Timelier referral to palliative care services (PCS) within the acute care setting is a health care priority. End-of-life consumes a disproportionate share of healthcare dollars with studies indicating PCS can save hospitals approximately $1.3 million annually, for every 500 consults completed. Strategies to increase timelier referral are needed. Integration of electronic clinical decision support and utilization of triggers to identify individuals who may benefit from palliative care, using an algorithm embedded with the electronic health record (EHR) may facilitate this identification, but lacks empirical support.
The purpose of this research was to utilize variables available in the electronic healthcare record (EHR) of palliative care patients receiving PCS in the acute care setting to identify triggers which could be used to identify individuals who should be referred for PCS.
Specific Aims:
Aim 1: Characterize EHR data related to palliative care consultations among severely and chronically ill patients in the acute care.
Aim 2: Examine the relationships between the list of clinical EHR data, select demographics, in a sample of palliative care patients
Methods:
A descriptive, correlational study using de-identified retrospective data, collected from January 1, 2013 to December 31, 2015. An institutionally derived list of variables was used to provide a foundation for clinical decision support and patient identification integrated into the Cerner EHR system. Data were derived from three hospitals of a large multi-community healthcare system in San Diego County. Descriptive and inferential statistical analyses conducted using SPSS version 23.
Results:
A randomized sample yielded 694 palliative care patients seeking acute care treatment at one of the three hospitals. Of these 51.7% were male, 65.4% White, 36.7% Christian, 80.8% English speaking, 49.7% Medicare recipients, 51.4% declared themselves as a ‘do-not-resuscitate’ and 97.6% were seen by a palliative care nurse. Significant associations were found between race/ethnicity/code status (X2 = 11.311, p .02), language/presence of advance directive (X2 = 13.845, p .008), and change of code status/loss of responsiveness (X2 =15.129, p<.001).
Conclusion:
Using a large sample, a number of statistically significant demographic, physiologic, and clinical variables were found that to identify individuals suitable for timely referral to palliative care services. The integration of an EHR-based trigger system can aid not only nursing, but the interdisciplinary team to identify and refer potential palliative care patients in a timelier manner. The findings lay an important foundation for increased refinement of electronic clinical decision support within the EHR.
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