Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study.
Aged
Aged, 80 and over
Decision Support Techniques
Electronic Health Records
Female
Hospitalization
/ statistics & numerical data
Humans
Male
Middle Aged
Non-Randomized Controlled Trials as Topic
Palliative Care
/ organization & administration
Patient Acceptance of Health Care
/ statistics & numerical data
Pilot Projects
Prospective Studies
Referral and Consultation
/ organization & administration
palliative care
prediction model
triggers
Journal
Journal of general internal medicine
ISSN: 1525-1497
Titre abrégé: J Gen Intern Med
Pays: United States
ID NLM: 8605834
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
31
01
2019
accepted:
24
06
2019
revised:
11
04
2019
pubmed:
18
7
2019
medline:
15
12
2020
entrez:
18
7
2019
Statut:
ppublish
Résumé
Development of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated. To assess the clinical impact of triggering palliative care using an EHR prediction model. Pilot prospective before-after study on the general medical wards at an urban academic medical center. Adults with a predicted probability of 6-month mortality of ≥ 0.3. Triggered (with opt-out) palliative care consult on hospital day 2. Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS). The control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P < .001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P = .02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P = .03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P = .01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P = .13), code status changes (42 [30] vs 39 [29]; P = .81), or consult requests for lower risk (< 0.3) patients (48/1004 [5] vs 33/798 [4]; P = .48). Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.
Sections du résumé
BACKGROUND
Development of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated.
OBJECTIVE
To assess the clinical impact of triggering palliative care using an EHR prediction model.
DESIGN
Pilot prospective before-after study on the general medical wards at an urban academic medical center.
PARTICIPANTS
Adults with a predicted probability of 6-month mortality of ≥ 0.3.
INTERVENTION
Triggered (with opt-out) palliative care consult on hospital day 2.
MAIN MEASURES
Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS).
KEY RESULTS
The control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P < .001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P = .02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P = .03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P = .01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P = .13), code status changes (42 [30] vs 39 [29]; P = .81), or consult requests for lower risk (< 0.3) patients (48/1004 [5] vs 33/798 [4]; P = .48).
CONCLUSIONS
Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.
Identifiants
pubmed: 31313110
doi: 10.1007/s11606-019-05169-2
pii: 10.1007/s11606-019-05169-2
pmc: PMC6712114
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1841-1847Références
J Pain Symptom Manage. 2018 Feb;55(2):226-235
pubmed: 28935130
J Palliat Med. 2013 Jun;16(6):661-8
pubmed: 23662953
JAMA Intern Med. 2018 Jun 1;178(6):820-829
pubmed: 29710177
N Engl J Med. 2007 Sep 27;357(13):1340-4
pubmed: 17898105
J Palliat Med. 2015 Dec;18(12):998-9
pubmed: 26556657
J Palliat Med. 2016 Jan;19(1):8-15
pubmed: 26417923
Support Care Cancer. 2018 Jan;26(1):175-180
pubmed: 28726065
Ann Intern Med. 2018 Dec 18;169(12):866-872
pubmed: 30508424
J Crit Care. 2016 Oct;35:7-11
pubmed: 27481729
J Palliat Med. 2011 Jan;14(1):17-23
pubmed: 21133809
Crit Care Med. 2018 Mar;46(3):460-464
pubmed: 29474328
Crit Care Med. 2018 Jun;46(6):e481-e488
pubmed: 29419557
JAMA. 2018 Apr 3;319(13):1317-1318
pubmed: 29532063
J Palliat Med. 2016 Mar;19(3):255-8
pubmed: 26849002
J Hosp Med. 2015 Jan;10(1):26-31
pubmed: 25263548
Am J Hosp Palliat Care. 2018 Jul;35(7):966-971
pubmed: 29169247
JAMA. 2016 Nov 22;316(20):2104-2114
pubmed: 27893131
J Palliat Med. 2016 Jul;19(7):696-7
pubmed: 27244246
Health Aff (Millwood). 2014 Jul;33(7):1139-47
pubmed: 25006139
J Pain Symptom Manage. 2011 Nov;42(5):657-62
pubmed: 22045368
J Palliat Med. 2015 Nov;18(11):956-61
pubmed: 26270277
J Pain Symptom Manage. 2010 Dec;40(6):899-911
pubmed: 21145468
Milbank Q. 2011 Sep;89(3):343-80
pubmed: 21933272
J Pain Symptom Manage. 2018 Feb;55(2):245-255.e8
pubmed: 28865870
West J Emerg Med. 2008 Jan;9(1):6-8
pubmed: 19561695
Health Aff (Millwood). 2016 Sep 1;35(9):1690-7
pubmed: 27605652
Ann Intern Med. 2018 Jan 2;168(1):71-72
pubmed: 29132161
AMIA Annu Symp Proc. 2018 Apr 16;2017:625-634
pubmed: 29854127
N Engl J Med. 2010 Aug 19;363(8):733-42
pubmed: 20818875
J Gen Intern Med. 2018 Jun;33(6):921-928
pubmed: 29383551
Respir Med. 2013 Nov;107(11):1731-9
pubmed: 23810150
J Hosp Med. 2018 Aug 29;13(12):868-871
pubmed: 30156581
BMJ Support Palliat Care. 2014 Mar;4(1):30-7
pubmed: 24644768
JAMA. 2018 Jul 3;320(1):27-28
pubmed: 29813156
Ann Intern Med. 2016 Jan 19;164(2):114-9
pubmed: 26595370
J Palliat Med. 2018 Mar;21(S2):S17-S27
pubmed: 29091522
Health Aff (Millwood). 2017 Jul 1;36(7):1265-1273
pubmed: 28679814
J Pain Symptom Manage. 2015 Oct;50(4):462-9
pubmed: 26087471
BMC Med Inform Decis Mak. 2018 Dec 12;18(Suppl 4):122
pubmed: 30537977
Crit Care Med. 1996 Jan;24(1):57-63
pubmed: 8565539
Arch Intern Med. 2008 Sep 8;168(16):1783-90
pubmed: 18779466
Am J Hosp Palliat Care. 2018 Mar;35(3):417-422
pubmed: 28571498
Ann Surg. 2019 Apr;269(4):652-662
pubmed: 29489489
J Palliat Med. 2017 Jul;20(7):745-751
pubmed: 28471732
Intensive Care Med. 2017 Dec;43(12):1847-1849
pubmed: 28656453
Ann Am Thorac Soc. 2016 Sep;13(9):1629-39
pubmed: 27348271
Crit Care Med. 2018 Jul;46(7):1125-1132
pubmed: 29629986