The pressure is on. As the population ages, nursing homes will have to play a more central role in helping realize the national mandate of reducing healthcare costs while improving its quality.
Keeping care in low-cost settings such as long-term care facilities is a major part of that mandate. Analytics and the Internet of Things — sensors, monitors and wireless devices that communicate patient information —will be a boon. They can identify risks that predict costly and avoidable hospital admissions. They can also support care at home, the least expensive locale.
Take the management of falls. Such an event will almost certainly land an elderly patient in the hospital. By putting a bracelet on a patient, for example, a long-term care facility can identify behaviors that pose the risk of a fall. Imagine that a patient is getting up frequently during the night to use the bathroom. Facility staff will be alerted so they can quickly identify and address causes such as medication problems or the onset of a urinary tract infection. Aggregating this and similar data on all patients will generate important early warning signs that facility staff can address.
The Internet of Things can also help keep more patients at home. Portable health monitors and mobile testing devices can provide steady streams of data including blood pressure, blood sugar levels and the effectiveness of medications. To see the future of home care, we should take a cue from Japan. The Japanese toilet maker Toto recently launched its Intelligent Toilette II. The fixture captures and analyzes important healthcare data including weight, BMI, blood pressure and blood sugar levels. The data is sent via WiFi to the patient’s desktop and to his or her doctor.
Equally important, analytics can steer long-term care facilities to the best and most appropriate care. The CMS-mandated minimum data sets (MDS) for long-term care provides powerful insights. One fourth of deaths, for example, occur in nursing homes,[i] yet many facilities fail to identify which patients are dying. By analyzing demographic data, clinical symptoms, medical history and factors such as increased need of daily living assistance, MDS statistics can be used to identify individuals who are in their final months of life. According to the Dartmouth Atlas of Healthcare, 80% of patients want to avoid aggressive end-of-life treatments that can be painful and often futile[ii]. When long-term care facilities consistently identify those approaching their end of life, professionals can have candid dialogues with patients to understand their wishes and provide treatment accordingly.
Professionals in long-term care facilities have always worked hard. They’ve always been smart. As long-term care facilities become a bigger part of managing the health of the growing elderly population, they need to work smarter. Analytics provides the new intelligence.
[i]Brown, U.S.o.M. Brown Atlas Site of Deaths 1987-1997. Brown University.
Drew Goddin is the managing director of health solutions at FTI Consulting.