The optimal design of pedagogical strategies for e-learning in graduate and postgraduate medical education remains to be determined. Video-based e-learning use is increasing, with initial research suggesting that taking short breaks while watching videos (independent of answering test questions) may improve learning by focusing attention on the content presented. Interspersed test questions may also improve knowledge acquisition and retention.To examine the effect of interspersed test questions and periodic breaks on immediate knowledge acquisition and retention at 6 months by pediatric residents engaged in video-based e-learning.First- and second-year pediatric residents were randomized to 1 of the following 3 groups: viewing the complete video uninterrupted (full video), viewing the video interrupted with unrelated logic puzzles (logic puzzles), or viewing the video interrupted with brief comprehension test questions (short answer questions). Residents answered pre- and post-tests before and after video viewing, followed by a retention test at 6 months. Primary outcome included comparison of the change in test scores between groups.F2,46=0.35, P=.71). Thirty-five residents completed retention testing with comparable degrees of knowledge retention in the full video and short answer test questions groups (P<.001), but no significant change in the logic puzzles group .A total of 49 residents completed the initial testing session. All 3 learning groups had comparable mean increases in immediate knowledge gain, but with no significant differences between groups (Improved knowledge gain was not demonstrated among residents answering interspersed questions or completing logic puzzles during interrupted online video viewing when compared with residents viewing uninterrupted video content. However, residents who either participated in uninterrupted video viewing or answered interspersed questions during interrupted video viewing demonstrated significant knowledge retention at 6 months. The recent introduction of e-learning initiatives in postgraduate medical education has been heralded as a disruptive change to efficiently scale knowledge and promote more effective learning. Although e-learning can deliver knowledge, research to inform the optimal learning design and teaching practices in this environment remains in its infancy -4. ReseaUsing interspersed test questions as an educational learning tool also allows for superior knowledge retention relative to passively restudying the same material when students are tested at extended intervals after the initial learning activity ,9. TestsDespite the increased use of e-learning platforms to educate postgraduate medical trainees, we were unable to identify any studies investigating the implementation of test-enhanced learning strategies in an e-learning platform for postgraduate medical education. Yet, many e-learning platforms and massive open online courses (MOOCs) utilize uninterrupted instructional videos as a means of learning, with or without pre- and post-tests for knowledge assessment -14.The purpose of this study was to evaluate the extent to which the use of interspersed test questions or taking periodic breaks while watching an online video would impact knowledge gain as compared with watching the same video without any breaks. As a proxy for knowledge gain, the primary study outcome was the difference between pre- and post-test scores between groups. Secondary outcomes included the difference between pre- and post-test scores within each group and the difference in retention test scores at 6 months compared with pretest scores between groups.We conducted a randomized, prospective, cohort study in 3 academic medical centers in Boston, Massachusetts between June 2014 and March 2016. Pediatric residents in their first or second year of postgraduate training were eligible to participate. Participation was completely voluntary, as this educational initiative was independent from educational obligations during their clinical pediatric intensive care unit (PICU) rotations. Residents were ineligible if they had previously completed a 4-week PICU rotation during residency because the study intervention assessed knowledge acquisition and retention of mechanical ventilation concepts that would have been encountered by residents on their first PICU rotation. Email invitations were sent to all eligible residents enrolled in the following pediatric residency programs: Boston Combined Pediatric Residency Program, Massachusetts General Hospital, and Tufts Floating Hospital for Children. Residents provided voluntary written consent for study inclusion. No residents who volunteered to participate actively refused participation at a subsequent time point during the study. Residents received a US $50 Amazon gift card and the chance to receive an iPad via random selection upon completion of 6-month retention testing. The Boston Children’s Hospital’s Institutional Review Board deemed this study exempt from informed consent, given no identifying data on study participants were collected. Affiliated institutions honored the exempt status. Initial testing was conducted at each of the 3 institutions with all sessions monitored by 1 of 2 study facilitators who were available to troubleshoot technical difficulties and monitor for dishonest behavior. Retention tests were administered via email. Residents were asked to abide by the honor code when completing the retention test.Pediatric residents were blindly randomized via concealed envelopes to 1 of the following 3 groups: full video, logic puzzles, or short answer questions . ResidenResidents completed all computer-based elements of this study via a single lesson plan created on the commercial e-learning platform Softchalk . All residents watched a peer-reviewed video about the basic principles of high frequency oscillatory ventilation (HFOV), assuming that pediatric residents would have limited baseline knowledge of this content as clinical exposure to ICUs is low early in residency training. In addition, this specific topic was chosen for educational use because although HFOV is an important mode of mechanical ventilation for advanced, refractory pediatric respiratory failure and requires basic conceptual understanding by residents, the use of this type of mechanical ventilation is an overall low-frequency event in most PICUs. The video was written and presented by a Harvard Professor who conducts research on HFOV, as part of an existing curriculum on OPENPediatrics, an open access, e-learning platform . This viThree content experts from the Division of Critical Care at Boston Children’s Hospital developed test questions and acceptable answers. All experts utilized the same content validity scoring system to evaluate questions. Questions scored as highly relevant  were included. All questions required free text answers. During the initial testing session, all residents completed a 10-question pretest prior to video watching and a 10-question posttest immediately afterwards. Six months after initial testing session completion, all residents were asked to complete a 10-question retention test. Different questions were included on pre-, post-, and retention tests, but all tested similar concepts. Three independent graders scored all test questions upon participant completion. Free text responses were scored on a binary scale . Scores were reported as percent correct, out of a possible 100% . No partial credit was given.F-tests to compare differences in the change in pre- and post-test scores among the 3 groups immediately and at 6-month follow-up  vs 39% [SD 16] vs P=.02).Pediatric residents were randomized to the following groups: full video (n=17), logic puzzles (n=16), and short answer questions (n=16). Mean initial pre- and post-test percent correct scores and 95% CIs for each group are reported in P=.80), all reporting caring for at least 3 patients receiving HFOV. No residents cared for more than 5 patients receiving HFOV (P=.67). Five residents reported repeated viewing of the HFOV video on OPENPediatrics prior to the 6-month posttest . Cross-reference of OPENPediatrics data verified that only 2 participants had actually watched the video in between the initial and 6-month retention testing. Mean retention test scores for each group are reported in Thirty-five residents 71%; 35/49) completed the 6-month retention test with similar numbers in each group . During this 6-month interval, 10 residents overall rotated through the PICU . Repeated measures mixed-model ANOVA demonstrated that the full video and short answer questions groups had a significant degree of knowledge retention at 6 months, although to a lesser degree than knowledge gained on initial mean posttest scores . The logic puzzle group did not demonstrate statistically significant knowledge retention at 6 months .One-way ANOVA revealed that all 3 groups had comparable degrees of mean knowledge retention between the initial pretest and retention test (P<.001), although to a lesser degree compared with the change in test scores between initial pre- and post-testing.This study did not demonstrate a significant difference related to overall initial knowledge gain when pediatric residents watched a video without structured breaks compared with those with interspersed breaks and completion of either logic puzzles or short answer questions. Despite this finding, we demonstrate that irrespective of group assignment, all groups had significantly higher mean percent correct test scores on initial posttests compared with pretests. This suggests that the online video itself is an effective teaching modality. Six-month retention analysis reveals a continued lack of statistical significance when comparing residents’ change in test scores between groups. However, there is an overall significant increase in the change in test scores between initial pretests and 6-month retention tests in the full video and short answer groups , but possibly due to time constraints related to clinical rotations or time away from residency during which they were unresponsive to email.We acknowledge several limitations to this study. First, the sample size reported here was designed to power to an 80% level, yet it is still possible that our sample size was too small to detect significant differences between groups. Second, the lack of a statistically significant difference in knowledge gain between groups may be related to several factors, including overall video duration, timing of when residents completed this study in the context of their clinical rotations, emotional state of residents during study completion , and their overall content interest. Third, this study may have some methodological insufficiency regarding the use of spaced learning for evaluation of knowledge