Quality improvement (QI) requires rectifying the differences between work-as-imagined (ie, people's assumptions about how work is done currently) and work-as-done (ie, the reality of what people do) and then reimagining work through analysis of and reflection on those gaps to better meet the needs of patients and health care workers. We identify gaps between our assumptions and reality in many ways (eg, formal and informal interviews and observations), but we often lack practical, objective data indicating how the work-as-done is achieved; in all but the most heavily resourced QI efforts, costs are too high or collection takes too long. However, advances in sensor technologies can complement our traditional QI methods. Sensing technologies are expanding rapidly in the workplace. In health care settings, real-time locating systems (RTLSs) are the most mature and commonly available and, consequently, most primed new technology to meet the needs of QI professionals.

WHAT ARE RTLSs?

RTLSs involve wearable and environmental sensors that detect when individuals or objects are at different physical locations within our facilities, such as staff or patient "badges" or "tagged" equipment. RTLS sensors use either infrared or radio signals and most frequently are adopted in the hospital setting to track expensive mobile equipment (eg, infusion pumps, ventilators). The benefit of RTLSs extends beyond asset management into understanding and improving work processes.

PROMISE OF RTLSs FOR QI

How can we leverage this RTLS infrastructure many of our organizations have already invested in? As the technology and related computational fields mature, a wider range of possibilities open. With modest effort, there are 2 broad areas where RTLSs can support QI efforts today.

Workflow mapping and workload assessments

RTLS data can augment and expedite many foundational QI practices. From the earliest scientific approaches to management up to present-day QI efforts, time and motion studies detailing what people do and how their work is sequenced have played a critical role in examining how work is done. Time and motion studies necessitate observers shadowing workers or time-intensive self-report instruments. While RTLS data do not provide the same understanding of what someone is doing that a traditional time and motion study does, understanding movement through physical space can generate important insights. For example, we have conducted studies of nursing workflow and workload in our intensive care units (ICUs) and found that patterns of physical movement of ICU nurses obtained from an RTLS were better predictors of their subjective physical and mental workload than the commonly used factors to assess patient complexity and design staff assignments.1 Spikes in and more chaotic movement patterns were the biggest predictors of strain. Better models and measures of workflow and workload can inform redesign of both the physical space and the staff assignment systems to improve care and mitigate staff burnout. This same approach can be applied to foundational QI efforts such as mapping the patient journey and patient flow in time-critical areas (eg, perioperative services, emergency departments).

Supporting reflective practices

QI is much more than promoting adherence to best practices: it is about exploring and discovering new ways for individuals and organizations to excel. Here, RTLS data can be used to engage stakeholders in sensemaking around work-as-done, such as in understanding resident time at the bedside.2 Time spent with patients is invaluable for resident learning and well-being as well as for patient care. However, residents spend very little of their time with patients. RTLS data have revealed important individual differences in how residents spend time at the bedside and differences across services.3 Although we currently do not have a detailed understanding of why or how individuals and services could change their practices to improve, we are engaging residents and physicians using RTLS data visualizations to facilitate discussions about what would have to happen for time at the bedside to play a more prominent role in resident learning.

Ambient intelligence and smart environments

As RTLSs and other capabilities mature, sensor data can be used more proactively to provide real-time support through ambient intelligence in physical spaces capable of sensing and responding to human behavior. These environments can improve the safety and quality of care at a large scale in acute and home care settings4 by, for example, minimizing documentation burdens while promoting compliance with the increasing number of protocols for mitigating risks of preventable harm. Although much work remains to be done, novel ambient intelligence solutions are one of the most promising approaches to maintaining high-quality care under new realities of workforce shortages.

SOCIAL AND TECHNICAL BARRIERS TO RTLS ADOPTION

Trust

Workplace monitoring has become ubiquitous in many industries, and it is rarely welcomed when first introduced. People value their autonomy and privacy, and monitoring of any type can be perceived as a threat to both. Addressing these concerns requires creating transparency around the data and staff engagement with its use. Resistance dissipates when staff find value in the data through insights and improvements in their work.

Variety in the technologies and data quality

There are many different RTLSs on the market, each with unique data models and analytical capabilities. When organizations begin to work with RTLSs, data quality should be scrutinized closely for factors such as the placement of environmental sensors and system rules for handling events, such as data drop off from moving to areas without environmental sensors. While many QI programs will have the technical expertise to do these assessments, partnerships with other organizational entities further strengthen these capabilities.

In summary: to facilitate organizational change, QI depends on data to shape understanding of where we can improve and from whom we can learn. RTLSs and other sensing technologies are increasingly commonplace in health care organizations, and QI professionals would benefit from being well versed in their use and potential to catalyze change. RTLSs complement QI methods, and they can open avenues to greatly extend our ability to understand, analyze, and reflect on work-as-done to the benefit of health care systems and the patients they serve.

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