Even as patient information moves to electronic records, important data is often siloed
Dr. Robert Walker, director of health innovation for the U.S. Army Surgeon General, has been more a frustrated data entry clerk in recent years than a physician, a frustration shared by thousands of his colleagues.
Instead of freeing him for more face-time with patients, the electronic health record (EHR) system he uses has become a third person in the exam room, drawing his attention away from patients. The issue isn’t the EHR Walker uses, however; it’s the shortcomings of technology in general.
“The electronic medical record has become an impediment versus something that was going to streamline your day,” Walker explained in a recent interview. “It took the focus away from the patient and put it all on the computer. People are clicking boxes and turning their backs to patients. It’s all about jamming data into this thing.”
EHRs make it possible for every medical care facility to electronically capture a patient’s family history, illnesses, treatments and current lifestyle. The promise of EHRs was that they would save the U.S. healthcare system up to $81 billion a year by streamlining workflows and creating massive clinical data warehouses that could be mined for information that could improve preventative care and disease treatment.
That has not yet happened, and doctors are less enamored with EHRs as a result. Last month, the American College of Physicians and AmericanEHR Partners released a survey of 4,279 physicians that showed fully 39% of them would not recommend their EHR to a colleague. That’s up from 24% who felt that way in 2010. And 34% said they are “very dissatisfied” with the ability of EHRs to decrease workload.
Under the auspices of the Health Information Technology for Economic and Clinical Health Act, (HITECH Act), the U.S. government is requiring healthcare providers — hospitals, clinics and private practices alike — to implement EHRs. Providers must also prove their meaningful use of those systems through a three-stage government process that is taking place over the next four years.
Despite what has so far been an uneven rollout of EHRs in the U.S., Walker and others are already, in effect, building what a treasure trove of patient information that can be tapped to improve patient care, a repository that will revolutionize medicine for decades to come. That is, if everyone can figure out how to categorize it, sort it and access it easily.
The promise
Big data analytics engines such as Hadoop have the capability to mine the clinical data warehouses created by EHRs, warehouses filled with valuable unstructured data that can be used to help doctors make decisions about patient treatment.
Today, physicians and pharmaceutical companies still rely largely on text books and infinitesimally small clinical studies that typically use healthy patients with only one disease. That pool of subjects hardly mimics most real-world patients, many of whom have more than one health problem.
About 25% of hospitals use some form of data analytics to mine traditional databases to learn more about past treatments and about how future treatments can be improved. But, what is contained in the columns and rows of databases represents an almost insignificant portion of the information about patients that’s been collected; the most important information lies in unstructured data – the physicians’ notes, radiological images and lifestyle information gathered from patients using mobile devices.
“That’s the real renaissance that’s going to happen in health care,” Walker said. “With big data, what happens in a doctor’s office is going to be vastly different from what we see today. The top five or 10 things that people die from in America are life-style induced. That’s absurd. Maybe instead of vital signs, I’m just going to look at what you buy in a grocery store.”
With big data, what happens in a doctor’s office is going to be vastly different….
Dr. Robert Walker, director of health innovation for the U.S. Army Surgeon General
Today, data analytics in most hospitals is used to manage costs and increase the quality of care. The more promising use for big data, however, is the ability to discover treatment-and-outcome correlations using physician and nurse notes and data driven by genetic profiles.
By combining big data and genetics analytics, scientists today can determine how a patient will react to a medication and may someday even be able to predict who may become ill and — if they do — what customized medications can best treat diseases.
“When I look at the historical growth rate, [big data] is definitely a hot application in the marketplace,” said James Gaston, senior director of clinical and business intelligence at the Healthcare Information and Management Systems Society (HIMSS).
Healthcare and IT
Currently, one of the more promising areas of big data analytics involves drug therapies devised through the study of genomics, also known as personalized medicine.
Genetic diseases are akin to buggy code in software; the key to finding the cause of an illness is to uncover that error in the code, according to Alexis Borisy, co-founder of Foundation Medicine, a cancer diagnostics company.
“Cancer, for example, is a disease of the genome where something has gone wrong with the programming code and a mutation occurred. There are actual errors in the code and that’s a core reason why cancer develops,” Borisy said.
While sequencing the first human genome took eight years and cost about $1 billion, genetic sequencing costs have fallen dramatically in the last decade. It now costs from $5,000 to $10,000 per human genome, and companies are working hard to cut that cost to $1,000 in the next few years. Sequencing a DNA strand is becoming so inexpensive that hospitals will soon be able to do it for on most patients and add the data to an EHR, according to according to Nigam Shah, an assistant professor of Medicine at Stanford University’s School of Medicine.
Shah works in biomedical informatics, meaning he works toward making sense of the information in clinical data warehouses.
Sequencing of a human genome yields a massive amount of data, and storing one person’s genetic code can require up to 1TB of data storage capacity, Shah said.
The human genome contains 3.2 billion lines of code, which means that finding a flaw in that code requires sophisticated computer algorithms and massive, clustered server farms. Adding to the complexity is that disease is often the result of multiple mutations, according to Shah.
While diseases such as Huntington’s or Alzheimer’s disease are caused by common genetic mutations, and are more easily spotted, most illnesses are caused by rare mutations. Diabetes, for example, is thought to be caused by a number of genetic mutations, which on their own confer a small amount of risk, but in combination can be more serious.
“If you genome type someone, and out of the 50 [mutations associated with diabetes] you have 10 of them, it’s very hard to say what’s going to happen to you,” Shah said. “Part of the problem is that we just need to do more research and collect more data, and some of it we just need better methods.”
But tremendous progress has been made. To date, scientists now know the genetic causes of about 5,000 rare diseases.
One of the most promising areas of genetic research is pharmacogenomics, which uses a person’s genetic makeup to determine how they’ll respond to drugs, tailoring treatments to specific mutations — even mutations found in cancer tumors.
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