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Institute
Background
The eResearch system “Prospective Monitoring and Management App (PIA)” allows researchers to implement questionnaires on any topic and to manage biosamples. Currently, we use PIA in the longitudinal study ZIFCO (Integrated DZIF Infection Cohort within the German National Cohort) in Hannover (Germany) to investigate e.g. associations of risk factors and infectious diseases. Our aim was to assess user acceptance and compliance to determine suitability of PIA for epidemiological research on transient infectious diseases.
Methods
ZIFCO participants used PIA to answer weekly questionnaires on health status and report spontaneous onset of symptoms. In case of symptoms of a respiratory infection, the app requested participants to self-sample a nasal swab for viral analysis. To assess user acceptance, we implemented the System Usability Scale (SUS) and fitted a linear regression model on the resulting score. For investigation of compliance with submitting the weekly health questionnaires, we used a logistic regression model with binomial response.
Results
We analyzed data of 313 participants (median age 52.5 years, 52.4% women). An average SUS of 72.0 reveals good acceptance of PIA. Participants with a higher technology readiness score at the beginning of study participation also reported higher user acceptance. Overall compliance with submitting the weekly health questionnaires showed a median of 55.7%. Being female, of younger age and being enrolled for a longer time decreased the odds to respond. However, women over 60 had a higher chance to respond than women under 60, while men under 40 had the highest chance to respond. Compliance with nasal swab self-sampling was 77.2%.
Discussion
Our findings show that PIA is suitable for the use in epidemiologic studies with regular short questionnaires. Still, we will focus on user engagement and gamification for the further development of PIA to help incentivize regular and long-term participation.
Purpose
This study aims to determine the intention to use hospital report cards (HRCs) for hospital referral purposes in the presence or absence of patient-reported outcomes (PROs) as well as to explore the relevance of publicly available hospital performance information from the perspective of referring physicians.
Methods
We identified the most relevant information for hospital referral purposes based on a literature review and qualitative research. Primary survey data were collected (May–June 2021) on a sample of 591 referring orthopedists in Germany and analyzed using structural equation modeling. Participating orthopedists were recruited using a sequential mixed-mode strategy and randomly allocated to work with HRCs in the presence (intervention) or absence (control) of PROs.
Results
Overall, 420 orthopedists (mean age 53.48, SD 8.04) were included in the analysis. The presence of PROs on HRCs was not associated with an increased intention to use HRCs (p = 0.316). Performance expectancy was shown to be the most important determinant for using HRCs (path coefficient: 0.387, p < .001). However, referring physicians have doubts as to whether HRCs can help them. We identified “complication rate” and “the number of cases treated” as most important for the hospital referral decision making; PROs were rated slightly less important.
Conclusions
This study underpins the purpose of HRCs, namely to support referring physicians in searching for a hospital. Nevertheless, only a minority would support the use of HRCs for the next hospital search in its current form. We showed that presenting relevant information on HRCs did not increase their use intention.
Purpose: The calculation of aggregated composite measures is a widely used strategy to reduce the amount of data on hospital report cards. Therefore, this study aims to elicit and compare preferences of both patients as well as referring physicians regarding publicly available hospital quality information.
Methods: Based on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit patients’ and referring physicians’ preferences. The DCEs were conducted using a fractional factorial design. Statistical data analysis was performed using multinomial logit models.
Results: Apart from five identical attributes, one specific attribute was identified for each study group, respectively. Overall, 322 patients (mean age 68.99) and 187 referring physicians (mean age 53.60) were included. Our models displayed significant coefficients for all attributes (p < 0.001 each). Among patients, “Postoperative complication rate” (20.6%; level range of 1.164) was rated highest, followed by “Mobility at hospital discharge” (19.9%; level range of 1.127), and ‘‘The number of cases treated” (18.5%; level range of 1.045). In contrast, referring physicians valued most the ‘‘One-year revision surgery rate’’ (30.4%; level range of 1.989), followed by “The number of cases treated” (21.0%; level range of 1.372), and “Postoperative complication rate” (17.2%; level range of 1.123).
Conclusion: We determined considerable differences between both study groups when calculating the relative value of publicly available hospital quality information. This may have an impact when calculating aggregated composite measures based on consumer-based weighting.