Predictive metrics can positively impact your weight loss practice business performance and patient outcomes. It’s time to not only analyze the past, but use predictive metrics to make decisions that can guide you towards stronger future performance.

 

While the methods can be quite involved, the ultimate goal of predictive analytics is to advance beyond knowing what happened in the past to providing best predictions of what will likely happen in the future. Predictive metrics relates to measuring the right things to ensure success. Essentially, instead of measuring an outcome, they measure a process or a behavior that influences the outcome.

Like many industries, health care has embraced the practice of thoroughly analyzing the abundance of historical and real-time business and patient data available. Through predictive analysis, this information is then used to improve patient care, determine accurate diagnoses, personalize patient treatments, enhance marketing efforts and automate repetitive tasks for better efficiency and performance, among many others. The use of predictive analytics is predicted to grow significantly in nearly every industry.¹

You likely track various metrics in your practice already as you strive to improve your patient and business outcomes. However, it is easy to measure metrics that are simple or common to track but not relevant to your goals. You may also understandably get caught up in the busy day to day operations and miss the value of thorough analysis and interpretation of what your metrics mean. Particularly, how they can improve your decisions moving forward.

Benefits of Predictive Metrics

Some of the potential benefits of predictive metrics specific to medical and surgical weight loss practices are listed below:

• Improved Patient Care
• Better Patient Engagement
• Improved Marketing ROI
• Increased Sales & Profitability
• Predictable & Efficient Operations

How to Integrate Predictive Metrics into Your Weight Loss Practice

If you have enough historical data, you may be able to build a predictive model or strategy from thorough data analysis. If so, then you can simply test your predictive model and integrate it into your business as appropriate. For example, analyzing your top patient nutrition product shopping patterns helps to define what they are more likely to buy. This allows you to create personalized offers including such patterns for faster sales. You can also develop recommendations (often seen on Amazon) that specifies ‘people who bought X product also bought Y product’ which often results in a higher overall sale.

However, more often than not, you will need to follow a model such as a Predictive Metrics Tree that will measure the behaviors that will drive your desired results.² A good way to manually (without use of AI – Artificial Intelligence or automation) determine predictive metrics that will drive better performance and decisions in your weight loss practice is to follow the four Predictive Metrics Tree steps:

1. Identify your primary problem and create a clear goal or question you want to answer.
2. Identify key drivers or actions that have (based upon past data analysis) or will have the greatest impact on goal attainment.
3. Narrow down the key initiatives for each action that will lead to accomplishing the goal. This is not a hypothesis, it is it is based upon data driven relationships.
4. Specify the predictive metrics which include the processes or behaviors that will drive progress toward the goal. Be sure to identify one metric to measure for each of the initiatives. You will also want to make sure that each predictive metric is specific in terms of a definition and frequency of data collection, unit of measurement and targeted value you are striving to attain.

Sound confusing?  Let’s simplify. Start with a problem that is most important to you. Be sure to include your team members who can/will impact the outcome for a better result. To assist, here is an example specific to a common goal many weight loss practitioners share: Increasing the total number of 5-Star online patient reviews.

1. Goal or Question You Want to Answer

How can I increase new 5-Star patient online reviews for our practice to at least five each month?

2. Key Drivers/Actions

• Patient Satisfaction
• Easy Access to Online Business Listing/Review Page
• Process for Timely Request of Online Reviews
• Timely Response to Reviews

3. Key Initiatives for Each Driver/Action

• Patient Satisfaction
  • 3 Question Survey Distributed Via E-mail/Text with Each Visit
  • Timely Response to Patient Inquiries
  • Predictable Patient Outcomes
• Easy Access to Online Business Listing/Review Page
  • Accurate Listing
  • Creation of QR Code/Tools for Immediate Access to Review Page
  • Review Links in E-Mail Signature for All Team Members
• Process for Timely Request of Online Reviews
  • Systematized Process for Online Review Requests
  • Lead Team Member Identified for Implementation
• Timely Response to Reviews
  • Lead Team Member Response to All Reviews Within 48 Hours

4. Specific Predictive Metrics

• Patient Satisfaction
  • At Least 10 Patient Surveys Completed Each Month, Reviewed, Summarized, Action Plan for Concerns, Summarized for Team Monthly
• Easy Access to Online Business Listing/Review Page
  • QR Code/Tools Shared by Team with at Least 4 Happy Patients/Week, Documented and Summarized for Team Monthly
• Process for Timely Request of Online Reviews
  • Team Leader Documents Process for Timely Request of Online Reviews Along with Total Number of Requests (4/Month) Expected for Team Members – Total Number of Requests Summarized Monthly for Team (Goal – At Least Five 5-Star Reviews/Month)
• Timely Response to Reviews
  • Team Leader Responds to All Online Reviews Within 48 hours and Summarized for Team Monthly

These drivers, initiatives and predictive metrics have been shown to produce the desired outcome when implemented properly with consistent team reporting. Modifications can be made along the way.

While predictive analytics is a promising tool to improve patient care and business performance, there are potential problems primarily surrounding automated machine-driven data such as data security and incorrect data interpretation resulting in poor predictions. Integrating human insight and intuition specific to your practice is essential for optimal results.

Source(s):

1. https://www.grandviewresearch.com/industry-analysis/predictive-analytics-market
2. https://www.tcgen.com/blog/predictive-metrics-tree-measuring-behaviors-drive-results/