BARS – Real World Data for COVID-19 Risk Stratification
High quality real world data (RWD) to characterise elderlies at risk
For more than 12 years, the existing data warehouse and web-based data presentation system BARS (Benchmarking and Reporting Service) has been used for quality assurance in healthcare in Austria and Germany. The system has been used by healthcare professionals in both countries to standardise RWD for patient care in geriatrics, diabetes, cardiovascular diseases and hepatitis C. Around 40% of all Austrian acute geriatrics and remobilization facilities use BARS and participate in an open audit and feedback quality assurance programme. This led to a unique high quality database on quality of care, for healthcare planning, and research with data acquired under routine conditions. The BARS system is currently being re-specified, modernised and linked to innovative eHealth systems for care process support and decision support to improve nursing and elderly care. The current COVID pandemic has shown the need for such systems to be used for regional risk stratification in order to supply pandemic models with high quality RWD. Regional statements could already be drawn from the broad implementation of BARS in Austria.
Unknown potential for risks (frailty, COVID-19, …)
The potential for risks is often unknown to healthcare professionals and healthcare decision makers, e.g. “What kind of complications have to be expected when surgery has to be performed in this particular patient?” or “How likely will a patient develop complications?”.
The required information for risk stratification is fragmented across multiple data sources and is often not available in a standardised way, although this information is necessary for efficient evidence-based decision-making and resource planning in healthcare.
Frailty is a common geriatric syndrome that entails an elevated risk of catastrophic declines in health and function among older adults. Frailty is characterised by a functional decline across multiple physiological systems, accompanied by an increased vulnerability to stressors and results in major implications for clinical practice and public health.
Targeted use of resources in the care of the elderly
A large meta-analysis showed that older people living with frailty benefit significantly from targeted interventions that improve health and independence (Ellis et al. 2011, Cochrane). Due to limited healthcare resources, access to specialised healthcare facilities is often restricted and patients have to be selected based on certain criteria. On the individual patient level, age alone has little prognostic use – a simple screening factor is needed.
Frailty screening is internationally recognised to identify patients who will profit from targeted interventions. Several different frailty-screening tools are available and widely used, most notably the Frailty Phenotype, the Electronic Frailty Index or the Clinical Frailty Scale. Recent studies have shown that frailty is also relevant for risk stratification in the current COVID pandemic, as COVID-19 disease outcomes were better predicted by frailty than either age or co-morbidity (Hewitt et al. 2020, Lancet).
Evidence-based resource planning
The modernisation of BARS and its link to innovative eHealth systems (Fig 1) started in 2018 and was funded by the health fund Styria and Carinthia (Austria). This included the automated allocation of RWD from care processes for quality assurance and to enable prospective and retrospective risk stratification in the care of the elderly.
Fig 1: Risk stratification and quality assurance using BARS and the linked innovative eHealth systems
The linked Guidance System can be used outside the geriatric acute care units for frailty risk screening in emergency departments, surgery departments, etc. This system is currently under development as software as a medical device (SaMD).
Inside the geriatric acute care units, a Therapy/Monitoring System offers process support and decision support to capture high quality and structured RWD. Multiple pilots in hospitals are planned next year in different Austrian regions. The collected RWD enables:
- risk stratification for healthcare professionals on an individual patient level through the BARS Guidance System
- regional risk stratification for a severe disease progression (e.g. COVID-19 or the flu)
In Austria, the structured RWD in BARS from many institutions and regions allows characterisation of the risk potential based on frailty, co-morbidities, demographics etc. using and applying a series of data models (Fig 2).
This enables informed, detailed resource planning for healthcare decision makers.
Fig 2: Evidence-based resource planning by means of: a) risk potential assessment based on frailty, co-morbidity, etc. b) demographic models, c) regional models, and d) pandemic models
Conclusion
BARS enables automated and standardised collection of RWD from risk groups as well as prospective and retrospective identification of patients at risk. RWD on risk potential can be used for regional resource planning and to define risk mitigation measures, such as where to vaccinate first.
The Clinical Decision Support group at JOANNEUM RESEARCH is working to improve medical treatment processes by developing advanced eHealth and mHealth solutions. JOANNEUM RESEARCH is a member of the BioNanoNet association. Together they develop an initiative to support digital healthcare and perform research and development in the field of innovative healthcare delivery in Europe.
Kontakt:
DI Dr. Klaus Donsa, Head of Clinical Decision Support,
JOANNEUM RESEARCH HEALTH - Institute for Biomedicine and Health Sciences