retention, and we believe that this would be an interesting hypothesis to incorporate in a future study.The overall duration of the video used in this study was relatively short 23 min) such that this may have contributed to not finding a meaningful effect size difference between groups who were taking breaks while watching the video. Yet, data regarding the optimal video length for learner engagement are conflicting. Research in disciplines other than medicine suggests that shorter duration is generally better and that including breaks within longer videos helps reduce cognitive load. Data from TED talks suggests that the optimal video length is around 18 min, which is short enough to hold attention, yet long enough to succinctly communicate complex topics, both of which decreases cognitive overload by limiting the amount of time of active brain engagement, and forces the speaker to be clear and concise . Data fr min suchIn addition, inattentiveness and mind-wandering have been linked to poor knowledge gain, and these behaviors occur more frequently when students are experiencing an underlying negative emotional state, lack engagement, or experience stress related to learning . These aP=.67). If this were clinically significant, we would have expected to observe a greater increase in knowledge retention within all groups at 6 months’ follow-up. Similarly, numbers of residents rotating through the PICU between initial testing and 6-month follow-up were low and not statistically different between groups (P=.80). We continue to acknowledge the overall small sample size in interpretation of our 6-month follow-up analysis.Finally, several limitations must be considered when reviewing our secondary outcome of retention test score analysis. First, residents were not directly observed for dishonest behavior on retention test completion, which could potentially falsely elevate test scores. Second, we did not specifically control for “on-the-job” training. However, given the overall small exposure to patients ventilated by HFOV as self-reported by residents across all groups between the initial testing and the 6-month follow-up, we do not believe this has greatly impacted our findings as differences in exposure between groups lacked statistical significance , in which interspersed test questions and periodic breaks integrated into an online statistics lecture improved knowledge gain among undergraduate students. However, when our findings are viewed together with other previous studies , in whic,10,21, w


Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from a fragment of Cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions  of the chambers were measured. The results showed the shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found distant from the mound of loose soil and were spaced apart and distributed laterally at the soil profile. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers were separated and distant. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara. Atta are structurally the most complex nests in the Attini tribe. For example, nests of the species Atta laevigata contain more than 7000 chambers and measure up to 8 m in depth [A. sexdens nests, the fungus and waste chambers have a semi-ellipsoidal shape and the waste chambers have arm-like prolongations. Both types of chambers are found below a mound of loose soil [A. laevigata and A. bisphaerica, the fungus chambers are spherical [Nests of species of the genus in depth . Internain depth . In A. sose soil . In A. lpherical ,3. HowevAtta nest is consequently much larger than the documented nests of other ant genera, such as those of Pogonomyrmex badius which contain 150 chambers, Solenopsis invicta which builds nests with numerous chambers connected by tunnels in a highly complex and elaborated system, Pheidole morrissi, Prenolepsis impairs, and Conomyrma [Acromyrmex is the other Attini genus, they do not build nests in comparable size to the genus Atta [An onomyrma ,5. Even nus Atta ,7,8.Despite this knowledge, the study of the internal architecture of the subterranean nests of ants has received little attention. Although there is a considerable number of studies mentioning the architecture of ant nests, many of them are based on qualitative descriptions accompanied by schematic drawings of the nests ,16,17,18Atta and of other ant genera is related to the ecology and behaviour of the species since the nests are considered a superorganism and the individuals behave in such a way that favors the development and specialization of this organism [The external and internal architecture of the nests of organism . In thisorganism ,21 and morganism .Atta bisphaerica, the chambers containing the fungus garden with freshly incorporated plant fragments are found near the soil surface [In  surface ,3. This  surface ,3. On th surface ,23,24, aAtta capiguara. Therefore, the objective of the present study was to provide a detailed description of the nest architecture of the grass-cutting ant, Atta capiguara, using cement moulding and excavation.Although several studies have investigated the nest architecture of leaf-cutting ants, addressing external and internal structures, little is known about these structures in grass-cutting ant species, especially The study was conducted at the Santana Farm , located near the Lageado Experimental Farm of UNESP, Botucatu, São Paulo, Brazil in a Cerrado fragment.Atta capiguara nests. Three nests were used in this study. The size of the nests was confirmed by mapping two or three active entrance holes near the nests. The holes were mapped using plastic straw baits of different colours and cut types  according to the method of Fowler et al. ( 1993) [The external area consisting of loose soil was used as a criterion of maturity of the  ( 1993) , modifie ( 1993)  for grasThe straws were cut into 3 to 4 mm pieces and immersed in a solution of concentrated orange juice containing sugar (forming a syrup). The straw fragments were removed with sieves and transferred to trays where a mixture of citrus pulp and sugarcane leaf powder was added. The pieces were carefully moved inside these trays so that the mixture would be well impregnated on the straws and they would be highly attractive to the ants. The straws were dried for 24 h at room temperature. After this period, the baits were stored in plastic bags identified with numbers according to the colour and type of straw to facilitate handling in the field.In the field, the plastic straw baits were placed in the active holes near the nests. The number corresponding to each type of bait was marked on the stake. After 24 h, the mound of loose soil to which each straw was returned by the ants was identified and the type and colour found, which indicate which entrance holes belong to each nest, were recorded.A. capiguara nests was measured. These measurements were obtained using the traditional method in which the mound area is calculated by multiplying the greatest length by the greatest width.After the area covered by the nests was determined, the mound area of the Atta capiguara nests  were chosen to visualize the internal architecture and tunnel structure. The nests were moulded using a mixture of 5 kg cement in 10 L of water, which was poured into all open holes above the soil mound of the nest  and into the holes spread on the soil surface (foraging holes) as described by Moreira et al. 2003 and 2004) [Three Prior to excavation, the external area of the nest was determined by the traditional method, which consists of measuring the greatest width and greatest length of the area comprising all mounds of loose soil (largest mound and rosettes). Two nylon strings were then stretched over the nest area, forming two orthogonal axes , whose center corresponded to the center of the nest area. These axes were used to identify the chambers and tunnels.The areas of the nests filled with cement were excavated using small manual tools to avoid their destruction. A 0.70-m wide and 1-m deep ditch was opened around the nest area and excavation was performed from the outside to the inside. The ditch was deepened until complete appearance of the chambers, which were numbered for subsequent measurement. After excavation, the chambers and tunnels were measured and photographed. The following parameters were obtained: dimension , depth from the soil surface, and position on the orthogonal axes . The mean, standard deviation and range of the measurements were calculated for statistical analysis.The volumes of the fungus and waste chambers were calculated and compared to geometric figures. Although they were not perfect figures, the models that best fitted the shape of each type of chamber were used. The formulas proposed by Forti (1985)  were app2 (hc + r0.67), where r = radius of the base of the chamber, and hc = height of the cylinder. The radius was subtracted from the maximum height of the chamber (h) to obtain hc, i.e., h − r = hc. The volume of the waste chambers was estimated by V = πr2h/3, where r = radius and h = height.Volume of fungus chamber = πrSince all fungus garden chambers were moulded with cement, the correction factor (1.37) obtained by Forti (1985)  was usedAtta capiguara nests had the typical shape consisting of several mounds of loose soil with unique features resembling a conic section [2 (C), 102.4 m2 (C4) and 64.8 m2 (C6).The  section ,29. The The fungus chambers were found distant from the mound of loose soil and were spaced apart and distributed laterally at the soil profile A. The waA. capiguara [A. sexdens [A. vollenweideri [A. capiguara differed from that of other Atta species. There was no network of tunnels or a concentrated chamber area resembling a “bunch of grapes” as observed in other species for which studies on nest architecture by cement moulding are available, such as A. sexdens [A. laevigata [A. bisphaerica [In leaf-cutting ant nests, the chambers vary in shape, location and dimension depending on the species and type of chamber . The fungus chambers found in the present study had an oval shape with a plane base A and theapiguara , in A. s sexdens  and in Anweideri ,30,31, w sexdens , A. laevIn the first nest excavated (C), the fungus chambers were connected through a main tunnel A,B. ThisThe chambers connected to the tunnels that branched along the main tunnel were found at an average depth of 2.1 m (SD ± 1.0) from the soil surface B. The deIn nests C6 and C4, the main tunnel was not as prominent as in the first nest excavated and the chambers were generally connected to branched tunnels which, in turn, connected to the main tunnel . In nestThe main tunnel was much wider than the branching tunnels . The conIn the first excavated nest, the areas where the fungus and waste chambers were concentrated was interconnected through a single tunnel . HoweverIn the first moulded nest, the waste chambers were found at a depth ranging from 0.43 to 1.6 m. In nest C6, the waste chambers were located at similar depths (0.7 to 1.75 m), while in C4 these chambers were found in deeper layers of 2 to 2.6 m from the soil surface. The waste chambers were quite large , with diThe volume of one of the chambers was much higher (535.5 L) than the total volume estimated for 11 waste chambers (166.5L) . HoweverThe tunnels of the three excavated nests were flat and elliptical in cross-section, but not as wide as the tunnels connecting to the waste chambers. The first moulded nest and nest C6 exhibited the same architectural pattern. In contrast, some differences in the arrangement of the tunnels were found in nest C4. The fungus chambers of this nest were located in deeper soil layers and were concentrated. Another difference was the large number of foraging tunnels, a fact that makes the outer ring not very visible. Nevertheless, it could be observed that the linear tunnel in this nest starts at one end, passes through the fungus chamber area and extends to the area called the dead zone where the waste chambers are concentrated. This tunnel measured 23.3 m in length from one end of the nest to the other A.However, it is possible that nest C4 was on the decline. There was no open entrance hole on the small soil mounds or on the largest mound (waste), and no fresh soil had been deposited recently on these mounds. We observed no signs of ant activity at this nest site during the daytime.However, as seen in nest C6, when the cement was poured into the three holes studied, workers reopened several foraging holes around the nest and on the rosettes and largest soil mound.Casting permitted the demonstration of a peculiar architecture of the nest built by this ant species that was very different from the nests of previously studied species ,21. SincAnother interesting finding was that only few chambers were found in the area where the foraging tunnels were concentrated . Did theCasting of the interior of the nests provided another important finding. The connection between the area of the fungus chambers and the area of the waste chamber was made by a single tunnel in the first excavated nest and in nest C4 , while iThe little contact between the two nest areas (live and dead zone)  is interAtta species and it is therefore possible to estimate the age of these nests. Nest C6 was the youngest nest, with a maximum of 5 years. The first excavated nest (C) was 8 to 10 years old and nest C4 was the oldest, with more than 10 years. Since nest C6 was the youngest, it still had several connections with the area where the waste chambers were concentrated. As the nest becomes older, the distance between the two nest areas increases. Nevertheless, the level of specialization of this species seems to be high when compared to other Atta species that do not separate the area of waste deposition from the fungus garden.The region where the nests were excavated has been used in previous studies investigating other Atta capiguara nests can be identified by conical mounds of loose soil visible on the soil surface. The fungus chambers were found, through excavation, to be distant (6.0 m +2.0) from those conical mounds of loose soil and were spaced (8.67 m +1.15) apart at different depths (3.20 m +1.91) in the soil profile.A. capiguara.The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers were separated and distant. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant


Multiple omic profiles have been generated for many cancer types; however, comprehensive assessment of their prognostic values across cancers is limited. We conducted a pan-cancer prognostic assessment and presented a multi-omic kernel machine learning method to systematically quantify the prognostic values of high-throughput genomic, epigenomic, and transcriptomic profiles individually, integratively, and in combination with clinical factors for 3,382 samples across 14 cancer types. We found that the prognostic performance varied substantially across cancer types. mRNA and miRNA expression profile frequently performed the best, followed by DNA methylation profile. Germline susceptibility variants displayed low prognostic performance consistently across cancer types. The integration of omic profiles with clinical variables can lead to substantially improved prognostic performance over the use of clinical variables alone in half of cancer types examined. Moreover, we showed that the kernel machine learning method consistently outperformed existing prognostic signatures, suggesting that including a large number of omic biomarkers may provide substantial improvement in prognostic assessment. Our study provides a comprehensive portrait of omic architecture for tumor prognosis across cancers, and highlights the prognostic value of genome-wide omic biomarker aggregation, which may facilitate refined prognostic assessment in the era of precision oncology. Advances in high-throughput genomic technologies and large-scale sequencing studies including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) project have generated a rich resource of multi-dimensional omics data. Building cancer prognostic models incorporating genomic data has the potential to improve the precision of predicting patient clinical outcomes, to help understand the mechanism of tumor progression, and to evaluate the clinical values of biomarkers in clinical trials. Meanwhile, the complexity of the tumor genome poses a great challenge for cancer prognostic assessment. Indeed, substantial omic heterogeneity has been revealed for histologically homogeneous tumors in terms of genomics4, epigenomics5, transcriptomics8, and proteomics9. Recognizing its importance and challenges, the Cancer Moonshot Blue Ribbon Panel has recently recommended prediction of patient outcomes as one research area poised for acceleration10.Developing models that accurately predict patient survival using prognostic and predictive biomarkers is increasingly important in clinical research and practice13. More recent works investigated multiple omic profiles for predicting survival in single cancer types15 and across few cancer types16. These studies focused on identifying a list of prognostic genes, molecules or signatures, excluded somatic mutations from analyses, and did not comprehensively consider the combination of multiple omic profiles. There is a need to conduct a pan-cancer analysis of prognostic accuracy for multiple omic profiles on a genome-wide scale, and to understand the shared patterns for prognostic performance of omic profiles across cancer types.Considerable effort has been devoted to incorporating omic profiles into prognostic assessments for various cancer types. The earlier analyses typically created prognostic indices consisting of a few dozen selected genes based on microarray gene expressionWe hypothesized that prognosis-relevant signals may come from multiple pathways and involve a large number of omic biomarkers, the effect of which may be visible only when aggregated. Indeed, the omic biomarkers with moderate or weak prognostic value likely failed to reach the genome-wide prognostic significance threshold and consequently were discarded from the model. Therefore, the potential prognostic value of omic biomarkers may be unfulfilled and the underlying assumption of omic architecture of tumor prognosis built upon sparsity  needs to be reconsidered. In such situations, a viable hypothesis is that a large number of omic biomarkers (with a continuum of effect size) are involved in prognosis. Hence, aggregating prognosis effects of all biomarkers (through a shrinkage model) may be more effective and stable, and provide improved assessments of prognostic performance of omic biomarkers.9. Thus, we included analysis of the RPPA data set for prognostic assessment in the TCGA pan-cancer cohort.To examine this hypothesis, we developed a multi-omic kernel machine learning method including all molecular markers of an omic profile simultaneously. The schematic analysis steps are illustrated in Fig. 17, but to aggregate the prognostic effects of all biomarkers across various omic profiles and to quantify the prognostic value of tumor molecular profiles, alone or combined with clinical factors across cancer types. The basic idea behind the proposed multi-omic kernel machine learning method is intuitively simple: a patient’s predicted outcome would be similar to that of other patients with similar clinical variables and omic profiles, i.e. “someone like you”, measured by omic similarity matrices.The ability of each omic biomarker to predict overall survival was treated as a random variable and shrunken toward zero with a data-driven degree of shrinkage. In other words, omic architecture involving tumor prognosis was built upon all biomarkers, whose prognosis effects varied from very weak (for most biomarkers) to moderate (for some biomarkers) to strong (for a few biomarkers) in a continuous spectrum. This approach was not intended to identify individual prognosis-related biomarkers in a particular omic profile, which likely requires thousands of samplesFor somatic mutation, DNA copy number, DNA methylation, mRNA and miRNA expression, an omic similarity matrix was computed for each omic data type using a linear kernel function that measures the similarity of omic profiles between subjects. Other kernel functions, including the Gaussian kernel, may be used, but at the cost of additional kernel parameters which may require a large sample for model tuning. We aggregated all biomarkers from each individual omic profile to create the corresponding omic similarity matrix (Methods). Fig. 18 to evaluate whether the omic similarity matrix defined by one omic profile  aligned well with that defined by another . The resulting profile alignment matrices measure the similarity between omic similarity matrices in each cancer type (Methods). Figure Next, we applied a kernel alignment approachTo identify if there are cross-cancer type similarities in profile alignment matrices, we performed cancer type alignment as illustrated in Fig. 20 as being associated with cancer risk rather than prognosis.We first applied the multi-omic kernel machine learning method to evaluate the prognostic performance of individual omic profile and clinical variables for 14 cancer types. The concordance index (C-index) was calculated to evaluate the concordance of the actual survival outcome and survival outcomes as predicted by either the kernel machine learning method based on the omic data or by a Cox proportional hazard model based on clinical variables. From a pan-cancer perspective, we observed that the prognostic power of clinical variables and some molecular