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Understanding Predictive Patient Care
Predictive modeling using leading Spire data delivers real-time insights for proactive interventions, redefining the quality and responsiveness of patient care.
Predictive Health Solutions Powered by Spire
- Utilizes proprietary Spire physiological data and predictive analytics.
- Leverages algorithms derived from the Spire Tag data to develop predictive models, driving timely interventions.
- Aims to identify and intervene early to help prevent adverse events, reducing costs and improving outcomes.
Predictive Analytics in Healthcare
Predictive analytics uses data algorithms and AI to detect deviations from clinical baselines, enabling the care team to identify health changes days before symptoms or decline appear.
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Enhanced Predictive Modeling
Data algorithms and AI which identify risks before they escalate.
Real-Time Data Analysis
Enables providers to easily monitor health changes and respond to emerging issues.
Risk Stratification
Data algorithms and AI which identify risks before they escalate.
Improved Patient Engagement
Digital tools and coaching to promote proactive health.
Risk Stratification – Classifies patients by risk level, helping healthcare teams deliver targeted interventions.
Risk Stratification – Classifies patients by risk level, helping healthcare teams deliver targeted interventions.
Continuous Learning
Refine predictive capabilities and improve accuracy by learning from new AI data.
Predictive Alerts
Detects changes in patient data to enable timely interventions.
Personalized Treatment Plan
Improves outcomes for individual patient needs.
Streamlined Workflow
Automate routine tasks with AI enabling providers to focus on patient care.
Cost Reduction
Enable early interventions to reduce acute events.
Predictive Patient Care Helps Pulmonologists Monitor Patients
End-to End Remote monitoring system
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Identify changes in patient’s condition and intervene.
2
Access high-quality patient data to enhance interactions and clinical assessments.
3
Increase frequency of patient touch points without impacting workflow or resources.
Stacie Bratcher
CEO, Wellinks
“We are creating something entirely novel - Predictive Patient Care. This enables Wellinks to passively monitor cardiopulmonary patients and use machine learning to predict complications.”
What Our Partners Have to Say
“Spire allows my practice to track their physiology in an effort to identify clinical deterioration early. Patients are sometimes contacted before they even recognize a problem.”
Michael Polsky
MD, Pulmonary Associates of Richmond
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“Spire has helped us identify and diagnose many patient issues before the patient knew there was something wrong. This has led to earlier intervention and better patient outcomes.”
Cary Weinstein
MD, Southwest Pulmonary Associates
“Deploying Spire at PCSI has been extremely easy, making everything from clinical care to billing seamless. We’d highly recommend Spire.”
Jay S. Maizes
M.D., F.C.C.P., Pulmonary, Critical Care & Sleep Disorders Institute of South Florida
Predictive Patient Outcomes
Improve patient health, lower costs, and boost efficiency.
44%
Reduction in ER visits
55%
Reduction in hospital admissions
57%
Reduction in Health Care Costs
83%
of Escalations RequiredIntervention
12 Polsky, Michael, et al. "Use of Remote Cardiorespiratory Monitoring is Associated with a Reduction in Hospitalizations for Subjects with COPD." International Journal of Chronic Obstructive Pulmonary Disease (2023): 219-229. 3 Moraveji, N, et al. "CLINICAL IMPACT OF ESCALATIONS TROGERED BY REMOTE PATIENT MONITORING IN COPD." Chest 162.4 (2022): A2616. Hendricks, Teresi Moravell Murray Polsky, Caceres. "Clinical interventions following escalations from a continuous respiratory monitoring service in patients with chronic obstructive pulmonary disease." In review.
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