In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community. Urban and domestic environments are becoming increasingly related to technology. Being able to automatically interpret human behaviors and people’s feelings could provide significant insights for the definition of systems that can automatically react depending on the interacting person.As a concrete example, let us consider a future scenario where self-driving vehicles are increasingly adopted in an urban environment. In the case of traditional vehicles, the establishment of eye contact with the driver provides a perception of safety to the pedestrian . ConsideAnother example in an urban environment where technologies capable of receiving feedback from citizens could be involved is the realization of traffic lights able to adapt their waiting time according to the presence or absence of people with impaired mobility.To realize such systems, an essential aspect consists in profiling and recognizing the categories of individuals with which these systems could interact. Within this context, subjects’ age is a relevant factor that should be considered. It has been shown that subjects of different ages react differently to stimuli both from an emotional  and behaNowadays, physiological responses have become useful to better understand how different types of people react to different situations. They can be considered reliable indicators of uncontrolled and fair human reactions to external stimuli and are now widely used to detect and recognize affective states  and humaNowadays, the significant improvement in wearable sensor technology and the progressive reduction in their cost allow their adoption in many new experimental scenarios, making them comfortable also for older adults or impaired subjects. Their utilization, as opposed to the deployment of more complex but more invasive devices, allows the recording of physiological signals during, for example, daily life activities , withoutQ1: Can physiological data, acquired with wearable sensors, be useful to define binary classification models that allow discriminating between young adults and older adults while they perform various tasks?Q2: Can a binary classification model, trained on two tasks of different cognitive load, be sufficiently generalizable to classify a new set of different binary tasks?Q3: Can physiological data be useful to discriminate contemporaneously among different tasks and ages?Q4: Can physiological data, potentially affected by movement noise, reveal the different behavior of subjects with respect to their age and walking activities?Relying on the data collected, the main research questions of this paper are:Research questions Q1, Q3 and Q4 will give hints about the feasibility of defining interacting systems that react differently with respect to the peculiar reaction of different individuals. Q2 is crucial when considering that real-life activities are variegate and it is not reasonable to imagine that for each of them a classification model should be trained. The requirement of generalizable models is thus mandatory. This paper is structured as follows: In Physiological signals are regulated by the parasympathetic and sympathetic systems, and thus are not directly controlled by the subjects and can be considered honest indicators of emotions and mood ,18. For Cognitive load has been widely analyzed in the study of an individual’s workload during a driving task. In this context, physiological signals have been considered to monitor the level of driver physiological arousal in response to cognitive load during car driving ,38, to eConcerning human activity recognition (HAR), the use of physiological signals combined with inertial and position data have shown positive results in the recognition of the person’s activities, even during movement tasks such walking or running . In thisFinally, in most of the studies reported so far, the populations considered are uniform with respect to age. However, physiological signals, as well as how a person reacts to stimuli or conditions, change with the age. In , for insThe CLAWDAS  dataset here presented has been collected in a controlled laboratory environment at the Research Center for Advanced Science and Technology (RCAST) at the University of Tokyo. In the experiment, two different groups of subjects were involved: a population of young adults, composed of 16 Japanese master and PhD students, , and a population of Japanese older adults (retired), 20 subjects, . Mental or heart diseases could influence the physiological responses, thus the healthy conditions were considered as inclusion criteria for the selection of the participants.All the participants performed the same tasks defined by the experimental protocol, lasting about one hour, and consisting of two sessions: a first part in which subjects sat at a desk performing several cognitive load or relaxing tasks, and a second part in which, instead, several walking conditions were proposed. The experiments in this paper have been reviewed and approved by the Research Ethics Committee at The University of Tokyo, Japan (No. 19-283 and 19-376).The two sessions of the experiment are described in more details below:Subject’s profiling carried out filling the STAI questionnaires , 3 min.Reading and comprehension  tasks. Two different texts are proposed: a Fairy-tale (“The Wolf and the Seven Little Kids”) and a philosophy text (“Kant’s Critique of Pure Reason”). The subjects have 2 min to read each text on a piece of paper and 1 min to answer self-assessment and reading comprehension questions, 6 min.Audio listening, performed using headphones, in which the relaxation is induced by natural and real-life sounds  right, 2Mental arithmetic calculations such as sums, subtractions and multiplications  left, 30Audio listening and math calculation  tasks composed of six repetitions of a two-step sequence consisting of:The audio tracks and the calculations proposed are the same for each subject but change according to the iteration, 15 min.Cognitive Load session: lasting about 30 min and composed of the following steps:Between each couple of tasks, a period of resting time , and go back in the opposite direction repeating the same actions. The obstacle is a moving pendulum composed of a swinging mattress activated manually by one of the experimenters.Collision avoidance: two subjects, at the same time, walk with their own pace along a U path  top leftForced speed walk: the participants walk with a forced speed based on the metronome ticking. Three speeds are considered: 70 bpm (F1), 85 bpm (F2) and 100 bpm (F3). The idea of adopting different walking paces was inspired by previous work on pedestrian flow analysis ,56.Free walk (FW): the participants walk freely, without constraints, following their own pace.Walking session: Three within subject walking conditions are considered:Between each couple of tasks, a period of resting time . These low-cost wearable sensors were already utilized in different experiments concerning physiological signal analysis and affective state recognition with encouraging results [During the whole experiment, physiological signals are collected using wearable sensors from the Irish company Shimmer  processed to reduce noise, (ii) normalized to disentangle subjects dependencies and (iii) segmented into different tasks. The data processing algorithms adopted in this work are described in detail in the next section . Once raSignal denoising: each signal can be affected by different types of noise and artifacts related to both the characteristics of the environment  or the experimental conditions . Thus, in Subject normalization: physiological signals not only depend on the induced stimulus, but also on subject’s characteristics. A proper normalization process is performed to reduce both inter- and intra-subject