Many technological steps are easy to identify, and some are tangible, thus easy to recognize what has gone into them. Other steps are ideas and concepts that may not be as easy to distinguish from previous advances. In some respects, this is exactly what prospective analytics is to many people. It is for this reason that it should be defined and identified more specifically in the public’s eye.
As with many definitions throughout various industries, meanings and classifications are ever changing. Some of this is due to expansion to a term or a movement within a trade to better define a process, lingo or system. Thus, the definition and encompassing factors that are currently laid out for prospective analytics may evolve over time. At present, it is the use of data in order to make more effective decisions, actions and outcomes, specifically at the point of care.
Prospective analytics uses the foundations of retrospective analytics and predictive analytics to make data-driven decisions, actions and improve outcomes. There are specific aspects that prospective analytics covers that neither of these other systems do. They include:
- Finding the gaps in care and assessment of patients
- Using electric forms of communications to coordinate care
- Coordinating care, especially with different doctors and facilities
- Using decision-making tools for support of patients
- Establishing additional appointments with patients as needed
- Educating patients as to their care and treatment
- Simplifying the continuity of care between patients and doctors
What separates the prospective and predictive analytics is probably where most of the subtly lies. While predictive works to tell physicians the likelihood of something happening, such as readmission, or comorbidity complexities, prospective works to show where focus should be placed with specific patients and also facility workflow. It is a higher level of thinking and evaluating the data that is available and to have that assessment more quickly into the hands of those that are making decisions.
Don’t start crying that this is only semantics, I explained that some of these terms are being developed and refined as time goes on. However, the small differences that do persist between the different analytics systems is enough to warrant the separation in systems. Much as a concert pianist can differentiate between music written in minor scale notes and those written in major scale notes, healthcare industry leaders understand that this move to incorporate a more sophisticated analytics system can be industry changing.
There may also be benefits that result from implementing a prospective analytics system.
- A higher return on investment (ROI)
- Reduction in unnecessary and wasteful costs
- Improvement to patient retention rates within programs
- Proactive management of high-risk patients
None of these reasons will be absolutes for bringing everyone on board, but are gems that help to reinforce why a new system and new way of thinking can be beneficial in the long-term. The most important feature of this all is that patients have a better opportunity for a positive outcome.
When finding areas that need changes, you have to have something to compare against, so you need to have something to measure or calculate. Performance measurements are easy to gauge, as long as you know what you are looking for. Luckily, you are not inventing the wheel, but following a newly fledged path. Performance measures include:
- Cost, resource and efficiency measures– tracking where resources are used, how often and how payments are received
- Process measures– tracking the adherence to established standards
- Structural measures– tracking the effectiveness of the care delivery system operations
- Outcome measures– tracking patient care, especially when it comes to safety, hospital acquired infections, adverse drug reactions, etc.
- Patient experience measures– tracking patient satisfaction through survey questionnaires
Prospective analytics is a set of clinical decision making tools that, when utilized correctly, make strong data-driven decisions possible. The whole hope is to drive improvement in healthcare practices and to do it at a low cost to all involved. This is a high standard to meet, but one that is possible as forward thought and progress is implemented. Change can be difficult when utilizing a new analytics system, however, change is necessary to move past the old and into a savvier new.