Self-Care Index and Post-Acute Care Discharge Score to Predict Discharge Destination of Adult Medical Inpatients: Protocol for a Multicenter Validation Study.
discharge planning
forecasting
logistic models
patient transfer
post-acute care discharge score
protocol
self-care index
sensitivity
specificity
validation study
Journal
JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504
Informations de publication
Date de publication:
14 Jan 2021
14 Jan 2021
Historique:
received:
16
06
2020
accepted:
01
12
2020
revised:
14
09
2020
pubmed:
3
12
2020
medline:
3
12
2020
entrez:
2
12
2020
Statut:
epublish
Résumé
Delays in patient discharge can not only lead to deterioration, especially among geriatric patients, but also incorporate unnecessary resources at the hospital level. Many of these delays and their negative impact may be preventable by early focused screening to identify patients at risk for transfer to a post-acute care facility. Early interprofessional discharge planning is crucial in order to fit the appropriate individual discharge destination. While prediction of discharge to a post-acute care facility using post-acute care discharge score, the self-care index, and a combination of both has been shown in a single-center pilot study, an external validation is still missing. This paper outlines the study protocol and methodology currently being used to replicate the previous pilot findings and determine whether the post-acute care discharge score, the self-care index, or the combination of both can reliably identify patients requiring transfer to post-acute care facilities. This study will use prospective data involving all phases of the quasi-experimental study "In-HospiTOOL" conducted at 7 Swiss hospitals in urban and rural areas. During an 18-month period, consecutive adult medical patients admitted to the hospitals through the emergency department will be included. We aim to include 6000 patients based on sample size calculation. These data will enable a prospective external validation of the prediction instruments. We expect to gain more insight into the predictive capability of the above-mentioned prediction instruments. This approach will allow us to get important information about the generalizability of the three different models. The study was approved by the institutional review board on November 21, 2016, and funded in May 2020. Expected results are planned to be published in spring 2021. This study will provide evidence on prognostic properties, comparative performance, reliability of scoring, and suitability of the instruments for the screening purpose in order to be able to recommend application in clinical practice. DERR1-10.2196/21447.
Sections du résumé
BACKGROUND
BACKGROUND
Delays in patient discharge can not only lead to deterioration, especially among geriatric patients, but also incorporate unnecessary resources at the hospital level. Many of these delays and their negative impact may be preventable by early focused screening to identify patients at risk for transfer to a post-acute care facility. Early interprofessional discharge planning is crucial in order to fit the appropriate individual discharge destination. While prediction of discharge to a post-acute care facility using post-acute care discharge score, the self-care index, and a combination of both has been shown in a single-center pilot study, an external validation is still missing.
OBJECTIVE
OBJECTIVE
This paper outlines the study protocol and methodology currently being used to replicate the previous pilot findings and determine whether the post-acute care discharge score, the self-care index, or the combination of both can reliably identify patients requiring transfer to post-acute care facilities.
METHODS
METHODS
This study will use prospective data involving all phases of the quasi-experimental study "In-HospiTOOL" conducted at 7 Swiss hospitals in urban and rural areas. During an 18-month period, consecutive adult medical patients admitted to the hospitals through the emergency department will be included. We aim to include 6000 patients based on sample size calculation. These data will enable a prospective external validation of the prediction instruments.
RESULTS
RESULTS
We expect to gain more insight into the predictive capability of the above-mentioned prediction instruments. This approach will allow us to get important information about the generalizability of the three different models. The study was approved by the institutional review board on November 21, 2016, and funded in May 2020. Expected results are planned to be published in spring 2021.
CONCLUSIONS
CONCLUSIONS
This study will provide evidence on prognostic properties, comparative performance, reliability of scoring, and suitability of the instruments for the screening purpose in order to be able to recommend application in clinical practice.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
UNASSIGNED
DERR1-10.2196/21447.