heterogeneity, as described in Signal segmentation: data are acquired continuously; thus, a proper segmentation is applied, adopting the markers introduced during the acquisition through Consensys Pro, the proprietary software of the Shimmer devices.PPG frequency normalization: in order to take into account frequency differences in subjects’ heartbeat, a frequency normalization strategy is applied to segmented data.Data augmentation: to balance the cardinality of the instances per classes, a proper data augmentation step is applied as described in Data processing algorithms applied to raw data are detailed in the following sections:All the analysis and pre-processing operations are performed using MATLAB 2020a.The raw signals collected during the experiment appear corrupted by noise and artifacts that could make them unusable or difficult to analyze. These artifacts could be due to the characteristics of the environment where the signals are acquired , to the errors in experimental setup (poor cohesion between the sensors and skin or artifact owing to subject’s breathing or movement) or to the influence of other physiological processes in the body  .jth wavelet coefficient [In order to remove these artifacts, the PPG raw signals are pre-processed using a multiresolution wavelet denoising strategy described in , and in fficient . Finallyfficient . In ordeposition .level” in this case is equal to seven.A similar denoising strategy based on multiresolution wavelet decomposition is adopted in pre-processing GSR signals. Following the positive results achieved in , a statiFinally, in the literature it is reported how the use of a multiresolution wavelet denoising strategy appears promising in removing noise also from EMG signals ,65. In pFor each analyzed signal, the denoising operations are followed by a normalization task performed to reduce both the inter- and intra-subjects’ heterogeneity. Concerning the PPG, the amplitude of the whole subject’s signal for each session is standardized by the application of the Z-score operation. Therefore, the mean and standard deviation of the whole subject session PPG signal are computed and used according to the formula ollowing , the GSRollowing , has beeBoth the denoising and amplitude normalization are applied to the whole signals collected from each subject during the two different sessions . Therefore, a segmentation operation is then performed to divide each signal into the different tasks as defined by the experimental protocol. In particular, for each participant, the markers manually recorded during the data acquisition phase are used to determine the beginning and end of each trail performed by the subject and, thus, to define the edge of the segments in which the session signal has to be divided. In The normalization applied so far to PPG signals allows us to uniform the signals concerning amplitude but does not take into account the differences in the subjects’ heartbeat that are more related to frequency. In , it is rIn order to increase the cardinality of the data and create more balanced classes for the next classification step, some segmented signals are divided into non-overlapping segments. In particular, the signals collected during the reading task (R) are divided into segments of 40 s using non-overlapping windows of fixed length, while the reading comprehension signals (C) are segmented into two parts of equal length. Thereby, the number of signals for reading task increases from 32 to 96 for young adults, and from 32 to 64 for older adults. Similarly, the cardinality of the signals related to the comprehension task increases from 40 to 120 for young adults and from 40 to 80 for older adults.A similar strategy is also adopted for the signals related to the free walking (FW) task in order to create more balanced classes during the classification analysis. In particular, all the instances collected during the pure free walking task are divided into two segments of equal length, increasing the cardinality of each analyzed physiological signal from 57 instances to 114.In this section, the classification settings adopted in the next data analysis are detailed. In particular, in Four statistical features –Peak rate, representing the mean number of peaks of each 128 subject-Normalized sample;–Inter-beat interval (IBI), representing the mean distance between two peaks in a row;–Root mean square of successive distance (RMSSD) representing the quadratic mean of the distance between two peaks .Three peak related features:For each analyzed signal, several features are extracted in order to highlight significant characteristics that could describe the subject’s physiological behavior in the different tasks. Considering the PPG signals, seven time-domain features are calculated:–Maximum, mean and variance of the phasic component of the signal–Peak rate, representing the mean number of peaks per second–Peak area and peak area per second, representing, respectively, the mean area under the peaks and the mean area under the peaks evaluated per second.–Peak height representing the mean height of the peak detected on the phasic component.–Rise time  defined as the mean number of samples from the onset of the skin conductance response to the top of the peak ,74Phasic Component: eight statistical and peak-related features:Tonic component: the Regression coefficient is considered as representative of the signal slope.In the analysis of GSR signals, both the phasic and tonic component time domain features are considered. Following , the GSRThe MATLAB codes to calculate all these features are available together with the dataset.n-th sample of the EMG signal, and N is the total number of samples.The mean power of the signal, calculated by the Root Mean Square (1)RMS=1The walking frequency, known as the Stride Frequency, evaluated in terms of the number of steps per second. This feature is calculated as deeply described in  and hereFor EMG signals, two features are considered:Many of the data analyses reported here consist in classification tasks aimed at recognizing the age of the subjects or the levels of arousal induced by the different tasks performed. In these analyses, only the features related to PPG and GSR signals are considered. In particular, three different feature sets are taken into account during the classification tasks: features extracted considering only PPG signals, features extracted considering only GSR signals and the joining of PPG and GSR features. It is important to underline that all the features are standardized using the z-score before using them as an input to the different classifiers. For each experiment, this standardization is applied to the whole set of analyzed instances before splitting it into training and test sets.Two well-known classifiers frequently used in the literature to classify physiological signals are trained using the standardized features: a classification and regression tree (Cart)  and a suTo evaluate the performance of the trained classifiers, a leave one subject out (LOSO) cross-validation strategy is employed. In this strategy, the analyzed data are divided into subsets according to the subject from which the data were acquired. At each iteration, all the instances of one subject are used to test the model, while the signals of the remaining subjects are used as a training set. This method allows us to generate more robust performances when considering new unseen data, as it prevents the classifier from being trained and tested simultaneously with instances acquired on the same subject . The conc is the class considered while c class. The F1-score metric gives a general hint in evaluating the classifier performance in recognizing a single class. As a harmonic mean of precision and recall, it provides information about the ability of the classifier in balancing precision and recall for the considered class, avoiding high recall at the expense of low precision and vice versa.Several metrics are computed in order to evaluate and compare the performance of the different classifiers. In particular, in addition to the traditional and well-known accuracy, precision and recall, a single class F1-score  is evalum is the number of classes considered, c, j-th class [Starting from the single class F1-score, we calculate the weighted F1-score metric . In all the classifications, two classes are taken into account: a class made up with the signals collected from young adults and a class related to the signals collected from older adults. In order to create balanced groups, the data augmentation strategy described in W-FW-FW-FIn general, the best performances are obtained considering both PPG and GSR signals, allowing to reach values of an As a further experiment, a multi-class classification analysis is conducted in order to discriminate not only the population group with respect to age but also the task performed. Therefore, six classes are considered: three activities , for the two population groups. The comprehension task, C, is not considered due to its lower cardinality, even in the case of data augmentation. Only the SVM with linear kernel trained using the union of PPG and GSR features is selected for this analysis as it is the classifier that allows us to reach the best performance in recognizing the two populations in the previously described binary classification tasks.The confusion matrix produced by this classifier is reported in Due to the positive results achieved in discriminating young adults from older adults, we have decided to consider the two population groups separately in the next analyses.The second analysis on data collected during the first session of the experimental protocol aims at defining classifiers able to discriminate between different cognitive load tasks. In particular, binary classification models are here investigated to recognize math calculation, MC, from audio listening tasks, AL. For each one of the two populations analyzed, the four classification models described in The performances obtained for the two analyzed populations appear very similar when the same set of features  are considered. In fact, in both young adults and older adults, the best performances are reached using the features extracted from GSR with accuracy values around 92%. In particular, in young adults, two classifiers (SVM-Linear and Cart) are able to achieve the highest accuracy value of 93% using GSR. Similarly, in older adults, the use of GSR features allows to reach the highest accuracy of 92% using either SVM-Linear or SVM-Gauss. Although positive, the lowest performances are observed in the classifiers trained using only features extracted from PPG signals. In this case, the use of SVM with linear kernel allows us to reach accuracy values around 80% in both young adults and older adults. Moreover, analyzing the per-class F1-scores, it emerges that both the two classes are well recognized, with similar values in the performance metrics.Finally, another interesting consideration regards the classifier that allows us to reach the best performances. In general, the use of SVM with a linear kernel allows us to achieve the highest accuracy value in almost all the conducted experiments. It is important to stress, however, that the same accuracy values are also reached by more than a classifier in the analysis using features computed from GSR. To this regard, for instance, in both populations, the Cart classifier generates the same performances of SVM-Linear when trained with the union of PPG and GSR features, as well as when trained with the GSR features extracted from the young adult signals. Likewise, in older adults, the two classifiers SVM with a linear and Gaussian kernel generate the same accuracy value when the features extracted from only GSR signals are considered. In general these results seem to confirm what is reported in the literature about the importance of the GSR in discriminating tasks having different levels of arousal ,83.Starting from the results obtained in the previous section, this analysis aims to evaluate the performance of the classifiers trained on MC and AL tasks in recognizing new tasks that imply different cognitive loads. In , it is rIn the following analysis, we tried to apply these classifiers to the signals collected during different cognitive activities such as, for instance, reading and comprehension. The idea is to automatically evaluate, using the classifier, the level of arousal induced in the subjects performing these two tasks and, at the same time, to evaluate the possibility of applying a classifier trained on known data to new unseen ones, collected during different tasks. In particular, the following three tasks have been considered: the baseline BAccording to the results obtained in The classification model so trained is then employed to classify the unseen data collected in the BIn all the analysis, most of the instances collected during the baseline are classified as inducing low arousal with percentages slightly higher in older adults  with respect to young adults . On the other hand, the instances related to text comprehension are mainly classified as high arousal with percentages usually between 70% and 85% in both the populations analyzed.Regarding the reading task, the percentage of instances predicted as inducing low or high arousal appears more balanced, especially in the case of older adults, while in the case of young adults there is a predominance of instances predicted as inducing low arousal. This difference could be related to the higher difficulty of older adults in reading long text because of age-related factors  that could lead to a higher cognitive load and, thus, to a greater emotional alteration. It is also worth noting that the young adults are students used to reading difficult texts.The first analysis on data collected during the walking session relies only on EMG signals, which can reveal different walking behaviors through the analysis of muscle activities. Another important aspect investigated in this section regards the contributions in the PPG variations related to movement and those related to changes in arousal due to an increase in stress. Finally, a task classifier is proposed to evaluate if it is possible to recognize different tasks, despite the interference of movement with the acquired signals.In this section, the three different tasks of the walking session are analyzed using the EMG features described in From the analysis of the estimated frequency stride during the forced speed tasks, reported in In the case of the fee walk task FW, the mean frequency stride detected is usually around 0.90 steps/s, appearing, in this way, in agreement with the metronome frequency indicated by the participants as the most preferred one, F3 and with the normal pace speed reported in the literature (between 0.90 and 1 steps/s as reported in ).In the collision avoidance task, a stride frequency evaluation and a signal energy analysis is performed. Analyzing the obtained stride frequency values in the portions of the path far away from the collision zone, WO see , we obtap-value returned by the test appear lower than a certain significance level , in accordance with what has already been observed in the EMG analysis.Moreover, from the analysis of the p-values obtained for both young adults and older adults, in most of the PPG features. An interesting exception is observed in the peak rate, while comparing F3 and WO in case of the older adults. In this case, the p-value appears lower than A comment worth of notice concerns the comparison between the two tasks F3 and WO. For the sake of clarity, we recall that a similar frequency stride has been observed in both the tasks from the EMG analysis. This similarity is also confirmed by the high r adults  right, ip-values generated by the test are greater than the level of significance considered. The only exception refers again to the peak rate, in case of the older adults, where the p-value appeared lower than Likewise, it is not possible to reject the null hypothesis between the two tasks of crossing, Obs, and free walking during the collision avoidance task, WO. In fact, in both the populations and in most of the features analyzed, the The aim of this study is to evaluate if different walking tasks, having similar walking pace, could lead to significant changes in the physiological signals and, thus, could be discriminated by a classifier.For each considered subject group, a different set of analyses is carried out due to slight differences in the experimental protocol, which make it impossible to directly compare all the results achieved in the two populations. Furthermore, only the signals related to heartbeat (PPG) and to sweet gland activity (GSR) are considered in this study. EMG data are excluded from the analysis as we only have data for half of the subjects.In older adults, three different binary classification tasks are considered, one for each couple of walking activities having similar walking pace . This choice is justified by the results described in In The same classification models, evaluation strategy and performance metrics are also adopted in the classification analysis of young adults walking tasks. As already mentioned, the slight difference in the experimental protocol between the two populations makes it not possible to replicate exactly the same analysis. Thereby, in the young adults analysis, we consider only one binary classification task comparing the signals collected during the whole collision avoidance task, WO + Obs  with the signals acquired during the highest frequency forced speed task (F3). The latter has been selected as representative of a free walking task due to his similarity in terms of frequency stride and because it was selected by the participants as preferred walking frequency among those constrained by the metronome.W-FThe performance achieved by the four classifiers is reported in The positive results achieved from the analysis of data acquired both during the cognitive load and walking sessions demonstrate that physiological signals can be adopted within machine learning models not only to discriminate among different tasks but also to discriminate among different population age.This allows to answer positively to the research questions Q1 and Q3 and to underline how people of different ages can react differently to similar stimuli, both from an emotional and behavioral point of view.The performances achieved in the classification of cognitive tasks appear slightly higher than those achieved in the literature in which accuracy values between 60% and 80% are reported ,86,87. AWith reference to the classification performance, PPG seems in general to be more useful in most of the classification tasks; however, the best results are achieved considering both PPG and GSR. Considering in particular the walking session from the results achieved in these experiments it emerges how the physiological signals allow to recognize well the signals collected during different tasks in spite of the walking speed factors. In particular, the analysis have highlighted how the physiological signals of a person tend to change when he/she approaches to a potential dangerous situation as a collision avoidance zone as well as during the crossing of the obstacle itself. Moreover, the good performances of the classification models applied to the signals collected during the walking session demonstrate that there are significant differences in physiological responses with respect to space varying conditions. These considerations answer to the research question Q4.A potential limit of the dataset collection concerns the subjects that have been selected as participants in the two populations. In fact, all the young adults selected were PhD students or young researchers of the RCAST at the University of Tokyo and, thus, are used to reading difficult texts or performing difficult calculations. On the other hand, the older adults have been selected by a company specialized in experimental subject recruitment and, thus, not representative of a generic elderly population. Of course the low number of instances adopted to train our classifiers can also introduce a bias. However, the cardinality of data collected is similar to other experiments in the state of the art. We have paid particular attention to remove bias related to oversampling as already commented, using a LOSO validation strategy. These results, together with the increasing availability and reliability of wearable devices, are promising in the perspective of the definition of systems that, interacting with subjects, can recognize their emotions and behaviors as well as their age group and consequently adapt. Concerning this topic, several factors such as different cultural aspects or daily habits could be taken into account in future analysis to create systems able to interact with the largest possible number of heterogeneous users. Furthermore, the age of the individuals could also be used as an additional input variable, together with other parameters such as the subject’s health, lifestyle or nutritional habits, in the definition of accurate measures of physiological age that could be used by industrial designers and product developers to guide their work in the development of appropriate technology able to provide efficient and personalized assistance to individuals of different ages and needs.Another general consideration concerns the classification setting used in the analysis. In the present work, we focused our attention on the analysis of statistical and time domain features, encouraged by the positive results achieved in other works of literature based on PPG and GSR ,88. In sRegarding the questionnaires that were filled out by the participants, we started looking at the results obtained by the STAI questionnaires and if they could be effectively correlated to the physiological data of the subjects. The initial results are quite interesting but, at the same time, given how we would need a much larger data cardinality in order to carry out more significant analyses, they cannot be considered significant. For this reason, we intend to concentrate on this kind of data in the future, when we will be able to have a larger amount of data on which perform our tests.Given the information the data analysis allowed us to gather regarding people’s behavior in response to particular stimuli, especially considering what was obtained from the walking tasks, we started hypothesizing how we could use the results we gathered. One of the research areas we started approaching is the one of agent simulation: the role emotions and affect have in modeling more realistic agent behaviors is gradually being investigated more and more, given the crucial part they play in the interactions a person has with the environment and with other people around him/her. For this reason, we started contemplating how to develop an affective agent model, namely, an affective multiagent system in which the agents’ behavior is also modeled relying on data collected through experiments, both in real-life or controlled laboratory scenarios . Our finIn their daily life, people are subjected to different stimuli that could affect their behavior and emotions. In particular, the age of a person seems a relevant factor in the definition of how an individual responds to specific stimuli. In this paper, different binary and multi-class classification tasks have proved that physiological signals permit to