Computerisation and decision making in neonatal intensive care: a cognitive engineering investigation

NEOREG: design and implementation of an online Neonatal Registration System to access, follow and analyse the data of newborns with congenital cytomegalovirus infection

Today's registration of newborns with congenital cytomegalovirus (cCMV) infection is still performed on paper-based forms in Flanders, Belgium. This process has a large administrative impact. It is important that all screening tests are registered to have a complete idea of the impact of cCMV. Although these registrations are usable in computerised data analysis, these data are not available in a format to perform electronic processing. An online Neonatal Registry (NEOREG) System was designed and developed to access, follow and analyse the data of newborns remotely. It allows remote access and monitoring by the physician. The Java Enterprise layered application provides patients' diagnostic registration and treatment follow-up through a web interface and uses document forms in Portable Document Format (PDF), which incorporate all the elements from the existing forms. Forms are automatically processed to structured EHRs. Modules are included to perform statistical analysis. The design was driven by extendibility, security and usability requirements. The website load time, throughput and execution time of data analysis were evaluated in detail. The NEOREG system is able to replace the existing paper-based CMV records.

A systematic review of the designs of clinical technology: findings and recommendations for future research

Human factors (HF) studies are increasingly important as technology infuses into clinical settings. No nursing research reviews exist in this area. The authors conducted a systematic review on designs of clinical technology, 34 articles with 50 studies met inclusion criteria. Findings were classified into 3 categories on the basis of HF research goals. The majority of studies evaluated effectiveness of clinical design; efficiency was fewest. Current research ranges across many interface types examined with no apparent pattern or obvious rationale. Future research should expand types, settings, and participants; integrate displays; and expand outcome variables.

Towards ethical decision support and knowledge management in neonatal intensive care

Recent studies in neonatal medicine, clinical nursing, and cognitive psychology have indicated the need to augment current decision-making practice in neonatal intensive care units with computerized, intelligent decision support systems. Rapid progress in artificial intelligence and knowledge management facilitates the design of collaborative ethical decision-support tools that allow clinicians to provide better support for parents facing inherently difficult choices, such as when to withdraw aggressive treatment. The appropriateness of using computers to support ethical decision-making is critically analyzed through research and literature review. In ethical dilemmas, multiple diverse participants need to communicate and function as a team to select the best treatment plan. In order to do this, physicians require reliable estimations of prognosis, while parents need a highly useable tool to help them assimilate complex medical issues and address their own value system. Our goal is to improve and structuralize the ethical decision-making that has become an inevitable part of modern neonatal care units. The paper contributes to clinical decision support by outlining the needs and basis for ethical decision support and justifying the proposed development efforts.

A Comparison of Graphical and Textual Presentations of Time Series Data to Support Medical Decision Making in the Neonatal Intensive Care Unit

OBJECTIVE

To compare expert-generated textual summaries of physiological data with trend graphs, in terms of their ability to support neonatal Intensive Care Unit (ICU) staff in making decisions when presented with medical scenarios.

METHODS

Forty neonatal ICU staff were recruited for the experiment, eight from each of five groups--junior, intermediate and senior nurses, junior and senior doctors. The participants were presented with medical scenarios on a computer screen, and asked to choose from a list of 18 possible actions those they thought were appropriate. Half of the scenarios were presented as trend graphs, while the other half were presented as passages of text. The textual summaries had been generated by two human experts and were intended to describe the physiological state of the patient over a short period of time (around 40 minutes) but not to interpret it.

RESULTS

In terms of the content of responses there was a clear advantage for the Text condition, with participants tending to choose more of the appropriate actions when the information was presented as text rather than as graphs. In terms of the speed of response there was no difference between the Graphs and Text conditions. There was no significant difference between the staff groups in terms of speed or content of responses. In contrast to the objective measures of performance, the majority of participants reported a subjective preference for the Graphs condition.

CONCLUSIONS

In this experimental task, participants performed better when presented with a textual summary of the medical scenario than when it was presented as a set of trend graphs. If the necessary algorithms could be developed that would allow computers automatically to generate descriptive summaries of physiological data, this could potentially be a useful feature of decision support tools in the intensive care unit.

