Browsing by Subject "health sciences"

Background: The complex, multidimensional nature of healthcare quality makes provider and treatment decisions based on quality difficult. Patient-reported outcome (PRO) measures can enhance patient centricity and involvement. The proliferation of PRO measures, however, requires a simplification to improve comprehensibility. Composite measures can simplify complex data without sacrificing the underlying information. Objective and methods: We propose a five-step development approach to combine different PRO into one composite measure (PRO-CM): (i) theoretical framework and metric selection, (ii) initial data analysis, (iii) rescaling, (iv) weighting and aggregation, and (v) sensitivity and uncertainty analysis. We evaluate different rescaling, weighting, and aggregation methods by utilizing data of 3145 hip and 2605 knee replacement patients, to identify the most advantageous development approach for a PRO-CM that reflects quality variations from a patient perspective. Results: The comparison of different methods within steps (iii) and (iv) reveals the following methods as most advantageous: (iii) rescaling via z-score standardization and (iv) applying differential weights and additive aggregation. The resulting PROCM is most sensitive to variations in physical health. Changing weighting schemes impacts the PRO-CM most directly, while it proves more robust towards different rescaling and aggregation approaches. Conclusion: Combining multiple PRO provides a holistic picture of patients’ health improvement. The PRO-CM can enhance patient understanding and simplify reporting and monitoring of PRO. However, the development methodology of a PROCM needs to be justified and transparent to ensure that it is comprehensible and replicable. This is essential to address the well-known problems associated with composites, such as misinterpretation and lack of trust.

Background: Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group. Objective: In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a “core” intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (1) incentives to promote consistent daily adherence to goals, and (2) web health coaching to teach effective problem solving focused on personalized barriers to self-management. We will use a novel digital direct-to-patient recruitment method and intervention delivery model that transcends the clinic. Methods: A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A1c ≥ 8.0%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A1c. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A1c improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components. Results: Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025. Conclusions: Successful completion of these aims will support dissemination and effectiveness studies of this intervention that seeks to improve glycemic control in this high-risk and understudied population of young adults with T1D.

Effective interventions for the prevention and treatment of child and adolescent obesity play an important role in reducing the global health and economic burden of non-communicable diseases. Although multi-component interventions targeting various health behaviors are deemed promising, evidence for their effectiveness is still limited. Self-regulation seems to be a relevant working mechanism in this regard. Therefore, we propose a playful, smartphone-based self-regulation training that also utilizes the health benefits of a slow-paced breathing exercise. The mobile app uses the microphone of the smartphone to detect breathing sounds (e.g. inhalation, exhalation) and translates these sounds into a visual biofeedback on the smartphone screen. The design and evaluation of a very first prototype is described in this interdisciplinary work of obesity experts, clinical psychologists, young patients, and computer scientists. The apps' breathing detection module uses a random forest tree for quasi real-time classification of the incoming audio samples and biofeedback generation. A study with 11 obese children and adolescents was conducted to assess the prototype. Results indicate overall positive evaluations and suggestions for improvement. Implications and limitations are discussed, and an outlook on future work is provided.

In contrast to many other European countries, ambulatory services in Germany are traditionally mostly provided outside of hospitals. This is evident in international comparisons when looking at the share of expenditures for outpatient care provided by hospitals (in Germany 2.8%, in Portugal 39.8%), the share of physicians working at hospitals, the share of day cases or the organisation of secondary care provision. However, in the context of emergency care services, patients’ expectations of receiving more convenient and better accessible care for non-urgent or minor problems have led to increasingly crowded emergency departments. International experience suggests that different activities aiming at a better coordination of care, such as integrated call centers, extending out-of-hours services and offering ambulatory services within or nearby hospitals, can help to steer patients to the most appropriate provider. However, innovative and integrated health care service models are in conflict with the fragmented payment and planning structures in Germany.

Background As part of the EuroDRG project, researchers from 11 countries (i.e., Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Sweden, and Spain) compared how their diagnosis-related groups (DRG) systems deal with appendectomy patients. The study aims to assist surgeons and national authorities to optimize their DRG systems. Methods National or regional databases were used to identify hospital cases with a diagnosis of appendicitis treated with a procedure of appendectomy. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that together comprised at least 97% of cases. Six standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. Results European DRG systems vary widely: they classify appendectomy patients according to different sets of variables (between two and six classification variables) into diverging numbers of DRGs (between two and 11 DRGs). The most complex DRG is valued 5.1 times more resource intensive than an index case in France but only 1.1 times more resource intensive than an index case in Finland. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the most complex case vignette amount to only 1,005€ in Poland but to 12,304€ in France. Conclusions Large variations in the classification of appendectomy patients raise concerns whether all systems rely on the most appropriate classification variables. Surgeons and national DRG authorities should consider how other countries’ DRG systems classify appendectomy patients in order to optimize their DRG system and to ensure fair and appropriate reimbursement.

Successful interventions to prevent and manage type 2 diabetes rely on long-term, day-today decisions which take place outside of clinical settings. In this context, human resources are difficult to scale up, and leveraging Conversational agents (CAs) could be one way to scale up healthcare to tackle the emerging epidemic of type 2 diabetes. The objective of this paper is to assess the degree to which CAs are employed by top-funded digital health companies that target the prevention and management of type 2 diabetes. Companies were identified via two venture capital databases, i.e. Crunchbase Pro and Pitchbook. Two independent reviewers screened results and the final list of companies was validated and revised by three independent digital health experts. The companies' digital services (usually mobile applications) were accessed and reviewed for the utilisation of CAs. To better understand the purpose of identified CAs, relevant publications were identified via PubMed, Google Scholar, ACM Digital Library and on the companies' website. Nine out of 15 companies' digital services were accessible to the authors and only in one case a CA was employed. The uptake of CAs by top-funded digital health companies targeting type-2 diabetes is still low.