Prevention of AcuTe admIssioN Algorithm (PATINA)

Prevention of AcuTe admIssioN Algorithm (PATINA) - The Effect of a Notification Algorithm and a Digital Decision Support Tool

Utilization of public health care mirrors the older individual's overall health, and increased need for health care may precede more clear symptoms of acute disease. In a previous retrospective study, we found a significant increase in municipal home care service (minutes/week) in a 12-month period prior to acute hospitalization. This PhD-study aims at examining the effect of a software algorithm monitoring older citizens utilization of municipal home care. When home care increases the community nurse is notified and special attention is paid to the individual's current health situation. Pro-active health care initiatives may be launched in collaboration with the primary care physician. The intervention is the algorithm.

Study Overview

• Status: Completed
• Conditions: Avoid Acute Hospitalizations
• Intervention / Treatment: Other: PATINA algorithm

• Study Type: Interventional
• Enrollment (Actual): 2464
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Denmark
  • Odense, Denmark, 5000
    • Odense University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 65 years and older (OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Community dwelling citizens (+ 65) receiving home and social care in Odense, Kerteminde and Svendborg Municipalities.

Exclusion Criteria:

• Non

Study Plan

How is the study designed?

Design Details

• Primary Purpose: PREVENTION
• Allocation: RANDOMIZED
• Interventional Model: PARALLEL
• Masking: NONE

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
EXPERIMENTAL: Intervention; Use of notification algorithm and nurse follow-up	Other: PATINA algorithm; Once a week community nurses receive a list of community dwelling citizens deemed at risk of deterioration that can lead to hospital admission. Nurses assess each citizens situation and initiate intervention if needed (not part of the intervention).
NO_INTERVENTION: Control; Standard regime (usual care)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Hospitalizations	Calculated based on data from the Danish National Patient Registry	30 days from identification by the algorithm (6 months for intervention phase)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Hospital readmissions	Calculated based on data from the Danish National Patient Registry	30 days from identification by the algorithm (6 months for intervention phase)
Outpatient contacts	Calculated based on data from the Danish National Patient Registry	30 days from identification by the algorithm (6 months for intervention phase)
Use of temporary care in a skilled nursing facility	Calculated based on data from the three participating municipalities electronic care records	30 days from identification by the algorithm (6 months for intervention phase)
Use of community home and social care services	Calculated based on data from the three participating municipalities electronic care records	30 days from identification by the algorithm (6 months for intervention phase)
Contacts to primary care physicians	Calculated based on data from the Danish National Health Insurance Service Register	30 days from identification by the algorithm (6 months for intervention phase)
Mortality	Calculated based on data from the Danish Cause of death register.	30 days from identification by the algorithm (6 months for intervention phase)

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Barriers and facilitators for nurse's acceptance of using the algorithm	Calculated based on a survey battery collected 1 month pre intervention start, 14-7 days pre intervention start, 1 month post intervention start	30 days from identification by the algorithm (6 months for intervention phase)
Health economic analysis	The analytic details will be based on the primary and secondary results of the trail	1 year

Collaborators and Investigators

• Sponsor: Region of Southern Denmark
• Collaborators: Odense Municipality, Denmark; Kerteminde Municipality, Denmark; Svendborg Municipality, Denmark
• Investigators: Principal Investigator: Anders Fournaise, MSPH, Region of Southern Denmark

Publications and helpful links

General Publications

• Fournaise A, Lauridsen JT, Bech M, Wiil UK, Rasmussen JB, Kidholm K, Espersen K, Andersen-Ranberg K. Prevention of AcuTe admIssioN algorithm (PATINA): study protocol of a stepped wedge randomized controlled trial. BMC Geriatr. 2021 Feb 27;21(1):146. doi: 10.1186/s12877-021-02092-2.

Study record dates

Study Major Dates

• Study Start (ACTUAL): June 1, 2020
• Primary Completion (ACTUAL): May 31, 2022
• Study Completion (ACTUAL): May 31, 2022

Study Registration Dates

• First Submitted: May 13, 2020
• First Submitted That Met QC Criteria: May 18, 2020
• First Posted (ACTUAL): May 21, 2020

Study Record Updates

• Last Update Posted (ACTUAL): November 3, 2022
• Last Update Submitted That Met QC Criteria: October 25, 2022
• Last Verified: October 1, 2022

More Information

Terms related to this study

• Keywords: Acute hospitalization; Algorithm; Health care utilization; Community dwelling
• Other Study ID Numbers: 19/29873; 9163-00002B (OTHER_GRANT: Innovation Fund Denmark); 19/20991 (OTHER_GRANT: Region of Southern Denmark); OP_1033 (OTHER: Open Patient data Explorative Network (OPEN))

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No