Community nursing and the digital technology revolution: the past, present and future.

While very much in its infancy in terms of becoming an established tool, the use of digital technology in community nursing is steadily growing, despite the persistent barriers to, and challenges encountered in its uptake and implementation. The mobile nature and high workload of a community nurse's daily practice should facilitate the rapid uptake of time-saving technology. However, there are indications that technology may not be the panacea it was originally proclaimed to be. Francesca Ramadan elaborates on the past and present applications of digital technology in community nursing and delves into the principles that should shape the future potential of tools such as artificial intelligence, automation technologies and clinical decision support systems.

Can Big Data guide prognosis and clinical decisions in epilepsy?

Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

Translating pharmacogenomics into clinical decisions: do not let the perfect be the enemy of the good.

The field of pharmacogenomics (PGx) is gradually shifting from the reactive testing of single genes toward the proactive testing of multiple genes to improve treatment outcomes, reduce adverse events, and decrease the burden of unnecessary costs for healthcare systems. Despite the progress in the field of pharmacogenomics, its implementation into routine care has been slow due to several barriers. However, in recent years, the number of studies on the implementation of PGx has increased, all providing a wealth of knowledge on different solutions for overcoming the obstacles that have been emphasized over the past years. This review focuses on some of the challenges faced by these initiatives, the solutions and different approaches for testing that they suggest, and the evidence that they provide regarding the benefits of preemptive PGx testing.

The Ottawa subarachnoid hemorrhage clinical decision rule for classifying emergency department headache patients.

BACKGROUND: Subarachnoid hemorrhage (SAH) is a serious cause of headaches. The Ottawa subarachnoid hemorrhage (OSAH) rule helps identify SAH in patients with acute nontraumatic headache with high sensitivity, but provides limited information for identifying other intracranial pathology (ICP). OBJECTIVES: To assess the performance of the OSAH rule in emergency department (ED) headache patients and evaluate its impact on the diagnosis of intracranial hemorrhage (ICH) and other ICP. METHOD: We conducted a retrospective cohort study from January 2016 to March 2017. Patients with acute headache with onset within 14 days of the ED visit, were included. We excluded patients with head trauma that occurred in the previous 7 days, new onset of abnormal neurologic findings, or consciousness disturbance. According to the OSAH rule, patients with any included predictors required further investigation. RESULTS: Of 913 patients were included, 15 of them were diagnosed with SAH. The OSAH rule had 100% (95% CI, 78.2%-100%) sensitivity and 37.0% (95% CI, 33.8-40.2%) specificity for identifying SAH. Twenty-two cases were identified as SAH or ICH with 100% sensitivity (95% CI, 84.6%-100%) and 37.3% (95% CI, 34.1%-40.5%) specificity. As for non-hemorrhagic ICP, both the sensitivity and negative predictive values (NPV) decreased to 75.0% (95% CI, 53.3%-90.2%) and 98.2% (95% CI, 96.1%-99.3%), respectively. CONCLUSIONS: The OSAH rule had 100% sensitivity and NPV for diagnosing SAH and ICH with acute headache. The sensitivity and specificity were lower for non-hemorrhagic ICP. The OSAH rule may be an effective tool to exclude acute ICH and SAH in our setting.

Measures of self-efficacy among doctors in conducting an online search for clinical decision making.

OBJECTIVES: To measure the perceived ability and level of confidence among doctors in performing the different tasks involved in conducting an online search for clinical decision making. METHODS: A large-scale cross-sectional survey was conducted in 36 District Headquarter Hospitals (DHQs), 89 Tehsil Headquarter Hospitals (THQs), 293 Rural Health Centers (RHCs) and 2455 Basic Health Units (BHUs) in Punjab, Pakistan. Using a quota sampling, data were collected from 517 doctors on a set of 11 statements. The collected data were analysed statistically. RESULTS: Of the 517 doctors, 73 (14.1%) had 'never accessed health care information online' for clinical decision making. Mean values of the doctors' response to the 11 statements ranged from 1.66 to 2.30 indicating that most of the doctors were 'not confident' in their ability to perform the tasks. CONCLUSION: The majority of doctors perceived themselves able to perform the different tasks involved in conducting an online search. Age and working experience were significant factors in the perception of their ability in performing the tasks. The study recommends promotional and educational activities to motivate interest, increase awareness, develop knowledge and skills for doctors to access information that would help in their clinical decision making.

Forecasting Content and Stage in a Nursing Home Information Technology Maturity Instrument Using a Delphi Method.

