Better Health Through Analytics
Doctors at one Texas health care organization are using analytics to identify the causes of hospital readmission. Once these causes are identified, the doctors assess treatment protocols and develop strategies to prevent patients from having to return to the hospital. In the first six months, the effort cut readmission by 22 percent.
With all the attention these days on health care costs and cutting government spending, smart doctors and hospitals are turning to sophisticated analytics to gain greater insight into patient readmissions.
Why patient readmissions? A University of California, San Francisco study found that in the United States, unplanned hospital readmission within 30 days occurs in nearly one in five Medicare patients. These readmissions are not only extremely costly, but the root causes for the readmissions can put patients at higher risk of increased illness, and in some cases death.
To address this problem, Southeast Texas Medical Associates, a primary health care group based in Texas, started using IBM analytics software to identify the causes that lead a patient back into the hospital after discharge.
The group’s 30 physicians compare patients who did not require readmission to those who did. Looking at both camps, the doctors collected and examined factors including ethnicity, socioeconomics, the follow-up care received, and how much and how quickly patients were able to receive that care.
Analyzing the data, the physicians have found similarities with the readmitted patients. Using this information, they have put in place new post-hospital treatment plans that include specific recommendations such as the need for immediate at-home care, or more aggressive support if the patient lives alone.
The doctors have also implemented preventative care programs by analyzing key data on their more than 7,500 patients. Here, they look at demographics, types of treatments, history of prescription care, risk factors and outcomes. Using the IBM analytics software, the doctors are better able to assess trends in their patients, so they may quickly adjust medications or treatments.
Additionally, physicians have been able to reduce the time needed to evaluate patients’ data prior to treatment. For example, the doctors are calculating cardiovascular risk factors at each and every office visit, something that was not practical before. What used to take a physician over an hour to sit and calculate just one patient's score by hand can now be done in less than a second.
With that information, a doctor can now point out key risk factors around relative heart age scores, so if the patient is 65 years of age but is showing a relative heart age of 75 years, it allows the physician to discuss ways they can work together, like adjusting lifestyle choices, to improve those numbers.
All of these efforts are paying off. In just the first six months of employing the advanced analytics practice, Southeast Texas Medical Associates has been able to cut the number of patient hospital readmissions by 22 percent.
That translates into huge cost savings for the patients, their insurance companies and programs like Medicare.
Source: smartertechnology
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