well discriminate between young adults and older adults, while performing different actions. In particular, the results obtained with the analysis of the EMG during different walking conditions confirm that physiological responses can give significant hints in studying subjects’ behavior and their reactions and confidence within different environments. Moreover, this analysis permits us to underline the different behavior of the older adults with respect to young adults. In particular, the two population groups would need a more specific and in-depth analysis to create ad hoc environments able to meet their needs.The results reached here can be useful in different analyses; for instance, they may be used to better understand the age-related behavior of subjects in real-world stressful urban environments such as, for example, busy streets or uncomfortable and crowded sidewalks. Moreover, the achieved results suggest that other population groups could be considered, in particular, children or impaired subjects, in order to classify and model their behavior in different real-life conditions, such as while interacting with autonomous vehicles. We were able to demonstrate the generalizability of our model, making physiological signals well suited to be adopted in different conditions. This research paves the way to a new field of research that, for instance, will permit proper forms of communications between pedestrians  and autonomous vehicles, taking into account their level of stress. In future scenarios, self-driving vehicles will circulate in urban environments, and will need to adapt to the feelings of pedestrians, being able to provide them effective feedback, properly tuned for the most vulnerable ones.Simulating a human subject and his/her behavior is an always up-to-date research topic, especially in the artificial intelligence field. Adopting an agent modeling approach often comes as the most natural way to represent a human, since the definition of an agent as a hardware or software system situated in an environment, autonomous, reactive, proactive and social, directly maps to the main features of a human too. The role of emotions and affect in producing more realistic agent behaviors is becoming increasingly crucial, given the important part they play in the interactions a person has with others and with the environment in which he/she lives.Thus in the definition of an affective agent, the influence of age in the emotional perception of the environment should also be modeled.


Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern clinical data warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema, and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multicentric data reuses as well as innovations in routine care. Reusing routine care data does not come free of charges. Attention must be paid to the entire life cycle of the data to create robust knowledge and develop innovation. Building upon the first overview of CDWs in France, we document key aspects of the collection and organization of routine care data into homogeneous databases: governance, transparency, types of data, data reuse main objectives, technical tools, documentation, and data quality control processes. The landscape of CDWs in France dates from 2011 and accelerated in the late 2020, showing a progressive but still incomplete homogenization. National and European projects are emerging, supporting local initiatives in standardization, methodological work, and tooling. From this sample of CDWs, we draw general recommendations aimed at consolidating the potential of routine care data to improve healthcare. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the data transformation tools and studies must improve to allow successful multicentric data reuses as well as innovations for the patient. Health information systems (HIS) are increasingly collecting routine care data –7. This In recent years, health agencies in many countries have conducted extensive work to better support the generation and use of real-life data ,17–19. SIn practice, the possibility of mobilizing these routinely collected data depends very much on their degree of concentration, in a gradient that goes from centralization in a single, homogenous HIS to fragmentation in a multitude of HIS with heterogeneous formats. The structure of the HIS reflects the governance structure. Thus, the ease of working with these data depends heavily on the organization of the healthcare actors. The 2 main sources of RWD are insurance claims—more centralized—and clinical data—more fragmented.Claims data is often collected by national agencies into centralized repositories. In South Korea, the government agency responsible for healthcare system performance and quality (HIRA) is connected to the HIS of all healthcare stakeholders. HIRA data consists of national insurance claims [e claims . Englande claims . This dae claims . In the e claims .Clinical data on the other hand, tends to be distributed among many entities, that made different choices, without common management or interoperability. But large institutional data-sharing networks begin to emerge. South Korea very recently launched an initiative to build a national wide data network focused on intensive care. United States is building Chorus4ai, an analysis platform pooling data from 14 university hospitals [ospitals . To unloospitals  created Israel stands out as one of the rare countries that pooled together both claims and clinical data at a large scale: half of the population depends on 1 single healthcare provider and insurer .An infrastructure is needed to pool data data from 1 or more medical information systems—whatever the organizational framework—to homogeneous formats, for management, research, or care reuses [e reuses ,29. Fig Collection and copying of original sources.Transformation: Integration and harmonization.Integration of sources into a unique database.Deduplication of identifiers.Standardization: A unique data model, independent of the software models harmonizes the different sources in a common schema, possibly with common nomenclatures.Pseudonymization: Removal of directly identifying elements.Provision of subpopulation data sets and transformed datamarts for primary and secondary reuse.Usages thanks to dedicated applications and tools accessing the datamarts and data sets.In France, the national insurer collects all hospital activity and city care claims into a unique reimbursement database . HoweverBased on an overview of university hospital CDWs in France, this study makes general recommendations for properly leveraging the potential of CDWs to improve healthcare. It focuses on: governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes.Interviews were conducted from March to November 2022 with 32 French regional and university hospitals, both with existing and prospective CDWs.This work has been authorized by the board of the French High Authority of Health (HAS). Every interviewed participant was asked by email for their participation and informed on the possible forms of publication: a French official report and an international publication. Furthermore, at each interview, every participant has been asked for their agreement before recording the interview. Only 1 participant refused the video to be recorded.Semi-structured interviews were conducted on the following themes: the initiation and construction of the CDWs, the current status of the project and the studies carried out, opportunities and obstacles, and quality criteria for observational research. The interview form was sent to participants in advance and then used as a support to conduct the interviews. The interviews lasted 90 min and were recorded for reference.Three tables detailed the structured answers in Outcome frequency: Incidence or prevalence estimation for a