware (like GPUs or TPUs) 2	Optimized for modern hardware (like GPUs or TPUs) 3	Optimized for modern hardware (like GPUs or TPUs) 4	Optimized for modern hardware (like GPUs or TPUs) 5
Fits with our existing software stack	Fits with our existing software stack 1	Fits with our existing software stack 2	Fits with our existing software stack 3	Fits with our existing software stack 4	Fits with our existing software stack 5

Question Title

12. If your organization uses or plans to use outside consultants, how important are the following requirements for a consulting company that will deliver part of all of your AI project? (1=Not Important, 5=Very Important)

	1	2	3	4	5	N/A
Experts in machine learning, deep learning, NLP, or computer vision	Experts in machine learning, deep learning, NLP, or computer vision 1	Experts in machine learning, deep learning, NLP, or computer vision 2	Experts in machine learning, deep learning, NLP, or computer vision 3	Experts in machine learning, deep learning, NLP, or computer vision 4	Experts in machine learning, deep learning, NLP, or computer vision 5	Experts in machine learning, deep learning, NLP, or computer vision N/A
Experts in healthcare data engineering, integration, and compliance	Experts in healthcare data engineering, integration, and compliance 1	Experts in healthcare data engineering, integration, and compliance 2	Experts in healthcare data engineering, integration, and compliance 3	Experts in healthcare data engineering, integration, and compliance 4	Experts in healthcare data engineering, integration, and compliance 5	Experts in healthcare data engineering, integration, and compliance N/A
Medical doctors or other clinicians in our project’s specialty	Medical doctors or other clinicians in our project’s specialty 1	Medical doctors or other clinicians in our project’s specialty 2	Medical doctors or other clinicians in our project’s specialty 3	Medical doctors or other clinicians in our project’s specialty 4	Medical doctors or other clinicians in our project’s specialty 5	Medical doctors or other clinicians in our project’s specialty N/A
Experts in MLOps (model deployment, operations, and governance)	Experts in MLOps (model deployment, operations, and governance) 1	Experts in MLOps (model deployment, operations, and governance) 2	Experts in MLOps (model deployment, operations, and governance) 3	Experts in MLOps (model deployment, operations, and governance) 4	Experts in MLOps (model deployment, operations, and governance) 5	Experts in MLOps (model deployment, operations, and governance) N/A
Product managers to design how the system will deliver business outcomes	Product managers to design how the system will deliver business outcomes 1	Product managers to design how the system will deliver business outcomes 2	Product managers to design how the system will deliver business outcomes 3	Product managers to design how the system will deliver business outcomes 4	Product managers to design how the system will deliver business outcomes 5	Product managers to design how the system will deliver business outcomes N/A
Local consultants who can work on-site with our team	Local consultants who can work on-site with our team 1	Local consultants who can work on-site with our team 2	Local consultants who can work on-site with our team 3	Local consultants who can work on-site with our team 4	Local consultants who can work on-site with our team 5	Local consultants who can work on-site with our team N/A
Education and on-the-job training to grow our in-house AI talent	Education and on-the-job training to grow our in-house AI talent 1	Education and on-the-job training to grow our in-house AI talent 2	Education and on-the-job training to grow our in-house AI talent 3	Education and on-the-job training to grow our in-house AI talent 4	Education and on-the-job training to grow our in-house AI talent 5	Education and on-the-job training to grow our in-house AI talent N/A
Faster time-to-market, compared to building the system in-house	Faster time-to-market, compared to building the system in-house 1	Faster time-to-market, compared to building the system in-house 2	Faster time-to-market, compared to building the system in-house 3	Faster time-to-market, compared to building the system in-house 4	Faster time-to-market, compared to building the system in-house 5	Faster time-to-market, compared to building the system in-house N/A
Cheaper than building an in-house team	Cheaper than building an in-house team 1	Cheaper than building an in-house team 2	Cheaper than building an in-house team 3	Cheaper than building an in-house team 4	Cheaper than building an in-house team 5	Cheaper than building an in-house team N/A
No sharing or derivative rights of the data or code with the consulting company	No sharing or derivative rights of the data or code with the consulting company 1	No sharing or derivative rights of the data or code with the consulting company 2	No sharing or derivative rights of the data or code with the consulting company 3	No sharing or derivative rights of the data or code with the consulting company 4	No sharing or derivative rights of the data or code with the consulting company 5	No sharing or derivative rights of the data or code with the consulting company N/A

Other (please specify)

Question Title

13. For which of these functions do you have a software package that you currently use, or expect to use before the end of 2021? (Select all that apply)

Automated Speech Recognition (ASR)

Business Intelligence (Reports and Dashboards)

Computer vision (CV)

Data annotation

Data science platform

Data warehouse

De-Identification

Healthcare data integration

Experiment tracking

Medical terminology

Model Deployment & Operations (MLOps)

Natural language processing (NLP)

Object Character Recognition (OCR)

Time Series Forecasting or Anomaly Detection

Question Title

14. How do you validate that an AI model is ready for production use? (Select all that apply)

Based on the model vendor’s representation

Based on 3rd party evaluation of the model (including academic papers or industry groups)

Based on in-house evaluation of the model on our own data

By tuning the model to our own data and population

By monitoring the model’s behavior in production and retraining it regularly

Other (please specify)