Health information technology capabilities in some healthcare sectors, such as nursing homes, are not well understood because measures for information technology uptake have not been fully developed, tested, validated, or measured consistently. The paper provides a report of the development and testing of a new instrument measuring nursing home information technology maturity and stage of maturity. Methods incorporated a four round Delphi panel composed of 31 nursing home experts from across the nation who reported the highest levels of information technology sophistication in a separate national survey. Experts recommended 183 content items for 27 different content areas specifying the measure of information technology maturity. Additionally, experts ranked each of the 183 content items using an IT maturity instrument containing seven stages (stages 0-6) of information technology maturity. The majority of content items (40% (n = 74)) were associated with information technology maturity stage 4, corresponding to facilities with external connectivity capability. Over 11% of the content items were at the highest maturity stage (Stage 5 and 6). Content areas with content items at the highest stage of maturity are reflected in nursing homes that have technology available for residents or their representatives and used extensively in resident care. An instrument to assess nursing home IT maturity and stage of maturity has important implications for understanding health service delivery systems, regulatory efforts, patient safety and quality of care.

Artificial intelligence: Implications for the future of work.

Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e-commerce sites, goods and services recommender systems, image and speech recognition, sensor technologies, robotic devices, and cognitive decision support systems (DSSs). As more applications are integrated into everyday life, AI is predicted to have a globally transformative influence on economic and social structures similar to the effect that other general-purpose technologies, such as steam engines, railroads, electricity, electronics, and the Internet, have had. Novel AI applications in the workplace of the future raise important issues for occupational safety and health. This commentary reviews the origins of AI, use of ML methods, and emerging AI applications embedded in physical objects like sensor technologies, robotic devices, or operationalized in intelligent DSSs. Selected implications on the future of work arising from the use of AI applications, including job displacement from automation and management of human-machine interactions, are also reviewed. Engaging in strategic foresight about AI workplace applications will shift occupational research and practice from a reactive posture to a proactive one. Understanding the possibilities and challenges of AI for the future of work will help mitigate the unfavorable effects of AI on worker safety, health, and well-being.

A crossroads in predictive analytics monitoring for clinical medicine.

A new goal for medical informatics is to develop robust tools that integrate clinical data on a patient in order to estimate the risk of imminent adverse events. This new field of predictive analytics monitoring is growing very quickly. Its claims, however, can be vulnerable when clinicians fail to use the best mathematical and statistical tools, when quantitative scientists fail to grasp the nuances of clinical medicine, and when either fails to incorporate knowledge of physiology. Its potential, though is clear: we can provide more effective clinical decision support and make better predictive analytics monitoring tools if we apply principles learned from physiology and mathematics to the right problems in clinical medicine.

[Electronic decision support to promote medication safety]. / Elektronische Entscheidungsunterstützung zur Annäherung an eine sichere Arzneimitteltherapie.

Because of its inherent complexity, it is a considerable challenge to tailor drug treatment to a prevalent disease and its subgroups, which are increasingly defined by genomic variability (personalized medicine) and require consideration of context information such as co-morbidity, co-medication, patient preferences, and the specific characteristics of the healthcare sector. Thus, optimum treatment decisions might not be taken intuitively any longer, because decisions must be made both rapidly and increasingly based on analyses of complex relations of numerous variables that exceed the processing performance of a human brain. Hence, computer support is indispensable to ensure error-free high-performance medicine. A key step in computer-supported medication safety is to implement a computerized physician order entry (CPOE) system that compiles a patient's medication in a structured and coded format enabling the link to clinical decision support (CDS) systems. Implementing a CPOE is hence a strategic step for a hospital, which is crucial to exhaustingly and consistently prevent medication errors. Thereby, the best performance of a CPOE is achieved if it is deeply integrated into an electronic patient record thus enabling access to relevant patient information, which again has to be structured to allow processing. To efficiently support drug treatment, CDS systems must fulfill high-quality standards with regard to underlying data, integration, and user-interaction to ensure that they support but do not impede the provision of care.

[E-Health and reality - what are we facing in patient care?] / E-Health und die Realität ­ was sehen wir heute schon in der Klinik?

