Spacelabs Healthcare
The Power of Real-World Predictive Analytics in Clinical Workflow

Spacelabs Healthcare, a leading patient monitoring and surveillance solutions company, is on a fast track to redefining clinical workflows with its focus on data science-driven predictive analytics solutions. At a time when most hospital analytics tools are descriptive in nature, and point-of-care predictive models have not yet been widely adopted, Spacelabs’ solutions stand out for their real-world validation and practical application.

Joseph Beals, PhD, Vice President of Data Science and Analytics Strategy, paints a vivid picture. “Inundated with an array of analytics tools, clinicians and quality leaders often find a disconnect between the anticipated performance of predictive analytics solutions and their utility in clinical settings. Numerous academic studies corroborate this. Retrospective performance or efficacy in trials is a very different thing from effectiveness in real-world practice. Compounding this is a still too common inattention to the generalizability and transferability of predictive models. These gaps obscure the true potential of predictive analytics in healthcare.”

Spacelabs employs a comprehensive strategy to navigate these challenges. Central to this approach is the empirical validation of its predictive models using real-world data– something the FDA publicly noted after it reviewed and cleared Spacelabs’ flagship patient surveillance solutions based on the Rothman Index (RI). Leveraging machine learning, the RI integrates various clinical inputs from electronic medical records (EMRs), including vital signs, laboratory results, and in-depth nursing evaluations of patients’ functional systems, to generate a unified patient risk score.

In addition to scientific rigor, a key element of Spacelabs’ success is the company’s strong emphasis on end-user experience. Delving deep into understanding clinical workflows on a hospital-by-hospital basis, Spacelabs tailors clinical deployments of its solutions to help with the day-to day challenges of front-line clinicians. Understanding that a one-size-fits-all approach is not effective in a diversified healthcare landscape, Spacelabs operationalizes its solutions according to the needs and context of specific hospital units and clinical roles.

The user-centric focus is embodied in the implementation strategies the company follows. This means that insights from its predictive analytics platform can be delivered to clinical professionals in a wide variety of formats and accessed from desktops, mobile devices, dedicated kiosk displays, and, of course, from directly within the EMR for easy and effective integration into clinical workflow. Roll-out plans and continued support are further guided by a data-driven understanding of care team processes, patient outcomes, and opportunities to impact the drivers of those outcomes.

Augmented Intelligence to Support Clinical Judgment

“Our intention through this solution is not to replace but to support and enhance clinical judgment—that's the central concept of augmented intelligence,” says Kathy W. Belk, Sr. Director of Analytics and Chief Data Scientist. Spacelabs recognizes that clinician expertise is paramount to patient care and that the role of artificial intelligence and data science is to provide insights to enhance diagnostic accuracy, treatment planning, and overall patient outcomes.

When bringing workflow enhancements to clinicians, Spacelabs looks at two key dimensions. The first, of course, involves providing actionable information to clinicians. The other equally important dimension is to consider subtractive enhancements to workflow. Providing insights that can simplify, accelerate, or even eliminate certain clinical tasks allows providers to spend more time focused on patients and less time on data collection, data entry, and data interpretation—tasks that should increasingly be the domain of analytics tools.

Understanding how analytic insights can lead to modifications in clinical practice for the better is a non-trivial component. There are many standardized response protocols and care pathways that clinicians follow for straightforward situations, but that’s not the focus of the Spacelabs’ data science team.

Instead, they concentrate on adding value in the “gray areas,” where clinicians have to make decisions under conditions of real uncertainty in a fast-paced environment. In these instances, harnessing the power of sophisticated data models to provide nudges to clinicians rather than providing overly simplistic prescriptive direction can be the best way to help them synthesize large volumes of information and make better decisions with greater confidence.

Improved Workflow Communication Leads to Improved Patient Care

Workflow optimization plays a pivotal role in addressing many longstanding challenges in patient care that influence patient outcomes. One of the key issues is the fragmentation of care arising during clinical handoffs throughout a patient’s hospital stay. The steady rise in patient acuity across the healthcare continuum, coupled with a persistent shortage of clinical staff, presents another challenge as clinicians continually manage an increased caseload of patients who have more complex and severe health conditions than in the past.

Spacelabs has directed considerable effort toward ameliorating these challenges through a comprehensive framework for evaluating and communicating patient health progression. The trends in a patient’s Rothman Index score and the underlying clinical drivers of those trends can play a pivotal role in providing a common reference point for inter disciplinary communication.

Having this objective, common language for patient acuity facilitates more effective and insightful exchanges during critical communication touchpoints, such as handoffs, safety huddles, multidisciplinary rounds, and even during urgent medical emergency team calls.

Better Patient Placement and Flow

In its day-to-day operations, Spacelabs’ analytics solutions are a major factor in optimizing the use of limited beds in Intensive Care Units (ICUs). In particular, drawing clinicians’ attention to subtle, potentially unnoticed deterioration outside the ICU can allow timely interventions and either avoid escalation to the ICU or prompt a more timely escalation. Proactive escalation allows the care team to shift the focus from managing crises reactively to preventing or mitigating potential health issues before they become critical. At the same time, using the RI to support decisions to safely downgrade a patient out of the ICU and prevent ICU readmissions, which are associated with prolonged hospital stays and increased mortality, is also important in balancing the efficiency of operations with patient safety considerations.

Yet another use case entails supporting or “nudging” physicians and/or palliative care teams to consider if a palliative care consultation may be appropriate for a patient. This can often lead to a better care path for the patient and reduce the non-beneficial utilization of both acute and critical care resources. Collectively, these approaches, and others like them, help improve patient throughput and bed management, ensuring patients receive the most appropriate level of care and do not remain hospitalized longer than necessary.

Responding to COVID-19’s Respiratory Challenges

Another example of Spacelabs’ adaptive approach in action can be seen in the context of COVID-19, particularly with the emergence of new respiratory risks. Particularly in pediatric populations, respiratory symptoms can be a critical concern, and the pandemic introduced unprecedented challenges. The convergence of respiratory syncytial virus (RSV), influenza, and COVID-19—often referred to as the ‘triple threat’— significantly complicated the clinical landscape, especially for children’s hospitals.

Spacelabs collaborated with clinicians at customer sites to address the problem. The company conducted detailed analyses, reviewed patient cases, and identified patterns in the clinical documentation that, when paired with clinician expertise, provided insight into the early indicators of respiratory-related pediatric patient deterioration. This collaborative effort led to the development of specialized, targeted warnings for more timely and effective risk identification and intervention, aiding clinicians in staying ahead of potential adverse events.

These successes are rooted in Spacelabs’ vivid understanding of specific clinician needs and ongoing partnerships with like-minded organizations to address burgeoning patient care needs.

By placing the daily workflow of healthcare professionals at the forefront of its solution development, Spacelabs’ data science offerings are not just technically impressive but are also effective tools to make a meaningful impact in healthcare practice.