Continuous Cardiac Monitoring Policy Implementation: Three-year Sustained Decrease of Hospital Resource Utilization

Applying Human Factors Engineering to Address the Telemetry Alarm Problem in a Large Medical Center

OBJECTIVE

Address the alarm problem by redesigning, reorganizing, and reprioritizing to better discriminate alarm sounds and displays in a hospital.

BACKGROUND

Alarms in hospitals are frequently misunderstood, disregarded, and overridden.

METHOD

Discovery-oriented, intervention, and translational studies were conducted. Study objectives and measures varied, but had the shared goals of increasing positive predictive value (PPV) of critical alarms by reducing low-PPV alarms in the background, prioritizing alarms, redesigning alarm sounds to increase information content, and transparently conveying who initiated alarms. An alarm ontology was iteratively generated and refined until consensus was achieved.

RESULTS

The ontology distinguishes five levels of urgency that incorporate likely PPV, three categories for who initiates the alarm (hospital staff, patient, or machine), whether it is clinical or technical, and clinical functions.

CONCLUSION

This unique collaboration allowed us to make progress on the alarm problem by making unintuitive leaps, avoiding common missteps, and refuting conventional healthcare approaches.

APPLICATION

Hospitals can consistently redesign, reorganize, reprioritize, and better discriminate alarms by priority, PPV, and content to reduce nurse response times.

Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization

BACKGROUND

Healthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship.

METHODS

This study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored.

RESULTS

GWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists.

CONCLUSIONS

Our findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.