profiles vary substantially across cancer types Fig. . For exa16. To systematically understand the variations in C-indices, we applied a linear mixed effects model to quantify the contribution of cancer type to the variation of the C-index for various omic profiles and clinical factors across cancer types, treating C-indices generated by individual molecular profiles and clinical variables as repeated prognosis evaluations for a given cancer type. The model suggested that the cancer type itself explained 74.7% of variation (see Methods) in prognostic performance while the remaining 25.3% of variation is due to clinical variables, molecular profiles, and other unidentified factors , there were a number of notable exceptions in specific cancer types , the best performance among the five omic profiles we analyzed. The second best omic profile is miRNA with a C-index above the average by 0.020 (P = 4.08*10−5), followed by DNA methylation with an elevated C-index above the average by 0.008 (P = 0.09). By contrast, the C-indices of copy number and somatic mutation are below the average prognostic power by 0.005 and 0.002 respectively. The differences, however, are not statistically significant (P = 0.219 and 0.618 respectively).Figure We next investigated whether combining multiple molecular profiles would further improve prognostic power in comparison with the individual molecular profiles. Although the molecular profile combination would lead to the strongest prognostic power in a few cancer types, such as HNSC and OV, the increment from the best individual omic profile is often minimal. Compared to the single molecular profiles, the combination of two molecular profiles would improve C-index by 0.008 (P = 0.04) on average. This may reflect the similarity between omic profiles both in terms of omic similarity matrices and C-indices by individual omic profiles.8 and chromosomal instability15), or agnostically selected mRNA transcripts . The underlying assumption of these studies is that tumor prognosis is driven by a relatively small number of mRNA transcripts, commonly less than one hundred. As will be illustrated shortly, a much larger number of mRNA transcripts, likely thousands of them, each with a weak prognostic effect individually, may be involved in the tumor prognosis for highly heterogeneous tumor types. Therefore, we systematically compared various established signatures with the kernel machine learning method we developed that aggregates prognostic effect across all transcripts of annotated genes from the mRNA profile.A number of mRNA-based prognostic signatures have been proposed and adapted for prognostic assessment. Prognostic signatures commonly consist of either pre-selected mRNA transcripts  the PAM50 breast cancer classifier21 or the MammaPrint signature22 that predicts distant metastasis for early stage breast cancer; (d) LGG subtypes defined by IDH1 mutation and co-deletion of chromosome 1p/19q; and (e) algorithmically selecting mRNA transcripts by L1 penalized Cox regression (LASSO)16.Specifically, we compared the prognostic powers of (a) the kernel machine learning method for genome-wide aggregation of mRNA transcripts; (b) pre-specified prognostic signatures, including the metagene signatures−4) respectively, across cancer types. For KIRC, the metagene and immune signatures lead to significantly lower C-indices (0.618 and 0.546) as compared to the kernel method (0.707). There are a few exceptions in which the metagene and immune signatures perform slightly better than the kernel method, including the metagene signature in UCEC (C-index = 0.728 v.s 0.667 by the kernel machine learning method), in LUAD (C-index = 0.626 v.s 0.606), in COAD (C-index = 0.604 v.s 0.583), and in GB (C-index = 0.585 v.s 0.572), as well as the immune signature in STAD (0.594 v.s 0.571) and in GB (C-index = 0.594 v.s 0.572). This implies that these two biological signatures may be relevant to the prognosis in particular cancer types but not universally.We found that the kernel machine learning method outperforms the metagene and immune signatures in 7 cancer types Fig. . On averIn LGG, the metagene and lasso signatures achieve slightly higher C-indices than the LGG subtypes Fig.  and the The model including only agnostically selected mRNA transcripts by LASSO performs worse in prognostic prediction than the kernel machine learning method  among 11 of 14 cancer types, most notably in OV . In the remaining three cancer types , C-indices of agnostically selected mRNA transcripts by LASSO  and aggregated mRNA transcripts combined via the kernel machine learning method  are very close, suggesting that either the prognostic power of mRNA is generally weak or the number of prognosis-related mRNA transcripts is limited in these cancer types.Taken together, these results suggest that traditional prognosis methods relying on a small number of molecular biomarkers, such as Lasso-derived genomic signatures, may not be sufficient to achieve the optimal prognostic performance in some cancer types. The kernel machine learning method provides more refined prognostic prediction by aggregating a large number of molecular features in the half of the cancer types we analyzed.23.Combining clinical variables with molecular profiles shows increased C-indices (up to 10.8% in HNSC for miRNA) compared to using clinical variables alone in 7 of 14 cancer types  platform. Since the number of subjects available in a given cancer type is much smaller than those for other omic platforms, we examined the RPPA data separately. First, we investigated whether aggregating protein levels by kernel machine learning method would improve prognostic prediction, compared with selecting protein biomarkers by penalized regression (Lasso) methods, across cancer types. Consistent with other omic profiles, the C-indices by kernel machine learning method were significantly higher that the C-indices based on the Lasso model  as a working example to examine how the C-index varies depending on the sample size and the number of mRNA transcripts used to build the kernel method through down-sampling analysis, which repeatedly samples part of subjects or genes with gradually reducing size and evaluates the prognostic performance (Methods).We observed that the C-index steadily increases with increasing sample size and did not plateau even with over nine hundred subjects Fig. . This suAccurate prognostic assessment of cancer is of great value for patients, oncologists, and cancer researchers. Yet it remains challenging due to inter-tumor heterogeneity. The increasing popularity of multi-omic profiling of tumors raises the hope of improving prognostic prediction through the integration of clinical and omic biomarkers. We systematically evaluated the integration of clinical variables and omic profiles for survival prediction using a novel kernel machine learning method, which we applied to over three thousand tumor samples across fourteen cancer types from the TCGA. The kernel machine learning method built upon omic similarity matrices provides a comprehensive framework to incorporate multiple clinical variables and omic platforms simultaneously, yet it is intuitively simple and interpretable in which subjects with similar clinical variables and omic profiles have similar prognostic outcomes.25, but little has been explored in the context of survival prediction using high-dimensional genomic markers. Our study provides several unique contributions. First, most existing studies focus on genomic similarity matrices defined by SNPs only. In contrast, the proposed approach exploits information from multiple omic profiles. Secondly, the existing kernel methods have mostly been applied to study heritability of traits or disease risk27 and to analyze associations between a genetic similarity matrix and trait information in linear28 or logistic regression32 frameworks. In this study, we proposed a new kernel based prediction method for cancer prognosis. This framework allows us to incorporate clinical information and multiple omic profiles for the systematic assessment of prognostic performance across multiple cancer types in thousands of tumor samples.Genomic similarity matrices have been extensively used in genetic epidemiology studiesThe pan-cancer prognostic assessment confirmed that the difficulty of prognostic prediction varies considerably across cancer types, and that the utilities of prognostic profiles are unequal. Some cancer types, like LGG, demonstrate superior prognostic accuracy than others, based on clinical variables or molecular markers. Indeed, cancer type itself explains more than two thirds of C-index variability across cancer types. Among the different omics data types, mRNA expression most frequently provides the highest C-index for predicting patients’ survival outcome compared to the other molecular profiles in our analysis, suggesting that the resulting expression of mutated genes may be more important for patients’ survival than the underlying mutational patterns. DNA methylation and miRNA expression data also provide good prognostic values in several cancer types.Cancer is extremely heterogeneous. We hypothesized that a small number of omic biomarkers would be unlikely to provide sufficient prognostic power. Instead, we proposed to aggregate numerous omic-wide prognostic biomarkers, using a genome-scale kernel machine learning method. Our approach consistently outperformed “condensed” signatures including the metagene signatures, immune signature, and lasso-regression derived prognostic signatures that rely only on the top-performing genes in the transcriptome. In addition, down-sampling of mRNA transcripts of BRCA suggested that thousands of transcripts are likely associated with prognosis. These observations were consistent with our hypothesis that a large number of biomarkers are involved in tumor prognosis in highly heterogeneous tumor types, and that each of the omic biomarkers has a small to moderate prognosis effect size following a distribution with long tail, and do not necessarily pass a genome-wide significance threshold. The omics-wide architecture of tumor prognosis we observed is different from the ones focusing on a relatively small number of omic biomarkers with relatively larger prognosis effect.34, it has been reported that hundreds of variants with gradually decreased effect sizes are associated with complex polygenic traits, such as height36. In discovery and saturate analysis of cancer genes, it suggested that in addition to over two hundred known cancer genes, larger sample size will discover many more with lower mutation frequency37. Such a long-tailed distribution possibly reflects the complex mechanisms underlying the tumor prognosis, genetic architecture of complex traits, and nature selection of