Advantages and Functions of Clinical and Decision Support Systems

Clinical Decision Support Systems (CDSSs) signify the framework shift in the medical sector in the modern age. CDSSs are utilized in augmenting healthcare facilities in the process of making complex clinical decisions. Since the first application of CDSSs in the 80s, the framework has witnessed significant transformation. The frameworks are now administered through electronic healthcare records with complex capacities. Irrespective of these complex advancements, there are existing questions concerning the impacts of CDSSs on service providers, healthcare costs, and patients’ records. There are many published texts concerning the success stories of CDSSs, but significant setbacks have proved that CDSSs are not without any potential risks. In this research, we provide critical analysis on the application of CDSSs in clinical setting, integrating various forms, present application cases with proven effectiveness, potential harms and common pitfalls. We therefore conclude with evidence-centered recommendation for mitigating the issues of CDSSs maintainability, evaluation, implementation and designing.

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

Effectiveness of Clinical Decision Support Systems on the Appropriate Use of Imaging for Central Nervous System Injuries: A Systematic Review

Background One of the best practices for timely and efficient diagnoses of central nervous system (CNS) trauma and complex diseases is imaging. However, rates of imaging for CNS are high and impose a lot of costs to health care facilities in addition to exposing patients with negative impact of ionizing radiation. Objectives This study aimed to systematically review the effects and features of clinical decision support systems (CDSSs) for the appropriate use of imaging for CNS injuries. Method We searched MEDLINE, SCOPUS, Web of Science, and Cochrane without time period restriction. We included experimental and quasiexperimental studies that assessed the effectiveness of CDSSs designed for the appropriate use of imaging for CNS injuries in any clinical setting, including primary, emergency, and specialist care. The outcomes were categorized based on imaging-related, physician-related, and patient-related groups. Result A total of 3,223 records were identified through the online literature search. Of the 55 potential papers for the full-text review, 11 eligible studies were included. Reduction of CNS imaging proportion varied from 2.6 to 40% among the included studies. Physician-related outcomes, including guideline adherence, diagnostic yield, and knowledge, were reported in five studies, and all demonstrated positive impact of CDSSs. Four studies had addressed patient-related outcomes, including missed or delayed diagnosis, as well as length of stay. These studies reported a very low rate of missed diagnosis due to the cancellation of computed tomography (CT) examine according to the CDSS recommendations. Conclusion This systematic review reports that CDSSs decrease the utilization of CNS CT scan, while increasing physicians' adherence to the rules. However, the possible harm of CDSSs to patients was not well addressed by the included studies and needs additional investigation. The actual effect of CDSSs on appropriate imaging would be realized when the saved cost of examinations is compared with the cost of missed diagnosis.

Attribution Markers and Data Mining in Art Authentication

Today’s global art market is a billion-dollar business, attracting not only investors but also forgers. The high number of forged works requires reliable authentication procedures to mitigate the risk of investments. However, with the developments in the methodology, continuous time pressure and the threat of litigation, authenticating artwork is becoming increasingly complex. In this paper, we examined whether the decision process involved in the authenticity examination may be supported by machine learning algorithms. The idea is motivated by existing clinical decision support systems. We used a set of 55 artworks (including 12 forged ones) with determined attribution markers to train a decision tree model. From our preliminary results, it follows that it is a very promising technique able to support art experts. Decision trees are able to summarize the existing knowledge about all investigations and may also be used as a classifier for new paintings with known markers. However, larger datasets with artworks of known provenance are needed to build robust classification models. The method can also utilize the most important markers and, consequently, reduce the costs of investigations.

