Avoid Acute Hospitalizations
Conditions
Keywords
Algorithm, Community dwelling, Health care utilization, Acute hospitalization
Brief summary
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.
Interventions
OTHERPATINA 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).
Sponsors
Odense Municipality, Denmark
Kerteminde Municipality, Denmark
Svendborg Municipality, Denmark
Region of Southern Denmark
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE
Intervention 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.
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Community dwelling citizens (+ 65) receiving home and social care in Odense, Kerteminde and Svendborg Municipalities.
Exclusion criteria
* Non
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Hospitalizations | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the Danish National Patient Registry |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Use of community home and social care services | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the three participating municipalities electronic care records |
| Contacts to primary care physicians | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the Danish National Health Insurance Service Register |
| Mortality | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the Danish Cause of death register. |
| Outpatient contacts | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the Danish National Patient Registry |
| Use of temporary care in a skilled nursing facility | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the three participating municipalities electronic care records |
| Hospital readmissions | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on data from the Danish National Patient Registry |
Other
| Measure | Time frame | Description |
|---|---|---|
| Barriers and facilitators for nurse's acceptance of using the algorithm | 30 days from identification by the algorithm (6 months for intervention phase) | Calculated based on a survey battery collected 1 month pre intervention start, 14-7 days pre intervention start, 1 month post intervention start |
| Health economic analysis | 1 year | The analytic details will be based on the primary and secondary results of the trail |
Countries
Denmark
Outcome results
None listed