somatic mutation respectively.The long-tailed distribution of effect size we proposed for tumor prognosis is not unique but has been also observed in other biomedical research areas. For risk prediction in GWASA number of limitations of our prognosis analysis based on TCGA data warrant more discussion. First, the samples and clinical records of TCGA were collected retrospectively, and tended to over represent cases with fresh-frozen specimens of high quality, and large tumor size in late-stage patients. Hence, the kernel machine learning model trained on the TCGA samples may not be directly applicable to the general population. Second, the sample size of a given cancer type is limited, which may lead to unsaturated prognostic power and overfitting. Further studies with larger size, patients more representative of the general population, and with improved clinical records are necessary to further delineate the omic architecture of prognosis and achieve the full prognostic power of the kernel machine learning method, which should be evaluated in completely independent studies before being applied to clinical practice. Third, the clinical follow up is insufficient in the TCGA study cohorts for some cancer types, including for BRCA and prostate adenocarcinoma (PRAD). Combined with small sample size, this may contribute to the lack of significant increase in prediction performance for the integrated analysis. Fourth, most of subjects in TCGA received the standard treatment, such as surgery, chemotherapy and/or radiation therapy. New studies to evaluate the prognostic utility of omic profiling for newly developed targeted therapies and immunotherapies would be desireable. Finally, although six omic profiles have been examined, additional data types could further improve the precision of predicting clinical outcomes, including measures of intra-tumor heterogeneity, imaging, proteomics, and immunological factors. Our framework can be extended to accommodate these additional data types.In conclusion, our work evaluates the prognostic value of multi-omic profiling integrated with clinical factors in thousands of samples across fourteen cancer types and proposes an omics-wide architecture of tumor prognosis. If confirmed in future studies, it suggests that genome-scale profiling platforms, instead of gene panels, should be preferred for future prognostic assessment and that the research focus should be shifted from molecular biomarker selection to large scale omic biomarker aggregation in the era of precision oncology.We evaluated the prognostic value of six omic profiles and their combinations across cancer types by a multi-omic kernel machine learning method, which includes creating omic similarity matrices using kernels, kernel integration and survival prediction using a Cox kernel machine regression framework, and prognostic performance evaluation.16, and knowledge-driven models based on pre-defined metagenes15 or immune cell infiltration score6, both of which have demonstrated strong prognostic associations in several cancer types. For BRCA, we also applied the Cox model using the PAM5021 classification and the MammaPrint gene signature22. To obtain unbiased performance evaluation, we used cross-validation, fitting model in training datasets and evaluating the prognostic performance in validation datasets using the C-index38. The C-index, as a generalization of area under the receiver operating characteristics curve, is a widely used measure for model assessment in survival analysis that evaluates the proportion of subjects with both longer observed survival time and higher predicted probability of survival . The larger the C-index, the better the prognostic performance is, with a value of one indicating a perfect prognosis prediction and a value of 0.5 indicating a random prediction.In addition to the multi-omic kernel machine learning method, we also considered several alternative approaches, including a conventional method considering clinical variables only, a sparse model involving variable selection39 implemented in the SVA package40 was used to adjust for batch effects. The TCGA datasets were obtained from the TCGA data portal (now the Genomic Data Commons) and the Broad Institute’s Firehose pipeline. Sample size, patient demographics, distribution of tumor stage and overall survival statistics are summarized in Supplementary Table We analyzed 3,382 samples across 14 TCGA cancer types. Rare cancer types were not included due to sample size limitations. Cancer types  with very few events (death) were also excluded. Six different molecular profiles were used, including germline variants (SNP6.0 array), somatic point mutation (whole-exome sequencing), DNA copy number (SNP6.0 array), DNA methylation (Illumina Human Methylation 450 K array), mRNA expression (mRNA sequencing), and miRNA expression . Patient samples with all data types available were included in the analysis. Principal component analysis was used to examine and visualize potential batch effects. Batch effects were identified in four methylation data sets . The ComBat algorithm41, and further reduced to a set of non-redundant regions as described in Mo et al.42. For the methylation data (Illumina Infinium 450k arrays), a beta-mixture quantile normalization43 was applied to normalize the beta-value. Methylation probes with >20% or more missing data and those corresponding to SNP and autosomal chromosomes were removed. RNAseq version 2 was used. MapSplice44 was used for sequence alignment and RSEM13 for the quantitation of gene expression. For mRNA and miRNA sequence data, lowly-expressed genes were excluded based on median-normalized counts.Each individual data type was pre-processed using the following procedure. Copy number alteration data were derived from the segmented data using the Circular Binary Segmentation algorithmM kinds of omic profiles. For the pm omic biomarkers for n subjects which were organized into an n × pm matrix Zm. Denote Z′m as the transpose of Zm and Zmj as its jth column. For mRNA, miRNA, methylation, and copy number profiles, Zmj is in the continuous scale and normalized with mean zero and variance one; for somatic mutation, Zmj was recorded as binary values with zero for observing no somatic mutation at the j th gene and one otherwise. The corresponding linear kernel, an n × n matrix, was defined asAssuming there are 18 measures the similarity between two kernels, namely K1 and K2, defined asith row and jth column. If Kernel alignmentAn omic similarity matrix could also be derived from the omic prognostic score, which is the weighted sum of all biomarkers in an omic profile with weights  following a univariate normal distribution with mean zero and a constant variance. Unlike the other prognosis indices commonly used with a handful of biomarkers, the omic prognostic score involves tens of thousands of biomarkers whose prognosis coefficients are treated as random and concentrate around zero. It can be easily shown that omic prognostic indices follow a multivariate normal distribution with mean zeros and covariate matrix proportional to the corresponding omic similarity matrix (details in the later section).i th subject, We propose a kernel-fusion Cox model as the multi-omic kernel learning method. Specifically, consider the Cox proportional hazards model,From the random effects perspective, we can show that jth clinical variable for subjects in the validation dataset. Finally, the predicted overall prognostic scores are given as We considered, evaluated, and compared several prognosis prediction methods through the Monte Carlo cross-validation. Given the method and cancer type, we randomly selected 80% of subjects as the training dataset and the remaining 20% of subjects as the validation dataset. For each training dataset, we fit (a) the Cox model with clinical variables only using R package “Survival”; (b) the kernel-fusion Cox models for one omic profile at a time and their pairwise combinations using R package “coxme”; (c) the kernel-fusion Cox models considering both clinical variables and omic profiles. To calculate the omic prognostic scores et al.16; (b) a Cox model with predefined prognosis signatures: immune and metagenes. The calculated immune signatures as the immune cell immune infiltration scores were downloaded from http://bioinformatics.mdanderson.org/estimate/. The metagene signatures included CIN, MES and LYM attractor metagenes, whose levels were calculated as the average of the mRNA expression levels of ten top-ranked genes15. For breast cancer, we also applied the Cox model using PAM5021 and mammaPrint gene list22.For mRNA profiles, we further considered (a) a Cox model with LASSO for the omic biomarker selection, the details of which was given by Yuan The prognostic powers of omic profiles, measured by C-indices, for a given cancer type were related. This relation was not surprising. The clinical variables and omic profiles were measured on the same subjects, and we have shown that the omic similarity matrices were well aligned. Hence, we applied linear mixed models to quantify and compare the contribution of clinical variables and omic profiles to prognostic powers across cancer types, while considering their between-cancer correlation. The linear mixed models were fit by the R package “lme4” and P-values were given by R package “lmerTest”.jth omic profile or clinical variables in the ith cancer type, the following model assumed that the variation of We first quantified the contribution of cancer type itself to the variation of C-indices for clinical variables and omic profiles. Denote kth profiles and zero otherwise; linear mixed model A number of modified linear mixed models were applied to compare between clinical variables and omic profiles, and between omic profiles themselves. For example, we used linear mixed model Among 16,598 mRNA transcripts, we randomly selected a subset of transcripts, built kernel, ran fivefold cross-validation for the kernel learning method, and calculated C-indices. The procedure was essentially the same as the one for the complete set of transcripts with one key difference: down-sampling of transcripts. The number of transcripts was gradually reduced until reaching one hundred. For a given number of transcripts, the down-sampling was conducted fifty times and the average C-index was calculated. Similarly, we carried out down-sampling of subjects, i.e. selecting a subset of subjects which gradually reduced until reaching two hundred. For selected subjects, we used all mRNA transcripts to build the kernel, carried out fivefold cross-validation for the kernel learning method, and obtained the C-index. This procedure was repeated fifty times for a given reduced sample size and the average C-index was reported.The TCGA datasets were obtained from the TCGA data portal (now the Genomic Data Commons) and the Broad Institute’s Firehose pipeline.Supplementary Information


