Academic journal article Creative Nursing

Nursing Operations Automation and Health Care Technology Innovations: 2025 and Beyond

Academic journal article Creative Nursing

Nursing Operations Automation and Health Care Technology Innovations: 2025 and Beyond

Article excerpt

This article reviews why nursing operations automation is important, reviews the impact of computer technology on nursing from a historical perspective, and considers the future of nursing operations automation and health care technology innovations in 2025 and beyond. The increasing automation in health care organizations will benefit patient care, staffing and scheduling systems and central staffing offices, census control, and measurement of patient acuity.


Registered nurses (RNs) comprise the largest segment of the U.S. workforce and the largest percentage of health care professionals. Of the 3.1 million licensed RNs in the United States, 2.6 million are employed in nursing (Health Resources and Services Administration, 2010). Nursing departments represent 43% of total employees in hospitals, and RNs comprise 65% of professional hospital staffs, the largest single component (Labor Management Institute [LMI], 2012).

Nursing is the single largest source of employee data for inpatient care delivery. Projections indicate that by 2025, the U.S. nursing shortage will grow to more than 250,000 RNs (Buerhaus, Auerbach, & Staiger, 2009). Increasing patient acuity will demand a 36% increase in inpatient RNs by 2020. The looming shortage of RNs demands efficiency and the elimination of redundant data entry and manual data collection. Unit leaders need automatic reporting and analysis of data for real-time decision making and financial management that enhances the quality of life for patients and staff, improves patient outcomes and patient and staffsatisfaction, and reduces adverse outcomes, all at the speed of sound-the sound of a mouse click.

The LMI surveys hospitals annually about the number of employees and RNs in their hospitals, nursing departments, and units; their managers' span of control; systems used for automation; and degree of automation provided to nursing operations. The 2011 PSS Annual Survey of Hours Report© revealed that of the 170 responding hospitals, most have no electronic interfaces to import or export data between their human resources and patient classification/acuity systems. The responding hospitals report their average manager has 114 employees directly reporting to him or her, yet employee scheduling/staffing software is used by only 39% of responding hospitals.

Of the hospitals that do employ staffing and scheduling systems, less than half are interfaced with human resources, and only 18% are interfaced with their patient classification/acuity system. Although 68% have interfaces to their time and attendance/ payroll systems, most report one-way exports of data that do not reflect actual real-time activity such as floating, extra hours and overtime, or tardiness/late or early out occurrences. The lack of real-time interfaces forces unit managers and nursing operations personnel to manually update multiple systems to have accurate data for management reporting-a frustrating and costly process (LMI, 2012).

In many hospitals visited by LMI for consultations and education programs, central staffing offices are underoperationalized, with limited numbers of personnel and staggering amounts of data to enter into schedules to reflect employeeand management-requested changes. Most decision making is paper-driven and manually updated, and most reports are manually maintained from data gathered manually from multiple sources.

For example, at some hospitals, an export file of shiftcensus data from the clinical workload system is created and manually copied into the scheduling/staffing software. Admission, discharge, and transfer data and non-census data (e.g., ED visits, deliveries, and procedures) are often not available to download, and many scheduling and staffing software systems only accommodate census as a workload type. Frequently, the workload data is 24 to 72 hours old rather than concurrent, forcing daily staffing decisions to be made manually to accommodate the delay in information. …

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