Patient Self-assessment and Symptom Checkers
Goal of the project is to learn more about the competence of laypersons in health self-assessment, as well as - whether, how and for whom "symptom checker" apps are helpful in this regard. In collaboration with Prof. Markus Feufel (Chair of Ergonomics, TU Berlin), methods from the field of Human Factors are used in the context of medical decisions.
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Complex decisions in the health care system are not only required from the service providers, but also from the patients: before the care process (diagnostics, therapy, rehabilitation, etc.) can even start, it is often up to the patient to decide which path to healthcare he or she chooses. In a more differentiated and thus more complex healthcare system, such decisions are not trivial. The claim that the treating physician should make clinical decisions together with the patient (keyword "Shared Decision Making") also expects decision-making competence and an understanding of healthcare on the side of the patient.
One contribution that digital health can make to improve healthcare is to support complex decisions through so-called "clinical decision support systems". Although a majority of these are aimed at healthcare professionals, perhaps the most widespread are those targeted at patients or medical laypersons, namely so-called "symptom checkers". These are web or smartphone applications (apps) that offer assessments of possible diagnoses and treatment urgency based on the symptom.
Innovations and perspectives
Patient-focused digital health applications ("DiGAs") have become part of the catalog of services provided by statutory health insurers with the Digital Health Care Act of 2019. The concept of most DiGAs still consists of providing knowledge, tracking information and instructing exercises. For such interventions, methods and frameworks already exists (from the time before digital applications) to assess the benefits and risks. For the assessment of clinical decision support systems, conversely, such a framework is still missing. Our research shall contribute to the emergence and development of methods and frameworks for this purpose.
Diagnose per App – wie gut ist der digitale Doktor auf dem Smartphone?
Lehrstuhl Prof. Dr. Markus Feufel, Fachgebiet Arbeitswissenschaft, Institut für Psychologie und Arbeitswissenschaft, Fakultät Verkehrs- und Maschinensysteme, Technische Universität Berlin: