palliative connect: triggered palliative care …...palliative care already consulted 6 (10.3) no...
TRANSCRIPT
Palliative Connect: Triggered Palliative Care Consultation Using an EHR Prediction ModelKatherine R. Courtright, MD, MS1,2,3, Corey Chivers, PhD4, Michael Becker, BS4, Susan H. Regli, PhD4, Linnea Pepper, MD3, Robert L. Stetson, MHA5, Michael Draugelis, BS4, Nina O’Connor, MD, FAAHPM1,3
o Frequency and timing of palliative care
consultation are highly variable among patients with different life-limiting
illnesses
o Triggering palliative care consultation based on certain diagnoses in the EHR is
increasing, but this strategy is a poor surrogate for actual needs and is neither
equitable nor sustainable
RATIONALE
1Palliative and Advanced Illness Research Center, Perelman School of Medicine; 2Center for Health Incentives and Behavioral Economics; 3Department of Medicine, Perelman School of Medicine; 4Predictive Analytics, University of
Pennsylvania Health System; 5Corporate Office of Strategic Decision Support, University of Pennsylvania Health Systems, all at the University of Pennsylvania
o Evaluate the feasibility and clinical
impact of triggering palliative care consultation based on predicted risk of
6-month mortality
OBJECTIVE
Prediction model development
o Model development phase: 70/30 split
of admissions (N=64,246) to 3 hospitals
at 1 academic center in 2016;
multivariate logistic regression model
including demographics, comorbidities
POA, laboratory values, admission type;
C-statistic and calibration curve
o Trigger evaluation phase: pre-post pilot
study on hospitalist services at 1 urban,
academic hospital; Intervention:
triggered (with opt-out) palliative care
consult on hospital day 2 if risk of 6-
month mortality ≥0.3 (IRR 100%);
Outcomes: palliative care processes,
clinical outcomes, and direct costs
METHODS
RESULTS
Figure 1. EHR 6-month mortality prediction model AUROC
CONCLUSIONS
Figure 2. EHR 6-month mortality prediction model calibration
Consult trigger evaluationTable 1. Characteristics of patients in the pre-post intervention cohorts
Characteristic* Control (n=142) Intervention (n=134) Patient age (year), median (IQR) 72.5 (65.5, 81.6) 72.6 (63.0, 83.0) Female, n (%) 54 (38.0) 58 (43.3) Race, n (%) White 83 (58.5) 69 (51.5) Black 52 (36.6) 59 (44.0) Asian 5 (3.5) 3 (3.3) Other/Unknown 2 (1.4) 3 (2.2) Married, n (%) 83 (58.5) 72 (53.7) Admission type urgent, n (%) 142 (100) 133 (99) Elixhauser Index, median (IQR) 9 (6, 12) 8 (6, 12) Palliative Connect score, mean (SD) 0.5 (0.2) 0.5 (0.2)
Abbreviations: IQR, interquartile range; SD, standard deviation *p<0.05 for all comparisons between control and intervention cohorts
Reasons for declined triggered consults (n=58) n (%) No palliative care needs at this time 24 (41.4) Primary team meeting palliative care needs 8 (13.8) Discharge anticipated soon 8 (13.8) Hospice already consulted 6 (10.3) Palliative care already consulted 6 (10.3) No reason provided 4 (6.9) Other 2 (3.5)
Table 2. Reasons from primary team for declining triggered palliative care consultation
Table 3. Intention-to-treat analysis of triggered palliative care consultation among patients with predicted 6-month mortality risk ≥0.3
Measure Control (n=142) Intervention (n=134) p-value Clinical outcomes In-hospital mortality, n (%) 7 (5.0) 2 (1.5) 0.11 Hospital length of stay (day), median (IQR) 5.7 (3.5, 9.8) 5.9 (3.9, 10.6) 0.50 ICU admission, n (%) 33 (23.2) 19 (14.2) 0.05 ICU length of stay (day), median (IQR) 4.4 (1.4,6.2) 2.7 (1.9,4.7) 0.50 30-day all-cause readmission* 29/130 (22.3) 23/127 (18.1) 0.40 Palliative care processes Palliative care consult order 23 (16.2) 84 (62.7) <0.001 Pre-consult length of stay (day), median (IQR) 2.8 (1.3, 5.8) 1.2 (0.7,2.7) 0.0013 Advance care planning documentation, n (%) 24 (16.9) 36 (24.9) 0.05 Change in code status, n (%) 43 (30.3) 40 (29.9) .094 Outpatient palliative care referral, n (%) 6 (4.3) 22 (16.4) <0.001 Hospice discharge*, n (%) 13 (9.2) 23 (17.2) 0.05 Economic outcomes Total hospital direct costs, median (IQR) $8,814 (5,623, 20,070) $9,088 (5,365, 16,648) 0.92
o An EHR risk stratification tool reliably identifies patients with high risk of
mortality within 6 months who would
not otherwise have received palliative
care consultation
o Triggering inpatient palliative care consultation based on predicted 6-
month mortality risk is feasible; leads to
earlier and more frequent high quality
palliative care; and may improve clinical outcomes
LIMITATIONS
o A single center study at a large academic
center with a multidisciplinary palliative
care team
o Non-randomized study design
o Several (n=14) eligible patients unable to be offered a triggered consult due to
palliative care team strain
FUTURE DIRECTIONS
o Expanded intervention across all medical
services at 2 hospitals (ongoing)
o Interviews with stakeholders to explore
perceptions of and preferences for EHR
triggers (ongoing)
o Determine optimal strategy for palliative
care delivery at different risk thresholds
This study was funded in part by a career development award from the National Palliative Care Research Center (KRC)Author contact: [email protected]