Heres what we all know: that a data-rich health care future is coming our way. And what it will look like, in large outlines. Health care reformers have learned that no single practice will improve the system. All of the following, which were discussed at OReillys recent Strata Rx conference, must fall in place.

Although this cocktail of treatments is complex, all commenters concur on the ingredients importance to a remarkable degree. There is no Greek or Jew, no Democrat or Republican in the consensus over health care: anyone who has looked at the system comes up with the same vision.

Ill warrant you cant find a single doctor who says, It works great to wait for people to get sick and then come to me to be fixed up. No insurer will say, Were happy taking our cut from the 18% of gross national product that goes to health care, and were looking forward to it reaching 24% (a figure Ive heard batted around for future costs). Everyone realizes the system will collapse, taking their livelihoods with it, unless we change.

In modern statistics, a model is not just a way of approaching problems mentally, but a set of directions to a computer program for solving those problems Tuan Dinh, who wrote a recent article on new medical practices, traced the history of model-based medicine at Strata Rx. Dinh rang up most of the themes of modern health reform: collecting data from multiple sources, patient engagement, analytics.

In the 1970s and 1980s (when the casual meaning of model applied), models were based on clinical judgment and expert opinion. They were not supported by well-established evidence, but were based on gross oversimplifications and errors.

Then evidence-based medicine (EBM) emerged in 1990s, based on systematic reviews of available evidence, of which randomized clinical trials are the gold standard. EMB is seen everywhere now: pay for performance, care processes, EHRs, etc.

But EBM was designed for the pre-computer era, to let doctors focus on one variable at a time. Dinh said there are already 10 established models for treating cardiovascular disease, 50 for diabetes, etc. But most are poor because they are based on a small and inappropriate selection. And different models give different advice, so what do doctors trust?

The upcoming stage of analysis, model-based medicine, requires the analysis of large numbers of variables, and huge sets of patient information that are not obtainable through clinical trials. Model-based medicine can handle information on real patients (clinical trials used idealized patientspeople who are healthy except for a single condition) and gather up complex inputs: lab information, genetic information, family history, comorbidities, and patient preference.

A number of talks at Strata Rx dealt with reducing readmissions shortly after a hospital discharge. Why the obsession with this particular cost reduction? Well, Medicare fairly recently announced strong penalties for hospital readmissions, so it catapulted suddenly to the health care fields favorite application of data analysis.

In one such talk, Miriam Paramore and David Talby showed the value of big data. There have been models for predicting readmissions for some time, but they were based on a single institution, or at best a single geographical area, and did not necessarily apply to other locations with different demographics. The older models were based on a few thousand to at most 1,700,000 samples. Paramores and Talbys was based on 4.7 billion medical claims, from 120 million patients seeing 500,000 providers.

See the article here:

Ticking all the boxes for a health care upgrade at Strata Rx