As an industry, real time location systems (RTLS) manufacturers and their software partners have made technological strides in terms of their systems’ reliability, accuracy, speed, ease of installation and applications. These combined improvements have helped deliver on the promises made to hospital operations and clinical units around asset tracking, temperature control, patient safety and other traditional RTLS use cases. In an age when “big data” and analytics are expected to change the game, RTLS systems are a source of near-constant data collection and reporting. The question is, and will continue to be: When hospitals apply analytics to the data collected by RTLS systems and software, what emerges?

Extrapolating from traditional use cases
The two basic inputs recorded by RTLS are location data and time data, although temperature and humidity monitoring is another capability. The data help populate a real-time virtual map of tagged items such as infusion pumps, addressing perennial issues like equipment shortages. Knowing where crucial assets like IV pumps and wheelchairs are in real time can and does contribute to concrete process improvements in the hospital. It helps individual nurses do their job more quickly. It minimizes the hoarding and hiding of equipment often caused by equipment scarcity. And it may even allow hospitals to save money by reducing their excess inventory. But hospitals and health systems are in a position where they need and want to look beyond adjustments to inventory, asking instead: How can the massive trove of RTLS data be used beyond core applications like asset tracking — and what can this data reveal when it is combined with other data sources?

A data analytics approach to RTLS information about infusion pumps or staff patterns might aim for broader operational, and perhaps even clinical, insights. It is possible to compare RTLS-enabled nurse call system data, including time spent at bedside, against key benchmarks for patient satisfaction. This integration of disparate data has helped hospitals investigate and understand the relationship between clinical time spent with the patient and patient satisfaction scores, which have rightly become a major area of concern for hospital leaders right up to the board of directors.

Steps in the right direction
Many RTLS companies have touted use cases beyond simple asset tracking for years. The major RTLS companies like Sonitor, Versus, Centrak, Ekehau/AristaFlow, Awarepoint and Aeroscout offer advanced applications such as locating patients at risk for wandering (typically dementia sufferers) or abduction (infants), hand-hygiene compliance (monitoring nurses’ use of hand-washing stations) and automated temperature monitoring and control for materials that require refrigeration. Industry thinkers have also identified other areas where RTLS reporting could save staff hours, improve the quality of care or flag patient safety issues, including:

• After a disease outbreak, hospitals could use the RTLS to manage the response by identifying any staff who had been in contact with specific patients or who had entered the contaminated area.
• RTLS reporting could track nurse rounding, automatically generating compliance reports about rounding frequency and time spent at the bedside.
• RTLS systems could also produce exception reports flagging any departures from hand hygiene protocol, thus aiding overall patient safety and infection control efforts.
• Asset distribution reporting could not only locate a missing asset, but identify utilization by facility, department, unit or floor. This information could be used to optimize the storage and/or sharing of specific assets.
• Insights into patient satisfaction could be uncovered by comparing patient satisfaction scores with a review of nurse and physician response time (via RTLS tracking).
• RTLS can help improve turnover times by immediately alerting staff about the completion of bed sanitation.
• By linking RTLS technology to rules-based software, hospitals could automatically assign an ED nurse to a patient if the clinician spends more than a certain amount of time in a room, alerting the ED system at the same time.

Some RTLS companies have worked with decision-makers during the construction of new hospitals to implement innovative patient-and caregiver-facing tracking technology. In one hospital, the patient’s family can now follow the progress of their loved one virtually as he or she moves through various clinical spaces, including the OR. This RTLS-enabled technology is intended to improve patient satisfaction by addressing widespread complaints about communication, transparency and wait times, which can be monitored by staff informed by the same location technology.

This information about an individual’s progress can do more, however, than just mollify a specific caregiver or nip an excessive wait time in the bud. By submitting a large set of these time-stamped paths to analysis, the hospital could achieve broad process improvements by revealing persistent bottlenecks, identifying the units with the smoothest transitions and uncovering unexpected trends in patient flow. Indeed, as one RTLS product manager has reported, RTLS users who have applied analytics to specific units have improved patient flow, increased capacity (in the emergency department, in this case) and made important strides in the coordination of surgical care.

Orlando-based Florida Hospital has used RTLS information to streamline its OR flow by visualizing turnaround times for different processes involved in surgery. Because a typical hospital’s surgical admissions can account for as much as half its annual revenue, delays in cases due to slow turnover or inefficiencies in the supply chain can have major financial consequences. An OR is thus ripe for efficiency gains that can yield financial benefits while improving patient satisfaction and safety.

At a recent HIMSS conference a presentation was delivered about Florida Hospital, which uses RTLS technology to automatically record different milestones, creating a record of patient flow and workflow — information that could be used to identify problem transitions, streamline workflows, increase patient volume and reduce overtime costs. Florida Hospital implemented a system that avoided the need for overworked staff to flag such milestones manually. The system was organized so that staff was still able to manually enter information relevant to any outlying data points. This last capability — and the larger recognition that big data must sometimes be tempered or qualified by individual input — was crucial to involving staff in the overall improvement project and to increasing staff morale.

New insights
RTLS hardware is used for answering on-the-ground questions while also providing the basis for asking, and answering, bigger, longer-term questions about operational efficiency. Retrospective analytics can draw on RTLS data to create a history of almost any asset in the hospital. This capability saves time for staff tasked with meeting grant reporting requirements, but it can also yield insight about underutilization or obsolete equipment. Retrospective analytics can help decision-makers and managers visualize how an asset interacts with staff flow and patient flow using time and location data points, illuminating patterns that can be compared to staffing levels, unnecessary wait times or even certain clinical outcomes. When combined with other data streams, these patterns and pathways might be more rigorously investigated for their relationship to metrics such as patient or staff satisfaction, spatial redesign efforts or some other area of inquiry.

Such investigations are not yet the norm in health care. In fact, even retrospective analysis requires that leaders take the time to step back, pose the right questions and drill down past the report to identify and evaluate the possible causes of any answer they get. Because other real-time decisions may feel more pressing, this kind of sleuthing can appear secondary, and typically gets de-emphasized.

According to McKinsey’s "The age of analytics: Competing in a data-driven world" (December 2016), one of two major barriers to the use of big data in health care is that it still “need[s] to demonstrate clinical utility to gain acceptance.” The report notes that location-based services and retail have made the best use of data and analytics. These sectors have captured the most value from analytics initiatives. In contrast, the authors note, “manufacturing, the EU public sector, and health care [in the United States] have captured less than 30 percent of the potential value we highlighted five years ago.”

Because of the basic facts it establishes — namely, the location of people and things and the time spent there — RTLS data is one of the data streams most fundamental to changing this situation. Learning who performed different interventions, how long they took, where they occurred and with what equipment — combined with the relevant data about the intervention’s outcomes for patient health, safety and satisfaction — would help put to rest certain questions about effectiveness and undoubtedly uncover new areas and insights of clinical utility. Analytics insights can be as bold, but typically no bolder, than the people asking the questions. In this new world of potential, hospitals must identify where they are confounded by “black box” situations, and decide where they stand to make the greatest improvements. By combining the robust data like that of RTLS with a flexible analytics package, hospitals can draw out correlations and operational knowledge that canbe tested for validity and refined.