This article reports a study of apost-discharge programme for elderly patients in Norway. It took place in anintermediate ward for transitional care and was based on collaboration between amunicipality and a hospital, which was part of a health enterprise. The aim ofthe study was to analyse the collaboration and its possible effects on thequality of patient care, and the economic efficiency of the project for theorganizations involved.A mixed-methods approach, consisting of interviews,questionnaires and analyses of official documents and statistics.The collaboration was working well on the top level of theorganizations, but was more problematic on the operative level. However, therewere clear signs of improvement. The patients who received transitional carewere more satisfied with their stay at the ward than their previous stay at thehospital. They were discharged to their homes more often and perceived to have ahigher level of functioning than the hospital patients. Average costs perpatient were also lower in the ward than in the hospital departments.The collaboration had mainly positive impacts on thequality of patient care and the economic efficiency of elderly care in themunicipality. However, the board of the health enterprise decided to close downthe intermediate ward. Integration has become an increasingly important issue in the development of themodern welfare society. At the same time, subspecialisation of professions incombination with decentralization has led to differentiation and fragmentationof welfare services, creating a need for integration between differentprofessions, organizations and sectors 2. This iElderly persons often have complex needs, requiring services from differentproviders, particularly within health care and social services. This means thatprofessionals from a number of organizations and sectors are involved in thecare of the elderly, leading to a risk of duplications, inconsistencies ordiscontinuities in the services provided. Transitions of older patients betweendifferent settings have also been associated with failures in care plans andtreatment, and strategies to improve transitional care have been lacking . As a reIn order to counteract such a development, there is a need for collaborationbetween professionals and organizations working with care or services for theelderly. There are different strategies to achieve or improve collaboration andquality of transitional care. Systematic reviews indicate a range of models ofinvolvement of hospital and municipal health services in the discharge processof elderly patients . One ove67In transitional care, the quality is strongly related to the transfer itself.Studies indicate that inappropriate medical prescriptions have been prevalentamong older people acutely admitted to hospital, and that the prevalence was notreduced during a stay at an intermediate unit especially designed for the careof these patients . In ordeIn addition to organizational change and improvement of specific procedures,there are also different models for collaboration between existingorganizations. There are structural models like case or care management,different forms of partnerships, co-location of professionals from differentorganizations, or financial coordination. There are also process-oriented modelsfor information exchange, more or less systematic interprofessional orinterorganizational meetings, or different forms of teamwork. These models areused not only for collaboration in elderly care, but also for example inpsychiatric care and vocational rehabilitation. They can also be combined indifferent ways 12. In th313Transitional care has become one of the most pressing topics in the globalefforts to improve quality of health services and patient safety. This has beendefined as a set of actions ensuring the coordination and continuity ofhealthcare as patients transfer among different locations and different levelsof care within the same location 15. In th1314In Norway there has been a need for integration primarily in the field of healthcare, but also in other welfare services. In 2006 there was a merger of theemployment service and social insurance combined with an increased collaborationbetween these agencies and the municipal social services within the framework ofthe Norwegian Labour and Welfare Administration. In health care, there is anongoing reform to improve collaboration between the municipal health and socialservices and particularly the specialized health services of the hospitals,which are organized in the form of health enterprises.The CoordinationReform for the health services was passed by the parliament [The planning of this reform started in 2005, when a parliamentary commissionpresented proposals for coordinated and seamless health services . As a reThe coordination reform was also a starting point for introducing mandatoryservice agreements between collaborating municipalities and health enterprises,which are required to have legally binding agreements since 2012. Studies offirst-generation agreements indicate that mutual sharing of knowledge can besignificant for improving the interorganizational collaboration, in response topatients’ needs for coordinated care . From 20Thus, the Norwegian coordination reform involves structural changes, financialincentives and legal obligations to improve interorganizational collaborationbetween municipalities and health enterprises. The question is what their dutyto collaborate really means. Experiences from other fields of collaboration showthat it is difficult to force organizations to collaborate if not all of themcan see some “collaborative advantage” . In addiExperiences also indicate that collaboration can be established if all theprofessionals involved have a strong commitment to the patients served. Such acommitment cannot be decided from above, but has to grow from below over alonger period of time .InterprBefore the implementation of the coordination reform, a three-year trial with apost-discharge programme in an intermediate ward for transitional care wasinitiated in a Norwegian community. The programme was established in April 2009as a collaboration project between a municipality and a hospital that was partof a health enterprise. The intermediate ward provided care for elderly patientsin transition from the hospital to the municipal health services, and theobjective was that the patients after their stay would be able to return totheir homes. The ward had eight beds divided into four rooms with two beds ineach. There were 13 employees, most of them nurses and auxiliary nurses, butalso a physician and a physiotherapist who were working part-time. The ward waslocated in a municipal nursing home and the personnel was employed by themunicipality.The collaboration project was based on an agreement between the municipality andthe health enterprise. According to this agreement, the intermediate ward wouldreceive patients who had been diagnosed and given most of their treatment at thehospital, so they could be sent home after two or maximum three weeks oftransitional care at the ward. There were restrictions, however, that thesepatients should be citizens of the municipality, 60 years of age or older, andnot be suffering from dementia. There was a project leader who was also thenursing manager of the intermediate ward. In addition, there was a project groupand a steering group with managers from the municipal administration and thehealth enterprise. The collaboration between the two organizations was takingplace in these groups and also in the daily contacts between the personnel atthe intermediate ward and at the hospital departments involved.Experiences from similar projects in Norway indicate that establishingintermediate wards can be challenging. Particularly, to see their ownorganizations as a part of a greater whole can be hard for the collaboratingpartners . Other N25252728Against this background, the aim of this study was to describe and analyse thecollaboration between the municipality and the health enterprise on differentorganizational levels. The focus was on the process of collaboration and itspossible effects on the quality of care and the economic efficiency of thecollaboration for the organizations involved. The quality assessment includedaspects of patients’ satisfaction with their stay at the hospital and theintermediate ward, which they were discharged to, and their subsequent needs forhome care or support. The economic assessment included the length of stay andthe average costs of patients at the ward and at the hospital, but also thecosts for home care and support from the municipality. Analysis ofcollaboration, quality and efficiency also involved issues of trust andintegrity.A mixed method design is comprised of a qualitative or quantitative core componentthat directs the theoretical drive, with one or more qualitative or quantitativesupplementary components . Such a The process of collaboration between the municipality and the health enterprise wasstudied through a collection and analysis of written documents like project plans,agreements, annual reports, notes and minutes from meetings, statistics andinformation materials. In addition, a number of persons from the municipality andthe health enterprise were interviewed. Some of these were selected strategically,others successively through “snowball sampling” .The interviews were made in 2011, during different periods of the year. There were 31interviews with 28 participants, who were in different ways involved in thecollaboration project. Among the participants were nine from different clinicaldepartments at the hospital, two from the top administration, one from the board ofthe health enterprise, six from the intermediate ward, six from the municipaladministration, three politicians from the municipality, and one participant fromthe local university college who had been involved in the establishment of the ward.Three of the participants were interviewed twice.All the interviews were made by two of the authors and conducted as informalconversations with the interviewees at their workplace. They were following aninterview guide with four thematic areas: the collaboration between the hospitaldepartments and the intermediate ward, the collaboration between the healthenterprise and the municipality, the barriers and the facilitators in thecollaboration. Each interview lasted between one and two hours and was documented inparallel notes by both interviewers. These notes were transcribed and a thematiccontent analysis was performed in a process where different impressions andinterpretations of the two authors were compared . The comThe effects on the quality of care were