Medicalisation and Overdiagnosis: What Society Does to Medicine

The concept of overdiagnosis is a dominant topic in medical literature and discussions. In research that targets overdiagnosis, medicalisation is often presented as the societal and individual burden of unnecessary medical expansion. In this way, the focus lies on the influence of medicine on society, neglecting the possible influence of society on medicine. In this perspective, we aim to provide a novel insight into the influence of society and the societal context on medicine, in particularly with regard to medicalisation and overdiagnosi

Medicalization Defined in Empirical Contexts – A Scoping Review

Background: Medicalization has been a topic of discussion and research for over four decades. It is a known concept to researchers from a broad range of disciplines. Medicalization appears to be a concept that speaks to all, suggesting a shared understanding of what it constitutes. However, conceptually, the definition of medicalization has evolved over time. It is unknown how the concept is applied in empirical research, therefore following research question was answered: How is medicalization defined in empirical research and how do the definitions differ from each other? Methods: We performed a scoping review on the empirical research on medicalization. The 5 steps of a scoping review were followed: (1) Identifying the research question; (2) Identifying relevant studies; (3) Inclusion and exclusion criteria; (4) Charting the data; and (5) Collating, summarizing and reporting the results. The screening of 3027 papers resulted in the inclusion of 50 empirical studies in the review.Results: The application of the concept of medicalization within empirical studies proved quite diverse. The used conceptual definitions could be divided into 10 categories, which differed from each other subtly though importantly. The ten categories could be placed in a framework, containing two axes. The one axe represents a continuum from value neutral definitions to value laden definitions. The other axe represents a continuum from a micro to a macro perspective on medicalization. Conclusion: This review shows that empirical research on medicalization is quite heterogeneous in its definition of the concept. This reveals the richness and complexity of medicalization, once more, but also hinders the comparability of studies. Future empirical research should pay more attention to the choice made with regard to the definition of medialization and its applicability to the context of the study

Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models

We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface. The system is trained on an individual user's utterances and the corresponding semantic frames representing controls. During training, no prior information on the alignment between utterance segments and frame slots and values is available. In addition, semantic frames in the training data can contain information that is not expressed in the utterances. To tackle this weakly supervised classification task, we propose a framework based on Hidden Markov Models (HMMs). Structural modifications, resulting in a hierarchical HMM, and an extension called expression sharing are introduced to minimize the amount of training time and effort required for the user. The dataset used for the present study is PATCOR, which contains commands uttered in the context of a vocally guided card game, Patience. Experiments were carried out on orthographic and phonetic transcriptions of commands, segmented on different levels of n-gram granularity. The experimental results show positive effects of all the studied system extensions, with some effect differences between the different input representations. Moreover, evaluation experiments on held-out data with the optimal system configuration show that the extended system is able to achieve high accuracies with relatively small amounts of training data

Measuring Active Purchasing in Healthcare:Analysing Reallocations of Funds Between Providers to Evaluate Purchasing Systems Performance in the Netherlands

Background: Purchasing systems aim to improve resource allocation in healthcare markets. The Netherlands is characterized by four different purchasing systems: managed competition in the hospital market, a non-competitive single payer system for long-term care (LTC), municipal procurement for home care and social services, and self-procurement via personal budgets. We hypothesize that managed competition and competitive payer reforms boost reallocations of provider market share by means of active purchasing, ie, redistributing funds from high-quality providers to low-quality providers. Methods: We define a Market Activity Index (MAI) as the sum of funds reallocated between providers annually. Provider expenditures are extracted from provider financial statements between 2006 and 2019. We compare MAI in six healthcare sectors under four different purchasing systems, adjusting for reforms, and market entry/exit. Next, we perform in-depth analyses on the hospital market. Using multivariate linear regressions, we relate reallocations to selective contracting, provider quality, and market characteristics. Results: No difference was found between reallocations in the hospital care market under managed competition and the non-competitive single payer LTC (MAI between 2% and 3%), while MAI was markedly higher under procurement by municipalities and personal budget holders (between 5% and 15%). While competitive reforms temporarily increased MAI, no structural effects were found. Relatively low hospital MAI could not be explained by market characteristics. Furthermore, the extent of selective contracting or hospital quality differences had no significant effects on reallocations of funds. Conclusion: Dutch managed competition and competitive purchaser reforms had no discernible effect on reallocations of funds between providers. This casts doubt on the mechanisms advocated by managed competition and active purchasing to improve allocative efficiency

Density of Patient-Sharing Networks: Impact on the Value of Parkinson Care

Background: Optimal care for Parkinson’s disease (PD) requires coordination and collaboration between providers within a complex care network. Individual patients have personalised networks of their own providers, creating a unique informal network of providers who treat (‘share’) the same patient. These ‘patient-sharing networks’ differ in density, ie, the number of identical patients they share. Denser patient-sharing networks might reflect better care provision, since providers who share many patients might have made efforts to improve their mutual care delivery. We evaluated whether the density of these patient-sharing networks affects patient outcomes and costs. Methods: We analysed medical claims data from all PD patients in the Netherlands between 2012 and 2016. We focused on seven professional disciplines that are commonly involved in Parkinson care. We calculated for each patient the density score: the average number of patients that each patient’s providers shared. Density scores could range from 1.00 (which might reflect poor collaboration) to 83.00 (which might reflect better collaboration). This score was also calculated at the hospital level by averaging the scores for all patients belonging to a specific hospital. Using logistic and linear regression analyses we estimated the relationship between density scores and health outcomes, healthcare utilization, and healthcare costs. Results: The average density score varied considerably (average 6.7, SD 8.2). Adjusted for confounders, higher density scores were associated with a lower risk of PD-related complications (odds ratio [OR]: 0.901; P <.001) and with lower healthcare costs (coefficients:-0.018, P =.005). Higher density scores were associated with more frequent involvement of neurologists (coefficient 0.068), physiotherapists (coefficient 0.052) and occupational therapists (coefficient 0.048) (P values all <.001). Conclusion: Patient sharing networks showed large variations in density, which appears unwanted as denser networks are associated with better outcomes and lower costs.Pattern Recognition and Bioinformatic