4 Healthcare Analytics Use Cases

The healthcare world is ripe with variables. The thing is, many of these are often far too complex or invisible to mean anything to the casual observer. When approached through the lens of healthcare data solutions, however, they can lead to profound answers. 

Here are four healthcare analytics use cases. 

Reducing Hospital Readmission Rates

Readmission rates are one of the most important statistics to track for healthcare providers, especially hospitals. There are several reasons why hospital readmission rates are so critical. 

The Hospital Readmissions Reduction program put in place a set of regulatory guidelines with the intention of lowering readmissions. This program was created along with the Affordable Care Act in 2010 with the goal of improving patient outcomes and reducing costs of care. Since the introduction of the program, readmissions have gone down. New studies, however, suggest this might just be due to an overall decline in admission rates, not because readmission rates have been materially improved. 

Hospital readmissions are bad for providers and patients alike. It’s a financial burden on both parties, and is incredibly hard physically and mentally on patients. Harnessing the power of healthcare data solutions can help healthcare providers to get a better idea of someone’s risk of readmission. By bringing more data into the fray, it can be possible to more accurately predict readmission based on multiple variables and adjust treatment accordingly to avoid the need for readmission. 

Real-Time Tracking of Vitals

Real-time data analytics allows for models to be updated with the collection of new data. There are some pretty clear advantages to this, especially in the healthcare world. 

For instance, someone with high blood pressure and an elevated cholesterol level is going to need to watch their vitals more closely than someone without those issues. Wearable technology can help provide real-time data on essential vital signs, and notify individuals and their doctors if anything seems off. 

Furthermore, critically ill patients in hospitals require a high level of attention that simply isn’t always available when teams are low on personnel. Real-time analytics can send alerts to hospital staff when someone is in need of immediate attention, potentially providing life-saving time and information. 

Improve Staffing and Scheduling

Staffing and scheduling issues have been an ongoing problem for the healthcare world, especially in hospitals. The scheduling process can be complex when coverage is absolutely essential. This can lead to people having to make last-minute changes on a regular basis, which can quickly lead to fatigue and burnout. 

With healthcare data solutions, it’s possible for managers to get better insights about staffing and scheduling needs. This can help ensure a better experience for healthcare workers, while also improving overall patient care. 

Predicting No-Shows

Patient no-shows can be a problem for doctors. On the one hand, having unexpected holes in the day means the healthcare provider is going to be losing out on income. It’s also frustrating for the doctors and nurses to have their time essentially wasted in that way. And moreover, no-shows are ultimately most damaging for the patients. 

By using healthcare data solutions, organizations can do a better job of predicting who is most likely to not show up for appointments. There are a few ways this information can be leveraged for better outcomes all around.

Of course, healthcare providers want patients to show up for their appointments. Identifying the people most at risk for not showing up can then lead to a procession of additional actions that can lower the chances of the no-show. These are a few potential practices:

Providers can send more reminders to patients identified as a greater risk for not showing up. These can be sent through multiple channels in order to have a greater likelihood of being read.
Offer transportation assistance to those who may need help getting to the appointment. 
Other appointment options can be offered to those who have a record of no-shows. This can help keep doctor workflows more constant, while also increasing the likelihood the patient will eventually get care. 

Identifying those most at-risk for no-shows can begin a dialogue about what would be helpful to that individual. Fostering greater levels of communication between physician and patient will make it so doctors can provide more optimal care.  

Healthcare data solutions are playing an increasingly important role in overall patient outcomes. Adopting healthcare analytics tools will help organizations streamline operations, while also improving lives.