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How genomic research could improve healthcare

Brian Eastwood | April 30, 2013
The cost of mapping an individual genome is quickly dropping. The potential benefits for improving the care individual patients as well as entire populations are immense. So, too, are the obstacles to getting all stakeholders--healthcare providers, researchers, pharmaceutical companies, insurance companies and the patients themselves--to share what they've learned.

Dr. Mark Davies, executive medical director of the Health & Social Care Information Centre within Britain's National Health Service, says physicians should have an "adult" relationship with patients-one that makes them feel like they're part of an equal partnership. This, in turn, must be coupled with a "bidirectional flow of insight" among patients, providers and patients, Reisman says. The benefit is bidirectional, too. Patients have better access to more robust personal health information, while patient-reported outcome measures can be used for quality, accountability and transparency improvement initiatives, Davies says.

For this to succeed, though, there must be a clear value for patients. Right now, unfortunately, that isn't the case, says Dr. John Halamka, CIO at Boston's Beth Israel Deaconess Medical Center. While the U.S. government's meaningful use incentive program does require healthcare providers to offer technology that lets patients download, request and transmit data, there is little "value add" for personal health record or disease management applications, Halamka notes.

In most cases, patients visit these apps once but don't come back. Poor usability and functionality are often to blame, Halamka says. "Go build apps that provide value."

Predictive Modeling Is Where the Value Is

For Julie Meek, clinical associate professor in the Indiana University School of Nursing, that value is in predictive modeling. Bringing together demographics, billing and pharmacy claims, lab test results, patient-supplied data and genomic research-and then incorporating it all into the clinical workflow via an EHR system-gives patients a much better sense of the health indicators than the height, weight, blood test and urine test of the annual physical ever could.

The key is making sure that no data sets are missed. Meek's predictive modeling-which is more than just an exercise in data mining, she says, because it incorporates logistic regression and model validation-considers 39 separate variables. Many stick to age and gender data, as both are readily available, but, as Meek puts it, "Cheap data is no substitute for legitimate inquiry."

She advocates such a comprehensive approach to population health management because the status quo isn't cutting it. Twenty percent of Medicare patients who are hospitalized are subsequently readmitted within 30 days-and many, for whatever reason, don't follow up with a physician in between hospital visits. This is costly and inefficient.

Determining who will come back isn't easy- John D'Amore, founder of clinical analytics software vendor Clinfometrics, says this analysis must take into account 60 variables-but it can be done. Take a group of 15 patients being discharged from the hospital and, D'Amore says, you can identify the five at the highest risk of being readmitted. That's important because, in that group of 15, 74 percent of the readmissions come are one of those five patients, he says.


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