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Determining Palliative Care Penetration Rates in the Acute Care Setting.
Journal of Pain and Symptom Management 2018 Februrary
CONTEXT: Intermountain Healthcare, in collaboration with Cerner Corporation, developed a hospital-based electronic palliative care algorithm.
OBJECTIVES: This study aims to improve identification of patients who would benefit from palliative care services, and calculate palliative care penetration rates.
METHODS: This study used a mixed-methods nonrandomized retrospective study design. Three 30-day iterations of clinical data were analyzed for patients identified by the electronic algorithm. During the second and third 30-day iterations, palliative care clinicians conducted chart reviews on a weekly basis for identified patients and determined whether the patients were appropriate for a palliative care consult. Positive predictive values (PPVs) were calculated. Based on the PPV, palliative care consult penetration rates were also calculated.
RESULTS: During the first iteration, the algorithm triggered 2995 times on 1384 unique patient encounters (69.3% of the total inpatient population). In the second iteration, the algorithm triggered 851 times on 477 unique patient encounters (26.4% of the total inpatient population). Eight hundred twenty-one chart reviews were completed on 420 unique patient encounters. The PPV was 68.3%. Based on the PPV, the projected palliative care penetration rate was 17.6%. During the third iteration, the algorithm triggered 1229 times on 539 unique patient encounters (33.3% of the total inpatient population). Nine hundred sixty-seven chart reviews were completed on 505 unique patient encounters. The PPV was 80.1%. Based on the PPV, the projected palliative care penetration rate was 26.4%.
CONCLUSION: This study successfully optimized an electronic palliative care identification algorithm with a PPV of 80.1% and indicates appropriate palliative care penetration rates may be as high as 26.4% of the total inpatient population.
OBJECTIVES: This study aims to improve identification of patients who would benefit from palliative care services, and calculate palliative care penetration rates.
METHODS: This study used a mixed-methods nonrandomized retrospective study design. Three 30-day iterations of clinical data were analyzed for patients identified by the electronic algorithm. During the second and third 30-day iterations, palliative care clinicians conducted chart reviews on a weekly basis for identified patients and determined whether the patients were appropriate for a palliative care consult. Positive predictive values (PPVs) were calculated. Based on the PPV, palliative care consult penetration rates were also calculated.
RESULTS: During the first iteration, the algorithm triggered 2995 times on 1384 unique patient encounters (69.3% of the total inpatient population). In the second iteration, the algorithm triggered 851 times on 477 unique patient encounters (26.4% of the total inpatient population). Eight hundred twenty-one chart reviews were completed on 420 unique patient encounters. The PPV was 68.3%. Based on the PPV, the projected palliative care penetration rate was 17.6%. During the third iteration, the algorithm triggered 1229 times on 539 unique patient encounters (33.3% of the total inpatient population). Nine hundred sixty-seven chart reviews were completed on 505 unique patient encounters. The PPV was 80.1%. Based on the PPV, the projected palliative care penetration rate was 26.4%.
CONCLUSION: This study successfully optimized an electronic palliative care identification algorithm with a PPV of 80.1% and indicates appropriate palliative care penetration rates may be as high as 26.4% of the total inpatient population.
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