Predictive Analytics: Saving Lives
I had many interesting conversations recently at the HIMSS conference in New Orleans. The healthcare industry is actively talking about shifting the focal point of care from illness to wellness but few folks really understood that predictive analytics is fundamental in that monumental shift. At present the healthcare system knows very little about us till we show up at the hospital or in a doctor’s office sick. In order to become preventive, the healthcare system will have to begin tracking our vitals decades before it currently does and listening in ways it currently does not.
As I was getting ready to see my next patient, I began what has now become a familiar ritual for doctors and nurses everywhere, washing my hands, doing my part to not become a vector for disease. As I toweled off my chapped hands for the umpteenth time today, a thought occurred to me: how come we’re not better at preventing hospital acquired infections (HAIs) despite all this hand washing? Across the country doctors and nurses are scrubbing our hands until they are impossibly chapped, yet HAIs abound. We’re doing our part but not really making much of a dent. I wondered if there might be a way that our IT systems and the immense quantity of data we collect on each and every patient might lend a helping hand…
I find it hard to imagine that Accountable Care Organizations (ACO) are going to be as wildly successful as projected without an ability to actively use the data they are obligated to collect. The Centers for Medicare & Medicaid Services (CMS) require the average ACO to compile data on their patients and then use that same data to demonstrate that they’ve met the CMS requirements of data use, reporting and improving quality. If those requirements aren’t met… well let’s just say that ACO might not be around for much longer.
You may not be aware that just a few weeks ago we released our Predixion Insight Developer Edition. I have to say I’m happily surprised at the amount of uptake and attention this release had generated, with new users coming on board and adopting Predixion Insight as their predictive platform of choice.
Predixion Insight Developer Edition is a completely free version of our Predixion Insight Enterprise that is licensed for development, testing, and evaluation purposes. This is the exact same service that we deploy in the cloud and with our enterprise customers – you get the whole enchilada that you can deploy right on your desktop or laptop.
Since Predixion Insight is a server product the pre-requisites and installation can be burdensome to the non-technical crew. If that describes you, and you still want to use Predixion Insight for dev, test, and eval purposes, you can use our cloud service – again, for free – under the same restrictions. Since it is a hosted service, you won’t get the full flexibility of the enterprise platform, but all of the Office-integrated analytical functionality will be at your disposal. Subscribe to the cloud service here.
If you are technical enough to install some server-type software, then read on.
With the first round of CMS penalties about to come out for hospitals with unacceptable readmission rates, there will be a lot of people suddenly interested in ways to decrease their readmission rates. I’m guessing that a lot of those people will want to use the LACE index, developed in Ontario, Canada, to predict the risk of patients for death or unplanned readmission within 30 days of discharge. LACE is an acronym for Length of Stay, Acuity of admission, Co-morbities (as measured by a Charlson Score) and number of previous ED visits in the last six (6) months preceding this admission. Read More
The most significant insights aren’t discovered in a vacuum. Predixion Insight provides the tools and methodologies allowing you to share predictive results with your colleagues from various aspects of your business in order to extract the most value from your data. This article will describe the variety of ways that you can share using Predixion Insight for collaboration and productionalization. Read More
One of the most frustrating things for me about starting a new clinical process improvement (PI) project is just how flipping long it takes to get actionable results. Most of us use the tried and true PDCA (Plan, Do, Check, Act) method of W. Edward Deming fame.
Predictive analytics short-circuits this PDCA process by looking at huge chunks of data (from your EHR, lab, pharmacy, etc.) and by simultaneously examining lots of variables associated with the process being improved, in order to show which of those variables have the strongest association with the outcome you are measuring. Read More
Using predictive analytics and modeling tools is becoming a common and often daily occurrence in the work of caring for patients and populations in the healthcare industry. But for someone who may be new to the field or has had limited exposure and/or experience with predictive analytics, the concept of mining data to predict the future may seem not only foreign, but a bit intimidating. Read More
Predictive Analytics are a powerful tool; of that there can be no doubt. However, like any tool, it only serves a purpose if it fulfills a useful function and almost as importantly is easy–to-use in fulfilling that purpose. For example, I can use a rock to drive a nail into a board, but the rock is hard to hold, difficult to use gently, easily breakable, etc. Ease of use for any tool means being able to complete a task as easily as possible, with minimum frustration, every single time. The same concept applies to the user interface (UI) of a seriously powerful tool like predictive analytics. The UI has to be effortless and intuitive, particularly if the end user is not a “super-user” and instead is a mere mortal who has many other things to do in their busy life (like most of us!). Read More
In 1897, an eight-year old Virginia Hanlon posed the question to the New York Sun – is there a Santa Claus? While the editors of the Sun could have easily disregarded and dismissed the child’s simple question, instead they took the opportunity to address the simple question metaphorically and inspirationally in a way that has impacted the American view of Christmas for over a century.
In the 13 years that I have been involved with Predictive Analytics, Machine Learning and Data Mining, I have been told countless times that there is no Santa Claus in this field. Industry experts, leaders in the fields, scientists and practitioners alike have all told me that this technology is simply out of reach for mere mortals. I’ve had a general manager of a prominent data mining product tell me that people simply can’t make good models. I’ve seen an analytics chief at a major American corporation tell his colleagues that they simply weren’t smart enough. I’ve seen a top level scientist at a software company claim that machine learning is just too hard for any but the most highly trained and sophisticated to comprehend. Read More