The terms e­Health and digitization are core elements of a change in our time. The main drivers of this change - in addition to a dynamic market - are the serious advantages for the healthcare sector in the processing of tasks and requirements. The large amounts of data, the intensively growing medical knowledge, the rapidly advancing technological developments and the goal of a personalized, customized therapy for the patient, make the application absolutely necessary. While e­Health describes the use of information and communication technologies in healthcare, the concept of digitization is associated with the underlying processes of change and innovation. Digital technologies include software and hardware based developments. The term clinical data intelligence describes the property of workability and also characterizes the collaboration of clinically relevant systems with which the medical user works. The hierarchy in digital processing maps the levels from pure data management through clinical decision support to automated process flows and autonomously operating units. The combination of patient data management and clinical decision support proves its value in terms of error reduction, prevention, quality and safety, especially in drug therapy. The aim of this overview is the presentation of the existing reality in medical centers with perspectives derived from the point of view of the medical user.

[Applications of machine learning in clinical decision support in the omic era].

With the development of the omic technologies, the acquisition approaches of various biological data on different levels and types are becoming more mature. As a large amount of data will be produced in the process of diagnosis and treatment of diseases, it is necessary to utilize the artificial intelligence such as machine learning to analyze complex, multi-dimensional and multi-scale data and to construct clinical decision support tools. It will provide a method to figure out rapid and effective programs in diagnosis and treatment. In this process, the choice of artificial intelligence seems to be particularly important, such as machine learning. The article reviews the type and algorithm of machine learning used in clinical decision support, such as support vector machines, logistic regression, clustering algorithms, Bagging, random forests and deep learning. The application of machine learning and other methods in clinical decision support has been summarized and classified. The advantages and disadvantages of machine learning are elaborated. It will provide a reference for the selection between machine learning and other artificial intelligence methods in clinical decision support.

When Machines Think: Radiology's Next Frontier.

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.

Clinical Decision Support to Efficiently Identify Patients Eligible for Advanced Heart Failure Therapies.

BACKGROUND: Patients who need and receive timely advanced heart failure (HF) therapies have better long-term survival. However, many of these patients are not identified and referred as soon as they should be. METHODS: A clinical decision support (CDS) application sent secure email notifications to HF patients' providers when they transitioned to advanced disease. Patients identified with CDS in 2015 were compared with control patients from 2013 to 2014. Kaplan-Meier methods and Cox regression were used in this intention-to-treat analysis to compare differences between visits to specialized and survival. RESULTS: Intervention patients were referred to specialized heart facilities significantly more often within 30 days (57% vs 34%; P < .001), 60 days (69% vs 44%; P < .0001), 90 days (73% vs 49%; P < .0001), and 180 days (79% vs 58%; P < .0001). Age and sex did not predict heart facility visits, but renal disease did and patients of nonwhite race were less likely to visit specialized heart facilities. Significantly more intervention patients were found to be alive at 30 (95% vs 92%; P = .036), 60 (95% vs 90%; P = .0013), 90 (94% vs 87%; P = .0002), and 180 days (92% vs 84%; P = .0001). Age, sex, and some comorbid diseases were also predictors of mortality, but race was not. CONCLUSIONS: We found that CDS can facilitate the early identification of patients needing advanced HF therapy and that its use was associated with significantly more patients visiting specialized heart facilities and longer survival.

Bits and bytes: the future of radiology lies in informatics and information technology.

Advances in informatics and information technology are sure to alter the practice of medical imaging and image-guided therapies substantially over the next decade. Each element of the imaging continuum will be affected by substantial increases in computing capacity coincident with the seamless integration of digital technology into our society at large. This article focuses primarily on areas where this IT transformation is likely to have a profound effect on the practice of radiology. KEY POINTS: • Clinical decision support ensures consistent and appropriate resource utilization. • Big data enables correlation of health information across multiple domains. • Data mining advances the quality of medical decision-making. • Business analytics allow radiologists to maximize the benefits of imaging resources.

Current applications of big data in obstetric anesthesiology.

PURPOSE OF REVIEW: The narrative review aims to highlight several recently published 'big data' studies pertinent to the field of obstetric anesthesiology. RECENT FINDINGS: Big data has been used to study rare outcomes, to identify trends within the healthcare system, to identify variations in practice patterns, and to highlight potential inequalities in obstetric anesthesia care. Big data studies have helped define the risk of rare complications of obstetric anesthesia, such as the risk of neuraxial hematoma in thrombocytopenic parturients. Also, large national databases have been used to better understand trends in anesthesia-related adverse events during cesarean delivery as well as outline potential racial/ethnic disparities in obstetric anesthesia care. Finally, real-time analysis of patient data across a number of disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is one promising application of big data technology on the labor and delivery unit. SUMMARY: 'Big data' research has important implications for obstetric anesthesia care and warrants continued study. Real-time electronic surveillance is a potentially useful application of big data technology on the labor and delivery unit.

