Electronic Health Records (EHR) store a wealth of information about patients, outcomes, clinical pathways, timing, and so much more. How can this information be tapped into to optimize nurse staffing? The wealth of clinical and census data allows hospitals to move beyond standard nurse to patient ratios. Patients require differing levels of nursing care and nurses have differing levels of expertise and efficiency in caring for patients. Taking patient and nurse differences into account allows for a safer and more effective balancing of workload throughout the hospital.  

Individual Patient Needs Drive Workload

Patients require differing amounts of nursing time based on a multitude of factors, some of which are:  

  • Diagnosis
  • Patient type (I.e. post-operative patient, oncology patient, etc.) 
  • Dietary status 
  • Presence and type of invasive devices 
  • Frequency of labs and vitals 
  • Types and frequency of medications being administered 
  • Ambulation and hygiene needs 

Furthermore, a patient’s nursing needs are very likely to change throughout the course of an inpatient stay. Many of the above factors can and do fluctuate. It is also important to note that a patient’s arrival or discharge from a unit often carries with it a significant bolus of work for the nursing staff. 

The Data Is Available

Fortunately, the bulk of the above information is available in one way or another within the EHR. Patient movement and census can be obtained from a combination of ADT data (admission/discharge/transfer) and orders. Flowsheet data can provide insight into frequency of vitals and lab draws. Medication orders and the MAR (medication administration record) provide insight into the type of medications, routes, and frequency required. Nursing notes can help inform of special conditions, dietary needs, hygiene needs, ambulatory needs, and social conditions that might necessitate more or less nursing time.  

Putting it All Together 

Taking all of these factors into account, and doing so over time, will help to robustly identify nursing workload. This can be broken down historically by unit, time of day and day of week to help make decisions about how many nurses to staff for a particular shift. 

While future planning based on historical data is helpful, this can go a step further. Providing this information in real time will help nurse managers to optimally allocate the nurses currently working to the units that need them most.  

Using EHR data to understand nursing workload beyond a simple patient census will have a dramatic impact on being able to have the appropriate nursing staff available. Incorporating all of the above factors isn’t a trivial effort. However, taking the time to work with nursing staff to understand and quantify how patient specific factors impact the time it takes to care for patients will help to maximize the amount of time that nursing units are staffed appropriately and safely.  

Once patient workload is understood, understanding the difference in nursing capability is the next step, but that is a topic for an upcoming blog.  

About the Author

Bill Ferris has been at IPS since 2010, and has been working in healthcare focusing on analytics, modeling, and performance improvement for over 20 years. His past experience includes projects focused on obstetrics, emergency departments, perioperative services, outpatient clinics, ancillary services, and inpatient units in both the private sector and military medicine. He holds degrees in Industrial Engineering and Information Technology Management. Currently he is the Director of Professional Services at IPS. He also really likes to fish before heading to the office.

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