Abstracts by keywords: health data analytics
The aging of society with a significant increase in the number of elderly and very old patients is known to be a socio-political and therefore also a medical challenge (World Population Aging 2017). Older patients usually suffer from several chronic illnesses and therefore have to take numerous medications at the same time: multimorbidity and multimedication, also known as polypharmacy, have therefore long been the focus of medical interest in this context (van den Akker et al. 1998, Kaufman et al. 2002). Therefore, it can be difficult for treating physicians to find the right balance between over- and under-care; It is important to avoid harmful drugs and not to forget cheaper drugs, i.e. to minimize risks (e.g. side effects) but also not to miss opportunities (e.g. fewer strokes) (Maher et al. 2014, Barnett et al. 2012). Older patients continue to be underrepresented in clinical research, so there is little evidence on the appropriateness and safety of many medications in old age (Wehling 2011). To support doctors, the FORTA classification (Fit for The Aged) and from it the FORTA list were developed in a Delphi consensus process (Wehling 2008, Kuhn-Thiel et al. 2014), in which active ingredients are combined with age-relevant indications are rated hierarchically from A (positive) to D (negative) by over 20 medical and pharmacological experts (A-bsolutely, B-eneficial, C-areful, D-on't). The use of the FORTA list was clinically successful in a randomized endpoint study (VALFORTA) and was relatively easy and efficient to teach as a learning tool (Wehling et al. 2016). This evaluation approach is now to be tested and used IT-based as an automated algorithm for medication optimization.