Since the outbreak of the corona pandemic, the eyes of the world have been firmly locked on statistics and data to stay abreast of the development of this new viral disease. Reminiscent of a fever curve, the course of the pandemic is documented and disseminated in real time. This is a new and unusual level of information transparency, especially in the healthcare sector. However, Covid-19 does indeed call for transparency, as the virus raises many questions and poses enormous challenges for those responsible. For science, corona has become a real-world laboratory. One of the central elements in this context is access to medical data in order to be able to predict how and within what timeframe the pandemic will develop.
Insufficient medical data
Austria has weaknesses in this respect, as several members of the scientific community recently pointed out. Scientists wrote an open letter to the Minister of Health to draw attention to the lack of access to data about the corona crisis. Seen from a historical perspective, the issues that have now come to light illustrate that, hitherto, transparency and quality measurements have played a less prominent role in the Austrian healthcare system than in other countries, for instance when compared to the United Kingdom. A lack of access to data for science is one result; another is too little detail in data collection, as Albrecht Becker, an accounting researcher from the University of Innsbruck, explains: “The medical data we currently operate with in Austria are insufficient.” In a project under the joint lead of Becker and Silvia Jordan that started in 2018 and is funded by the Austrian Science Fund FWF, their team has been exploring quality measurement in the Austrian healthcare system.
A lack of context-relevant information
One of the aspects Becker and Jordan have noted in their ongoing analyses is a frequent lack of context-relevant information required to understand the figures. “This quickly stymies you even when it comes to very simple questions, such as the total number of intensive care patients, where information is lacking as to why this number is going up or down,” explains Silvia Jordan. Were patients transferred or did they die? Questions like these are currently left unanswered in quality measurements. Nor is there any publicly accessible information about the kinds of pre-existing health conditions found in cases of corona deaths. Such information is the only way, however, to better understand the virus and to protect risk groups. Becker and Jordan also note that the models do not provide much information about their underlying assumptions. Especially when making forecasts for the future, it is important to know these underlying assumptions.
Use of routine data for quality management
The four-member research team in Innsbruck gains insights into the status and transparency of data within the Austrian healthcare system through their research on the system for measuring the quality of in-patient hospital stays, introduced throughout Austria in 2013 as part of the healthcare reform. Quality indicators, the so-called A-IQIs (Austrian Inpatient Quality Indicators), are continuously recorded on the basis of diagnoses and forms of treatment. The elements measured include the mortality rates, complication rates and care progress. As a basis for the calculation of the indicators the system uses data that hospitals need to collect in any case to invoice their services, i.e. so-called routine data. One of the reasons for choosing this solution is that hospitals already have very extensive documentation requirements and hence staff were not to be burdened even more.
Data collection unclear and isolated
The system also comes with disadvantages, as Silvia Jordan and Albrecht Becker explain. “The A-IQIs are about measuring the quality of results, but there is no general agreement as to what ‘results’ means and which standard levels they are to be measured against,” says Jordan. In addition, the hospital cases are recorded in relatively loose categories. “The situation is different in Germany, for example, where more or less the same system exists, but it is more detailed.” As a result, Austrian mortality data cannot be compared with those from Germany, to give just one example. According to the researchers, this also makes it more difficult to achieve learning effects, because measurements are not selective enough.
Another aspect of the current system that outside experts often criticize is the lack of documentation as to how different service providers in the healthcare system cooperate. The Austrian healthcare system still does not systematically analyse the development of medical histories after an inpatient stay. “There are discussions, however, about extending this documentation across the different service sectors and merging the data,” says Becker. Currently, the results of the A-IQI surveys are published in an annual report by the Ministry of Health and communicated to the public in a highly condensed form on kliniksuche.at. But they do not allow conclusions to be drawn about individual hospitals, and they also provide little of the contextual information that would be important for interpreting the data.
Promoting discourse and surveying interests
In their basic research project, which will run until 2021, the Innsbruck research team is also conducting case studies in selected hospitals. In order to gain a detailed insight into the hospitals’ internal processes, the researchers conduct interviews with various interest groups such as hospital management, doctors or nursing staff. The project team aims to bring together the different perspectives and interests and to open up discussion on the causes of the current lack of transparency and on how quality data are used.
According to the initial conclusions of the researchers, the current quality system is geared neither to internal quality management nor to information for patients. More transparency and discourse on the key figures would also be desirable for international country comparisons. “There are learning opportunities that are being missed, for example when it comes to understanding why some things cannot be compared,” explains Jordan. Perhaps experience from the corona crisis will see the learning curve rise in the future. One learning effect would be that data are useful for scientific and social learning processes only where they are not just publicly accessible, but where the conditions under which they were generated are discussed transparently.
Silvia Jordan is a Professor of Management Accounting at the Department of Organization and Learning, University of Innsbruck. Her research interests include interdisciplinary work in the areas of accounting, risk and regulation and organizational learning. Jordan is the principal investigator of the FWF project “Healthcare Quality Assessment in Austria ” (2018-2021).
Albrecht Becker is a Professor of Management Accounting at the Department of Organization and Learning, University of Innsbruck. His research focus is on social studies of accounting and controlling in non-profit contexts such as healthcare, universities, international development and philanthropy.