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Save Face or Save Life: Physicians’ Dilemma in Using Clinical Decision Support Systems

Humans think both rationally and heuristically. So do physicians. Clinical decision support systems (CDSSs) provide advice to physicians that could save patients’ lives, but they could also make physicians feel face loss because of submission to machine intelligence, leading to a perplexing dilemma. Thinking rationally, physicians focus on fulfilling their professional duty to save patients and should follow advice from CDSS to improve care quality. Thinking heuristically, they focus on protecting their authoritative image to maintain face and are inclined to avoid embarrassment by resisting CDSS. Through a longitudinal survey and follow-up interviews with a group of Chinese physicians, we find that the dilemma does exist. Moreover, face loss has a stronger effect on CDSS resistance when physicians have high autonomy. When time pressure is high, perceived usefulness more strongly reduces, whereas face loss more strongly increases CDSS resistance, worsening the dilemma. As face is a universal social concern existing in both Eastern and Western cultures, this research generates insights regarding why physicians are slow in adopting information technology innovations.

A systematic review on the effectiveness of clinician directed interventions to improve compliance to post polypectomy surveillance guidelines

Introduction Clinical practice guidelines recommend periodic colonoscopy surveillance following colorectal adenoma excision. Inappropriate use of post polypectomy surveillance is common and lead to improper resource utilisation.The aim of this review is to identify structured interventions which can affect post polypectomy surveillance practices and to evaluate the effectiveness of these various interventions in improving clinician adherence to post polypectomy surveillance guidelines. Methods A computerised search was performed to identify relevant studies between 1997 to November 2020. Two investigators identified eligible studies and extracted data independently. The quality of the included studies was assessed by the Newcastle-Ottawa risk of assessment scoring system. Results The search identified 5602 citations. Forty-one articles were retrieved for full text analysis and seven studies met the inclusion criteria. Compliance to PPS guidelines were higher following interventions which included medical education, specialist nurse coordinators facilitation, continuous quality improvement and clinical decision support systems. Conclusion This study demonstrates that medical education, specialist nurse coordinators, continuous quality improvement and clinical decision support systems are effective in improving clinicians’ compliance to post polypectomy surveillance guidelines and is associated with reduction in over- and underutilisation of colonoscopy surveillance resources.

Effect of computerised, knowledge-based, clinical decision support systems on patient-reported and clinical outcomes of patients with chronic disease managed in primary care settings: a systematic review

ObjectivesChronic diseases are the leading cause of disability globally. Most chronic disease management occurs in primary care with outcomes varying across primary care providers. Computerised clinical decision support systems (CDSS) have been shown to positively affect clinician behaviour by improving adherence to clinical guidelines. This study provides a summary of the available evidence on the effect of CDSS embedded in electronic health records on patient-reported and clinical outcomes of adult patients with chronic disease managed in primary care.Design and eligibility criteriaSystematic review, including randomised controlled trials (RCTs), cluster RCTs, quasi-RCTs, interrupted time series and controlled before-and-after studies, assessing the effect of CDSS (vs usual care) on patient-reported or clinical outcomes of adult patients with selected common chronic diseases (asthma, chronic obstructive pulmonary disease, heart failure, myocardial ischaemia, hypertension, diabetes mellitus, hyperlipidaemia, arthritis and osteoporosis) managed in primary care.Data sourcesMedline, Embase, CENTRAL, Scopus, Health Management Information Consortium and trial register clinicaltrials.gov were searched from inception to 24 June 2020.Data extraction and synthesisScreening, data extraction and quality assessment were performed by two reviewers independently. The Cochrane risk of bias tool was used for quality appraisal.ResultsFrom 5430 articles, 8 studies met the inclusion criteria. Studies were heterogeneous in population characteristics, intervention components and outcome measurements and focused on diabetes, asthma, hyperlipidaemia and hypertension. Most outcomes were clinical with one study reporting on patient-reported outcomes. Quality of the evidence was impacted by methodological biases of studies.ConclusionsThere is inconclusive evidence in support of CDSS. A firm inference on the intervention effect was not possible due to methodological biases and study heterogeneity. Further research is needed to provide evidence on the intervention effect and the interplay between healthcare setting features, CDSS characteristics and implementation processes.PROSPERO registration numberCRD42020218184.