Central station data displays: an experimental evaluation of observer performance. Part 1: Number of displays and observation time.

16 experienced ICU nurses monitored simulated central station VDU displays for the occurrence of ectopic beats and for signs of deterioration in general cardiovascular status. Each period of monitoring lasted for 1 h, and on separate occasions each nurse monitored 1, 2, 4 or 6 displays simultaneously. As the number of observed screens increased, performance declined significantly, with reduced identifications of discrete events (e.g., ectopic beat detections), and with increased delays in determining a deterioration in cardiovascular status; in addition, a secondary task was less accurately and efficiently performed, and the nurses tended to become increasingly fatigued. Efficient observing was maintained over the 1 h monitoring period, but only at considerable cost in terms of fatigue and a loss of alertness. The results suggest that there may be restrictions in the use of complex central station facilities at first-line observation posts for patient care.

Central station data displays: an experimental evaluation of observer performance. Part 2: Factors affecting performance, and a comparison of analogue and digital data.

16 experienced ICU nurses monitored simulated central station VDU displays for the occurrence of ectopic beats and for signs of deterioration in general cardiovascular status. Each period of monitoring lasted for 1 h, and on separate occasions each nurse monitored 1, 2, 4 or 6 displays simultaneously. For primary clinical tasks, the data showed that the nurses' performance improved with each test. The identification of ventricular ectopic beats (VEBs) improved within the 1 h of each test except when 6 screens were being viewed simultaneously, with a similar result for the identification of deterioration of cardiovascular status. The nurses showed a marked preference for analogue signals over digits for the identification of VEBs. The highest priority clinical task was the identification of deteriorating cardiovascular status. When this task was performed efficiently less attention was given to minor tasks, and the nurse rated herself as very tired.

A sampling study of bedside nursing activity in a cardiac surgical intensive care unit. Part 1: Analysis of the elements of activity in the nursing workload, and its variation with patient age group and pathology.

For a selected group of 17 patients following cardiac surgery, 33 discrete elements of nursing workload have been defined. It was possible to identify 3 broad types of activity undertaken by the bedside nurse; technical nursing care (Type 1), intermittent nursing care (Type 2), and learning activities (Type 3). The latter is the balance of the nurses time which is largely concerned with observation and liaison with other staff. The 17 patients comprised 4 groups of pathologies, 2 each representative of the adult and paediatric workload. The primary bedside nurse performs virtually all of the Type 1 activities, recruiting the assistance of a secondary nurse principally to change the patients' posture (a Type 2 activity). The only practically significant period of time spent by the secondary nurse is in clinical discussion with the bedside nurse, largely at change of shift. Statistically significant differences in bedside nursing activities between age groups can be explained by the relative physical size of the patients and equipment. However the nurse maintains a higher level of awareness with shorter periods of inattention when nursing children. A greater number of significant differences between pathologies were found within the paediatric group of patients; this may indicate that a broader spectrum of such differences is to be found in this age group. An estimate was made of the impact of computer technology on the nurse's bedside workload.

A sampling study of bedside nursing activity in a cardiac surgical intensive care unit. Part 2. The effect of time and shift on the nursing workload.

For a selected group of 17 patients following cardiac surgery, 33 discrete elements of nursing workload have been defined, and the nurse's bedside activities logged at 1 min intervals throughout the 24 h immediately following the patient's return from the operating theatre. It is possible to identify three broad types of activity undertaken by the bedside nurse; technical nursing care (Type 1), intermittent nursing care (Type 2), and the balance of the nurse's time, largely concerned with observation and liaison with other staff--learning activities (Type 3). The time devoted to technical nursing care reduces significantly over the patient's first 24 h in the ward. On the other hand the time devoted to intermittent nursing care and learning activities is shown to be shift dependent. The effect of the efficient utilisation of computer technology is estimated, and it is demonstrated that the implementation of such technology would result in the expansion of the time available for the nurse to observe the patient. The potential benefits and drawbacks of this are discussed.

Does the ICU computer improve patient care?

The introduction of sophisticated microprocessor-based instruments into the intensive care environment promises to relieve clinical staff of many routine duties and to provide more time for patient observation. A recent English survey investigates how much time can actually be saved thereby, and in what kinds of duties, and offers ideas on how to maintain clinical awareness when data logging has been automated.

Preliminary studies in the identification of cardiac status in a cardiac surgical intensive therapy unit.

Sets of the most commonly monitored cardiovascular data have been collected retrospectively in four categories of patients following cardiac surgery: survivors and non-survivors with and without inotropic support. A data set was recorded in the surviving categories when the patient had achieved an optimum cardiovascular state, and in the nonsurviving categories 2 h before death. Three statistical methods of discriminating between the categories are compared. The best discriminants of cardiovascular status are peripheral skin temperature, mean arterial blood pressure, and urine output; there is little to be gained by using more than three variables. A method is suggested of reducing the relevant information in cardiovascular data to a single variable which can be used to predict future status.