From intelligence to wisdom

Importance of radiology as a profession is massive. Rarely will any hospitalised patient finish his journey without visiting the radiology department. So, if we develop a system that will help radiology to increase productivity and accuracy while keeping the patient safe, the effects of this technology will also be massive. Artificial Intelligence (AI) has shown a potential to be one possible technology to revolutionise the radiology service. Over the last ten years, publications on AI in radiology have increased from 100–150 per year to 700–800 per year (Pesapane et al., 2018) highlighting the importance of the topic. In 2016 was predicted that „machine learning will displace much of the work of radiologists and anatomical pathologists“ (Obermeyer and Emanuel, 2016), and that machines will replace doctors because „when professional work is broken down into component parts, many of the tasks involved turn out to be routine and process-based. They do not, in fact, call for judgment, creativity, or empathy“ (Susskind and Susskind, 2016). There is more evidence of scientists overestimated the potential of AI, and probably the most famous one was from one of the AI pioneers, winner of the Association for Computing Machinery Turing Award, who stated in 2016: „People should stop training radiologists now“ (Geoff Hinton: On Radiology – YouTube, 2016)

Semantic Search Engine as tool for clinical decision support in Register for Acute Coronary Syndrome

This paper presents the implementation and use of Semantic Search Engine (SSE) as part of knowledge management system functionalities in Register for Acute Coronary Syndrome (REACS). REACS SSE is part of a clinical decision support system and is used as an aid in decision making in clinical processes related to the care and treatment of patients with Acute Coronary Syndrome (ACS)

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Background: Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed. Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully. Methods: We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist. Results: We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy. Conclusions: The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed

A decision support system for surgery sequencing at UZ Leuven's day-care department.

In this paper, we test the applicability of a decision support system (DSS) that is developed to optimize the sequence of surgeries in the day-care center of the UZ Leuven Campus Gasthuisberg (Belgium). We introduce a multi-objective function in which children and prioritized patients are scheduled as early as possible on the day of surgery, recovery overtime is minimized and recovery workload is leveled throughout the day. This combinatorial optimization problem is solved by applying a pre-processed mixed integer linear programming model. We report on a 10-day case study to illustrate the performance of the DSS. In particular, we compare the schedules provided by the hospital with those that are suggested by the DSS. The results indicate that the DSS leads to both an increased probability of obtaining feasible schedules and an improved quality of the schedules in terms of the objective function value. We further highlight some of the major advantages of the application, such as its visualization and algorithmic performance, but also report on the difficulties that were encountered during the study and the shortcomings that currently delay its implementation in practice, as this information may contribute to the success rate of future software applications in hospitals.Decision support system; Optimization; Visualization; Health care application;

Description and pilot evaluation of the Metabolic Irregularities Narrowing down Device software: a case analysis of physician programming

Background: There is a gap between the abilities and the everyday applications of Computerized Decision Support Systems (CDSSs). This gap is further exacerbated by the different ‘worlds’ between the software designers and the clinician end-users. Software programmers often lack clinical experience whereas practicing physicians lack skills in design and engineering. Objective: Our primary objective was to evaluate the performance of Metabolic Irregularities Narrowing down Device (MIND) intelligent medical calculator and differential diagnosis software through end-user surveys and discuss the roles of CDSS in the inpatient setting. Setting: A tertiary care, teaching community hospital. Study participants: Thirty-one responders answered the survey. Responders consisted of medical students, 24%; attending physicians, 16%, and residents, 60%. Results: About 62.5% of the responders reported that MIND has the ability to potentially improve the quality of care, 20.8% were sure that MIND improves the quality of care, and only 4.2% of the responders felt that it does not improve the quality of care. Ninety-six percent of the responders felt that MIND definitely serves or has the potential to serve as a useful tool for medical students, and only 4% of the responders felt otherwise. Thirty-five percent of the responders rated the differential diagnosis list as excellent, 56% as good, 4% as fair, and 4% as poor. Discussion: MIND is a suggesting, interpreting, alerting, and diagnosing CDSS with good performance and end-user satisfaction. In the era of the electronic medical record, the ongoing development of efficient CDSS platforms should be carefully considered by practicing physicians and institutions

The effectiveness of M-health technologies for improving health and health services: a systematic review protocol