assessed in a retrospective and a prospectivestudy of patients at the intermediate ward and the hospital. The retrospective studywas conducted by means of a postal questionnaire sent to all the patients who hadstayed at the intermediate ward in 2010. There were questions about theirsatisfaction with different aspects of the care received, and also questions aboutwhere they had been discharged to after their stay at the ward and their needs formunicipal care or services when they returned to their homes. The questionnaire wasdeveloped and validated by the Norwegian Knowledge Centre for the Health Services,but some adjustments were necessary.The prospective study was conducted mainly during 2011 and 2012 following aquasi-experimental research design, where a study group of patients who had beentransferred from the hospital to the intermediate ward was compared with a controlgroup of similar patients at the hospital. Because of the small number of patientsat the ward it was not possible to make a random selection of patients to the twogroups. Instead, the control group was composed of patients who fulfilled all thecriteria for admission to the ward except that they were not citizens of themunicipality. The patients in the two groups were compared using a similarquestionnaire as in the retrospective study.The results from the retrospective study (n = 62) and some of the results from theprospective study were analysed using descriptive statistics . For theThe question of whether the patients in the study group were more satisfied withtheir stay at the intermediate ward than at the hospital was analysed using a pairedsamples t-test. Because some respondents did not answer all questions, the samplesizes vary slightly in the analysis of the different questions. Post hoc poweranalysis was conducted, with significance level set to the conventional p =0.05.The Regional Committee for Medical and Health Research Ethics approved both studiesin December 2010. They were part of a larger research project on quality in the careof elderly patients, which will be reported in more detail elsewhere.The economic effects were studied mainly through an analysis of the length of stayand the average costs per patient and day at the intermediate ward compared to theaverage costs per patient and day of the clinical departments at the hospital. Thisanalysis was based on financial reports and official statistics from themunicipality and the health enterprise. However, because of the small number ofpatients involved, it proved difficult on the basis of this information to calculateany direct savings for the health enterprise by transferring patients from thehospital to the intermediate ward.There were great differences in average costs between different hospital departmentsand also between different phases of treatment. Therefore, only departments withsimilar patients to the intermediate ward should be considered and only the lastphases of treatment. There are also alternative costs in terms of lost revenues fornew patients due to bed occupancy at the hospital that should be considered. It wasnot possible, however, to extract any information on alternative costs and averagecosts during the last phases of treatment from the financial systems. Instead, thepotential savings of the health enterprise were estimated by comparing the averagecosts of patients at the ward with the average costs of similar patients at thehospital departments.In the same way, it proved difficult to calculate any direct savings for themunicipality on the basis of information from the existing financial systems,because of the small number of persons involved. Therefore, a simple questionnairewas constructed by the researchers and distributed by the municipal administrationto all the clinical managers within the municipal home care. There were questionsconcerning the needs for care and support of elderly persons who had been staying atthe intermediate ward compared to persons who had been sent home directly from thehospital. Based on this questionnaire, it was possible to discuss the potentialsavings of the collaboration project for the municipality.The initiative to the collaboration project came from the top managers of thehealth enterprise, who heard about the positive experiences of intermediatewards for transitional care in other parts of the country. The health enterprisefinanced 2/3 of the project, while the remaining 1/3 was financed by themunicipality. In spite of the different financial contributions to the project,the collaboration between the health enterprise and the municipality worked wellon the top level of the two organizations.“it is valuable to test the collaboration between themunicipality and the health enterprise, as it will anyway become a duty inconnection with the coming coordination reform”. An importantfacilitating factor was that none of the managers saw it as a loss of prestigeif the project should not turn out to be successful. “We will inany case learn from the experiences”.There was a good climate of collaboration in the project group and the steeringgroup, where different managers represented both parties. The politicians werealso very supportive of the collaboration project. All of them saw a great valuein a common project like this. According to one of the managers,“the ward is expensive and a waste ofresources that could be used better at the hospital”.Moreover, they did not trust the personnel at the ward. “They donot have the competence required to take care of ourpatients”.Unlike the management relations, the contact between the personnel at theintermediate ward and the hospital departments was more problematic. This wasthe case particularly in the contacts between the ward and the departments ofgeneral surgery and internal medicine. Many of the interview persons from thesedepartments felt that this project was decided from above and forced upon them.In their opinion, the intermediate ward should be closed down immediately. Asone of them pointed out, The personnel at the hospital departments saw a number of barriers tocollaboration. The strict criteria for admission to the intermediate ward madeit difficult to find patients at the hospital who could be transferred there.Even when such patients were identified, the ward was sometimes unable toreceive them. This could be due to a lack of personnel at the ward, or becauseit was impossible to place male and female patients in the same room. When theward refused to receive patients who fulfilled all the admission criteria, thepersonnel at the hospital departments felt themselves cheated. It was alsodifficult for them to reach the personnel at the ward and discuss these mattersbecause of limited telephone hours. The result was increasing annoyance and ahesitation to send patients to the ward.“they want to get rid of certain patients and when we are notaccepting them, they are punishing us by not sending any patients atall”.The personnel at the intermediate ward had a positive view of the collaborationproject, but they felt that there was a strong resistance from some of thedepartments at the hospital. They experienced that the personnel at thesedepartments were badly informed about the project and the agreements between themunicipality and the health enterprise. This was an important barrier tocollaboration. They also felt that the personnel at the hospital departmentswere cheating them. According to one of the nurses at the intermediate ward,“there wereinformation meetings where no one came”.Thus, while both the managers of the municipality and the personnel at theintermediate ward had a positive view of the collaboration project, there weredifferent opinions among the managers of the health enterprise and the managersand personnel at some of the hospital departments. The managers at thesedepartments were not able to convince their personnel of the needs and theusefulness of an intermediate ward, which might have facilitated thecollaboration with the personnel at the ward. Additionally, they were not ableto order the hospital personnel to collaborate. There was not a great deal ofinterest in the project, particularly among the physicians at the hospital.According to one of the managers in the project group, “it is probably more expensive and worse for thepatients to stay at the hospital”.The contacts and communication between the hospital departments and theintermediate ward were problematic from the start of the project. There weresigns, however, that they were overcoming some of the barriers and that theircollaboration and relations of trust were slowly improving. There were morecontacts between the part-time physician at the intermediate ward and thephysicians at the hospital. The contacts between the personnel at the ward andthe hospital were also improving. There were mutual adjustments of the routinesfor transferring patients from the hospital, and the telephone hours of the wardwere extended. In addition, there was an increased understanding of theadvantages of the intermediate ward. As one of the nurses at the hospitaladmitted, According to the results of the retrospective study, 95 % of the patients whoreturned the questionnaire (n = 62) were satisfied or very much satisfied withtheir stay at the intermediate ward. They appreciated particularly theprofessional competence of the personnel, their concern for the patients and thequality of the food.The patients in the study group, who were treated both at the hospital and theintermediate ward (n = 52), were less satisfied with their treatment and care atthe hospital than the patients in the control group (n = 28), and more satisfiedwith their treatment and care at the intermediate ward than at the hospital,although the differences were not statistically significant . However, the group of patients who were treated both at the hospital andthe intermediate ward were slightly less satisfied with their total treatmentand care  than the patients in the control group, althoughthe difference was not statistically significant .The patients’ satisfaction with different aspects of treatment and care wasalso analysed, and the results are summarized in Table Regarding readmission and discharge, only 3% of the patients in the retrospectivestudy were readmitted to the hospital after their stay at the intermediate ward.In the prospective study, there was a low rate of hospital readmission amongboth the study group and the control group. However, results from theprospective study show that the patients who were transferred to theintermediate ward (n = 57) could return to their own homes more often than thepatients in the control group (n = 30), although the difference between thegroups was not statistically significant p = 0,12). A larger percentage of thepatients from the hospital were instead sent to short term stays