A consensus for the development of a vector model to assess clinical complexity

Intern Emerg Med. 2017 Dec;12(8):1313-1318. doi: 10.1007/s11739-017-1709-6. Epub 2017 Jul 14.

Abstract

The progressive rise in multimorbidity has made management of complex patients one of the most topical and challenging issues in medicine, both in clinical practice and for healthcare organizations. To make this easier, a score of clinical complexity (CC) would be useful. A vector model to evaluate biological and extra-biological (socio-economic, cultural, behavioural, environmental) domains of CC was proposed a few years ago. However, given that the variables that grade each domain had never been defined, this model has never been used in clinical practice. To overcome these limits, a consensus meeting was organised to grade each domain of CC, and to establish the hierarchy of the domains. A one-day consensus meeting consisting of a multi-professional panel of 25 people was held at our Hospital. In a preliminary phase, the proponents selected seven variables as qualifiers for each of the five above-mentioned domains. In the course of the meeting, the panel voted for five variables considered to be the most representative for each domain. Consensus was established with 2/3 agreement, and all variables were dichotomised. Finally, the various domains were parametrized and ranked within a feasible vector model. A Clinical Complexity Index was set up using the chosen variables. All the domains were graphically represented through a vector model: the biological domain was chosen as the most significant (highest slope), followed by the behavioural and socio-economic domains (intermediate slope), and lastly by the cultural and environmental ones (lowest slope). A feasible and comprehensive tool to evaluate CC in clinical practice is proposed herein.

Keywords: Comorbidity; Internal medicine; Medical decision making; Patient complexity; Polytherapy; Quality of healthcare.

MeSH terms

  • Consensus*
  • Decision Support Techniques*
  • Humans
  • Multimorbidity
  • Patient Acuity*
  • Risk Assessment / methods