Category Archives: Data Management

The Ethics of AI in Healthcare

I’m looking to participating in this panel discussion on September 29th for #aimed. There are no #AI shortcuts to #aihealthcare. Like anything else, you have to build a solid foundation to be successful. I discuss many of these concepts in my book Competing on Healthcare Analytics and in my analytics classes at Northwestern University School of Professional Studies.

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Need a Data Scientist? Try Building a ‘DataScienceStein’.

Organizations are finding that hiring qualified Data Scientist is a real challenge. Experienced Data Scientists are expensive and are usually employed elsewhere. This high demand, low supply economics is leading to a situation of the ‘haves’ versus the ‘have-nots’, where the larger, financially rich organizations in the ‘sexy’ industries are most capable of attracting and hiring data scientists, while the lesser companies will have to make do without one. Organizations are looking at new approaches to finding data scientists. Some are able to attract them with more than money like autonomy and development opportunities. Others are training current staff to become more data literate through professional development programs. Once trained, these individuals typically must work 12 to 24 months at the organization or have to pay back the amount spent on their training. There is another approach that should be considered.
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Predictive Analytics World Chicago 2016 Recap

I attended Predictive Analytics World in Chicago the week of June 20 to June 23. I met a lot of new people and was reacquainted with several other colleagues. As I listened to 2 days of workshops and the pre- and post-conference workshops, some common themes emerged. Most of these themes confirmed what I have been touching on in the presentations I’ve made at conferences over the last few years and discussed in my book, Competing On Healthcare Analytics, but it was reassuring to hear the same concepts presented by others.

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The Foundational Approach to Population Health Analytics

The rate of change in the healthcare industry has been staggering. From Electronic Health Records to ICD-10 to Population Health, few industries have undergone such change in such a short amount of time. The silver lining in this change is the treasure trove of digital data, which will enable providers to analyze and compare information across thousands of patients instead of relying on the anecdotal evidence they previously used.

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The Importance of Adding a Data Strategy to Your Business Strategy

Category : Data Management , General

A business strategy is something that is considered sacred by most companies. Offsite meetings with top executives are held to prepare and discuss the strategy. The business strategy usually goes through several iterations before it is presented to the Board of Directors for review and approval. Sales are discussed. Marketing plans are floated. Production/Purchasing is scheduled. IT gets new hardware and software. Data is…

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Six Myths About Business Intelligence

Category : Analytics , Data Management

An effective business intelligence (BI) strategy is one of the most powerful tools available to companies today. It enables companies to better monitor and track the state of the business by providing the user with actionable information which can lead to better decisions. BI provides for data consistency since it is accessed through a common platform versus the disparate applications (ERP, CRM, HR, etc.) the data is derived from. Thus the information is timelier as it is typically accessed in real-time or near real-time.

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Healthcare Big Data is Déjà vu All Over Again

My colleagues and I chuckle when we hear about all the ‘new benefits’ from Big Data. We chuckle because we have been using large datasets (what today is called Big Data) for years to help companies glean insights and intelligence from customer data. The goal has been to get the “right offer, to the right person, at the right time”. This means that we sought to understand the customer and their needs enough to be able to make the right product offer to them when they were most likely to accept or respond to the offer.  To accomplish this, behavior analysis and predictive modeling was used to create more effective acquisition and retention programs and to build actionable customer segmentations. The segmentation schemes enabled the company to manage similar customers as a group with emphasis on keeping the top customers in their segments and motivating the other customers to ‘move up’ the segmentation schema.

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