Conceptual Framework of Knowledge Management for Ethical Decision-Making Support in Neonatal Intensive Care

This research is built on the belief that artificial intelligence estimations need to be integrated into clinical social context to create value for health-care decisions. In sophisticated neonatal intensive care units (NICUs), decisions to continue or discontinue aggressive treatment are an integral part of clinical practice. High-quality evidence supports clinical decision-making, and a decision-aid tool based on specific outcome information for individual NICU patients will provide significant support for parents and caregivers in making difficult "ethical" treatment decisions. In our approach, information on a newborn patient's likely outcomes is integrated with the physician's interpretation and parents' perspectives into codified knowledge. Context-sensitive content adaptation delivers personalized and customized information to a variety of users, from physicians to parents. The system provides structuralized knowledge translation and exchange between all participants in the decision, facilitating collaborative decision-making that involves parents at every stage on whether to initiate, continue, limit, or terminate intensive care for their infant.

Mismatched concepts in a neonatal intensive care unit (NICU): further issues for computer decision support?

BACKGROUND

Common concepts and definitions are important for the effective practice of medicine. In an intensive care unit clear understanding of terminology and communication between different staff groups may be critical for optimal care. If computerised decision support tools are to be successfully deployed in these high intensity environments, all staff must understand the concepts and information that is to be portrayed.

OBJECTIVE

To examine the similarity of language and concepts related to newborn infants and their care in staff groups with different experience.

METHODS

An experimental study in a tertiary regional neonatal intensive care unit involved 32 staff in 4 groups with varying experience (junior and senior / nurses and doctors). A psychologist developed a lexicon of clinical actions and a second lexicon of possible patient descriptors applicable to newborn infants receiving all degrees of neonatal care by conducting interviews with staff on the unit. Card sorting experiments were performed on the terms in the action and descriptor lexicons, the staff being asked to group words that were related to the same or similar concepts. The card sort data were analysed using conventional cluster analysis to produce tree-diagrams or dendrograms and then by distance matrix analysis to give cumulative probability plots.

RESULTS

Differences were shown in the way various classes of staff and staff with different experience mentally map clinical concepts. Clinical actions were grouped more randomly by nurses and by those with less experience with a polarisation between senior doctors and junior nurses. Descriptors were classed more definitively and similarly by junior and senior nurses and senior doctors but more randomly and quite differently by junior doctors. Thus there were differences seen between nurses and doctors with different roles within the unit and differences related to experience.

CONCLUSIONS

Concepts are used differently by various staff groups in a neonatal unit: this may have an impact on the effectiveness of computerised decision aids unless it is taken into account during their development.

Human factors: imperative concepts for information systems in critical care

This article provides an overview of human factors, ergonomics, human-computer interaction, and usability concepts as they relate to critical care settings. The use of these concepts can improve patient safety and the productivity of nurses, especially as they relate to the use of information systems. A framework for human-computer interaction is presented. Examples illustrate how the suite of human factors concepts is used to create intuitive, effective data presentations for use in critical care including an intensive care unit (ICU) summary display, an alternative graphic display, and modeling information sources of decision making in a neonatal ICU. The importance of integrating these concepts into advanced practice nurses' environments is made apparent.

Role and experience determine decision support interface requirements in a neonatal intensive care environment

The aim of this paper is to describe a novel approach to the analysis of data obtained from card-sorting experiments. These experiments were performed as a part of the initial phase of a project, called NEONATE. One of the aims of the project is to develop decision support tools for the neonatal intensive care environment. Physical card-sorts were performed using clinical "action" and patient "descriptor" words. Thirty-two staff (eight junior nurses, eight senior nurses, eight junior doctors, and eight senior doctors) participated in the actions card-sorts and the same number of staff participated in separate descriptors card-sorting experiments. To check for consistency, the card-sorts were replicated for nurses during the action card-sorts. The card-sort data were analysed using hierarchical cluster analysis to produce tree-diagrams or dendrograms. Differences were shown in the way various classes of staff with different levels of experience mentally map clinical concepts. Clinical actions were grouped more loosely by nurses and by those with less experience, with a polarisation between senior doctors and junior nurses. Descriptors were classed more definitively and similarly by nurses and senior doctors but in a less structured way and quite differently by junior doctors. This paper presents a summary of the differences in the card-sort data for the various staff categories. It is shown that concepts are used differently by various staff groups in a neonatal unit and that this may diminish the effectiveness of computerised decision aids unless it is explored during their development.