Project:

PATIS - A patient safety intelligence system and framework
for the secondary use of multimodal clinical data
to assess and improve patient safety

   
Funding:

FWF - Austrian Science Fund (P29076-N33)

   
Duration: 2016 - 2021
   
Partner

Department of Neurology, Medical University Innsbruck

   

Project member at UMIT

Elske Ammenwerth, Werner Hackl, Bogdan Ianosi, Renate Nantschev, Michael Schaller, Lukas Huber

   
Summary

Within the PATIS project, we contributed methodologically to patient safety. Patient safety means that the risk that patients are harmed during their stay in a health care institution is minimized as far as possible. Patient safety is an essential issue for health care, as around 10% of patients in a hospital are subject to such risky situations or even harmed by errors (e.g., medication errors).

We approached this important topic from the perspective of medical informatics. Thus, we first investigated which indicators are helpful to measure patient safety. We then conducted case studies in various fields such as neurology or gerontopsychiatry. We found here that a large amount of data is already available that may be helpful to monitor patient safety indicators without the need to collect additional data.

Then, we investigated in more detail for a selected set of indicators whether it is indeed practically possible to used valid routine patient data that allow monitoring these indicators. Here, we used routine clinical data from patients in neurology or gerontopsychiatry. We were able to show that for some patient safety indicators, we can indeed use routine clinical data. Routine data has the advantage that it is already available so that patient safety could be monitored regularly.

However, routine clinical data is not always of sufficient quality. We thus performed some methodological work on the question of the quality of routine clinical data. For example, routine data from neurological monitoring of an intensive care patient comprises many data generated every second. This data is automatically monitored, but manual activities (such as injection of medication) may have an unnoticed impact on this data. Thus, routine clinical data must be synchronized with documented manual activity to avoid drawing false causal conclusions.

Our cooperation partners in neurology used the clinical routine data for in-depth analysis on specific clinical questions based on our methodological work. For example, they found that some patient outcome indicators might worsen when a particular standard therapy is applied in a specific sub-group of patients.

Our project results show that a careful analysis of available clinical routine data can help monitor health care quality and thus contribute to patient safety. Overall, our work contributes in a methodological way to the idea that available clinical routine data can be used to derive meaningful information for quality management and patient data. Given that more and more routine patient data is available electronically and standardized, we expect that application areas for using this data will further grow.


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v.l.n.r.: Prof. Dr. Erich Schmutzhart, Department of Neurology; Prof. Dr. Elske Ammenwerth, UMIT; PD Dr. Raimund Helbok, Department of Neurology; Ass.-Prof. Dr. Werner Hackl, UMIT.

   
Publications:

(please find a complete list of publications in our list of publications)