Prevention of AcuTe admIssioN algorithm (PATINA): study protocol of a stepped wedge randomized controlled trial

Background The challenges imposed by ageing populations will confront health care systems in the years to come. Hospital owners are concerned about the increasing number of acute admissions of older citizens and preventive measures such as integrated care models have been introduced in primary care. Yet, acute admission can be appropriate and lifesaving, but may also in itself lead to adverse health outcome, such as patient anxiety, functional loss and hospital-acquired infections. Timely identification of older citizens at increased risk of acute admission is therefore needed. We present the protocol for the PATINA study, which aims at assessing the effect of the ‘PATINA algorithm and decision support tool’, designed to alert community nurses of older citizens showing subtle signs of declining health and at increased risk of acute admission. This paper describes the methods, design and intervention of the study. Methods We use a stepped-wedge cluster randomized controlled trial (SW-RCT). The PATINA algorithm and decision support tool will be implemented in 20 individual area home care teams across three Danish municipalities (Kerteminde, Odense and Svendborg). The study population includes all home care receiving community-dwelling citizens aged 65 years and above (around 6500 citizens). An algorithm based on home care use triggers an alert based on relative increase in home care use. Community nurses will use the decision support tool to systematically assess health related changes for citizens with increased risk of acute hospital admission. The primary outcome is acute admission. Secondary outcomes are readmissions, preventable admissions, death, and costs of health care utilization. Barriers and facilitators for community nurse’s acceptance and use of the algorithm will be explored too. Discussion This ‘PATINA algorithm and decision support tool’ is expected to positively influence the care for older community-dwelling citizens, by improving nurses’ awareness of citizens at increased risk, and by supporting their clinical decision-making. This may increase preventive measures in primary care and reduce use of secondary health care. Further, the study will increase our knowledge of barriers and facilitators to implementing algorithms and decision support in a community care setup. Trial registration ClinicalTrials.gov, identifier: NCT04398797. Registered 13 May 2020.