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Biomedical subjects

J F Hurdle

Publications and source records attributed to J F Hurdle.

3 recordsLinked to original sources

Interventions for disruptive behaviors. Use and success.

Health care providers deal with disruptions from geriatric patients routinely. Despite the negative impact on provider efficiency, provider-patient relations, and patient well-being, there have been no systematic clinical studies of the impact of disruptive behaviors on geriatric inpatient care. This article presents a taxonomy for these behaviors, applying them to a study of disruptive behaviors and concomitant nursing interventions on a geriatric evaluation and management (GEM) unit. The sample, consisting of 23 nursing staff (16 RNs, 4 LPNs, and 3 nurse aides), was followed over 8 weeks (five shifts per week, distributed randomly over day, evening, and night shifts). An experienced pair of RN observers logged all disruptive behaviors and the associated interventions employed by the nursing providers. The taxonomy was validated on 97 disruptive events (113 disruptive behaviors) initiated by 87 patients. The major findings of the study were: (a) disruptive behaviors are common on a GEM unit; (b) behaviors that disrupt care are recognized only 50% of the time by nursing staff; (c) interventions, when used singly, were found successful 45% of the time; (d) multiple simultaneous interventions may be more successful than single interventions but were used in only 16% of cases; and (e) selection of interventions may be associated with staff education level.

Aged↗

Lightweight fuzzy processes in clinical computing.

In spite of advances in computing hardware, many hospitals still have a hard time finding extra capacity in their production clinical information system to run artificial intelligence (AI) modules, for example: to support real-time drug-drug or drug-lab interactions; to track infection trends; to monitor compliance with case specific clinical guidelines; or to monitor/ control biomedical devices like an intelligent ventilator. Historically, adding AI functionality was not a major design concern when a typical clinical system is originally specified. AI technology is usually retrofitted 'on top of the old system' or 'run off line' in tandem with the old system to ensure that the routine work load would still get done (with as little impact from the AI side as possible). To compound the burden on system performance, most institutions have witnessed a long and increasing trend for intramural and extramural reporting, (e.g. the collection of data for a quality-control report in microbiology, or a meta-analysis of a suite of coronary artery bypass grafts techniques, etc.) and these place an ever-growing burden on typical the computer system's performance. We discuss a promising approach to adding extra AI processing power to a heavily-used system based on the notion 'lightweight fuzzy processing (LFP)', that is, fuzzy modules designed from the outset to impose a small computational load. A formal model for a useful subclass of fuzzy systems is defined below and is used as a framework for the automated generation of LFPs. By seeking to reduce the arithmetic complexity of the model (a hand-crafted process) and the data complexity of the model (an automated process), we show how LFPs can be generated for three sample datasets of clinical relevance.

Biopsy, Needle↗

Inter-rater reliability and review of the VA unresolved narratives.

To better understand how VA clinicians use medical vocabulary in every day practice, we set out to characterize terms generated in the Problem List module of the VA's DHCP system that were not mapped to terms in the controlled-vocabulary lexicon of DHCP. When entered terms fail to match those in the lexicon, a note is sent to a central repository. When our study started, the volume in that repository had reached 16,783 terms. We wished to characterize the potential reasons why these terms failed to match terms in the lexicon. After examining two small samples of randomly selected terms, we used group consensus to develop a set of rating criteria and a rating form. To be sure that the results of multiple reviewers could be confidently compared, we analyzed the inter-rater agreement of our rating process. Two rates used this form to rate the same 400 terms. We found that modifiers and numeric data were common and consistent reasons for failure to match, while others such as use of synonyms and absence of the concept from the lexicon were common but less consistently selected.

Medical Records Systems, Computerized↗