By DAN HOUSMAN

First of all I have to admit that I am a convert and not an original believer in the Data Lake and late binding approaches to data analytics. I do not think it is my fault or at least I have a defense of sorts. I grew up in a world where my entrepreneurial heroes were people like Bill Gates, Larry Ellison, and Steve Jobs and it seemed that structured systems like operating systems that allowed many developers to work against a common standard were the way to go.

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By DAN HOUSMAN

In my last blog I riffed on prospect theory and how it applies to health care data sharing. In essence, prospect theory suggests two categories:

1. People are extremely unwilling to accept risk when the consequences are unknown (patients avoid sharing data if they don’t know how it will benefit or harm them)

2. People are more willing to accept the risk when the reward is achievable, and the alternative is very harmful (patients with severe illnesses would readily share data when there is a possibility it could save their life or eliminate significant suffering)

Scenario 1, risk adversion, is more common across all constituents including providers, healthy patients and families, political leaders and philanthropists. Generally, the benefits of sharing health care data are foggy and unclear, while the alternatives to keeping the data private are not life threatening.

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By DAN HOUSMAN

We have been talking about Precision Medicine for a long time now but so far we are still in the infancy of using genetics to impact medical decision making. The human genome was sequenced in 2003,with the promise of rapid medical advances and genetically tailored treatments.  However, development and adoption of these treatments has been slow. Today with the advent of large cohorts, and in particular, the construction of the US Government’s Precision Medicine Cohort,[1] conditions are being set up for precision medicine to flourish.  In the PMI infographic,it states three reasons for ‘Why now?’ – sequencing of the human genome, improved technologies for biomedical analysis, and new tools for using large data sets. While I agree this is progress, I believe there area few fundamental other areas to be tackled in order to really get to the promise of precision medicine.

1. The concept of protocols is designed for mass production not mass personalization

Medicine is practiced using protocols and documented in EHRs. When written down they can look like a cook book or a choose-your-own-adventure story book. The intent is to codify medical knowledge into a guide that can be consistently used by all physicians to obtain the ideal outcome. But as a result of strict adherence to medical practice standards, they inherently choose paths that are well defined based on the assumption that most people are similar in their response to treatments. So over time the protocol is enhanced to suggest which decision is the right one to make with a given patient and a protocol will more often than not be ‘anti-precision,’ just in the same way that a factory is designed to make one size of jeans at a time rather than make a custom set of jeans for each customer visiting the store.

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By DAN HOUSMAN

I have spent several years working with specialty medical offices like oncology centers, diabetes clinics, IPAs (Independent Practice Associations), and disease advocacy groups seeking to build health care data warehouses and analytics solutions. During that time, I have seen the same concerns pop up over and over: “How can we understand the value and impact of our care if we only see the component of care that we provide? If we can’t understand our value, then how can we make sure that we are optimizing our care, getting reimbursed for our impact, and executing leading research in our specialties that helps find better medical treatments for our patients? How can we really care for patients effectively in the first place?”

Organizations are highly restricted in the ways they share data. HIPAA allows for data sharing between entities, but doesn’t provide for any mechanism or incentives to do so efficiently or in a scalable method. Also, the groups who should be sharing may find themselves in competitive situations where sharing could be perceived as risky. But in spite of this, some exciting developments have quietly been moving forward in the past few years that can help fill in pieces of the data last mile.

The rise of Meaningful Use 2 (MU2) compliant electronic medical records (EMR) with the objective to enable health information exchange (HIE) between systems now represents a potential solution to this challenge that has been exacerbating the fragmentation of the health care industry for years. Public HIEs have not yet demonstrated that they can resolve analytics issues or workflow changes. Instead, there are some new and useful models of HIE that show great promise that are likely being adapted from the lessons learned from the original HIE designs.

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