Prevention of AcuTe admIssioN Algorithm (PATINA)

Study Overview
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Brief Summary
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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.

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

Conditions
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Avoid Acute Hospitalizations

Intervention / Treatment
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* Other: PATINA algorithm


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Community dwelling citizens (+ 65) receiving home and social care in Odense, Kerteminde and Svendborg Municipalities. Exclusion Criteria: Non

Ages Eligible for Study
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Minimum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Step-wedge, open label, cluster randomized controlled trial with 1 year follow up. Area home care teams from three Danish municipalities are randomized to cross-over from the control phase to the intervention phase. Participants automatically follow their area home care team into the allocated group.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention<br>Use of notification algorithm and nurse follow-up | Other: PATINA algorithm<br>* 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).<br>|
| No Intervention: Control<br>Standard regime (usual care) | |

What is the study measuring?
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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) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Algorithm, Community dwelling, Health care utilization, Acute hospitalization