Identifiants
pubmed: 33263553
pii: v10i1e21447
doi: 10.2196/21447
pmc: PMC7843199
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e21447Informations de copyright
©Antoinette Conca, Daniel Koch, Katharina Regez, Alexander Kutz, Ciril Bächli, Sebastian Haubitz, Philipp Schuetz, Beat Mueller, Rebecca Spirig, Heidi Petry. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 14.01.2021.
Références
J Am Geriatr Soc. 2008 Dec;56(12):2171-9
pubmed: 19093915
BMC Health Serv Res. 2015 Jun 25;15:246
pubmed: 26108373
J Hosp Med. 2012 Feb;7(2):73-8
pubmed: 22173979
Med Care. 1989 Feb;27(2):112-29
pubmed: 2918764
BMJ. 2013 Feb 05;346:e5595
pubmed: 23386360
J Med Internet Res. 2020 Apr 28;22(4):e15573
pubmed: 32343248
BMC Pulm Med. 2010 Mar 11;10:12
pubmed: 20222964
Eur Respir J. 2013 Oct;42(4):1064-75
pubmed: 23349444
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
J Aging Health. 2015 Jun;27(4):670-85
pubmed: 25414168
Prof Case Manag. 2011 Sep-Oct;16(5):240-50; quiz 251-2
pubmed: 21849873
Krankenpfl Soins Infirm. 2013;106(1):20-3
pubmed: 23405447
Aust Health Rev. 2017 Mar;41(1):54-62
pubmed: 27028335
PLoS Med. 2013;10(2):e1001380
pubmed: 23393429
Swiss Med Wkly. 2011 Jul 30;141:w13237
pubmed: 21805408
BMC Health Serv Res. 2019 Apr 23;19(1):237
pubmed: 31014343
BMC Health Serv Res. 2008 Jul 22;8:154
pubmed: 18647410
PLoS One. 2019 Mar 28;14(3):e0214194
pubmed: 30921356
J Gerontol A Biol Sci Med Sci. 1996 Sep;51(5):M189-94
pubmed: 8808987
BMC Health Serv Res. 2018 Feb 13;18(1):111
pubmed: 29439684
BMC Health Serv Res. 2020 Mar 4;20(1):161
pubmed: 32131817
Stat Med. 2008 Jan 30;27(2):157-72; discussion 207-12
pubmed: 17569110
Med Decis Making. 2006 Nov-Dec;26(6):565-74
pubmed: 17099194
Geriatr Gerontol Int. 2016 Mar;16(3):314-21
pubmed: 25752922
J Am Geriatr Soc. 2011 Jul;59(7):1206-16
pubmed: 21649616
J Clin Epidemiol. 2016 Jun;74:167-76
pubmed: 26772608
Eur J Public Health. 2006 Apr;16(2):203-8
pubmed: 16076854
PLoS Med. 2013;10(2):e1001381
pubmed: 23393430
BMC Med Res Methodol. 2014 Mar 19;14:40
pubmed: 24645774
Ann Intern Med. 2015 Jan 6;162(1):W1-73
pubmed: 25560730
Ann Intern Med. 2015 Jan 6;162(1):55-63
pubmed: 25560714
BMC Res Notes. 2015 Oct 28;8:612
pubmed: 26510822
J Clin Epidemiol. 2018 Jun;98:133-143
pubmed: 29174118
Swiss Med Wkly. 2017 Nov 09;147:w14515
pubmed: 29120010
Neurology. 1999 Jan 15;52(2):224-7
pubmed: 9932934
J Clin Med Res. 2012 Dec;4(6):402-9
pubmed: 23226173
Age Ageing. 2005 Sep;34(5):467-75
pubmed: 16043443
Epidemiology. 2010 Jan;21(1):128-38
pubmed: 20010215
Med Decis Making. 2013 May;33(4):490-501
pubmed: 23313931
Enferm Clin. 2016 Mar-Apr;26(2):121-8
pubmed: 26777483