4 Essential Lessons for Adopting Predictive Analytics in Healthcare

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Information plus context equals knowledge. But predictions made solely for the sake of making a prediction are a waste of time and money.

In healthcare and other industries, predictors are most useful when their knowledge can be transferred into action. The willingness to intervene is the golden key to harnessing the power of historical and real-time data. More importantly, to best judge the efficacy and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred.  Case in point: using predictive analytics in healthcare to mediate hospital readmissions.

The Economy of Prediction

Academically speaking, predicting hospital readmissions is a very active topic. Thus far in 2013, 36 peer-reviewed journal articles have been published on the subject along with three additional review articles. Highlighting this rapidly growing interest are recent papers focused on simplified readmission scoring for elderly patients1, the relationship between readmission and mortality rates 2, and a systematic review of tools for predicting severe adverse events 3. Prediction discussions associated with specific areas such as heart failure 4,5 or within pediatric populations are also very active 6,7.

This year alone, researchers are on track to publish approximately the same number of papers on using prediction in healthcare as were published in the entire 1990s. The motivation? Improving patient care while avoiding financial and reimbursement penalties for hospitals.

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

4 to Lessons from Predictive Analytics

Figure 1.  Opportunities for improved patient care and motivation to avoid financial penalties have resulted in a rapidly growing interest in predicting hospital readmission.

What You See is What You Get

Evidence-based medicine is a powerful tool to help minimize treatment variation and unexpected costs.  Best-practice guidelines contribute further to the goal of standardized patient outcomes and controlling costs.  But a well-known Chinese proverb states, “jùng dōu shòu dōu, jùng gwà, shòu gwà.” (If you plant beans, you get beans. If you plant squash, you get squash.)  Algorithm and computer types know this better as “garbage in – garbage out.”  Similar to notation in the Six Sigma approach to quality improvements, for predictive analytics to be effective, Lean practitioners must truly live the process to best understand the type of data, the actual workflow, the target audience and what action will be prompted by knowing the prediction.  In military intelligence terms, this is “boots on the ground”; in a hospital setting, “nurses on the floor.”

In short, decision makers cannot be isolated or far removed from the actual point of decision. At the same token, to best leverage the data, predictors should also not be used in “isolation,” although in the healthcare industry today, readmission risk profilers are often used as standalone applications.

Returning to the Chinese garden, Confucius is credited with saying, “If you think in terms of a year, plant a seed; if in terms of 10 years, plant trees; if in terms of 100 years, teach the people.”  For the long-term success of predictive analytics in healthcare, it’s necessary to do all of the above. However, the process can be jump-started by learning from other industries and expertise.

Existing Expertise

Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. Take, for example, the casino industry, which carefully models and manages population risk in terms of how many people walk through the door and how much the pay outs will cost the casino8,9. Similarly, the statistical work of actuaries in the task of managing population life insurance risk and payout is also well understood10,11.

Beyond industry expertise, studying history will likely ease some of the potential pains and pitfalls that could accompany healthcare’s adoption of predictive analytics.  Given that predictive analytics are listed as level 7 out of the 8 possible levels on the Healthcare Analytics Adoption Model, there are many keys and pitfalls that can occur at such a level if not properly prepared.  Key lessons are listed below; illustrations of each follow12.

  1. More data does not equate to more insight: It can be difficult to extract robust and clinically relevant conclusions, even from reams of data.
  2. Insight and value are not the same: While many solid scientific findings may be interesting, they do little to significantly improve current clinical outcomes.
  3. Ability to interpret data varies based on the data itself: 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, however, may require a significant investment of time and capital and alignment of economic interests.

For the healthcare industry, like other industries, predictors will always be more useful in the framework of a more complete set of data, where the knowledge can be fully leveraged to action. Furthermore, full clinical utility of prediction or risk stratification is only possible in a data-rich enterprise warehouse environment. But perhaps most importantly, these predictor-intervention sets can best be monitored and measured within that same data warehouse environment.  If the predictor is used standalone or housed elsewhere (siloed), this important evaluation step may not be possible.

Specific Trumps Global

Lesson #1: Don’t confuse more data with more insight. This first lesson from history is similar to the love affair we humans have with new technology.  The sometimes not-so-obvious irony is that without having the proper technology framework in place, with context and metadata for meaningful use, new technology is really not very useful. In fact, it is often a waste of time and money. Thus in healthcare, the irony of a technology-driven, more generalized prediction model that inputs big data and global features is that the targeted utility is usually lost. Prediction focused on a specific clinical setting or patient need will always trump a generic predictor in terms of accuracy and utility. The following example from genetics illustrates this concept.

Thyroid cancer can be caused by mutations in the RET gene.  This proto-oncogene is a tyrosine kinase that modulates phosphorylation signals on the cell surface into the cell.  In RET mutations, a very specific protein location (transmembrane) and certain amino acid residue (cysteine) is

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