A SUB-OPTIMAL PATIENT ASSIGNMENT PROCESS

Can a sub-optimal patient assignment process be preventing your unit or department from delivering quality care?

Let’s face it. Managing the patient assignment process day-in and day-out is not easy. It requires not only balancing nurse schedules, but it also means attempting to factor in patient acuity, geography and a host of other variables that affect care. There’s also the human factor of trying balance workloads so they are fair, equitable and don’t result in overburdened workloads.

This is not a trivial matter either.

Burnout is a significant problem in nursing today and workload is a big reason.

In one study, researchers summarized their observations this way: …a heavy nursing workload adversely affects patient safety. Furthermore, it negatively affects nursing job satisfaction and, as a result, contributes to high turnover and the nursing shortage.1

In another study, researchers said “there is a direct relationship between nurses’ workload, patient outcomes and nurse-reported quality of care.”2

The simple reality is – assigning patients is difficult and can have a tremendous impact on nursing workloads and workflow.

THE UNDERAPPRECIATED PATIENT ASSIGNMENT PROCESS

And yet, it’s one of the most underappreciated functions in hospitals today.

Some units have tried to better manage the patient assignment process with patient acuity tools. While this helps identify patients who will require more time and attention so they can be assigned accordingly, it doesn’t address other elements such as geography or continuity of care.

Other patient classification systems software programs have tried to address this issue of complexity. But they often don’t go far enough or are difficult to use. Some try to develop a tool in-house, only to find the task daunting and complicated.

A BETTER PATIENT ASSIGNMENT PROCESS

What is a better solution?

  • What if there was a software program that automatically and intelligently matched the right patient with the right nurse using multiple variables? Thus, it included patient acuity and geography, but it also included continuity of care and many others.
  • And what if the software automatically and intelligently balanced workloads so they were fair and equitable?
  • What the software was easy to use and was customizable so only those variables that were important to a particular unit or department were deployed?
  • And what if the software automatically pulled the census from the EHR, automatically pulled the nurse schedule, then matched those two, and finally automatically developed a list that could be distributed electronically and/or in print?
  • What if the software meant the entire patient assignment process took only a few minutes to complete?
  • And what if the ROI on the software was clear and compelling?

That product by called ASSIGN for Nurses.

A SOPHISTICATED AND INTELLIGENT PATIENT ASSIGNMENT PROCESS

ASSIGN for Nurses is a much more advanced and sophisticated way to organize and manage the patient assignment process.

Don’t let a sub-optimal patient assignment process result in sub-optimal care. Automate the process, using evidence-based intelligent algorithms to balance workloads, support continuity of care, and avoid medical errors. It’s time to finally fix the patient assignment process with a better solution.

Want to learn more or see a demo?

Click here or call (617) 896-4000.

NURSING WORKFLOW AND WORKLOAD STUDIES

To view more than 60 studies related to patient assignments, nurse workloads, missed patient care and nurse burnout – click here.

1NURSING WORKLOAD AND PATIENT SAFETY—A HUMAN FACTORS ENGINEERING PERSPECTIVE. CARAYON P1, GURSES AP2. EDITOR HUGHES RG PATIENT SAFETY AND QUALITY: AN EVIDENCE-BASED HANDBOOK FOR NURSES. ROCKVILLE (MD): AGENCY FOR HEALTHCARE RESEARCH AND QUALITY (US); 2008 APR. CHAPTER 30. ADVANCES IN PATIENT SAFETY.
2BALANCING NURSES’ WORKLOAD IN HOSPITAL WARDS: STUDY PROTOCOL OF DEVELOPING A METHOD TO MANAGE WORKLOAD. W F J M VAN DEN OETELAAR, H F VAN STEL,1 W VAN RHENEN, R K STELLATO, AND W GROLMAN. BMJ OPEN. 2016; 6(11): E012148. PUBLISHED ONLINE 2016 NOV 10. DOI:  10.1136/BMJOPEN-2016-012148