Performance management and hybridization of healthcare – case of the accountable care organization

PurposeThe article seeks to explain whether and how value-based healthcare principles lead to hybridization. The public management literature has been increasingly interested in hybrid forms of governance and hybrid performance management, but empirical studies are still rare. Further, the article studies the design of performance management and accounting systems as healthcare organizations reorganize their care processes applying value-based healthcare principles.Design/methodology/approachThis article first connects the theoretical discussions on value-based healthcare and performance management for hybrids. The conceptual understanding of performance management in hybrid healthcare uses a case study of a Finnish healthcare organization with documentary data and transcribed interviews with healthcare professionals from both the strategic and operative levels of healthcare.FindingsThe article illustrates and analyses how new policy-level objectives and principles of value-based healthcare led to hybridity in healthcare, manifest in mixed ownership of a particular care path and new forms of social and financial control. Further, the article provides empirical evidence of how increased hybridity necessitated new organizational modes and roles, new managerial tools for performance management and created a need to develop the capability to account and measure entire integrated care processes. Important enabling factors for the integration of care and hybrid performance management were commitment created in dialogue, voluntary-based trust and technology to generate factual shared information.Practical implicationsThe study is informative for stakeholders, funders and managers of healthcare organizations, namely new knowledge for the discussion of hybrid governance in healthcare, including a critical account of the applicability and impact of a hybrid service model in healthcare management. Moreover, the article illustrates what needs to be reconsidered in performance management and accounting practices when reorganizing care processes according to the principles of value-based healthcare.Originality/valueThe article extends the analysis of performance management in hybrids and sheds new light on hybridization in healthcare. It also provides much-needed empirical evidence on the processes and practices of accounting and performance management after implementing a value-based healthcare strategy.

Zoom: A case study

The paper 'Zoom: a case-study' explores the process of the staging of a hybrid performance that took place in September 2021 in Malta as a response to the Covid scenario. This production was watched online and live, with actors performing, using both realities.  The project also explores the notion of space, whether it is the virtual, as opposed to the 'real', and the different spatial dynamics that this performance occurred in. In fact, the performance happened in two 'real' spaces, connected one to the other through an intricate use of screens and cameras, in such a way that whichever way the audience decided to watch the performance in, they could still understand the narrative of the play. The paper is analysed by juxtaposing literature on hybridity against the experiences of the different stakeholders involved in this production, namely, the co-producers, the director (the researcher of this paper), the technical director, the actors and members of the audience, with the aim of analysing and evaluating the dynamics of the production as a model of good practice and discern whether it can provide a framework to work in for the restricted reality that the local industry is in currently, as well as for the future.

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

Multi-omics-based prediction of hybrid performance in canola

Key message Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment

High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.