medically well-defined target population.Population characterization: Characterization of a specific set of covariates. Feasibility and prescreening studies belong to this category [category .Risk factors: Identification of covariates most associated with a well-defined clinical target . These studies look at association study without quantifying the causal effect of the factors on the outcome of interest.Treatment effect: Evaluation of the effect of a well-defined intervention on a specific outcome target. These studies intend to show a causal link between these 2 variables [ariables .Development of diagnostic and prognostic algorithms: Improve or automate a diagnostic or prognostic process, based on clinical data from a given patient. This can take the form of a risk, a preventive score, or the implementation of a diagnostic assistance system. These studies are part of the individualized medicine approach, with the goal of inferring relevant information at the level of individual patient’s files.Medical informatics: Methodological or tool oriented. These studies aim to improve the understanding and capacity for action of researchers and clinicians. They include the evaluation of a decision support tool, the extraction of information from unstructured data, or automatic phenotyping methods.Studies were classified according to this nomenclature based on their title and description.Grand-Est region, are distinguished by an initial, institutional, and high-level support.The CDWs have so far been initiated by 1 or 2 people from the hospital world with an academic background in bioinformatics, medical informatics, or statistics. The sustainability of the CDW is accompanied by the construction of a cooperative environment between different actors: Medical Information Department (MID), Information Systems Department (IT), Clinical Research Department (CRD), clinical users, and the support of the management or the Institutional Medical Committee. It is also accompanied by the creation of a team, or entity, dedicated to the maintenance and implementation of the CDW. More recent initiatives, such as those of the HCL  or the The CDW has a federating potential for the different business departments of the hospital with the active participation of the CRD, the IT Department, and the MID. Although there is always an operational CDW team, the human resources allocated to it vary greatly: from half a full-time equivalent to 80 people for the AP-HP, with a median of 6.0 people. The team systematically includes a coordinating physician. It is multidisciplinary with skills in public health, medical informatics, informatics , data engineering, and statistics.Historically, the first CDWs were based on in-house solution development. More recently, private actors are offering their services for the implementation and implementation of CDWs (15/21). These services range from technical expertise in order to build up the data flows and data cleaning up to the delivery of a platform integrating the different stages of data processing.Before starting, projects are systematically analyzed by a scientific and ethical committee. A local submission and follow-up platform is often mentioned (12/21), but its functional scope is not well defined. It ranges from simple authorization of the project to the automatic provision of data into a Trusted Research Environment (TRE) . The proOngoing studies in CDWs are unevenly referenced publicly on hospital websites. Some institutions have comprehensive study portals, while others list only a dozen studies on their public site while mentioning several hundreds ongoing projects during interviews. In total, we found 8 of these portals out of 14 CDWs in production. Uses other than ongoing scientific studies are very rarely documented. The publication of the list of ongoing studies is very heterogeneous and fragmented between several sources: clinicaltrials.gov, the mandatory project portal of the Health Data Hub  or the wCDW data reflect the HIS used on a daily basis by hospital staff. Stakeholders point out that the quality of CDW data and the amount of work required for rapid and efficient reuse are highly dependent on the source HIS. The possibility of accessing data from an HIS in a structured and standardized format greatly simplifies its integration into the CDW and then its reuse.Although the software landscape is varied across the country, the main functionalities of HIS are the same. We can therefore conduct an analysis of the content of the CDWs, according to the main categories of common data present in the HIS.The common base for all CDWs is constituted by data from the Patient Administrative Management software  and the billing codes. Then, data flows are progressively developed from the various softwares that make up the HIS. The goal is to build a homogeneous data schema, linking the sources together, controlled by the CDW team. The prioritization of sources is done through thematic projects, which feed the CDW construction process. These projects improve the understanding of the sources involved, by confronting the CDW team with the quality issues present in the data.Today, the main use put forward for the constitution of CDWs is that of scientific research.The studies are mainly observational . Grand-Ouest region of France) or via European calls for projects (EHDEN). These 2 research networks are made possible by supra-local governance and a common data schema, respectively, eHop [Grand-Est region is building a network of CDW based on the model of the Grand-Ouest region, also using eHop.The CDWs are used extensively for internal projects such as student theses (at least in 9/21) and serve as an infrastructure for single-service research: their great interest being the de-siloing of different information systems. For most of the institutions interviewed, there is still a lack of resources and maturity of methods and tools for conducting inter-institutional research . The clinical texts gathered in the same database are queried using keywords to facilitate the structuring of information. The data are then aggregated into indicators, some of which are reported at the national level. The construction of indicators from clinical data can also be used for the administrative management of the institution. Finally, closer to the clinic, some actors state that the CDW could also be used to provide regular and appropriate feedback to healthcare professionals on their practices. This feedback would help to increase the involvement and interest of healthcare professionals in CDW projects. The CDW is sometimes of interest for health monitoring  or pharmacovigilance (13/21).Some CDWs develop specific applications that provide new functionalities compared to care software. Search engines can be used to query all the hospital’s data gathered in the CDW, without data compartmentalization between different softwares. Dedicated interfaces can then offer a unified view of the history of a patient’s data, with inter-specialty transversality, which is particularly valuable in internal medicine. These cross-disciplinary search tools also enable healthcare professionals to conduct rapid searches in all the texts, for example, to find similar patients . Uses foThe technical architecture of modern CDWs has several layers:Data processing: connection and export of source data, diverse transformation .Data storage: database engines, file storage (on file servers or object storage), indexing engines to optimize certain queries.Data exposure: raw data, APIs, dashboards, development and analysis environments, specific web applications.Supplementary cross-functional components ensure the efficient and secure operation of the platform: identity and authorization management, activity logging, automated administration of servers and applications.The analysis environment  is a key component of the platform, as it allows data to be processed within the CDW infrastructure. A few CDWs had such operational datalab at the time of our study (6/21) and almost all of them have decided to provide it to researchers. Currently, clinical research teams are still often working on data extractions in less secure environments.Systematic data quality monitoring processes are being built in some CDWs. Often (8/21), scripts are run at regular intervals to detect technical anomalies in data flows. Rare data quality investigation tools, in the form of dashboards, are beginning to be developed internally (3/21). Theoretical reflections are underway on the possibility of automating data consistency checks, for example, demographic or temporal. Some facilities randomly pull records from the EHR to compare them with the information in the CDW.Grand-Ouest region of France, the CDWs use the eHop warehouse software. The latter uses a common data model also named eHop. This model will be extended with the future warehouse network of the Grand Est region also choosing this solution. Including this grouping and the other establishments that have chosen eHop, this model includes 12 establishments out of the 32 university hospitals. This allows eHop adopters to launch ambitious interregional projects. However, eHop does not define a standard nomenclature to be used in its model and is not aligned with emerging international standards.No single standard data model stands out as being used by all CDWs. All are aware of the existence of the OMOP (research standard)  and HL7 Half of the CDWs have put in place documentation accessible within the organization on data flows, the meaning and proper use of qualified data (10/21 mentioned). This documentation is used by the team that develops and maintains the warehouse. It is also used by users to understand the transformations performed on the data. However, it is never publicly available. No schema of the data once it has been transformed and prepared for analysis is published.We give the first overview of the CDWs in university hospitals of France with 32 hospitals reviewed. The implementation of CDW dates from 2011 and accelerated in the late 2020. Today, 24 of the university hospitals have an ongoing CDW project. From this case study, some general considerations can be drawn that should be valuable to all healthcare system implementing CDWs on a national scale.As the CDW becomes an essential component of data management in the hospital, the creation of an autonomous internal team dedicated to data architecture, process automation, and data documentation should be encouraged . This muThe resources specific to the warehouse are rare and often taken from other budgets or from project-based credits. While this is natural for an initial prototyping phase, it does not seem adapted to the perennial and transversal nature of the tool. As a research infrastructure of growing importance, it must have the financial and organizational means to plan for the long term.The governance of the CDW has multiple layers: local within the university hospital, interregional, and national/international. The first level allow to ensure the quality of data integration as well as the pertinence of data reuse by clinicians themselves. The interregional level is well adapted for resources mutualization and collaboration. Finally, the national and international levels assure coordination, encourage consensus for committing choices such as metadata or interoperability, and provide financial, technical, and regulatory support.Health technology assessment agencies advocate for public registration of comparative observational study protocols before conducting the analysis ,17,49. TFrom a patient’s perspective, there is currently no way to know if their personal data is included for a specific project. Better patient information about the reuse of their data is needed to build trust over the long term. A strict minimum is the establishment and update of the declarative portals of ongoing studies at each institution.When using CDW, the analyst has not defined the data collection process and is generally unaware of the context in which the information is logged. This new dimension of medical research requires a much greater development of data science skills to change the focus from the implementation of the statistical design to the data engineering process. Data reuse requires more effort to prepare the data and document the transformations performed.The more heterogeneous a HIS system is, the less qualitative would be the CDW built on top of it. There is a need for increasing interoperability, to help EHR vendors interfacing the different hospital softwares, thus facilitating CDW development. One step in this direction would be the open source publication of HIS data schema and vocabularies. At the analysis level, international recommendations insist on the need for common data formats ,54. HoweMany ongoing studies concern the development of decision support processes whose goal is to save time for healthcare professionals. These are often research projects, not yet integrated into routine care. The analysis of study portals and the interviews revealed that data reuse oriented towards primary care is still rare and rarely supported by appropriate funding. The translation from research to clinical practice takes time and need to be supported on the long run to yield substantial results.Tools, methods, and data formats of CDW lack harmonization due to the strong technical innovation and the presence of many actors. As suggested by the recent report on the use of data for research in the UK , it woulThese platforms should favor open-source solutions to assure transparency by default, foster collaboration and consensus, and avoid technological lock-in of the hospitals.Quality is not sufficiently considered as a relevant scientific topic itself. However, it is the backbone of all research done within a CDW. In order to improve the quality of the data with respect to research uses, it is necessary to conduct continuous studies dedicated to this topic ,54–56. TFinally, there is a need for open-source publication of research code to ensure quality retrospective research ,57. ReceThe interviews were conducted in a semi-structured manner within a limited time frame. As a result, some topics were covered more quickly and only those explicitly mentioned by the participants could be recorded. The uneven existence of study portals introduces a bias in the recording of the types of studies conducted on CDW. Those with a transparency portal already have more maturity in use cases.For clarity, our results are focused on the perimeter of university hospitals. We have not covered the exhaustive healthcare landscape in France. CDW initiatives also exist in primary care, in smaller hospital groups and in private companies.The French CDW ecosystem is beginning to take shape, benefiting from an acceleration thanks to national funding, the multiplication of industrial players specializing in health data and the beginning of a supra-national reflection on the European Health Data Space . HoweverThe priority is the creation and perpetuation of multidisciplinary warehouse teams capable of operating the CDW and supporting the various projects. A combination of public health, data engineering, data stewardship, statistics, and IT competences is a prerequisite for the success of the CDW. The team should be the privileged point of contact for data exploitation issues and should collaborate closely with the existing hospital departments.The constitution of a multilevel collaboration network is another priority. The local level is essential to structure the data and understand its possible uses. Interregional, national, and international coordination would make it possible to create thematic working groups in order to stimulate a dynamic of cooperation and mutualization.A common data model should be encouraged, with precise metadata allowing to map the integrated data, in order to qualify the uses to be developed today from the CDWs. More broadly, open-source documentation of data flows and transformations performed for quality enhancement would require more incentives to unleash the potential for innovation for all health data reusers.Finally, the question of expanding the scope of the data beyond the purely hospital domain must be asked. Many risk factors and patient follow-up data are missing from the CDWs, but are crucial for understanding pathologies. Combining city data and hospital data would provide a complete view of patient care.