Tackling polypharmacy : a multi-source decision support system

Managing the use of multiple medicines, also known as polypharmacy, is a challenge for physicians, pharmacists and patients alike, and is a particular concern for patients with multiple chronic conditions (aka multimorbidity). Patients with multimorbidity are often required to take a considerable number of medications for their different ongoing conditions, and managing/revising these medications effectively is a challenge. There is a need to periodically rearrange drugs taking into account patient’s preferences and avoiding adverse drug reactions. We present an incremental, constraint solver based framework for a clinical decision support system that makes it possible to check drug prescriptions using information from multiple sources, including a constraint database and patient records. We illustrate how it can be used to manage clinical conditions while reducing polypharmacy problems and undesired side effects in a patient-centric approach.Publisher PD

A clinical decision support system for detecting and mitigating potentially inappropriate medications

Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly. Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects. Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches. Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice

A simulation-based approach for the behavioural analysis of cancer pathways

Cancer pathway is the name given to a patient’s journey from initial suspicion of cancer through to a confirmed diagnosis and, if applicable, the definition of a treatment plan. Typically, a cancer patient will undergo a series of procedures, which we designate as events, during their cancer care. The initial stage of the pathway, from suspected diagnosis to confirmed diagnosis and start of a treatment is called cancer waiting time(CWT). This paper focuses on the modelling and analysis of the CWT. Health boards are under pressure to ensure that the duration of CWT satisfies predefined targets. In this paper, we first create the visual representation of the pathway obtained from real patient data at a given health board, and then compare it with the standardised pathway considered by the board to find and flag a deviation in the execution of the cancer pathway. Next, we devise a discrete event simulation model for the cancer waiting time pathway. The input data is obtained from historical records of patients. The outcomes from this analysis highlight the pathway bottlenecks and transition times which maybe used to reveal potential improvements for CWT in the future.Postprin

Estimating capacity and resource allocation in healthcare settings using business process modelling and simulation

Healthcare involves complex decision making from planning to resource management. Resources in hospitals are usually allocated by experienced managers,however, due to an inherent process complexity, decisions are surrounded by uncertainties, variabilities, and constraints. Information Systems must be robust enough to provide support to stakeholders, capable of controlling and support work flows. The present work explores the required synergy when combining business processes with discrete event simulation. The objective is to estimate performance indices and address capacity management of a surgical center as a case study.Postprin

An ontology-based approach for detecting and classifying inappropriate prescribing

Funding: Bowles is partially supported by Austrian FWF Meitner Fellowship M-3338 N.The Beers Criteria, widely used by healthcare professionals, list so-called Potentially Inappropriate Medications (PIMs) which older adults in certain circumstances should avoid. Manually identifying medications that belong to the Beers Criteria can be time-consuming and error-prone, as the criteria are complex and subject to frequent updates. Moreover, it is not available in a (formal) representation that health systems can interpret and reason with automatically. This paper proposes an ontology as a formal representation of the Beers Criteria, and describes the elements and the taxonomy underlying the ontology. We include inference rules to enable automated detection and categorisation of drugs classified as PIMs. By automatically detecting drugs that belong to the Beers Criteria, the ontology, once linked with decision support systems, can be used to support healthcare providers in ensuring that older adults receive safe and effective medical care.Publisher PD

An ontology-based approach for detecting and classifying inappropriate prescribing

The Beers Criteria, widely used by healthcare professionals, list so-called Potentially Inappropriate Medications (PIMs) which older adults in certain circumstances should avoid. Manually identifying medications that belong to the Beers Criteria can be time-consuming and error-prone, as the criteria are complex and subject to frequent updates. Moreover, it is not available in a (formal) representation that health systems can interpret and reason with automatically. This paper proposes an ontology as a formal representation of the Beers Criteria, and describes the elements and the taxonomy underlying the ontology. We include inference rules to enable automated detection and categorisation of drugs classified as PIMs. By automatically detecting drugs that belong to the Beers Criteria, the ontology, once linked with decision support systems, can be used to support healthcare providers in ensuring that older adults receive safe and effective medical care