Background
The challenges imposed by demographic changes will confront health care systems in the years to come [
1]. It is a global concern that even highly effective health care systems will struggle with meeting the demands of ageing populations [
2], as higher age is associated with multimorbidity, health deterioration with functional decline, and subsequent increased utilization of health care services [
3‐
6].
In older citizens acute hospitalization can be highly necessary and lifesaving, but may also lead to adverse consequences, such as hospital-acquired infections, anxiety and distress, poorer functional health, and death [
3,
7‐
10]. Prevention of acute admission is therefore exceptionally important in higher age groups, but requires timely detection of disease symptoms, functional and mental deterioration and health care interventions. However, early recognition of disease is hampered by diagnostic challenges following older citizens’ higher prevalence of multimorbidity, polypharmacy, functional impairment, and social issues, which altogether yield complex interactions and increases the risk of mismanagement [
11]. In addition, atypical presentation of symptoms may delay timely diagnosis, which is why novel predictive tools are needed for timely recognition of older citizens at increased risk of acute disease and subsequent acute hospitalization [
12].
Prediction models using data from electronic health records to identify those in the highest risk of acute hospitalization have been increasingly studied. A systematic review from 2014 identified 27 prediction models for acute admissions. However, only half of the models were targeted older citizens, and in addition, the predictive models in general required large administrative or clinical data sets, which were analyzed retrospectively [
13]. Most models are based on data from electronic hospital records and many have been found effective in predicting risk of acute admissions [
13‐
19]. Only a few studies have focused on prediction models solely based on home care data [
20], and very few studies have implemented and tested a prediction model in practice, mainly due to ethical and economic considerations [
21], barriers among professionals, such as trust in the technology [
22], and the fact that data on health and care is registered primarily for the use and support for health professionals, not for input to algorithms [
23]. Thus, datasets are more often designed for retrospective analysis than for prospective use [
24].
In a tax-funded (Beveridgian) health care system free of charge for health care receivers utilization of health and home care services mirrors older citizen’s overall health [
25]. An increased need of health care may be the first sign of emerging acute disease. In an earlier retrospective study, we found a significant increase in municipal home care service (hours/week) over a 12-month period prior to acute hospitalization [
3]. Based on this finding we have developed the Prevention of AcuTe admIssioN Algorithm (PATINA), a novel predictive model that analyzes administrative data on home care utilization and yields a warning to community nurses (hereafter referred to as nurses) about citizens at increased risk of acute hospitalization. Further, to assist nurses in their assessment of citizens identified by the algorithm as being ‘at risk’, a decision support tool has been developed. The intervention is presented in the method section.
The primary objective of the PATINA project is to implement and evaluate the combined effect of the ‘PATINA algorithm and the decision support tool’ on the prevention of acute hospital admissions of older community-dwelling citizens [
3]. Further, we will evaluate the effects on citizens utilization of health care service in the primary and secondary health care sector and estimate costs. A second objective is to investigate nurses’ role in achieving the desired effects of the implementation, by linking employees’ motivation and attitudes toward the ‘PATINA algorithm and the decision support tool’ to the primary and secondary outcome of the study.
In this paper, we present the study protocol for the PATINA project, where we use a stepped-wedge randomized controlled trial, explaining the methods, design and intervention of the study. The study protocol (version 1.2, 13 May 2020) follows the SPIRIT statement [
26] and has been adapted to suit the format of an article.
Discussion
Demographic changes towards older populations pressure most industrialized countries health care systems to adapt integrated care models, as this may reduce the use of secondary health care services, but also means an expansion of the primary health care services [
42]. To prevent the need for treatment in secondary health care, it is essential to provide sufficient treatment and care for older citizens, especially those who are frail, as this population have greater need for health care than younger citizens.
Even though it is still a novel area of research, prediction models developed to predict the risk of acute hospitalization among older community-dwelling citizens have been found to be effective in achieving predictive performance [
18‐
20]. These models have primarily been developed based on health care data from electronic hospital records. To our knowledge the PATINA study will be one of the first studies to implement and test the effect of a prediction algorithm based on home care utilization in a real-life setting [
3]. The algorithm and the tailored decision support tool will help nurses to systematically assess the health status and risk of acute hospitalization of older citizens. The expected benefits are reduced use of secondary health care services and improved patient outcomes. Further, we expect that the project will introduce a more detailed and suiting professional language for describing the unclear symptoms that may precede acute disease in older citizens. Such a language can be utilized both inside the municipal organization and in the cross-sectoral collaboration.
The stepped-wedge randomized controlled trial is well suited for studying and evaluating of service type interventions [
43]. The random and sequential cross-over is pragmatic to implement as it mimics how large organizations involved in delivering health care service implement interventions. The stepped-wedge study design is especially relevant when evidence in support of an intervention already exist, as the intervention can be fully implemented after the completion of the study [
43]. Further, the design is well suited for politically organized public authorities such as the Danish regions and municipalities, as it allows to incorporate rigorous scientific evaluations when implementing large interventions or changes without delaying the implementation process.
The sequential cross-over also allows researchers to study temporal effect with high accuracy compared to other cluster designs [
43,
44]. However, in the PATINA study the cross-over must be handled with care as some area home care teams in Kerteminde and Svendborg municipalities are not geographically separated. Further, the design introduces a potential risk of secular trends unrelated to the PATINA algorithm and decision support tool. The study design also introduces a risk of unequal exposure to seasonal trends, as more citizens will be exposed to the algorithm in the end of the study [
43,
44]. During the study we will keep track of changes and other interventions implemented in the three municipalities as well as on a regional and national level. Further, we will be aware and adjust for both the clustered design and confounding effect of time in our statistical analyses [
41].
Like many other welfare states’ health care systems Danish regions and municipalities are economically incentivized to prevent unnecessary use of secondary health care services, especially acute admissions and readmission of older citizens. By completing an economic analysis focusing on the cost of changes in both municipal and regional health care utilization as well as cost of implementation it is our hope that we will be able to highlight potential economic incentives of introducing the PATINA algorithm and decision support tool. Further, by studying the barriers and facilitators for nurses’ acceptance and use of the ‘PATINA algorithm and decision support tool’, we hope to deliver key-insights at the managerial level when implementing interventions such as the PATINA algorithm.
Study status
After achieving the necessary ethical and governance approvals the study began including area home care teams in the three Danish municipalities on the June 1, 2020 with an expected end date of May 31, 2021. The study start was postponed from April 1, 2020 (two months) due to COVID-19 and has run continuously since.
Acknowledgements
The authors would like to acknowledge Marie Birkemose (University of Southern Denmark) for her assistance and input during the development and preparation of the PATINA algorithm. Finally, the authors also acknowledge David Hass from OPEN, Odense University Hospital, Region of Southern Denmark for assistance in developing the data management setup for the project.
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