Rounds

Patient Engagement Solution

Round on patients and staff with this iPad-based tool designed in alignment with your workflows and goals.

  • Patient experience and safety rounding: Address service issues proactively and responsively. Prioritize patients for rounding based on fall or infection risk factors.
  • Staff engagement rounding: Round on staff to coach on behaviors that affect quality and safety.
  • Operational quality rounding: Monitor compliance with operational requirements, track environment-of-care issues, and support supervision of the general safety of the facility.

See Vocera Rounds in Action

See Vocera Rounds in Action

Patient Experience Rounding

Help improve the patient and staff experience, regulatory compliance, and quality of care using a single communication and workflow platform. Engage patients in a data-driven, systematic way across the ontinuum of care from pre-arrival through post-discharge supported by deep consultative expertise in designing, implementing, and managing patient and staff experience solutions.

Staff Engagement Rounding

Gain insight to inform environment-of-care issue remediation; provide protocols for staff coaching in areas such as process improvement or hand hygiene, and for expressing gratitude, ensuring staff feel safe, and more. Use custom filters to identify staff with high risk for dissatisfaction or need for intervention, to prioritize them for staff rounding.

Operational Quality Rounding

Monitor compliance with operational requirements; for example, fire safety. Track environment-of-care issues and gain insights needed for remediation. Support supervision of the general safety of the facility.

Custom Filters

View patients and staff by category to prioritize them for rounding intervention.

Proactively identify and stratify patients with dissatisfaction risk or by category, such as those who have been in the ED for a certain length of time or who’ve had a specific procedure.

Automated Sentiment Analysis to Support Data-Driven Transformation

Screen rounding comments for positive, negative, neutral, or mixed sentiment in order to identify trends in patient perception of care in near-real time, using machine learning and artificial intelligence.

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