August 17, 2018

Why Your Medical Department Needs Labor Analytics

3 min read

Written by: James Nichols  |  Share:

Labor analytics and the ability to find useful, effective labor data can be a black hole for your department. With workforce issues, leaders often struggle to understand what data to analyze even before tackling the problem of how to access and manipulate the data effectively. These issues make data-driven staff scheduling decisions a challenge.

Common Issues with Current Methods

So what data do you need? Effective clinician staffing starts with demand analysis. This seems obvious, but analysis must go beyond coarse measures like "How many surgeries do we do a year?" It must go deeper than asking "How many patients do we admit and discharge in a year?" Deep understanding of demand means you have enough data to understand why a Monday in January is different from a Wednesday in the summer, why facility A is busier at 3:00 pm than facility B, or how one department compares to another. That level of granularity can be difficult to achieve with manual data gathering and analysis.

Furthermore, your current methods of data analysis likely don't show you the outcome of your decisions. You should see planned versus actual staffing levels to understand what happened. Who showed up? (Who was supposed to?) How long were they there? (How long were they supposed to be there?) What did they do? (Did the demand really mean they were required?)

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Labor analytics is three-pronged. Achieving it means you need to know what you're trying to understand, how to obtain the data, and how to access it and understand it easily on an ongoing basis.

What Happens If You Can't Analyze Your Workforce?

When your organization doesn't have that clear view, what are the consequences? Poor retention? Poor patient care? Financial losses?

In demand-based specialty areas, particularly in surgical areas, you tend to see “just in case” staff scheduling designed to match expected peaks in demand. Yet those decisions are not often data driven —and if they are, that data is too coarse to really create a lean staffing approach. In the worst cases, the peak times are so often perceived and not actual. You know what busy feels like and the impact it can have, so staffing strategy ultimately becomes an insurance policy against shortage and thus potential patient safety issues. Accounting for sick days and personal time in this process can also lead to overstaffing. What happens as a result?

• Too many staff on one shift (leading to redundant staff or staff that are not fully tasked)
• Increased unnecessary expenses (especially for nonexempt employees)
• General staff dissatisfaction about lack of schedule insight and lead time

To prevent these consequences, leading organizations are using labor analytics to practice “lean staffing”. With labor analytics, you could discover whether the 30 minutes at the beginning of the day is productive every day or if your shift patterns can change. Why pay for extra capacity if you can staff in a more lean fashion? Labor Analytics allows you to see patterns and trends in staffing and production (by location, team, day and time down to 15-minute increments) and leverage that information to improve your staffing approach in an iterative way.

The Benefits of Fine-Grain Analysis

Looking at measures of throughput and cost tend to be quite coarse. What if, instead, you could see the compliance needs and acuity of the department at any given time? You can make better staffing decisions on a day-by-day and shift-by-shift basis.

Later, after these decisions have been implemented, you should also have access to the outcome. What was the demand likely to be? What did you schedule? Were you correct? What did it produce? Getting away from coarse benchmarks and moving toward granularity allows you to make even more optimized decisions in the future. Additionally, labor analytics provide the information you need to have the tougher conversations about demand and staffing. You can come to the table with information that reveals, in full, how much staff you need, when, why, and the projected outcomes of this decision. Not only will this net you greater buy-in from your executive team, but it will also help you manage a team of individual clinicians fairly and accurately.

 

"Closing the Gap: How clinician staffing levels can match patient demand"

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Topics: Workforce Analytics