The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser. We recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations. A total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%). Conclusion: The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders. Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213 : 11.09.2018.

Aims and objectives: This paper aims to provide an updated comprehensive review of the research‐based evidence related to the transitions of care process for adolescents and young adults with chronic illness/disabilities since 2010.Background: Transitioning adolescent and young adults with chronic disease and/or disabilities to adult care services is a complex process, which requires coordination and continuity of health care. The quality of the transition process not only impacts on special health care needs of the patients, but also their psychosocial development. Inconsistent evidence was found regarding the process of transitioning adolescent and young adults.Design: An integrative review was conducted using a five‐stage process: problem identification, literature search, data evaluation, data analysis and presentation.Methods: A search was carried out using the EBSCOhost, Embase, MEDLINE, PsycINFO, and AustHealth, from 2010 to 31 October 2014. The key search terms were (adolescent or young adult) AND (chronic disease or long‐term illness/conditions or disability) AND (transition to adult care or continuity of patient care or transfer or transition).Results: A total of 5719 records were initially identified. After applying the inclusion criteria a final 61 studies were included. Six main categories derived from the data synthesis process are Timing of transition; Perceptions of the transition; Preparation for the transition; Patients’ outcomes post‐transition; Barriers to the transition; and Facilitating factors to the transition. A further 15 subcategories also surfaced.Conclusions: In the last five years, there has been improvement in health outcomes of adolescent and young adults post‐transition by applying a structured multidisciplinary transition programme, especially for patients with cystic fibrosis and diabetes. However, overall patients’ outcomes after being transited to adult health care services, if recorded, have remained poor both physically and psychosocially. An accurate tracking mechanism needs to be established by stakeholders as a formal channel to monitor patients’ outcomes post‐ transition.

Objectives.: To profile Australian nurse practitioners and their practice in 2009 and compare results with a similar 2007 census. Methods.: Self-administered questionnaire. Results.: A total of 293 nurse practitioners responded (response rate 76.3%). The majority were female (n = 229, 81.2%); mean age was 47.3 years (s.d. = 8.1). As in 2007, emergency nurse practitioners represented the largest clinical specialty (n = 63, 30.3%). A majority practiced in a metropolitan area (n = 133, 64.3%); a decrease from 2007. Consistent with 2007, only 71.5% (n = 208) were employed as a nurse practitioner and 22.8% (n = 46) were awaiting approval for some or all of their clinical protocols. Demographic data, allocations of tasks, and patterns of practice remained consistent with 2007 results. ‘No Medicare provider number’ (n = 182, 91.0%), ‘no authority to prescribe using the Pharmaceutical Benefits Scheme’ (n = 182, 89.6%) and ‘lack of organisational support’ (n = 105, 52.2%) were reported as ‘limiting’ or ‘extremely limiting’ to practice. Conclusions.: Our results demonstrate less than satisfactory uptake of the nurse practitioner role despite authorisation. Barriers constraining nurse practitioner practice reduced but remained unacceptably high. Adequate professional and political support is necessary to ensure the efficacy and sustainability of this clinical role. What is known about the topic?: The nurse practitioner is a developing new model of healthcare delivery that performs an advanced clinical role and is becoming increasingly important in the overburdened Australian healthcare system. Our census conducted in 2007 indicated that nurse practitioners perceived many barriers to their practice and were underutilised in the Australian healthcare workforce, specifically because of their inability to prescribe medications. What does this paper add?: This paper provides a second census of Australian nurse practitioners in 2009. Similar to the results in 2007, the study indicates that nurse practitioners remain underutilised, with many unable to perform roles within their defined scope of practice because of limitations, such as inability to prescribe medications, lack of a Medicare provider number and awaiting approval for clinical protocols. Lack of support from within healthcare organisations and the nursing profession also were found. What are the implications for practitioners?: Nurse practitioners are not being utilised to their maximum clinical capacity despite increasing pressures on the health system. Many of the barriers to nurse practitioner practice that were flagged in 2007 remained issues in 2009. It is hoped the current legislative reform through the Health Legislation Amendment (Midwives and Nurse Practitioners) Act 2010 (Cth) will adequately address these issues.

Objective To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs). Design Systematic review. Data source CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017. Eligibility criteria Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included. Data extraction and synthesis Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form. Results Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS. Conclusions This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients<12 months or between 13–18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients.

Background: Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. Objective: The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. Methods: Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. Results: The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. Conclusions: The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments.