gms | German Medical Science

18. Deutscher Kongress für Versorgungsforschung

Deutsches Netzwerk Versorgungsforschung e. V.

09. - 11.10.2019, Berlin

An empirically derived taxonomy of telemedicine – development of a standardized codebook

Meeting Abstract

  • Lorenz Harst - Zentrum für Evidenzbasierte Gesundheitsversorgung (ZEGV), Forschungsverbund Public Health Sachsen, Dresden, Germany
  • Patrick Timpel - Medizinische Fakultät Carl Gustav Carus der TU Dresden, Prävention und Versorgung des Diabetes, Medizinische Klinik und Poliklinik III, Dresden, Germany
  • Lena Otto - Technische Universität Dresden, Fakultät Wirtschaftswissenschaften, Lehrstuhl für Wirtschaftsinformatik, insb. Systementwicklung, Dresden, Germany
  • Peggy Richter - Technische Universität Dresden, Fakultät Wirtschaftswissenschaften, Lehrstuhl für Wirtschaftsinformatik, insb. Systementwicklung, Dresden, Germany
  • Bastian Wollschlaeger - Technische Universität Dresden, Fakultät Informatik, Institut für Angewandte Informatik, Professur Technische Informationssysteme, Dresden, Germany
  • Hendrikje Lantzsch - Medizinische Fakultät Carl Gustav Carus der TU Dresden, Masterstudiengang Gesundheitswissenschaften/Public Health am Institut und Polyklinik für Arbeits- und Sozialmedizin, Dresden, Germany
  • Katja Winkler - Technische Universität Dresden, Fakultät Wirtschaftswissenschaften, Lehrstuhl für Wirtschaftsinformatik, insb. Systementwicklung, Dresden, Germany
  • Hannes Schlieter - Technische Universität Dresden, Fakultät Wirtschaftswissenschaften, Lehrstuhl für Wirtschaftsinformatik, insb. Systementwicklung, Dresden, Germany

18. Deutscher Kongress für Versorgungsforschung (DKVF). Berlin, 09.-11.10.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. Doc19dkvf024

doi: 10.3205/19dkvf024, urn:nbn:de:0183-19dkvf0248

Veröffentlicht: 2. Oktober 2019

© 2019 Harst et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Background: Taxonomies are a useful tool for systemizing existing telemedicine projects according to predefined criteria, which serve as a basis for telemedicine evaluation (Usman et al. 2017; Neugebauer et al. 2017). Existing taxonomies of telemedicine, however, lack several categories for systemization, such as professionals involved (Bashshur et al. 2011), target populations (Tulu, Chatterjee, and Laxminarayan 2005) or diagnoses targeted (Vincent et al. 2007). Also, they are mainly based on literature studies, instead of being empirically derived from current telemedicine initiatives (Fong, Fong, and Li 2011).

Research question: How can an empirically sound taxonomy for telemedicine be derived from existing initiatives?

How can such a taxonomy be used to derive relevant building blocks for future telemedicine initiatives?

Method: Based on taxonomies from information systems development and health care sciences, a standardized coding scheme was developed using qualitative content analysis (Michie and Prestwich 2010; Mayring 2000). Topics of the coding scheme included type of telemedicine application, intended outcome, target disease, personnel involved, technology used and data provision. To validate the coding scheme and derive an empirically sound taxonomy, quantitative content analysis was deployed, analyzing projects available on the German telemedicine project database “vesta” (vesta Informationsportal). Projects were eligible for analysis when fitting into the telemedicine definition provided by Sood et al. 2007.

After a pretest involving seven coders a suitable inter-coder-reliability of 0.83 (according to Krippendorf (Krippendorff 2013) was achieved. All eligible projects were coded by a pair of coders in order to minimize coder bias.

Fisher’s exact test was computed in R Studio to detect correlations between application types and other categories of the coding scheme. This serves as a validation.

Results: For the validation of the coding scheme by quantitative content analysis, 110 projects from the vesta database were relevant as they fell under the telemedicine definition. The majority of applications were tele- or self-monitoring applications, accounting for 44.5 % of all application types (n = 49 of 110), followed by teleconsulting applications (18.2 %, n = 20 of 110), tele-diagnosis tools (13.6 %, n = 15 of 110) and tele-rehabilitative applications, which account for 7.3 % of all types (n = 8 of 110). Tele-ambulance systems (6.4 %, n = 7 of 110), tele-self-management tools (5.5 %, n = 6 of 110) and health education delivered via telemedicine (4.5 %, n = 5 of 110) are less prevalent.

Significant correlations were found between application type and each of the following variables: diagnoses targeted (p < 0.001), intended outcome (p < 0.001), personnel involved (p < 0.001), technology used (p < 0.01), mode of data provision (p < 0.001) and location of the application (p < 0.001).

Results for the vesta database show that tele-consultation and tele-diagnosis are mainly applied in cases of neurological diseases, especially stroke. For both, the optimization of the care process is an important outcome, while tele-diagnosis also intends to improve early detection of disease symptoms or their worsening. For tele- or self-monitoring applications, common diseases are cardiovascular diseases and diabetes (type 1 and type 2), and the major intended outcomes are the optimization of care processes and the improvement of both hard (clinical) and soft (e.g. quality of life) outcomes.

Accordingly, the newly developed taxonomy was consolidated to comprise the following categories:

Application type (tele-health education, tele-consultation, tele-diagnosis, tele-ambulance, tele-monitoring, tele-rehabilitation, digital disease-management)
Personnel involved (patients, health care providers)
Target populations (diagnoses, demographics)
Setting (health care institution, home, portable)
Technology used for data provision (e.g. web, smartphone)
Intended outcome

Discussion: Even though derived from a sound coding scheme, some categories relevant for the effectiveness applications are missing. Research stresses the importance of targeting the applications not only according to age (Zhang et al. 2017), but also according to specific disease characteristics (Su et al. 2016) or time passed since diagnosis (Wu et al. 2018). Plus, improving quality of care was revealed as an outcome category too unspecific for evaluation purposes.

Implications: The taxonomy allows for defining target groups (Fu et al. 2017), diagnosis (Rush et al. 2018) and medical specialists (Lee et al. 2017) involved in a telemedicine project, as well as basal technologies (Shen et al. 2018) to be used when designing an application. It can therefore serve not only as a reporting guideline for telemedicine projects, but also provides building blocks for future telemedicine initiatives, as well as outcomes and predictors for evaluation.