Enhancing pain care with the American Pain Society Patient Outcome Questionnaire for use in the emergency department (APS-POQ-RED): validating a patient-reported outcome measure.

BACKGROUND: In general, the quality of pain care in emergency departments (ED) is poor, despite up to 80% of all ED patients presenting with pain. This may be due to the lack of well-validated patient-reported outcome measures (PROMs) of pain care in the ED setting. The American Pain Society-Patient Outcome Questionnaire-Revised Edition (APS-POQ-R), with slight modification for ED patients, is a potentially useful PROM for the adult ED, however it is yet to be completely validated. METHODS: Adult patients, who had presented with moderate to severe acute pain, were recruited at two large inner-city EDs in Australia. A modified version of the APS-POQ-R was administered at the completion of their ED care. Responses were randomly split into three groups and underwent multiple rounds of exploratory and confirmatory factor analysis with testing for construct, convergent, divergent validity and internal consistency. RESULTS: A total of 646 ED patients (55.6% female), with a median age of 48.3 years, and moderate to severe pain on arrival, completed the ED-modified APS-POQ-R. Psychometric evaluation resulted in a reduced nine-question tool, which measures three constructs (pain relief and satisfaction (α=0.891), affective distress (α=0.823) and pain interference (α=0.908)) and demonstrated construct, convergent, divergent validity, and internal consistency. CONCLUSIONS: This new tool, which we refer to as the American Pain Society-Patient Outcome Questionnaire-Revised for the ED (APS-POQ-RED), should form the basis for reporting patient-reported outcomes of ED pain care in future quality improvement and research.

Developing Robust Clinical Text Deep Learning Models - A "Painless" Approach.

The success of deep learning in natural language processing relies on ample labelled training data. However, models in the health domain often face data inadequacy due to the high cost and difficulty of acquiring training data. Developing such models thus requires robustness and performance on new data. A generalised incremental multiphase framework is proposed for developing robust and performant clinical text deep learning classifiers. It incorporates incremental multiphases for training data size assessments, cross-validation setup to avoid test data bias, and robustness testing through inter/intra-model significance analysis. The framework's effectiveness and generalisation were confirmed by the task of identifying patients presenting in 'pain' to the emergency department.

Wait times are not the problem! Detailed analysis of unsolicited patient complaints from a metropolitan Australian emergency department.

OBJECTIVE: To describe characteristics of ED admissions that resulted in unsolicited complaints and compare with overall ED admissions. The site utilised is an inner city tertiary hospital, with 630 beds, with approximately 82 600 annual presentations, where 32.5% were children. METHODS: Complaints between the dates of 27 November 2012 and 10 March 2016 were reviewed. Performance indicators and the distribution of presentations by diagnostic code were reviewed. RESULTS: A total of 572 different complaint reasons were found and grouped into 12 categories. The most common reasons for complaints were treatment (33.2%) and communication (28.3%), and most complaints concerned medical staff. Other variables including wait times have no effect on patient complaints. Utilising aggregate numbers, the overall paediatric complaint ratio was 0.98:1000 presentations, and the total adult department complaints were 1.78:1000 presentations. CONCLUSION: As seen in this study the vast majority of patient complaints were associated with treatment and communication issues and skewed towards doctors. It may be feasible for medical staff to undertake communication training as clinician-patient communication in the ED is an important aspect in the improvement of patient satisfaction and in decreasing patient complaints as waiting times and triage categories had no major influence on patient complaints.

Possibilities and pitfalls of outsourcing.

Outsourcing can save healthcare organizations costs related to staffing and training. Organizations should ensure that a vendor's staff is credentialed, knowledgeable, and properly trained. Outsourcing firms should ensure the confidentiality and security of the information they will handle. Outsourcing carries risks for providers, including potentially negative impact on tax-exempt status and loss of control over business processes.