Reference no: EM131637440

Personalized medicine brings to mind researchers doing complicated analysis of a single patient’s genetic makeup, and fine-tuning medicine and other treatments to those results. But Duke University Health System is using everyday data from the electronic medical records of patients, combined with an analytics tool to personalize its to treating patients. County health officials recently asked Duke how many of its patients would need priority access to the H1N1 flu vaccine. Duke used IBM’s Cognos to sift through information on the more than 20 million patients in its Oracle-based clinical data repository and in an hour was able to identify about 120,000 of them with risk factors such as age, pregnancy, respiratory, and other conditions that made them vulnerable to complications from swine flu. And now that the H1N1 vaccine is available, Duke is letting those patients know that they’re first in line to get it. “We put an analytics engine on top of our clinical repository and were able to stratify by age and key illnesses millions of records, and streamline who was most at risk,” says Asif Ahmed, chief information officer of diagnostics services for the Duke system, which runs three

hospitals and about 100 clinics in the Raleigh/Durham, North Carolina, area and treats more than 1 million patients a year.

This is a practical example of how health care IT is being used to personalize medical care in ways that help doctors make smarter decisions and tailor treatment to an individual’s needs. This evolving field covers a broad range of efforts. Beyond analytics systems like Duke’s, it includes decision support tools that help doctors pick the best tests and treatments for patients, remote monitoring tools that provide close to real-time care, as well as software that helps researchers identify the best candidates to participate in trials or experimental treatments. At Beth Israel Deaconess Medical Center in Boston, helping doctors make better treatment choices and arrive at more personalized medicine. One example is clinical support software to help its 1,600 staff and affiliated physicians choose the best radiology tests for patients. When ordering CT scans, MRIs, X-rays, ultrasounds, and other radiology tests, doctors enter a patient’s electronic medical record number into the Anvita Health decision support system. Data from Beth Israel’s records system, such as recent lab tests and allergies, are automatically loaded into the software. The doctor then adds information on the current complaint, such as symptoms, which area of the body is a concern, and the suspected diagnosis, as well as whether the person has any implants that might interfere with radiology treatment. The software analyzes the data and rates the best tests for the patient, giving five stars for the top choices and one for the worst ones based on the risks and benefits of each. It can also recommend that the patient forgo radiological testing. The system can catch details that might otherwise elude a doctor, such as a previous blood test indicating decreased kidney function that could mean the patient can’t metabolize the dyes used in certain radiological tests. It also checks how much radiation the patient has already been exposed to. “Excessive radiation can cause second malignancies,” says Dr. Richard Parker, medical director of Beth Israel’s physician organization. “The system takes that into account when ordering a scan.” For instance, the software might point out that a patient suspected of having pneumonia has enough symptoms and clinical indicators to make that the most likely diagnosis, and that treating the patient for it would be better than exposing him or her to a chest X-ray. During the three years that the hospital system has used the Anvita software, it has cut out about 5 percent of tests as unnecessary or inappropriate, Parker says. Beth Israel launched a related pilot project six months ago to analyze a doctor’s thought processes when ordering radiology tests. When a doctor orders a test, the system asks what diagnosis the physician is leaning toward, with what percentage of certainty. After the test, the system follows up with an e-mail asking the doctor whether the test confirmed the original diagnosis. The study aims at gaining insight into how doctors decide which tests to use, and learning in which situations doctors are most likely to prescribe the wrong test for a given set of symptoms. Information technology isn’t just helping doctors choose the right test for a patient; it’s also making more personalized medical tests possible. For example, diagnostic testing services provider Quest Diagnostics and Vermillion, a molecular diagnostic test developer, have developed a test to assess the likelihood that women diagnosed with pelvic masses have ovarian cancer as opposed to benign tumors. The test is helping those women who are most at risk for cancer be referred to specialists faster. Many of the newest personalized medicine efforts are focused on giving analytics and decision support tools to doctors and other clinicians. But medical researchers are also still focused on the more complex efforts to analyze genomic data and use the results to create individualized treatments that doctors will use in the future. One such initiative is Cancer Biomedical Informatics Grid, or caBig, a biomedical informatics network that the National Cancer Institute launched in 2004 with the mission of developing more personalized cancer treatments and getting them into the hands of doctors faster. Researchers at the about 100 academic and community- based cancer centers that make up caBig use the network to share data and research results. They can make use of the data in analytics, data mining, decision support, and other software tools. Members are using the network’s data and software today to identify the best patients to participate in clinical trials of experimental cancer treatments. Multiple myeloma, a cancer that strikes white blood cells and eventually bone marrow, can be difficult to treat. Now the Dana-Farber Cancer Institute in Boston is harnessing the dual power of business intelligence and Web 2.0-based scientific search tools to gather complex, scattered data to better treat patients and work toward a cure for this formidable disease. Dana-Farber is a treatment, research, and teaching facility affiliated with Harvard Medical School. Its physicians and researchers regularly slog through complex calculations to find connections between data gleaned from tumor biopsies and other clinical samples and the vast genetic research housed within the organization or spread among three massive public-domain databases. Dana-Farber officials are working to leverage grant money and other resources to blend data warehousing capabilities with Web-based data collection tools, since vital connections between patient samples and analytical data will almost certainly prove the crux of both effective patient treatment and any potential breakthroughs tied to the disease, according to researchers. To make the hunt for precious genetic information easier, Dana-Farber officials have stitched together a system that wraps in Oracle’s Healthcare Transaction Base, a service oriented architecture that supports the medical industry’s HL7 standard for the electronic exchange of clinical data.

Increased use of e-medical records should make more patient data available for research, says Ken Buetow, director of the center of bioinformatics and IT at the National Cancer Institute. Ultimately, Buetow expects the caBig network, combined with doctors’ growing use of electronic data, will shorten the time it takes for research findings to show up as clinical treatments. “We think this could be one of those moments for a big shift,” he says. John Glaser, chief information officer at Partners Healthcare, which operates several Boston-area hospitals, including Massachusetts General and Brigham and Women’s, sees that shift coming. As the use of EMRs become more pervasive and the amount of digitized clinical data increases, it will be easier to provide patients with more personalized care, says Glaser, who also is an adviser on the Health IT Policy Committee at the U.S. Department of Health and Human Services. EMRs make data on patients easier to search and analyze. Doctors using them are also more likely to use decision support tools, Glaser says.

“Science is moving rapidly,” he says, and health IT helps capture and disseminate to doctors perspectives and research findings that are impossible for even the most diligent physicians to keep up with. Once the use of EMRs is standard practice, the federal government is likely to put greater emphasis on personalized medicine initiatives, Glaser predicts. In the future, health care providers could be rewarded in terms of patient outcomes, and personalized medical treatments are one of the most likely ways to improve outcomes and improve health care across the board.

Questions:

1. What are the benefits that result from implementing the technologies described in the case? How are those different for hospitals, doctors, insurance companies, and patients? Provide examples of each from the case.

2. Many of the technologies described in the case require access to large volumes of data in order to be effective. At the same time, there are privacy considerations involved in the compiling and sharing of such data. How do you balance those?

3. What other industries that manage large volumes of data could benefit from an approach to technology similar to the one described in the case? Develop at least one example with sample applications.