Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial.
AI
Artificial intelligence
Electronic medical record
Palliative care
Pragmatic clinical trials
Stepped wedge trials
Journal
Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253
Informations de publication
Date de publication:
16 Sep 2021
16 Sep 2021
Historique:
received:
02
11
2020
accepted:
16
08
2021
entrez:
17
9
2021
pubmed:
18
9
2021
medline:
21
9
2021
Statut:
epublish
Résumé
Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance. This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor. ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.
Sections du résumé
BACKGROUND
BACKGROUND
Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care.
METHODS
METHODS
To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance.
DISCUSSION
CONCLUSIONS
This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor.
TRIAL REGISTRATION
BACKGROUND
ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.
Identifiants
pubmed: 34530871
doi: 10.1186/s13063-021-05546-5
pii: 10.1186/s13063-021-05546-5
pmc: PMC8444160
doi:
Banques de données
ClinicalTrials.gov
['NCT03976297']
Types de publication
Clinical Trial Protocol
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
635Informations de copyright
© 2021. The Author(s).
Références
J Pain Symptom Manage. 2006 Apr;31(4):285-92
pubmed: 16632076
Stat Methods Med Res. 2013 Jun;22(3):324-45
pubmed: 22491174
J Clin Oncol. 2003 Mar 15;21(6):1133-8
pubmed: 12637481
BMC Palliat Care. 2013 Feb 15;12:7
pubmed: 23414145
J Am Med Inform Assoc. 2021 Jun 12;28(6):1065-1073
pubmed: 33611523
J Pain Symptom Manage. 1999 Apr;17(4):240-7
pubmed: 10203876
J Pain Symptom Manage. 1999 Apr;17(4):231-9
pubmed: 10203875
BMC Palliat Care. 2016 Feb 22;15:21
pubmed: 26906043
J Biomed Inform. 2019 Apr;92:103115
pubmed: 30753951
BMC Med Inform Decis Mak. 2018 Dec 12;18(Suppl 4):122
pubmed: 30537977
J Clin Oncol. 2008 Aug 10;26(23):3860-6
pubmed: 18688053
J Investig Med. 2012 Jun;60(5):768-75
pubmed: 22525233
Crit Care Med. 2019 Dec;47(12):1707-1715
pubmed: 31609772
Minerva Anestesiol. 2015 Dec;81(12):1318-28
pubmed: 25616205
J Clin Oncol. 2012 Feb 1;30(4):394-400
pubmed: 22203758
J Gen Intern Med. 2019 Sep;34(9):1841-1847
pubmed: 31313110
Arch Intern Med. 2009 Mar 9;169(5):480-8
pubmed: 19273778
Cancer. 2014 Jun 1;120(11):1743-9
pubmed: 24967463
J Palliat Care. 1996 Spring;12(1):5-11
pubmed: 8857241
N Engl J Med. 2010 Aug 19;363(8):733-42
pubmed: 20818875
Mayo Clin Proc. 2013 Aug;88(8):859-65
pubmed: 23910412
BMJ Open. 2018 Jan 21;8(1):e015550
pubmed: 29358415
Nat Biotechnol. 2016 Oct 11;34(10):1016-1018
pubmed: 27727210
J Palliat Med. 2005 Oct;8(5):1025-32
pubmed: 16238515