BACKGROUND: The application of mobile computing and communication technology is rapidly expanding in the fields of health care and public health. This systematic review will summarise the evidence for the effectiveness of mobile technology interventions for improving health and health service outcomes (M-health) around the world. FINDINGS: To be included in the review interventions must aim to improve or promote health or health service use and quality, employing any mobile computing and communication technology. This includes: (1) interventions designed to improve diagnosis, investigation, treatment, monitoring and management of disease; (2) interventions to deliver treatment or disease management programmes to patients, health promotion interventions, and interventions designed to improve treatment compliance; and (3) interventions to improve health care processes e.g. appointment attendance, result notification, vaccination reminders.A comprehensive, electronic search strategy will be used to identify controlled studies, published since 1990, and indexed in MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, the Cochrane Library, or the UK NHS Health Technology Assessment database. The search strategy will include terms (and synonyms) for the following mobile electronic devices (MEDs) and a range of compatible media: mobile phone; personal digital assistant (PDA); handheld computer (e.g. tablet PC); PDA phone (e.g. BlackBerry, Palm Pilot); Smartphone; enterprise digital assistant; portable media player (i.e. MP3 or MP4 player); handheld video game console. No terms for health or health service outcomes will be included, to ensure that all applications of mobile technology in public health and health services are identified. Bibliographies of primary studies and review articles meeting the inclusion criteria will be searched manually to identify further eligible studies. Data on objective and self-reported outcomes and study quality will be independently extracted by two review authors. Where there are sufficient numbers of similar interventions, we will calculate and report pooled risk ratios or standardised mean differences using meta-analysis. DISCUSSION: This systematic review will provide recommendations on the use of mobile computing and communication technology in health care and public health and will guide future work on intervention development and primary research in this field

Clinical decision support system (CDSS) – effects on care quality

Purpose – Despite their efficacy, some recommended therapies are underused. The purpose of this paper is to describe clinical decision support system (CDSS) development and its impact on clinical guideline adherence. Design/methodology/approach - A new CDSS was developed and introduced in a cardiac intensive care unit (CICU) in 2003, which provided physicians with patient-tailored reminders and permitted data export from electronic patient records into a national quality registry. To evaluate CDSS effects in the CICU, process indicators were compared to a control group using registry data. All CICUs were in the same region and only patients with acute coronary syndrome were included. Findings – CDSS introduction was associated with increases in guideline adherence, which ranged from 16 to 35 per cent, depending on the therapy. Statistically significant associations between guideline adherence and CDSS use remained over the five-year period after its introduction. During the same period, no relapses occurred in the intervention CICU. Practical implications – Guideline adherence and healthcare quality can be enhanced using CDSS. This study suggests that practitioners should turn to CDSS to improve healthcare quality. Originality/value – This paper describes and evaluates an intervention that successfully increased guideline adherence, which improved healthcare quality when the intervention CICU was compared to the control group

Cancer Multidisciplinary Team Meetings: Evidence, Challenges, and the Role of Clinical Decision Support Technology

Multidisciplinary team (MDT) model in cancer care was introduced and endorsed to ensure that care delivery is consistent with the best available evidence. Over the last few years, regular MDT meetings have become a standard practice in oncology and gained the status of the key decision-making forum for patient management. Despite the fact that cancer MDT meetings are well accepted by clinicians, concerns are raised over the paucity of good-quality evidence on their overall impact. There are also concerns over lack of the appropriate support for this important but overburdened decision-making platform. The growing acceptance by clinical community of the health information technology in recent years has created new opportunities and possibilities of using advanced clinical decision support (CDS) systems to realise full potential of cancer MDT meetings. In this paper, we present targeted summary of the available evidence on the impact of cancer MDT meetings, discuss the reported challenges, and explore the role that a CDS technology could play in addressing some of these challenges

Informatics Technology Mimics Ecology: Dense, Mutualistic Collaboration Networks Are Associated with Higher Publication Rates

Information technology (IT) adoption enables biomedical research. Publications are an accepted measure of research output, and network models can describe the collaborative nature of publication. In particular, ecological networks can serve as analogies for publication and technology adoption. We constructed network models of adoption of bioinformatics programming languages and health IT (HIT) from the literature

A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department

Objectives: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. Methods: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. Results: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE – a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. Conclusions: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs