CALLING THE SHOTS

Blog » 4 Tips for Adopting Predictive Analytics in Nursing

4 Tips for Adopting Predictive Analytics in Nursing

Created Oct 20 2016, 8:00 PM by Lippincott Solutions
  • data mining
  • hospital readmissions
  • big data
  • EHR
  • predictive analytics

Friday, October 21, 2016

Predicting future trends is a great way to stay on top, in any business. The best method to accomplish this is through predictive analytics. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends, behavior patterns, and unknown future events. 

Predictive analytics involves many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to make predictions about the future.

A Need for More Information

Predicting hospital readmissions is very important these days as reimbursement is dependent upon reducing readmissions. Predictive analytics also helps providers look at the relationship between readmission and mortality rates. The goal is to improve patient care while avoiding financial and reimbursement penalties for hospitals.

Research aside, the common challenge remains: how can predictive analytics be used specifically to help control costs and improve patient care? And how can the industry move a promising idea like this from academic research to fully developed and working implementations in a live hospital IT environment? 

Tips for Adoption

It's a smart idea to gather and analyze your patient data. But if you don't know where to start, here are some tips for performing data analysis in healthcare, via Health Catalyst:  

1. More data does not necessarily mean more insight. It can be difficult to extract strong and clinically relevant conclusions, even from tons of data. The best results come when specific variables are gathered, a targeted clinical need is met, and participants are willing to act.

2. Insight and value are not the same. While many solid scientific findings may be interesting, sometimes they do little to significantly improve the clinical outcomes. At the same time, assessing only part of a picture often yields an incorrect view.

3. The ability to interpret data varies based on the data itself. Treatment decisions made on incomplete information and educated guesses are quite common in the current health system. Sometimes even the best data may afford only limited insight into clinical health outcomes.

4. Implementation itself may prove a challenge. Leveraging large data sets successfully requires a hospital system to be prepared to embrace new methodologies. This may require a significant investment of time, money, and alignment of economic interests. So many options exist when it comes to developing predictive algorithms. This presents a challenge to health care personnel tasked with sorting through all the data.  Healthcare providers need to partner with groups that have a keen understanding of the leading academic and commercial tools, and the expertise to develop appropriate prediction models. 

How do you think could employing data analytics at your facility improve patient care and outcomes? Leave us a comment!