Adaptive Nutzenbewertung für Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen

Das GKV-VSG führte erstmalig die Nutzenbewertung von Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen und invasivem Charakter ein. Zukünftig wird die Entscheidung über die Erstattungsfähigkeit von der Dokumentation von Nutzen und Schaden abhängig sein. Grundsätzlich kann die Nutzenbewertung in drei Phasen unterteilt werden: Messen von kausalen Effekten einer Intervention, Bewertung der gemessenen Effekte und Entscheidung über die Erstattungsfähigkeit eines aggregierten Gesamtnutzens. Um den Anforderungen zu begegnen, stellen adaptive Studiendesigns, die multikriterielle Entscheidungsanalyse und die adaptive Nutzenbewertung ein zukunftsfähiges Konzept für einen schnellen Zugang von Patienten zu innovativen Behandlungsmethoden bei hoher Qualität und Sicherheit dar

What factors influence HIV testing? Modeling preference heterogeneity using latent classes and class-independent random effects.

Efforts to eliminate the HIV epidemic will require increased HIV testing rates among high-risk populations. To inform the design of HIV testing interventions, a discrete choice experiment (DCE) with six policy-relevant attributes of HIV testing options elicited the testing preferences of 300 female barworkers and 440 male Kilimanjaro mountain porters in northern Tanzania. Surveys were administered between September 2017 and July 2018. Participants were asked to complete 12 choice tasks, each involving first- and second-best choices from 3 testing options. DCE responses were analyzed using a random effects latent class logit (RELCL) model, in which the latent classes summarize common participant preference profiles, and the random effects capture additional individual-level preference heterogeneity with respect to three attribute domains: (a) privacy and confidentiality (testing venue, pre-test counseling, partner notification); (b) invasiveness and perceived accuracy (method for obtaining the sample for the HIV test); and (c) accessibility and value (testing availability, additional services provided). The Bayesian Information Criterion indicated the best model fit for a model with 8 preference classes, with class sizes ranging from 6% to 19% of participants. Substantial preference heterogeneity was observed, both between and within latent classes, with 12 of 16 attribute levels having positive and negative coefficients across classes, and all three random effects contributing significantly to participants' choices. The findings may help identify combinations of testing options that match the distribution of HIV testing preferences among high-risk populations; the methods may be used to systematically design heterogeneity-focused interventions using stated preference methods

Preferences for treatment of Attention-Deficit/Hyperactivity Disorder (ADHD): a discrete choice experiment

<p>Abstract</p> <p>Background</p> <p>While there is an increasing emphasis on patient empowerment and shared decision-making, subjective values for attributes associated with their treatment still need to be measured and considered. This contribution seeks to define properties of an ideal drug treatment of individuals concerned with Attention-Deficit/Hyperactivity Disorder (ADHD). Because of the lack of information on patient needs in the decision-makers assessment of health services, the individuals' preferences often play a subordinate role at present. Discrete Choice Experiments offer strategies for eliciting subjective values and making them accessible for physicians and other health care professionals.</p> <p>Methods</p> <p>The evidence comes from a Discrete Choice Experiments (DCE) performed in 2007. After reviewing the literature about preferences of ADHS we conducted a qualitative study with four focus groups consisting of five to eleven ADHS-patients each. In order to achieve content validity, we aimed at collecting all relevant factors for an ideal ADHS treatment. In a subsequent quantitative study phase (n = 219), data was collected in an online or paper-pencil self-completed questionnaire. It included sociodemographic data, health status and patients' preferences of therapy characteristics using direct measurement (23 items on a five-point Likert-scale) as well as a Discrete-Choice-Experiment (DCE, six factors in a fold-over design).</p> <p>Results</p> <p>Those concerned were capable of clearly defining success criteria and expectations. In the direct assessment and the DCE, respondents attached special significance to the improvement of their social situation and emotional state (relative importance 40%). Another essential factor was the desire for drugs with a long-lasting effect over the day (relative importance 18%). Other criteria, such as flexibility and discretion, were less important to the respondents (6% and 9%, respectively).</p> <p>Conclusion</p> <p>Results point out that ADHD patients and their family members have clear ideas of their needs. This is especially important against the backdrop of present discussions in the healthcare sector on the relevance of patient reported outcomes (PROs) and shared decision-making. The combination of the methods used in this study offer promising strategies to elicit subjective values and making them accessible for health care professionals in a manner that drives health choices.</p

Using discrete choice experiments to design interventions for heterogeneous preferences: protocol for a pragmatic randomised controlled trial of a preference-informed, heterogeneity-focused, HIV testing offer for high-risk populations.

INTRODUCTION: Approximately one million undiagnosed persons living with HIV in Southern and Eastern Africa need to test for HIV. Novel approaches are necessary to identify HIV testing options that match the heterogeneous testing preferences of high-risk populations. This pragmatic randomised controlled trial (PRCT) will evaluate the efficacy of a preference-informed, heterogeneity-focused HIV counselling and testing (HCT) offer, for improving rates of HIV testing in two high-risk populations. METHODS AND ANALYSIS: The study will be conducted in Moshi, Tanzania. The PRCT will randomise 600 female barworkers and 600 male Kilimanjaro mountain porters across three study arms. All participants will receive an HIV testing offer comprised of four preference-informed testing options, including one 'common' option-comprising features that are commonly available in the area and, on average, most preferred among study participants-and three options that are specific to the study arm. Options will be identified using mixed logit and latent class analyses of data from a discrete choice experiment (DCE). Participants in Arm 1 will be offered the common option and three 'targeted' options that are predicted to be more preferred than the common option and combine features widely available in the study area. Participants in Arm 2 will be offered the common option and three 'enhanced' options, which also include HCT features that are not yet widely available in the study area. Participants in Arm 3, an active control arm, will be offered the common option and three predicted 'less preferred' options. The primary outcome will be uptake of HIV testing. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Duke University Health System IRB, the University of South Carolina IRB, the Ethics Review Committee at Kilimanjaro Christian Medical University College, Tanzania's National Institute for Medical Research, and the Tanzania Food & Drugs Authority (now Tanzania Medicines & Medical Devices Authority). Findings will be published in peer-reviewed journals. The use of rigorous DCE methods for the preference-based design and tailoring of interventions could lead to novel policy options and implementation science approaches. TRIAL REGISTRATION NUMBER: NCT02714140

Adaptive Nutzenbewertung für Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen

Das GKV-VSG führte erstmalig die Nutzenbewertung von Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen und invasivem Charakter ein. Zukünftig wird die Entscheidung über die Erstattungsfähigkeit von der Dokumentation von Nutzen und Schaden abhängig sein. Grundsätzlich kann die Nutzenbewertung in drei Phasen unterteilt werden: Messen von kausalen Effekten einer Intervention, Bewertung der gemessenen Effekte und Entscheidung über die Erstattungsfähigkeit eines aggregierten Gesamtnutzens. Um den Anforderungen zu begegnen, stellen adaptive Studiendesigns, die multikriterielle Entscheidungsanalyse und die adaptive Nutzenbewertung ein zukunftsfähiges Konzept für einen schnellen Zugang von Patienten zu innovativen Behandlungsmethoden bei hoher Qualität und Sicherheit dar

Patient-centeredness in integrated healthcare delivery systems: Needs, expectations and priorities for organized healthcare systems

Introduction: Patient-centred healthcare is becoming a more significant success factor in the design of integrated healthcare systems. The objective of this study is to structure a patient-relevant hierarchy of needs and expectations for the design of organised healthcare delivery systems.Methods: A questionnaire with 84 items was conducted with N = 254 healthcare experts and N = 670 patients. Factor analyses were performed using SPSS©18. The number of factors retained was controlled by Kaiser's criterion, validation of screeplots and interpretability of the items. Cronbach's α was used to assess the internal consistency of the subscales.Results: Exploratory factor analysis led to 24 factors in the expert sample and 20 in the patient sample. After analysing the screeplots, confirmatory factor analyses were computed for 7-factor solutions accounting for 42.963% of the total variance and Kaiser–Meyer–Olkinof 0.914 for the patients (experts: 38.427%, Kaiser–Meyer–Olkin = 0.797). Cronbach's α ranged between 0.899 and 0.756. Based on the analysis, coordinated care could be differentiated into seven dimensions: access, data and information, service and infrastructure, professional care, interpersonal care, individualised care, continuity and coordination.Conclusion and Discussion: The study provides insight into patient and experts expectations towards the organisation of integrated healthcare delivery systems. If providers and payers can take into account patient needs and expectations while implementing innovative healthcare delivery systems, greater acceptance and satisfaction will be achieved. In the best case, this will lead to better adherence resulting in better clinical outcomes. <br /

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment

BackgroundStrokes pose a particular challenge to the health care system. Although stroke-related mortality has declined in recent decades, the absolute number of new strokes (incidence), stroke deaths, and survivors of stroke has increased. With the increasing need of neurorehabilitation and the decreasing number of professionals, innovations are needed to ensure adequate care. Digital technologies are increasingly used to meet patients’ unfilled needs during their patient journey. Patients must adhere to unfamiliar digital technologies to engage in health interventions. Therefore, the acceptance of the benefits and burdens of digital technologies in health interventions is a key factor in implementing these innovations. ObjectiveThis study aims to describe the development of a discrete choice experiment (DCE) to weigh criteria that impact patient and public acceptance. Secondary study objectives are a benefit-burden assessment (estimation of the maximum acceptable burden of technical features and therapy-related characteristics for the patient or individual, eg, no human contact), overall comparison (assessment of the relative importance of attributes for comparing digital technologies), and adherence (identification of key attributes that influence patient adherence). The exploratory objectives include heterogeneity assessment and subgroup analysis. The methodological aims are to investigate the use of DCE. MethodsTo obtain information on the criteria impacting acceptance, a DCE will be conducted including 7 attributes based on formative qualitative research. Patients with stroke (experimental group) and the general population (control group) are surveyed. The final instrument includes 6 best-best choice tasks in partial design. The experimental design is a fractional-factorial efficient Bayesian design (D-error). A conditional logit regression model and mixed logistic regression models will be used for analysis. To consider the heterogeneity of subgroups, a latent class analysis and an analysis of heteroscedasticity will be performed. ResultsThe literature review, qualitative preliminary study, survey development, and pretesting were completed. Data collection and analysis will be completed in the last quarter of 2023. ConclusionsOur results will inform decision makers about patients’ and publics’ acceptance of digital technologies used in innovative interventions. The patient preference information will improve decisions regarding the development, adoption, and pricing of innovative interventions. The behavioral changes in the choice of digital intervention alternatives are observable and can therefore be statistically analyzed. They can be translated into preferences, which define the value. This study will investigate the influences on the acceptance of digital interventions and thus support decisions and future research. International Registered Report Identifier (IRRID)DERR1-10.2196/4605