BACKGROUND
Although the vast majority of children with acute infections are managed at home, this is one of the most common problems encountered in children attending emergency departments (EDs) and primary care. Distinguishing children with serious infection from those with minor or self-limiting infection is difficult. This can result in misdiagnosis of children with serious infections, which results in a poorer health outcome, or a tendency to refer or admit children as a precaution; thus, inappropriately utilising secondary-care resources.


OBJECTIVES
We systematically identified clinical features and laboratory tests which identify serious infection in children attending the ED and primary care. We also identified clinical prediction rules and validated those using existing data sets.


DATA SOURCES
We searched MEDLINE, Medion, EMBASE, Cumulative Index to Nursing and Allied Health Literature and Database of Abstracts of Reviews of Effects in October 2008, with an update in June 2009, using search terms that included terms related to five components: serious infections, children, clinical history and examination, laboratory tests and ambulatory care settings. We also searched references of included studies, clinical content experts, and relevant National Institute for Health and Clinical Excellence guidelines to identify relevant studies. There were no language restrictions. Studies were eligible for inclusion if they were based in ambulatory settings in economically developed countries.


REVIEW METHODS
Literature searching, selection and data extraction were carried out by two reviewers. We assessed quality using the quality assessment of diagnostic accuracy studies (QUADAS) instrument, and used spectrum bias and validity of the reference standard as exclusion criteria. We calculated the positive likelihood ratio (LR+) and negative likelihood ratio (LR-) of each feature along with the pre- and post-test probabilities of the outcome. Meta-analysis was performed using the bivariate method when appropriate. We externally validated clinical prediction rules identified from the systematic review using existing data from children attending ED or primary care.


RESULTS
We identified 1939 articles, of which 35 were selected for inclusion in the review. There was only a single study from primary care; all others were performed in the ED. The quality of the included studies was modest. We also identified seven data sets (11,045 children) to use for external validation. The most useful clinical features for ruling in serious infection was parental or clinician overall concern that the illness was different from previous illnesses or that something was wrong. In low- or intermediate-prevalence settings, the presence of fever had some diagnostic value. Additional red flag features included cyanosis, poor peripheral circulation, rapid breathing, crackles on auscultation, diminished breath sounds, meningeal irritation, petechial rash, decreased consciousness and seizures. Procalcitonin (LR+ 1.75-2.96, LR- 0.08-0.35) and C-reactive protein (LR+ 2.53-3.79, LR- 0.25-0.61) were superior to white cell counts. The best performing clinical prediction rule was a five-stage decision tree rule, consisting of the physician's gut feeling, dyspnoea, temperature ≥ 40 °C, diarrhoea and age. It was able to decrease the likelihood of serious infections substantially, but on validation it provided good ruling out value only in low-to-intermediate-prevalence settings (LR- 0.11-0.28). We also identified and validated the Yale Observation Scale and prediction rules for pneumonia, meningitis and gastroenteritis.


LIMITATIONS
Only a single study was identified from primary-care settings, therefore results may lack generalisability.


CONCLUSIONS
Several clinical features are useful to increase or decrease the probability that a child has a serious infection. None is sufficient on its own to substantially raise or lower the risk of serious infection. Some are highly specific ('red flags'), so when present should prompt a more thorough or repeated assessment. C-reactive protein and procalcitonin demonstrate similar diagnostic characteristics and are both superior to white cell counts. However, even in children with a serious infection, red flags will occur infrequently, and their absence does not lower the risk. The diagnostic gap is currently filled by using clinical 'gut feeling' and diagnostic safety-netting, which are still not well defined. Although two prediction rules for serious infection and one for meningitis provided some diagnostic value, we do not recommend widespread implementation at this time. Future research is needed to identify predictors of serious infection in children in primary-care settings, to validate prediction rules more widely, and determine the added value of blood tests in primary-care settings.


FUNDING
The National Institute for Health Research Health Technology Assessment programme.

Final report for HTA Project 07/37/05: Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care

This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant (P < 0.01). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant (P < 0.05). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion.