at nursinghomes, or rehabilitation clinics. These results are summarized in Figure 2. A largAccording to the retrospective study, 62.5% of the patients who were transferredto the intermediate ward had some needs for municipal care or services when theyreturned to their homes. Results from the prospective study show that thepatients who were discharged from the intermediate ward (n = 50) experienced agreater need for home care and services than the patients in the control group(n = 23), and this difference is statistically significant . For thegiven effect size, statistical power is 0,640. The results also show, however,that the control group received help and care from a larger number of providersonce they were home than the study group. This difference is also statisticallysignificant . For the given effect size, the statistical power is0,766.During the period studied, the patients at the intermediate ward stayed for anaverage length of 12.2 days before they were sent home. This length of stay iswithin the two or maximum three weeks that were expected for transitional careat the ward. However, there was great differences in bed occupancy during theperiod studied. On the average, the bed occupancy was only 68% during 2009 and2010, but it increased to 82% in 2011. Even so, the beds at the intermediateward were much less occupied than the beds at the hospital. During the sameperiod, the departments of general surgery, orthopaedic surgery and internalmedicine had a constant bed occupancy rate of 100%, which means that there was agreat pressure on the hospital beds and also a need to relieve thispressure.Table This means that the costs of the health enterprise could be reduced bytransferring patients from the hospital to the intermediate ward, although suchsavings were not possible to extract from the financial information systems. Thesavings would be even greater with higher rates of bed occupancy at the ward.This could also relieve the pressure on the hospital beds, or make it possiblefor the hospital to admit new patients, which would generate more revenues forthe health enterprise.The intermediate ward may also have brought some savings for the municipality.The patients from the intermediate ward were more often discharged to their ownhome, which in most cases is less costly for the municipality than a dischargeto a municipal institution. At the same time, they seemed to require more helpand support in their homes than the patients in the control group, although theresults are inconclusive. However, a questionnaire answered by all the clinicalmanagers of the municipal home care indicated that there were many differentneeds for care and services, depending on the individuals concerned. A commonview was that patients from the hospital were more demanding than patients fromthe intermediate ward. In addition, the home care had better collaboration withthe ward than with the hospital departments. Although it is difficult to draw afirm conclusion from the available data, it seems that the intermediate ward mayhave brought some financial gains to the municipality.This study has shown that the collaboration between the municipality and the healthenterprise was working well on the top management level of the two organizations. Itwas more problematic on the operative level between the personnel at theintermediate ward and the clinical departments at the hospital. The collaborationbetween the managers of the municipality and the health enterprise was characterisedby mutual interest in and support of the project, while the contact between thepersonnel at the intermediate ward and the hospital departments was characterisedmainly by conflicts and lack of trust. The managers could see some collaborativeadvantage in the project and so could also the personnel at the intermediate ward,while the personnel at the hospital felt that the project was forced upon them andthat it was a waste of time and resources.There were clear signs, however, that the collaboration problems were reduced. Thetrust was increasing during the time of the project through improved communication,adjustments of transfer routines and an increased understanding of the mutualadvantages and challenges of collaboration. In addition, the bed occupancy rate ofthe intermediate ward was increasing, probably as a result of better information andimproved collaboration between the personnel at the intermediate ward and thehospital departments. This development is not surprising. Reviews of internationalresearch in the field show that it is usually a long process and takes a lot of timeand energy to establish and maintain collaboration 12. In ecResearchers dealing with care of the elderly point out that there are often problemswith continuity and quality of care as well as economic efficiency 4. Manyo674142The assessment of quality of care showed that the patients on the whole weresatisfied with their stay at the intermediate ward. They were also slightly lesssatisfied with their stay at the hospital than the patients who completed theirtreatment and care there. Earlier studies of patient satisfaction have shown thatsuch positive results are not unusual 4546. Thu45In addition, the patients from the ward were more often discharged to their homesafter their stay, and were perceived by the home care managers to have a higherlevel of functioning than the patients from the hospital. However, they seemed tohave significant more need for municipal care and support compared with patients whowere discharged directly from the hospital, but received help and care from a lowernumber of providers once they were home.The economic assessment showed that the intermediate ward had lower average costs perpatient and day than the departments at the hospital. Even if the ward in certainperiods had not been able to fill their beds, meaning that the costs of the wardwere divided between fewer patients, the average costs of the ward were still lowerthan the average costs of the hospital departments.It was difficult to show any direct savings for the health enterprise and themunicipality. Even so, there seemed to be positive economic effects of transferringpatients to the intermediate ward before they were sent home. This may have reducedthe pressure on the hospital beds, even if it is was not possible to show in thisstudy. It may also have reduced the pressure on temporary care in nursing homes, asthe patients from the ward were discharged to their own home more often than thepatients from the hospital. At the same time, however, they needed more care andsupport than the patients from the hospital after returning home. Thus, it isdifficult to show any direct savings for the municipality.Other studies have indicated that patients had a higher level of functioning after astay at intermediate wards than the patients who were discharged directly fromhospitals . In thisIn addition to effects on quality and efficiency, the data indicated both positiveand negative collaborating relations. As care and transfer of elderly patients maybe characterized by a high degree of differentiation and fragmentation, a high levelof integration is required. There were positive contacts and a state of integrationbetween managers and units. For example, in the project group and the steeringgroup, representatives from both organizations had a good climate of collaboration.However, the interface between hospital departments and the intermediate ward wascharacterized by antagonistic contacts and conflicts, which is a state ofdisintegration. As suggested, the personnel at the ward felt themselves cheated bycolleagues at the hospital departments. They even suggested that their colleagueswanted to punish them by not sending any patients at all. Seemingly, a bad climatemay be destructive and compromise the interprofessional and interorganizationalcollaboration. However, a state of disintegration can also be necessary to manageconflicts and contribute to negotiations and integration.Findings of good and bad relations are not surprising, as facilitators and barriersof interorganizational collaboration are typically connected with factors ofcommunication, commitment and trust . ForexaOver time, feelings of being alienated to interorganizational processes and forced todo what you do not want to do may induce integrity pressure with potentiallynegative effects on work health and wellbeing . IntegriAs mentioned before, research indicates that it takes a lot of time and energy toestablish and maintain collaboration . AcontiThis article reports a study of a post-discharge programme for elderly patients inNorway. The programme took place in an intermediate ward for transitional care,which was based on collaboration between a municipality and a hospital belonging toa health enterprise. The patients on the whole were satisfied with their stay at theward and most of them could be sent home after two weeks of transitional care.The intermediate ward had lower average costs than the clinical departments at thehospital. This means that there could be positive economic effects for the healthenterprise by transferring patients to the ward. There were probably positiveeconomic effects also for the municipality, as the patients from the ward weredischarged to their homes rather than to short term stays at municipal institutions,which in most cases is less costly for the municipality. Although they seemed tohave a greater need for care and support in their homes than the patients who weredischarged from the hospital, the patients from the ward were perceived by themunicipal team managers to have a higher level of functioning.There were a number of barriers to collaboration on the operative level, related bothto formal obstacles, lack of trust and negative attitudes to collaboration among thehealth personnel. There were, however, signs of mutual adjustments and improvedcollaboration between the two groups of personnel, which indicate that thedevelopment of collaboration has to grow from below over a longer period of time.Positive and negative collaborating relations impacted values of quality andefficiency, and also made current issues of trust and integrity. Thus,interorganizational collaboration can also be related to interorganizationalhealth.In spite of increasingly and mainly positive results in an atmosphere of improvingcollaboration, the board of the health enterprise decided to close down theintermediate ward in 2012. This decision was based mainly on short-termintraorganizational and economic considerations. Although the intermediate ward wasclosed down, the findings of the study suggest that a continuation of thisinterorganizational collaboration might have had positive long-term effects on thequality of elderly care and also on the economic situation of both the healthenterprise and the municipality.