Outpatient diabetes clinical decision support: current status and future directions.

Outpatient clinical decision support systems have had an inconsistent impact on key aspects of diabetes care. A principal barrier to success has been low use rates in many settings. Here, we identify key aspects of clinical decision support system design, content and implementation that are related to sustained high use rates and positive impacts on glucose, blood pressure and lipid management. Current diabetes clinical decision support systems may be improved by prioritizing care recommendations, improving communication of treatment-relevant information to patients, using such systems for care coordination and case management and integrating patient-reported information and data from remote devices into clinical decision algorithms and interfaces.

How I use clinical decision support to improve red blood cell utilization.

Despite 20 years of published medical society guidelines for blood transfusion and a pivotal clinical trial in 1999 providing Level 1 evidence that restrictive transfusion practices can be utilized safely, blood transfusions did not begin to decline in the United States until 2010. Widespread adoption of electronic medical records allowed implementation of computerized systems such as clinical decision support (CDS) with best practice alerts to improve blood utilization. We describe our own experience using well-designed and highly targeted CDS to promote restrictive transfusion practices and improve red blood cell utilization, with a 42% reduction in blood transfusions from 2009 through 2015, accompanied by improved clinical outcomes.

Computer decision support for acute kidney injury: current and future.

PURPOSE OF REVIEW: Growing awareness regarding the impact of acute kidney injury (AKI) as a grave consequence of critical illnesses resulted in the expansion of the need for early detection and appropriate management strategies. Clinical decision support systems (CDSS) can generate information to improve the care of AKI patients by providing point-of-care accurate patient-specific information and recommendations. Our objective is to describe the characteristics of CDSS and review the current knowledge regarding the impact of CDSS on patients in the acute care settings, and specifically for AKI. RECENT FINDINGS: Several recent systematic analyses showed the positive impact of CDSS on critically ill patients care processes. These studies also highlighted the scarcity of data regarding the effect of CDSS on the patient outcomes. In the field of AKI, there have been several reports to describe development and validation of homegrown CDSS and electronic alert systems. A large number of investigations showed the implementation of CDSS could improve the quality of AKI care; although, only in a very small subgroup of these studies patient outcomes improved. SUMMARY: The heterogeneity of these studies in their size, design, and conduct has produced controversial findings; hence, this has left the field completely open for further investigations.

Automatic detection of ventilatory modes during invasive mechanical ventilation.

BACKGROUND: Expert systems can help alleviate problems related to the shortage of human resources in critical care, offering expert advice in complex situations. Expert systems use contextual information to provide advice to staff. In mechanical ventilation, it is crucial for an expert system to be able to determine the ventilatory mode in use. Different manufacturers have assigned different names to similar or even identical ventilatory modes so an expert system should be able to detect the ventilatory mode. The aim of this study is to evaluate the accuracy of an algorithm to detect the ventilatory mode in use. METHODS: We compared the results of a two-step algorithm designed to identify seven ventilatory modes. The algorithm was built into a software platform (BetterCare® system, Better Care SL; Barcelona, Spain) that acquires ventilatory signals through the data port of mechanical ventilators. The sample analyzed compared data from consecutive adult patients who underwent >24 h of mechanical ventilation in intensive care units (ICUs) at two hospitals. We used Cohen's kappa statistics to analyze the agreement between the results obtained with the algorithm and those recorded by ICU staff. RESULTS: We analyzed 486 records from 73 patients. The algorithm correctly labeled the ventilatory mode in 433 (89 %). We found an unweighted Cohen's kappa index of 84.5 % [CI (95 %) = (80.5 %: 88.4 %)]. CONCLUSIONS: The computerized algorithm can reliably identify ventilatory mode.

Introduction to systematic reviews and meta-analysis.

Systematic reviews provide a method for collating and synthesizing research, and are used to inform healthcare decision making by clinicians, consumers and policy makers. A core component of many systematic reviews is a meta-analysis, which is a statistical synthesis of results across studies. In this review article, we introduce meta-analysis, focusing on the different meta-analysis models, their interpretation, how a model should be selected and discuss potential threats to the validity of meta-analyses. We illustrate the application of meta-analysis using data from a review examining the effects of early use of inhaled corticosteroids in the emergency department treatment of acute asthma.