Efficiency in Health Practice: Improving Staff Scheduling and Overhead

Like any other business, medical clinics must make crucial decisions about how to keep their operations running as smoothly and efficiently as possible.

For example, staff scheduling, inventory, patient flow, a doctor’s time and equipment usage all contribute either to the efficiency of inefficiency of a medical practice. Staffing is a particularly sticky area since busy and lean times in the clinic are hard to predict.

This is where data analysis comes in. Analytics can reveal patterns in patient demands that you can act upon to align your staff schedules to anticipated patient needs. This will save you on overhead and staffing costs.

 Here we dive into how data analytics works for medical practices, the benefits of an analytical approach to operations, and how to translate data analytics into practice for clinical setting.

Applying Data Analytics to Medical Practices

Predictive analytics rely on patient records and clinical data in general. “With hundreds of patients in any given hospital, the amount of data associated with each person can easily reach colossal amounts,” writes Cindy Maike at Healthcare Global. This gives “practitioners a more comprehensive view of the patient at any given time.”

With this amount of data, Maike concludes, the future of healthcare lies with predictive analytics. So, how can data be translated into predictions?

A good case study for predictive analytics at work comes from Johns Hopkins Hospital. Kumba Sennaar at Tech Emergence describes how the hospital’s command center, equipped with predictive analytics, receives more than 500 messages each minute and integrates data across all of Johns Hopkins’ disparate IT systems. Real-time analysis of this combined data allows administrative staff to identify the highest priorities in patient flow. Sennaar reports that the system led to a 60-percent improvement in patient flow efficiency.

While a complex example of data analytics, this case study from Johns Hopkins makes the usefulness of predictive analytics clear. Rich Krueger at Electronic Health Report contrasts the ability to analyze datasets from existing IT systems with time-consuming spreadsheets, “back-of-the-envelope math” and less than reliable first-hand experience.

In other words, Krueger is saying many care providers are failing to realize the benefits of analytics because they continue to plot their operations manually — including tasks as crucial as staff scheduling.

Implementing Analytics to Improve Staff Efficiency and Overhead Costs

By having data analytics on hand, administrators, CTOs and CFOs alike can dive into the why of clinical and overhead costs. Writing at Health Catalyst, Chris Rains and Michael McCuistion write that a business application of data analytics “enables clinical and technology teams to use discovery tools to dig deep into the data to discover the root cause of the trends.”

What can implementing data analytics look like in practice? In their white paper with Kairoi Health, Dr. Jed Weissberg and Alide Chase point to three critical ways medical practices can improve efficiency in the clinic:

  1. Finding low-cost ways to maximize ROI.
  2. Focusing on increasing availability of appointments.
  3. Experimenting with their own data to highlight and fix inefficiencies.

The implications for improving staff efficiency are readily apparent. Traditionally, administrators would have to make educated guesses about how to staff their facilities. This often led to either overstaffing or understaffing.

That inefficiency goes right out the door with an effective data analytics tool in place. With the clinic’s historical data available and structured, predictive analytics can determine peak times when the most staff will be needed. Predictive analysis, writes Vinod Saratchandran of Fingent, quickly resolves the issue of staff inefficiency by “predicting admission rates” and allocating staff accordingly.

The benefits of analytics don’t stop with staff efficiency. As Susanne Madden at PhysiciansPractice.com points out, one of the major challenges for any medical practice is staying on top of expenses — and data can certainly reveal places where smaller, day-to-day expenses can streamlined. 

Taking data analytics into account can reduce overhead costs for medical clinics of all sizes. For example, Momina Sohail at Technology Advice highlights how clinical staff can use data analysis to stay on top of inventory management. By monitoring inventory, staff can automate the way utilization and maintenance records are maintained. This saves the staff time, and it saves the clinic money.

The goal of implementing analytics into assessing costs is to find efficiency in every operation, piece of equipment and step of inventory management.

The Bottom Line: Data Analytics as Long-Term Investment

Running a successful healthcare practice is full of dynamic decision-making and complex processes. The key to addressing all these complexities? Data analytics software designed to fit your practice.

That said, temper your expectations when rolling out an analytics platform. In a recent tweet, healthcare IT analyst John Moore noted that while enterprise software adoption may translate into cost overruns or productivity losses in the short term, the long-term benefit is similar to the strides seen in the manufacturing sector, which absorbed short-term productivity losses in exchange for more efficient technology in the long run.

It will take time for the efficiencies and their benefits to manifest. That's why so many providers still prefer the pen-and-paper approach to scheduling, Jacqueline LaPointe at Practice Management News writes.

The key, then, is to find a software solution that can bring this disparate sources of information together in a customized package that fits your clinical setting. One solution is to partner with a data analytics vendor that can combine multiple data sources to create an integrated picture of how your organization operates. When done right, a data analytics solution can provide insight in all of these areas:

  • Team efficiency.
  • How assets are utilized.
  • Patient flow day to day.
  • Allocation of resources.
  • Cost and profitability of each visit.

As Chase and Dr. Weissberg pointed out above, improving your bottom line is all about finding low-cost ways to improve your ROI. With data analytics, these opportunities will become apparent. Then, it’s simply a matter of driving informed, organization-wide improvements.

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