Hospitals capture enormous amounts of data, and this data overload can lead to confusion, ineffective processes, and decreased performance. To minimize confusion, correlate data points to provide insight into why adverse events take place. This insight leads to more specific actionable interventions that can positively impact performance.
Correlating data
An example of data correlation is to take a data point such as patient falls and examine it next to a second data point such as Patient Satisfaction Responsiveness scores and look for trends. If the trend shows an increase in the number of patients fall and a decrease in Patient Satisfaction Responsiveness scores you could infer a correlation.
Next dig into the data a little deeper.
There are two predominant Patient Satisfaction Responsiveness questions.
- The first asks if a patient receives help toileting as soon as they want, and
- the other asks if the patient received help quickly after depressing the call button.
If the responses to these questions are not favorable, it can be inferred the patient may have elected to exit their bed without assistance to toilet or do another activity in their room related to that perceived lack of responsiveness of the hospital staff, putting the patient at risk for injury or falling. Investigate what time of day patients are falling. Is there a higher prevalence of patients falling during shift change or after they have finished their meals?
Collectively and through data pooling this additional information provides a clearer understanding of a potential root cause for patient falls. Impactful action plans and interventions to prevent falls can be developed from the information gathered.
Studies indicate that a substantial number of falls occur during bathroom activities. If the patients call for assistance and they do not receive assistance in a reasonable amount of time, they may get up on their own. If the number of patient falls are going up and the Patient Satisfaction Responsiveness scores are decreasing, there could be a correlation.
Identifying correlations between different data components can assist with understanding how and why things have happened, and more importantly, are the basis for change and nursing preventative intervention.
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Using Correlated Data to Impact Performance
Present the correlation analysis to the unit’s Shared Governance representatives to assist with developing interventions to address the issue. This empowers the frontline staff to be actively involved in the decision-making process that will affect their practice and care being provided to patients.
This also promotes shared accountability.
Involving other departments is also important to ensure that everyone who interacts with patients assists with the interventions implemented on the unit. This includes departments such as Dietary, Radiology, Environmental Services, and physician groups.
Based on the analysis presented, below are some examples of interventions to decrease patient falls and improve Patient Satisfaction Responsiveness scores.
- Ensure there is someone available to respond to call lights during shift change.
- Round on patients just after they eat and offer to assist them to the bathroom.
- Before leaving a patient’s room always offer to assist them to the bathroom
- Before leaving a patient’s room always ask if there is anything they need that is not in reach of them (i.e., bag, phone)
- Remind patients and their families of the importance of calling for assistance prior to getting up.
