Here is a simple, easy to read ACO article on NPR
http://www.npr.org/2011/04/01/132937232/accountable-care-organizations-explained
Tuesday, February 21, 2012
Monday, February 6, 2012
Predictive Modeling in Healthcare
Ok, so these are my musings over predictive modeling, not a knock on it. (That is the disclaimer). So, as I was sitting around, pretending to watch TV and ignoring the dog who seemed to want to go out and play, I thought about Predictive modeling, in a healthcare setting. Specifically, in a Healthcare Provider setting. I actually asked this question on several groups on LinkedIn and asked folks if they have had success with it. The only person who responded with a success story was Mr. Alex Zverev (you can view his profile on LinkedIn here: http://www.linkedin.com/pub/alex-zverev/1/a01/b03), and the scenarios in which he has had successes here (http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=90342387&gid=93115&commentID=65616962&goback=%2Egmp_93115%2Eamf_93115_10544349&trk=NUS_DISC_Q-ncuc_mr#commentID_65616962). So here are some of my thoughts.
My primary interest is in the Return on Investment of using predictive modeling. I see quite a few software providers out there touting to have predictive modeling capabilities, but haven't heard of a lot of success stories, especially in a healthcare provider setting. Even lesser information is available on the ROI of implementing a predictive modeling solution.
To me, an ultimate predictive modeling solution would be something that can predict the stock market, which has infinite number of variables to consider. But if it were that simple, everyone would be doing it. On the other hand, in healthcare, people are touting Clinical Decision Support capabilities using predictive modeling. "Which patient of yours is most likely to develop cancer?", for example. In my humble opinion, that again is quite a stretch, because of the number of variables that need to be taken into account, not to mention "objective research" that is available to create the model in the first place.
For example, it would be easy to say that a smoker of Asian descent between the ages of 18-40 may develop cancer quicker than others. But what if he is a smoker with healthy eating habits and hits the gym 4 days a week? What if that person only smokes three cigs a day? What if he has no genetic predisposition to cancer? To me, this a cool exercise to conduct and eventually, as you gather more and more data and "evidence" really starts supporting your research in cancer, your model becomes much more reliable and this will start generating a measurable ROI, by reducing the cost of treating a patient through early screening and through preventive medicine.
My thought is that if you are going to do predictive modeling, start with an area with a limited number of variables. Your "bang for the buck" would be realized sooner and it would be greater in that scenario. For example, the scenarios that Alex describes (Capacity Planning and Measured Display Times for Display Stations) have a better chance of an "immediate" ROI than, let's say, a cancer predicting algorithm. Now, if you are reading this, and have had successes with using Predictive modeling in different settings other than the ones described above, please let me know. I'd like to hear your stories.
My primary interest is in the Return on Investment of using predictive modeling. I see quite a few software providers out there touting to have predictive modeling capabilities, but haven't heard of a lot of success stories, especially in a healthcare provider setting. Even lesser information is available on the ROI of implementing a predictive modeling solution.
To me, an ultimate predictive modeling solution would be something that can predict the stock market, which has infinite number of variables to consider. But if it were that simple, everyone would be doing it. On the other hand, in healthcare, people are touting Clinical Decision Support capabilities using predictive modeling. "Which patient of yours is most likely to develop cancer?", for example. In my humble opinion, that again is quite a stretch, because of the number of variables that need to be taken into account, not to mention "objective research" that is available to create the model in the first place.
For example, it would be easy to say that a smoker of Asian descent between the ages of 18-40 may develop cancer quicker than others. But what if he is a smoker with healthy eating habits and hits the gym 4 days a week? What if that person only smokes three cigs a day? What if he has no genetic predisposition to cancer? To me, this a cool exercise to conduct and eventually, as you gather more and more data and "evidence" really starts supporting your research in cancer, your model becomes much more reliable and this will start generating a measurable ROI, by reducing the cost of treating a patient through early screening and through preventive medicine.
My thought is that if you are going to do predictive modeling, start with an area with a limited number of variables. Your "bang for the buck" would be realized sooner and it would be greater in that scenario. For example, the scenarios that Alex describes (Capacity Planning and Measured Display Times for Display Stations) have a better chance of an "immediate" ROI than, let's say, a cancer predicting algorithm. Now, if you are reading this, and have had successes with using Predictive modeling in different settings other than the ones described above, please let me know. I'd like to hear your stories.
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