Assessing the motivation of MDs to use computer-based support at the point-of-care in the emergency department

A significant body of research investigates the acceptance of computer-based support (including devices and applications ranging from e-mail to specialized clinical systems, like PACS) among clinicians. Much of this research has focused on measuring the usability of systems using characteristics related to the clarity of interactions and ease of use. We propose that an important attribute of any clinical computer-based support tool is the intrinsic motivation of the end-user (i.e. a clinician) to use the system in practice. In this paper we present the results of a study that investigated factors motivating medical doctors (MDs) to use computer-based support. Our results demonstrate that MDs value computer-based support, find it useful and easy to use, however, uptake is hindered by perceived incompetence, and pressure and tension associated with using technology.

The potential for pharmacists to manage children attending emergency departments

AIM: There have been concerns about maintaining appropriate clinical staff levels in Emergency Departments in England.1 The aim of this study was to determine if Emergency Department attendees aged from 0-16 years could be managed by community pharmacists or hospital independent prescriber pharmacists with or without further advanced clinical practice training. METHOD: A prospective, 48 site, cross-sectional, observational study of patients attending Emergency Departments (ED) in England, UK was conducted. Pharmacists at each site collected up to 400 admissions and paediatric patients were included in the data collection. The pharmacist independent prescribers (one for each site) were asked to identify patient attendance at their Emergency Department, record anonymised details of the cases-age, weight, presenting complaint, clinical grouping (e.g. medicine, orthopaedics), and categorise each presentation into one of four possible categories: CP, Community Pharmacist, cases which could be managed by a community pharmacist outside an ED setting; IP-cases that could be managed at ED by a hospital pharmacist with independent prescriber status; IPT, Independent Prescriber Pharmacist with additional training-cases which could be managed at ED by a hospital pharmacist independent prescriber with additional clinical training; and MT, Medical Team only-cases that were unsuitable for the pharmacist to manage. An Impact Index was calculated for the two most frequent clinical groupings using the formula: Impact index=percentage of the total workload of the clinical grouping multiplied by the percentage ability of pharmacists to manage that clinical group. RESULTS: 1623 out of 18,229 (9%) attendees, from 45 of the 48 sites, were children aged from 0 to 16 years of age (median 8 yrs, range 0-16), 749 were female and 874 were male. Of the 1623 admissions, 9% of the cases were judged to be suitable for clinical management by a community pharmacist (CP), 4% suitable for a hospital pharmacist independent prescriber (IP), 32% suitable for a hospital independent pharmacist prescriber with additional training (IPT); and the remaining 55% were only suitable for the Medical Team (MT). The most frequent clinical groups and impact index for the attendees were General Medicine=10.78 and orthopaedics=10.60. CONCLUSION: Paediatric patients attending Emergency Departments were judged by pharmacists to be suitable for management outside a hospital setting in approximately 1 in 11 cases, and by hospital independent prescriber pharmacists in 4 in 10 cases. With further training, it was found that the total proportion of cases that could be managed by a pharmacist was 45%. The greatest impact for pharmacist management occurs in general medicine and orthopaedics.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

Clinical decision support system for point of care use: ontology driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms-desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.

Stress, burnout and coping strategies in the emergency and intensive care hospital departments

Doctors and nurses working at the accident and emergency (A&E), and intensive care departments are at risk of burnout. They often spend substantial time in intense interactions with other people, centered on patients? health problems (physical, psychological and social) that may lead to feelings of anger, anxiety and frustration, and eventually to burnout. Burnout is a syndrome of emotional exhaustion, depersonalization and reduced personal accomplishment (Maslach & Jackson, 1981) The purpose of this chapter is to assess work stressors, burnout and stress-coping mechanisms among doctors and nurses at the A&E and intensive care departments. A quantitative design using the survey approach was used to collect data from a sample of 200 participants with a response rate of 71% (n=154) Work stressors were associated with burnout in both doctors and nurses. Workload was the most salient work stressor in the sample. Nurses experienced more stress (M=1.5, SD=0.4) than doctors (M=1.2, SD=0.4) in all the work stressor variables examined. The A&E department was reported as more stressful than the intensive care department. Avoidance-oriented and task-oriented coping were the most and the least frequently reported coping strategies respectively. Additionally, only emotion-oriented coping strategy was significantly different between doctors and nurses, and this strategy was also significantly positively correlated with all the variables in the adapted nursing stress scale, and the three burnout variables. Death and dying was most strongly correlated with emotion-oriented coping. This chapter provides an assessment of stress, burnout and coping experienced by both doctors and nurses within the A&E and intensive care departments. Methods that may mitigate stress in these environments may be adequate staffing, supportive management, stress management programs, as well as improvement in communication strategies between doctors and nurses.