TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) Project: Protocol for an International Longitudinal Multicenter Study.

adverse events electronic health record hospital care no-harm incidents nurse staffing nursing care nursing-sensitive events patient safety systematic record review

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
22 Apr 2024
Historique:
received: 19 01 2024
accepted: 06 03 2024
revised: 04 03 2024
medline: 22 4 2024
pubmed: 22 4 2024
entrez: 22 4 2024
Statut: epublish

Résumé

Nursing-sensitive events (NSEs) are common, accounting for up to 77% of adverse events in hospitalized patients (eg, fall-related harm, pressure ulcers, and health care-associated infections). NSEs lead to adverse patient outcomes and impose an economic burden on hospitals due to increased medical costs through a prolonged hospital stay and additional medical procedures. To reduce NSEs and ensure high-quality nursing care, appropriate nurse staffing levels are needed. Although the link between nurse staffing and NSEs has been described in many studies, appropriate nurse staffing levels are lacking. Existing studies describe constant staffing exposure at the unit or hospital level without assessing patient-level exposure to nurse staffing during the hospital stay. Few studies have assessed nurse staffing and patient outcomes using a single-center longitudinal design, with limited generalizability. There is a need for multicenter longitudinal studies with improved potential for generalizing the association between individual nurse staffing levels and NSEs. This study aimed (1) to determine the prevalence, preventability, type, and severity of NSEs; (2) to describe individual patient-level nurse staffing exposure across hospitals; (3) to assess the effect of nurse staffing on NSEs in patients; and (4) to identify thresholds of safe nurse staffing levels and test them against NSEs in hospitalized patients. This international multicenter study uses a longitudinal and observational research design; it involves 4 countries (Switzerland, Sweden, Germany, and Iran), with participation from 14 hospitals and 61 medical, surgery, and mixed units. The 16-week observation period will collect NSEs using systematic retrospective record reviews. A total of 3680 patient admissions will be reviewed, with 60 randomly selected admissions per unit. To be included, patients must have been hospitalized for at least 48 hours. Nurse staffing data (ie, the number of nurses and their education level) will be collected daily for each shift to assess the association between NSEs and individual nurse staffing levels. Additionally, hospital data (ie, type, teaching status, and ownership) and unit data (ie, service line and number of beds) will be collected. As of January 2024, the verification process for the plausibility and comprehensibility of patients' and nurse staffing data is underway across all 4 countries. Data analyses are planned to be completed by spring 2024, with the first results expected to be published in late 2024. This study will provide comprehensive information on NSEs, including their prevalence, preventability, type, and severity, across countries. Moreover, it seeks to enhance understanding of NSE mechanisms and the potential impact of nurse staffing on these events. We will evaluate within- and between-hospital variability to identify productive strategies to ensure safe nurse staffing levels, thereby reducing NSEs in hospitalized patients. The TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) study will focus on the optimization of scarce staffing resources. DERR1-10.2196/56262.

