The Use of Predictive Analytics in Hospice
The need for predictive analytics systems is an important development in the care for hospice patients. The capabilities offered by this technology is transformative for both caregivers and patients alike. Critical data used to ensure patients and families receive the right care at the right time is life changing. It is clear that agencies recognize the significance of these systems and the need to incorporate data into how they provide care to their patients.
What is predictive analytics?
Predictive analytics systems use real world evidence in the form of statistical patient data. It learns from historical data in order to make predictions about a future outcome. This data is garnered from electronic health records that predicts a patient’s condition and potential outcome in order to provide necessary care at an appropriate time. The use of machine learning provides empirical correlations in the data to provide real-time patient information. As healthcare professionals begin to include predictive analytics in their profession, it will enable them to make better decisions for more personalized care.
Predictive analytics in end-of-life care
End-of-life care tends to be costly and intrusive at times. Using predictive analytics allows healthcare professionals to be mindful of care that a patient may not need at a delicate time in their life. Wholistic data based off of predictive models reduces unnecessary interventions that further protects the patient from preventable discomfort. These predictive models help healthcare professionals determine a thorough and thoughtful care plan.
A clear example and technology that uses predictive models in healthcare successfully is the Imminent Mortality Predictor in Advanced Cancer (IMPAC). It is a predictive model that uses data from electronic health records to reveal life expectancy probabilities. This technology is used to predict and provide care to patients in end of life circumstances.
Predictive analytics are also helpful when caretakers need to schedule visitations. According to a report by the Government Accountability Organization that studied both for-profits and non-profits, “83 providers did not have hospice staff visit beneficiaries within the last 3 days of their life.” Technology produced by Muse addresses this concern as the technology leverages machine learning models to learn from each patient visit to predict a potential outcome for a patient. It is also capable of alerting clinical staff when a patient may benefit from additional care. As a result, the tool improves the coordination of care and the quality of the care in a patient’s final days.
The technology produced by Muse has the potential to change the hospice industry. Patient care will gradually improve as care providers use data to glean insights into how and when care is administered. This in turn improves how hospices are run as patients receive the care and support they need at a relevant time.
The transformative technology of predictive analytics will change how care and treatment are managed in healthcare. Healthcare professionals can either choose to stay stagnant or learn to adapt and embrace this technology to provide better care for their patients.