Fully scoped de-identification software is the ultimate multi-tool enabling healthcare, research and government organizations to use the wealth of data available to them while maintaining strict compliance with privacy regulations. Beyond healthcare, there are broader use case applications of de-identified data sets across countless industries and applications.   

3 uses for de-identified data:

1. Transform Clinical Data into Business Efficiencies     

Data insights can optimize staff planning, facility utilization, cost avoidance strategy, inventory, and supply chain management, ensuring that healthcare resources are used efficiently.   

Healthcare business leaders can exercise the strategic advantage of data-driven decision-making when the data gathered in clinical care is anonymized and integrated with operational and financial datasets. Leadership can make informed decisions with data unique to their market, geography, and patient mix.  

2. Predict Chronic Illnesses for Population Health Management   

Anonymized datasets can uncover risk factors and comorbidities, anticipate health trends and devise targeted strategies that enhance patient outcomes and reduce costs for specific populations.   

De-identified healthcare records provide researchers with access to real-world data to test and verify their findings, uncovering insights into disease patterns and treatment outcomes including co-morbidities like blood pressure, diabetes, chronic lung disease, kidney diseases, and obesity, enabling at-risk patients to be identified and resources to be allocated more effectively in line with the principles of Value Based Care.     

3. Train & Test Healthcare Machine Learning Models   

De-identified data can be used to create, train and test ML models, allowing them to learn from clinically meaningful scenarios without compromising patient privacy.     

Using wide and deep datasets stripped of personal identifiers, ML Models can provide clinicians with powerful tools to predict outcomes and make informed decisions. Patterns in anonymized data can help predict which patients are at higher risk for chronic illnesses, enabling early intervention and better care management.   

Key questions to ask when purchasing De-Identification Software 

  • What types of data and formats can the tool ingest?   
  • Does it detect and identify PHI to guide users on best practices?   
  • De-identification techniques the tool enables?  
  • Capabilities for reidentification of de-identified data?   
  • Processes for manual review and validation of deidentified data?  
  • Tools for accessing and minimizing data loss during de-identification?   
  • Is it compliant with regulatory requirements?  
  • Can it generate audit logs and reports to demonstrate compliance?  
  • How will the solution integrate with your existing systems and pipelines? 
  • Can it scale as your needs grow?  
  • Reported processing times of de-identification actions?   
  • What level of support does the vendor provide?   
  • What are the upfront and recurring costs, including licensing?  
  • Any additional costs for customization, integration, upgrades or support services? 
Read the eBook

Want more insights? Check out key excerpts from Chapter Three in our previous blog: Choosing the Right De-identification Product for Your Organization.