Each piece of the Pulse platform drives valuable improvements to planning, patient care, and documentation throughout the patient journey:
Pulse Transitions analyzes visit-by-visit patient data to provide home health teams with all the information they need to identify and provide better care to patients most likely to benefit from end-of-life care.
Each piece of the Pulse platform drives valuable improvements to planning, patient care, and documentation throughout the patient journey:
AI-powered review of complete referral records, condensed into a single-page smart summary. Cross-references referral documents, the OASIS assessment, and clinical notes for improved documentation, coding, and reimbursement accuracy.
Evaluates clinical data to stratify patients by risk for optimal utilization. Provides visit-by-visit patient snapshots to guide proactive care and improve end-of-episode decisions.
Compares patients against unmatched industry-wide data to identify those most likely to require end-of-life care, helping providers ensure compassionate end-of-life planning for families.
Patient risk ranking based on unmatched industry-wide data drives action on every patient who needs it, not just a fixed cross section.
More appropriate patient settings: Decreased deaths on the home health census and increased patient days in hospice.
Visit-by-visit predictive updates and intelligent insights for focused, high-impact care decisions.
Workflow improvements and branch collaboration tools for simplified, efficient care provision.
Right-sized utilization and reduced rehospitalizations, improving both care quality and resource allocation.
Improved care quality and patient satisfaction for a better, more compassionate family experience.
A 2021 study from the Stanford School of Medicine found that although 80% of Americans say they would prefer to die at home, only 20% do. Pulse Transitions supports patients’ wishes, using machine learning to identify patients earlier who are most likely to benefit from hospice. This leads to dramatic improvements in care quality and efficiency.
Our predictive models activate patient data to generate an ordered list of patients to be clinically evaluated for whether hospice is more appropriate for them.
Specifically identifies patients on the home health census who are likely to pass away in the next 90 days.
Provides transparent workflow to move patients through a customized virtual care path toward hospice or further home health care.
Dashboards and reports that monitor utilization and patient outcomes.
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