IHE releases a white paper on family planning
Maintaining confidentiality when de-identifying data’s important for healthcare’s credibility. Integrating the Healthcare Enterprise (IHE) has released a white paper, Analysis of Optimal De-Identification Algorithms for Family Planning Data Elements, proposing a new technical framework for comment. It describes the rationale for selecting de-identification algorithms for each IHE Quality, Research and Public Health (QRPH) Family Planning data element. The Family Planning Annual Report (FPAR) de-identification analysis balanced two conflicting perspectives:
- Clinical subject matter expert who tends to want to keep as many data elements as possible at as high a level of fidelity as possible
- Security and privacy subject matter expert who aim to apply the most restrictive algorithm possible to safeguard the overall data set as much as possible.
To do this, IHE:
- Identified whether each data element is a direct identifier, indirect identifier, or data that does not need to be de-identified
- Discussed the purpose and need for each data element.
Simple so far, but it triggered 32 questions about de-Identification family planning spreadsheet data. After the first set of answers and revised method selected, the set of de-identification algorithms was reviewed to evaluate their effectiveness at reducing risk and identifying if any de-identification algorithms went too far and negatively impacted the performance measures relying on the data. Further passes through the data set and algorithm fine-tuning are set out in the white paper.
As HIV/AIDS policies and strategies are directly associated with family planning services, it’s important that Africa’s health systems informatics teams working in family planning contribute to IHE’s new technical framework. Participating offers considerable opportunities for learning and personal development, so double benefits.