A Rule-Based Model for Compliance of Medical Devices Applied to the European Market

Introduction

Based on the Global Medical Device Nomenclature Agency¹, there are more than 2 million different types of medical devices on the world market, with this number growing constantly. The global medical device market is forecast to grow at a Compound Annual Growth Rate (CAGR) of 4.5% from 2018 to 2023 (Reportlinker, 2018) with an increasing market demand. It is expected that there will be a significant rise in remote monitoring, patient-managed diagnostic devices, smart wearable or implantable devices, e-health applications for smart phones, devices with nano-scale or 3D manufacturing, and other state-of-the-art technologies. This growth requires from companies to remain antagonistic in a global market and launch innovative medical devices products (EMBASE, 2015), which will need to be proven and verified according to the relevant regulations. Medical device companies are more than interested in learning how to deal with and automate the internal processes of pre-market approval paperwork and secure that regulatory submissions are thorough and on time (Ranise & Siswantoro, 2017).

This increased need of computational medical records is usually supported by ontologies for taxonomic organization of information as well as legal-based rules for medical tests, procedures, and registrations, so that the quality of healthcare is secured and improved. Ontologies in the Semantic Web — represented with formal languages, such as Description Logics (DLs) — provide the representation for different types of medical knowledge, such as the OpenGalen ontology (Rector, Nowlan, & Consortium, 1994; Rector & Rogers, 2006) where methods were applied for restriction of medical terms to sensible classes. Similar techniques have been used to various medical nomenclature including the MeSH (Medical Subject Heading) (Soualmia, Golbreich, & Darmoni, 2004), the FMA (Foundational Model of Anatomy) (Rosse & Mejino, 2004), and the ICD10(International Classification of Diseases) (Heja, Surjan, Lukacsy, Pallinger, & Gergely, 2007). However, a demand has already been identified for expressive power beyond what is offered by DL-based ontology languages (Antoniou et al., 2005). Many health care procedures, such as inpatient clinical information systems (Cresswell & Sheikh, 2017), antibiotics prescription (Lezcano, Sicilia, & Rodríguez-Solano, 2010), and risk assessment of pressure ulcers (Rector & Rogers, 2006), are supported by computer aided decision making leading to increased interest in rule-based systems (Lezcano et al., 2010). In spite of existing theoretical issues of the complementary nature between ontology and rules languages, there is a need of Semantic Web Technologies for integrated formalisms that can provide advanced reasoning capabilities (Eiter, Ianni, Krennwallner, & Polleres, 2008), such as in SNOMED CT (Standardized Nomenclature of Medicine Clinicalterms) (Navas et al., 2010) which proposed rules expressed in DLs for consistency checking of terms. Medical applications that combine ontologies with rule languages can be used, e.g., as clinical guidelines (Casteleiro & Diz, 2008; Chen, 2010) and for medical decision support (Djedidi, 2007), which can be subjects of privacy and regulatory compliance as well. Thus, in some applications, it can be practical to regulate compliance process by using formalized parts of applicable laws.