The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

Abstract

Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.

Applying process mining and semantic reasoning for process model customisation in healthcare

“…According Bilici, Despotou and Arvanitis (2018), approaches for developing clinical decision support systems include decision rule models (e.g., Karadimas, Ebrahiminia, and Lepage, 2015), documentary models (e.g., Shiffman et al, 2001) or process-flow models also known as task-network models (TNMs). Regarding the TNMs models, some of the most known systems are: Guideline Interchange Format version 3 (GLIF3) (Peleg, Boxwala, Ogunyemi, et al, 2000) aims to create standards in health care enabling institutions and information systems to share the guidelines; Asbru (Shahar, Miksch, and Johnson, 1998), which consist of skeletal plans that represent the guidelines, but provides flexibility for executing specific activities; EON (Tu and Musen, 1999) provides recommendations based on the formalized clinical guideline and the patient's information; GUIDE (Quaglini et al, 2001) integrates the clinical guidelines into organizational workflows and applies Petri Nets to test and optimize the workflow model.…”

Alerts for community pharmacist-provided medication therapy management: recommendations from a heuristic evaluation

“…This study extends prior research on alert design to identify leverage points for MTM alerts used by community pharmacists during medication reviews. Our research addresses a growing need by focusing on pharmacists as end users, the community pharmacy setting, and MTM alerts targeting complex, multimorbid patients--for which there is an overall paucity of literature on alerts [1, 7, 38]. Although extensive human factors studies of alert design have been conducted in other domains [17, 21], recent literature reviews have noted a critical need for further attention given to human factors principles in alert design in the healthcare domain [39, 40].…”

Ontology of anomalous processes diagnosis

“…When solving the diagnosis problem different researchers and practitioners use knowledge models of varying complexity within their approaches: taking into account dynamics (what ranges of values are expected at what moments or periods) or taking into account the simplified “temporal aspect” (e.g., temperature decreases; vibration intensifies) or without them; taking into account the influence of factors on the dynamics of the values of signs (manifestations), or without taking into account such influence; taking into account the possibility of the existence of several types of deviations at once 7 (e.g., “combined pathology” in medicine; contact dis‐connection and contamination of contacts at once in technics) or with the expectation that an object has only one problem; taking into account different diagnostic methods or the only one; grouping anomalies or highlighting their subclasses (clarifying diagnoses) or considering their list.…”

“…taking into account the possibility of the existence of several types of deviations at once 7 (e.g., “combined pathology” in medicine; contact dis‐connection and contamination of contacts at once in technics) or with the expectation that an object has only one problem;…”