Semmel Hand Hygiene

Do More With Semmel
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No more paper.  Easy to use.  Loved by thousands of observers.

100% Digital Observation

Unlock the power of direct observation
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Missing quota resulting in uneven monthly sample size significantly affects data quality and

Never Miss Another Quota

Real-time progress report to stay on track
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Nurture self accountability with contextual feedback that adds meaning to compliance reports.
See Report

Understand The Context

Capture clinical activities of each observation and add meaning to compliance reports.
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Give Meaningful Feedback

Capture clinical activities of each observation and add meaning to compliance reports.
Turn on automate feedback function and let Semmel uses clinical activities that are captured to explain hand hygiene to a staff, like ‘after taking the patient’s blood pressure’ for a Moment 4 or ‘after you move the patient’s belongings’ to a housekeeper for a Moment 5. This brings concepts down to practice and people can relate to it and take ownership of it when they truly understand the context of what they are doing.
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One Yardstick To Measure

One Definition.  One Method.  One Consistent Data Set.  Uniformed.

Data variability is rampant which render results unreliable and trending ensure data consistency.

Difference in the data collection methods can happen due to individual or local interpretation by each hospital in your network. Unless data collection definitions and methods are unambiguous, published, supervised, and regularly monitored, variations may occur which affect the validity of the data. And you do not get the assurance of infection prevention and control practice. Variability creates noise in your data which diminishes trending clarity, intervention programme effectiveness becomes harder to detect and inter-hospital performance across network hospitals are incomparable.

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Consistent and Uniform Solution

Consistent and Uniform Solution To Scale Across Your Network

Data variability is rampant which render results unreliable and trending

Difference in the data collection methods can happen due to individual or local interpretation by each hospital in your network. Unless data collection definitions and methods are unambiguous, published, supervised, and regularly monitored, variations may occur which affect the validity of the data. And you do not get the assurance of infection prevention and control practice. Variability creates noise in your data which diminishes trending clarity, intervention programme effectiveness becomes harder to detect and inter-hospital performance across network hospitals are incomparable.

It effectively creates a single inter-operable standard to measure and compare performance over time and across hospitals in your network.