A Clear Need for Validation Daniel Young 19 July 2022

A Clear Need for Validation

Check Marks DHWBlog shutterstock 1714693012
Checklist to validate a product

There is a need for validation… ⭐️

There are a wide range of digital health products. As many of these products are intended to be used in a clinical application, you would expect to see solid evidence underlying each.

But it’s not there. 🙁

A recent analysis of the market found that over 40% lacked clinical trials or relevant regulatory filings. 😟

That’s a problem.

How can one know if a digital health venture’s claims are valid? Without controlled validation, you can’t.

A digital health venture needs good evidence that their product works as intended.

The process of validating a product may be intimidating at first, when broken into steps, it’s doable even for smaller ventures.

✅ Start by verifying that your technology functions as design: sensor-level data outputs are calibrated; software passes QA; controlled unit-level tests yield expected results.

Verification tests are engineering tests that confirm all components in the system, including the flow of data, functions within specifications.

✅ The next stage is the analytical validation from testing your product under controlled conditions, where observed outputs match expected (or referenced) outputs. The process overlaps engineering and clinical, evaluating the performance of the algorithms and the product.

In addition to testing the product, I recommend using this stage to test your internal systems and logistics. These too should be considered part of your product, as you may need to include parts of this in your documentation and regulatory filings.

If everything goes according to plan, and you have successfully demonstrated that your digital health product does what you designed it to do. Your results can be used for regulatory filings, journal articles, etc. 🗄 🗂 🥳

✅ Clinical validation is the final stage, where the clinical efficacy of the product is evaluated as applied to a specific context of use. As clinical study design is a massively detailed topic, I’ll touch on this in a future post.

Going back to the start, its best to break the validation process into incremental steps beginning with a plan on which your team can align. Setting the expectations, objectives, and anticipated timelines before you start, will go a long way to making this process smooth and productive.

Having strong evidence of positive clinical outcomes from your digital health product will drive adoption, usage, and ultimately your revenues. 💰💰💰

A Clear Need for Validation Daniel Young 19 July 2022

A Clear Need for Validation

Check Marks DHWBlog shutterstock 1714693012
Checklist to validate a product

There is a need for validation… ⭐️

There are a wide range of digital health products. As many of these products are intended to be used in a clinical application, you would expect to see solid evidence underlying each.

But it’s not there. 🙁

A recent analysis of the market found that over 40% lacked clinical trials or relevant regulatory filings. 😟

That’s a problem.

How can one know if a digital health venture’s claims are valid? Without controlled validation, you can’t.

A digital health venture needs good evidence that their product works as intended.

The process of validating a product may be intimidating at first, when broken into steps, it’s doable even for smaller ventures.

✅ Start by verifying that your technology functions as design: sensor-level data outputs are calibrated; software passes QA; controlled unit-level tests yield expected results.

Verification tests are engineering tests that confirm all components in the system, including the flow of data, functions within specifications.

✅ The next stage is the analytical validation from testing your product under controlled conditions, where observed outputs match expected (or referenced) outputs. The process overlaps engineering and clinical, evaluating the performance of the algorithms and the product.

In addition to testing the product, I recommend using this stage to test your internal systems and logistics. These too should be considered part of your product, as you may need to include parts of this in your documentation and regulatory filings.

If everything goes according to plan, and you have successfully demonstrated that your digital health product does what you designed it to do. Your results can be used for regulatory filings, journal articles, etc. 🗄 🗂 🥳

✅ Clinical validation is the final stage, where the clinical efficacy of the product is evaluated as applied to a specific context of use. As clinical study design is a massively detailed topic, I’ll touch on this in a future post.

Going back to the start, its best to break the validation process into incremental steps beginning with a plan on which your team can align. Setting the expectations, objectives, and anticipated timelines before you start, will go a long way to making this process smooth and productive.

Having strong evidence of positive clinical outcomes from your digital health product will drive adoption, usage, and ultimately your revenues. 💰💰💰

A Clear Need for Validation Daniel Young 19 July 2022

A Clear Need for Validation

Check Marks DHWBlog shutterstock 1714693012
Checklist to validate a product

There is a need for validation… ⭐️

There are a wide range of digital health products. As many of these products are intended to be used in a clinical application, you would expect to see solid evidence underlying each.

But it’s not there. 🙁

A recent analysis of the market found that over 40% lacked clinical trials or relevant regulatory filings. 😟

That’s a problem.

How can one know if a digital health venture’s claims are valid? Without controlled validation, you can’t.

A digital health venture needs good evidence that their product works as intended.

The process of validating a product may be intimidating at first, when broken into steps, it’s doable even for smaller ventures.

✅ Start by verifying that your technology functions as design: sensor-level data outputs are calibrated; software passes QA; controlled unit-level tests yield expected results.

Verification tests are engineering tests that confirm all components in the system, including the flow of data, functions within specifications.

✅ The next stage is the analytical validation from testing your product under controlled conditions, where observed outputs match expected (or referenced) outputs. The process overlaps engineering and clinical, evaluating the performance of the algorithms and the product.

In addition to testing the product, I recommend using this stage to test your internal systems and logistics. These too should be considered part of your product, as you may need to include parts of this in your documentation and regulatory filings.

If everything goes according to plan, and you have successfully demonstrated that your digital health product does what you designed it to do. Your results can be used for regulatory filings, journal articles, etc. 🗄 🗂 🥳

✅ Clinical validation is the final stage, where the clinical efficacy of the product is evaluated as applied to a specific context of use. As clinical study design is a massively detailed topic, I’ll touch on this in a future post.

Going back to the start, its best to break the validation process into incremental steps beginning with a plan on which your team can align. Setting the expectations, objectives, and anticipated timelines before you start, will go a long way to making this process smooth and productive.

Having strong evidence of positive clinical outcomes from your digital health product will drive adoption, usage, and ultimately your revenues. 💰💰💰