Sections du résumé

BACKGROUND BACKGROUND
Nursing-sensitive events (NSEs) are common, accounting for up to 77% of adverse events in hospitalized patients (eg, fall-related harm, pressure ulcers, and health care-associated infections). NSEs lead to adverse patient outcomes and impose an economic burden on hospitals due to increased medical costs through a prolonged hospital stay and additional medical procedures. To reduce NSEs and ensure high-quality nursing care, appropriate nurse staffing levels are needed. Although the link between nurse staffing and NSEs has been described in many studies, appropriate nurse staffing levels are lacking. Existing studies describe constant staffing exposure at the unit or hospital level without assessing patient-level exposure to nurse staffing during the hospital stay. Few studies have assessed nurse staffing and patient outcomes using a single-center longitudinal design, with limited generalizability. There is a need for multicenter longitudinal studies with improved potential for generalizing the association between individual nurse staffing levels and NSEs.
OBJECTIVE OBJECTIVE
This study aimed (1) to determine the prevalence, preventability, type, and severity of NSEs; (2) to describe individual patient-level nurse staffing exposure across hospitals; (3) to assess the effect of nurse staffing on NSEs in patients; and (4) to identify thresholds of safe nurse staffing levels and test them against NSEs in hospitalized patients.
METHODS METHODS
This international multicenter study uses a longitudinal and observational research design; it involves 4 countries (Switzerland, Sweden, Germany, and Iran), with participation from 14 hospitals and 61 medical, surgery, and mixed units. The 16-week observation period will collect NSEs using systematic retrospective record reviews. A total of 3680 patient admissions will be reviewed, with 60 randomly selected admissions per unit. To be included, patients must have been hospitalized for at least 48 hours. Nurse staffing data (ie, the number of nurses and their education level) will be collected daily for each shift to assess the association between NSEs and individual nurse staffing levels. Additionally, hospital data (ie, type, teaching status, and ownership) and unit data (ie, service line and number of beds) will be collected.
RESULTS RESULTS
As of January 2024, the verification process for the plausibility and comprehensibility of patients' and nurse staffing data is underway across all 4 countries. Data analyses are planned to be completed by spring 2024, with the first results expected to be published in late 2024.
CONCLUSIONS CONCLUSIONS
This study will provide comprehensive information on NSEs, including their prevalence, preventability, type, and severity, across countries. Moreover, it seeks to enhance understanding of NSE mechanisms and the potential impact of nurse staffing on these events. We will evaluate within- and between-hospital variability to identify productive strategies to ensure safe nurse staffing levels, thereby reducing NSEs in hospitalized patients. The TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) study will focus on the optimization of scarce staffing resources.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/56262.

Identifiants

pubmed: 38648083
pii: v13i1e56262
doi: 10.2196/56262
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e56262

Informations de copyright

©Stefanie Bachnick, Maria Unbeck, Maryam Ahmadi Shad, Katja Falta, Nicole Grossmann, Daniela Holle, Jana Bartakova, Sarah N Musy, Sarah Hellberg, Pernilla Dillner, Fatemeh Atoof, Mohammadhossein Khorasanizadeh, Paula Kelly-Pettersson, Michael Simon. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 22.04.2024.

Auteurs

Stefanie Bachnick (S)

Department of Nursing Science, University of Applied Sciences, Bochum, Germany.

Maria Unbeck (M)

School of Health and Welfare, Dalarna University, Falun, Sweden.
Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.

Maryam Ahmadi Shad (M)

Institute of Nursing Science, Department Public Health, Faculty of Medicine, University of Basel, Basel, Switzerland.

Katja Falta (K)

Department of Nursing Science, University of Applied Sciences, Bochum, Germany.

Nicole Grossmann (N)

Institute of Nursing Science, Department Public Health, Faculty of Medicine, University of Basel, Basel, Switzerland.

Daniela Holle (D)

Department of Nursing Science, University of Applied Sciences, Bochum, Germany.

Jana Bartakova (J)

Institute of Nursing Science, Department Public Health, Faculty of Medicine, University of Basel, Basel, Switzerland.
Health Economics Facility, Department of Public Health, University of Basel, Basel, Switzerland.

Sarah N Musy (SN)

Institute of Nursing Science, Department Public Health, Faculty of Medicine, University of Basel, Basel, Switzerland.

Sarah Hellberg (S)

Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
Department of Orthopaedics, Danderyd University Hospital, Stockholm, Sweden.

Pernilla Dillner (P)

Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
Department of Neonatology, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.

Fatemeh Atoof (F)

Social Determinants of Health Research Center, Kashan University of Medical Sciences, Kashan, Iran.

Mohammadhossein Khorasanizadeh (M)

Trauma Nursing Research Center, Kashan University of Medical Sciences, Kashan, Iran.

Paula Kelly-Pettersson (P)

Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
Department of Orthopaedics, Danderyd University Hospital, Stockholm, Sweden.

Michael Simon (M)

Institute of Nursing Science, Department Public Health, Faculty of Medicine, University of Basel, Basel, Switzerland.

Classifications MeSH