Arrow Left Health care

Use case: Distributed calculation of quality indicators

Outcome of health care and individual information about patient’s conditions are often registered in electronic health records and transferred to specific health care registries. This information can be used to derive indicators for successful treatments. In this project we want to use the PHT infrastructure to calculate quality indicators in a decentralized setting, which can be more frequently monitored than the current data delivery procedures.

Medical relevance

This will reduce the registration burden of healthcare professionals

Description

We think the measurement, analysis and comparison of data on healthcare quality can be done easier and faster by applying the Personal Health Train (PHT).

The PHT enables (re)use of health data to provide good health care.

Health care professionals still manually upload data relevant for health care quality in data-collection systems. This data is copied directly from the health care professionals’ systems into quality registries. This procedure is prone to errors and increases the administrative burden of health care professionals.

Moreover, this procedure slows down feedback-loops. It may take up to 1,5 years until quality-reports from different hospitals – or other health care organisations – are compared, analysed and published. It is not desirable to have to wait for 1,5 years to gain insights in the quality of health care one provides or to figure out from whom one may learn in order to improve the quality of health care.

In summary, health care quality registries at this moment are not used optimally and cause unnecessarily work-load for health care professionals. This even negatively effects the efficacy and efficiency of our health care system.

Therefore, we want to use the PHT infrastructure to calculate quality indicators in a decentralized setting, which can be more frequently monitored than the current data delivery procedures. An additional advantage is there is no central registry which needs to be maintained and updated. Hence, there is a mitigation of privacy risks, as data is analysed locally and personal data do not have to be transferred to a central location.

Project details

Project leader

André Dekker
Funders National Health Care Institute (ZIN)
Collaboration partners MUMC+
National Health Care Institute (ZIN)
Medical Data Works B.V.
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