Confidence lacking in secure data sharing, survey finds

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More than two out of three healthcare organizations aren't completely confident they can share data safely while still protecting patient privacy, according to a new survey

Privacy Analytics, a vendor of de-identification technology, conducted with survey with Electronic Health Information Laboratory, a group that conducts theoretical and applied research on the de-identification of health information.

It polled 271 professionals who handle protected health information in settings including hospitals, doctor's offices, payers, research organizations and agencies.

Among the findings:

  • More than half the respondents said they play to increase the volume of data stored or shared within 12 months and two-thirds currently release data for secondary use, such as analysis, research, safety measurement, public health, payment or marketing.
  • Individuals aren't familiar with advanced methods of de-identifying data, cited by 51 percent, leading them to release data stripped of its usefulness or that puts them at high risk of a breach.
  • More than 75 percent of respondents said their data-management practices include one or more approaches including data-sharing agreements (50 percent), data masking (31 percent) and Safe Harbor methodology (28 percent), according to an announcement.

Researchers have shown how easy it is to re-identify patients in de-identified data, yet de-identified data can lose its value as more identifying factors are stripped out. Researchers from Vanderbilt University and elsewhere recently published work exploring policy options that balance risk of violating a patient's privacy vs. the use of data for society.

Earlier this year, the Health Information Trust Alliance released a framework providing guidance on use of de-identification in a simplified and streamlined way through standards and controls that also adhere to HIPAA's privacy rules.

To learn more:
- find the announcement
- read the report

Related Articles:
Researchers examine balancing privacy risk, utility of de-identified health data
HITRUST creates framework for de-identification of data
Patient de-identification needs to balance privacy, value of analytics
De-identification effective in maintaining patient privacy if done right

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