/pdf/artificial-intelligence-technology-based-medical-information-30yc9hm0.pdf

Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management

The Agency for Healthcare Research and Quality (AHRQ) report "Diagnostic Errors in the Emergency Department" generated significant mass media interest with negative implications for the safety of contemporary emergency care. The assumptions and methodology underlying this report are problematic, while multiple ongoing efforts to improve the quality and quantity of diagnostic research are missed, neglected, or ignored. The AHRQ report identifies reasonable target diseases for targeting diagnostic quality improvement efforts, as well as viable methods by which to measure any initiatives impact on diagnostic error. We note additional opportunities to improve the status quo by funding emergency department-based diagnostic research and healthcare system-level patient safety research and highlighting innovative approaches to diagnostic science within emergency medicine. 

A Critical Appraisal of AHRQ's "Diagnostic Errors" Report.

Shared decision-making (SDM) is a conversational dynamic in which clinicians and patients discuss the best available evidence relevant to a medical decision. A patient's ability to engage in conversation and to understand the risks and benefits of diagnostic testing options is essential to effective SDM. In urban U.S. emergency departments (ED) the average health literacy is at a seventh grade level and that of rural EDs remains unquantified. SDM in emergency medicine can improve the quality, safety, and outcomes of ED patients. The process of SDM can occur informally, via conversation, or in a more standardized fashion using decision aids. Decision aids are evidence-based tools designed to increase patient understanding of medical options and possible outcomes, facilitate conversation between patients and clinicians, and improve patient engagement. 

Shared Decision‐Making in Diagnostic Testing

This chapter focuses on the United States, which spends more on health care than any other country. Diagnostic testing makes up a significant and growing proportion of national healthcare expenditures. The chapter discusses the role of diagnostic testing on healthcare costs, identifies practice changes that physicians can implement to help improve the appropriateness of testing, and discusses policy approaches to reducing the use of testing. Diagnostic testing accounts for a significant and rising proportion of national health expenditures. As a proportion of diagnostic tests are inappropriate and potentially avoidable, there is great potential to reduce the direct cost of diagnostic testing. Equally important as the direct costs of diagnostic tests are the downstream costs associated with incidental findings on unnecessary tests. Inappropriate use of diagnostic tests can be classified into the categories of underuse, overuse, and misuse. 

Appropriate Testing in an Era of Limited Resources

The emergency department (ED) is a unique practice environment that functions simultaneously as a place for high-acuity care of life-threatening illness and injury and also as a safety net for patients with chronic untreated disease. Patient presentations reflect not only pathophysiological derangements in individuals but also the consequences of social dysfunction as well as of healthcare itself, the latter related to the contrasting harms of poor access (in many cases) and excessive intervention (in many others). As changes in the larger healthcare system lead to more frequent use of the ED, clinicians have less time to spend with increasingly sick patients, resulting in more testing and less listening,1 as well as burnout among providers and medical harm and financial cost for patients.1–3

In attempting to address overuse in medicine, the Choosing Wisely campaign asked medical specialty societies to develop lists of diagnostic and therapeutic interventions that are being undertaken too frequently, leading to waste and harm.4 While different individuals and groups might not agree on every item identified, the ‘top 5’ lists that emerged from this process reflected in part an attempt to avoid controversy and left some important items—indeed some critical ‘elephants in the room’—unmentioned. While specialty societies do undertake advocacy work to address the health needs of the public, they also have a fundamental duty to advocate for and protect the interests of their specialty. Furthermore, healthcare dollars that are ‘wasted’ are of course not actually thrown away but rather end up in someone’s pocket; thus, there is clearly a conflict of interest when specialty societies address the overuse of extremely lucrative medical procedures that provide substantial income to their members.

The Right Care Alliance (RCA) is a US-based collaborative effort of healthcare practitioners and patients to address systemic issues of both overuse and underuse …

https://emj.bmj.com/content/emermed/early/2019/12/24/emermed-2019-209031.full.pdf

Erin Wilkes

Papers

Bringing value, balance and humanity to the emergency department: The Right Care Top 10 for emergency medicine