Ready for full-fledged use?
Some think predictive analytics tools are too new and untested to take the chance of using at such a sensitive time. “I think we are some distance from the use of data analytics in end-of-life discussions,” says Linda Harrington, RN-BC, PhD, an independent consultant on health informatics and digital strategy; professor at Baylor College of Medicine; past chair of the American Association of Critical-Care Nurses (AACN) Certification Corporation national board of directors; and technology department editor for AACN Advanced Critical Care. “Leading healthcare organizations, grappling with the use of data analytics to solve issues, are confronting challenges largely surrounding data quality, analytic tools, and talent to do the work.”
Today, data analytics alone are insufficient to counsel patients and families about chances of recovery or survival, she says. “End-of-life decisions are very individual and complex, requiring data that may not be currently available or held in one database, such as an electronic health record. In addition to the patient’s medical and psychosocial data, an analysis of data in related research, family history, genetics, resource availability, and more can impact survivability. Data analytics that create a holistic view may one day enable better support for patients and families.”
More than 40,000 studies have been published over the last decade on end-of-life care, and 10,000 on data analytics, according to Teresa Rincon, RN, enterprise critical care champion for the EHR design team at UMassMemorial Health Care. Still, she says, fewer than 100 of these studies have specifically investigated the use of analytics in end-of-life care. While EHRs contain an enormous amount of data, she cautions that this data varies in its completeness and detail. She adds that EHRs are used primarily for clinical and financial functions, and may not contain the data elements and formatting necessary to use in data analytics for the purposes of end-of-life care.