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The goal of population health is to use data to identify those who will benefit from intervention sooner, typically in an effort to prevent unnecessary hospital admissions. Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions.

The combination of the two has enormous potential. However, many of the factors that will determine success or failure have nothing to do with technology and should be considered before investing in machine learning or population health.

Population health software, with or without machine learning, only produces suggestions. Getting a team to take action, particularly if that action is different, is one of the hardest things to do in healthcare. You will not succeed without executive support. Executives will not support you without significant incentive to do so.

Here's an easy surrogate for whether there is enough of that incentive: whether those executives jobs are in jeopardy if too many people go to the hospital. If not, the likelihood that an investment will lead to measurable improvement is minimal.

If youve been ordered to "do" population health, your best bet is to install a low cost risk score or have your team write a query to identify the oldest sickest people with the most readmissions. Either will return the same results more or less and your team of care managers are used to ignoring said results without rocking the boat. If there is sufficient incentive, read on.

Henry Ford is credited with saying, "If I asked people what they wanted, they would have said faster horses." Its human nature to try to apply a new technology in an old way.

Economists have named this the IT Productivity Paradox and have studied the cost of applying new technical capabilities in old ways. There are signs that healthcare organizations are unknowingly walking this plank.

For decades, risk scores were designed to identify the costliest patients with little consideration of the types of costs, the diseases they suffer, whether or not those costs are preventable, etc.

As a result, according to a systematic review of 30 risk stratification algorithms appearing in the Journal of the American Medical Association, "most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly." A recently published study in Science also showed that prioritizing based on cost discriminates against people of color. Applying more data and better math to solve the problem in the old way is an expensive way to propagate existing shortcomings.

The opportunity now made possible is the ability to match individuals to interventions. Patients with serious mental illness that are most likely to have an inpatient psychiatric admission are very different than those with serious illnesses that might benefit from home-based palliative care. Clinicians wouldnt treat them the same, neither should our approach to prioritization.

However, you will need to design for this and clinical teams should be prepared for the repercussions. Patients identified with rising risk (as opposed to peak utilization) will not seem as sick.

Clinical teams trained to triage may feel like theyre not doing their jobs if the patients arent as obviously acute. Its important to discuss these repercussions and prepare in advance of the introduction of new technology.

Using technology to send more of the right people into a program that doesnt have an impact only adds to the cost of an already failing program. Surprisingly, very few programs have ever measured the impact of their interventions.

Those that have, often rely on measuring patients before and after they enter into care management programs which is misleading and biased on many levels.

If you are not confident that the existing program makes a difference, invest in measuring and improving the existing programs performance before investing additional resources. A good read on the pros and cons of different approaches to measuring impact is here.

Starting with a program of measurement can create a culture of measurement, improvement, and accountability - a great foundation for a pop health effort. Involving the clinical team in the definition of measures that matter will go a long way.

Another important consideration is whether your intervention is costly to deliver. The more costly it is to steer resources toward the wrong people, the more likely your program is to benefit from smarter prioritization.

For both reasons above if your program is entirely telephonic and targets older people with chronic complex diseases, you may want to invest in program design and measurement before investing in stratification technology.

Youre in great shape, and your odds of success are exponentially higher. Youre also better informed, as you and the team shift focus to decisions such as whether to build versus partner, what unique data you collect that can be used to your advantage and how youll measure algorithm and program performance.

Leonard DAvolio, PhD is an assistant professor at Harvard Medical School and Brigham and Womens Hospital, and the CEO and founder of Cyft. He shares his work on LinkedIn and Twitter.

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3 questions to ask before investing in machine learning for pop health - Healthcare IT News

We found that if Caterpillars (NYSE: CAT) stock drops 10% in a week (5 trading days), there is a solid 25% chance that it will rise by 10% over the subsequent month (20 trading days).

Caterpillar stock has seen significant volatility this year. While the company is being impacted by growing headwinds to the global economy, the uncertainty surrounding the trade war between the U.S. and China, relatively mixed quarterly earnings reports, as well as slowing sales, its relatively high capital returns, and strong balance sheet have supported the stock to an extent.

Considering the recent price swings, we started with a simple question that investors could be asking about Caterpillar stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Caterpillar stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 23%. Quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in CAT stock become more likely after a drop?

Answer:

Consider two situations:

Case 1: CAT stock drops by 5% or more in a week

Case 2: CAT stock rises by 5% or more in a week

Is the chance of say a 5% rise in CAT stock over the subsequent month after Case 1 or Case 2 occurs much higher for one versus the other?

The answer is absolutely!

Turns out, chances of a 5% rise over the next month (20 trading days) is meaningfully more for Case 1, where the CAT has just suffered a big loss, versus Case 2.

Specifically, chances of a 5% rise in CAT stock over the next month:

= 40% after Case 1, where CAT stock drops by 5% in a week

versus,

= 32% after Case 2: where CAT stock rises by 5% in a week

Question 2: What about the other way around, does a drop in CAT stock become more likely after a rise?

Answer:

Consider, once again, two cases

Case 1: CAT stock drops by 5% in a week

Case 2: CAT stock rises by 5% in a week

Turns out the chances of a 5% drop after Case 1 or Case 2 has occurred, is actually quite similar, both pretty close to 23%.

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, absolutely!

Given a drop of 5% in CAT stock over a week (5 trading days), while there is only about 21% chance the CAT stock will gain 5% over the subsequent week, there is more than 50% chance this will happen in 6 months, and 62% chance itll gain 5% over a year (about 250 trading days).

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in CAT stock are about 24% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 23% when the waiting period is a year (250 trading days).

The table below shows the trend:

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

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Machine Learning Answers: If Caterpillar Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes