A couple of weeks ago I was honored to participate in the Accenture “Journey to Analytics ROI” panel in San Francisco. To say that I was enamored with the location, organization and quality of attendees is an understatement. You see we have been working with Accenture for much of the last 12 months and, most recently, in addition to inviting Predixion Software to be a global alliance partner, Accenture led our most recent round of funding as a company alongside GE. 

Check Out What’s New in Predixion Enterprise Insight™ 3.1

The 3.1 release furthers Predixion’s revolutionary technology – enabling non-technical users to take advantage of advanced predictive analytics or skilled business analysts and data scientists to expedite the creation, re-use, sharing and deployment of their predictive models and applications.

Predictive Analytics: Saving Lives

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.

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.

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.

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…

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.