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Children and young people’s (CYP) mental health is worsening, and an increasing number are seeking psychiatric and mental health care. Whilst many CYPs with low-to-medium levels of psychiatric distress can be treated in outpatient services, CYPs in crisis often require inpatient hospital treatment. Although necessary in many cases, inpatient care can be distressing for CYPs and their families. Amongst other things, inpatient stays often isolate CYPs from their support networks and disrupt their education. In response to such limitations, and in order to effectively support CYPs with complex mental health needs, intensive community-based treatment models, which are known in this paper as intensive community care services (ICCS), have been developed. Although ICCS have been developed in a number of settings, there is, at present, little to no consensus of what ICCS entails.A group of child and adolescent mental health clinicians, researchers and academics convened in London in January 2023. They met to discuss and agree upon the minimum requirements of ICCS. The discussion was semi-structured and used the Dartmouth Assertive Community Treatment Fidelity Scale as a framework. Following the meeting, the agreed features of ICCS, as described in this paper, were written up.outside of hospital in community settings such as the school or home. Alongside this, ICCS should provide at least some out-of-hours support, and a minimum of 90% of CYPs should be supported at least twice per week. The maximum caseload should be approximately 5 clients per full time equivalent (FTE), and the minimum number of staff for an ICCS team should be 4 FTE. The group also confirmed the importance of supporting CYPs engagement with their communities and the need to remain flexible in treatment provision. Finally, the importance of robust evaluation utilising tools including the Children’s Global Assessment Scale were agreed.ICCS was defined as a service which provides treatment primarily This paper presents the agreed minimum requirements of intensive community-based psychiatric care. Using the parameters laid out herein, clinicians, academics, and related colleagues working in ICCS should seek to further develop the evidence base for this treatment model. The COVID-19 pandemic prompted the worsening of mental health symptoms, namely anxiety and depression, of children and young people (CYPs) around the world . BetweenICCS were generally considered to have a number of advantages when compared to inpatient services as described above. ICCS models are often community-centred and treatment takes place in locations preferred by the CYP or family such as the home , school,The World Health Organization  describeWhilst the move towards intensive community-based psychiatric care is very welcome, there is a need to define the scope and role of intensive community-based services such as ICCS. At present, ICCS and related teams operate in isolation from one another, and there is very little shared or best practice. As it is now, intensive community-based mental health teams are given a variety of names, have different structures and offer differing levels of support to CYPFs . Such seThis paper presents the findings of a meeting and discussion which took place in January 2023. A small group of experienced clinicians, academics and researchers met to debate and agree upon various factors relating to ICCS. The findings presented herein are the minimum requirements, as agreed by the group, that services should aim to adhere to in order to qualify as ICCS.A panel of nineteen experts, whose work relates to child and adolescent mental health, convened in London in January 2023. The small group, who were from the UK, Austria, France, Germany and Spain, was made up of experienced clinicians  as well as researchers and academics from across the field of child and adolescent mental health. The group members were identified largely via their pre-existing role in a national randomised controlled trial named IVY: Comparison of Effectiveness and Cost-Effectiveness of Intensive Community Care Services versus Usual Inpatient Care for Young People with Psychiatric Emergencies (IVY): An Internal Pilot followed by a Randomised Controlled Trial Comprising All Intensive Community Service Care Teams in Great Britain. The IVY study (ISRCTN: ISRCTN42999542) seeks to evaluate the effectiveness and cost-effectiveness of ICCS compared to usual care  in young people with severe psychiatric disorders.The purpose of the meeting described in this paper was to generate discussion and agreement regarding the nature and scope of ICCS for child and adolescent psychiatry and mental health. The meeting was semi-structured and used the Dartmouth Assertive Community Treatment Fidelity Scale (DACTS)  as a fraThe above method was selected in order to facilitate an open and free-flowing group discussion on the topic in question. Throughout the discussion, which took place in-person and in real-time, both concurring and divergent views on each topic area were sought and consensus was reached via group decision-making.Following the discussion, the agreed features of ICCS, as described in this paper, were written into a single document. The document was then circulated to group members and invitees who were unable to attend the in-person meeting and comments were welcomed. At this stage, the document was also shared with a former service user who had been identified as an expert by experience, having received ICCS support when they were a young person (< 18 years). The individual, who was an adult at the time of study, shared valuable insights on the discussion and the subsequent creation of guidelines. The individual also contributed to the critical revision of the manuscript and consented to be named as a co-author of this paper.https://www.hra-decisiontools.org.uk/ethics/), ethical approval was deemed not needed for this conduct of this study. Ethical approval was deemed not necessary as this study was born out of a meeting of professionals and with a youth former service user (now adult) with lived experience. The ex-service user was already known to the authors and was not recruited via NHS services. In other studies, this process has been confirmed by Health Research Association’s Quality and Performance Manager to not require ethical approval.Following an enquiry to the NHS “Do I need NHS REC review?” decision tool Intensive Outpatient ServicesStationsäquivalente Behandlung Discharge Support TeamsSupported Discharge Service TeamsMulti-Systemic Therapy TeamsAdolescent Intensive Support ServicesThe expert group determined that the defining feature of ICCS is the nature of the service to provide treatment primarily Please note these titles reflect only a small, and geographically limited, sample of those which may reflect the practice of ICCS.The group also discussed models which are not considered to be ICCS. Amongst others, standard community services, liaison teams, assessment teams and others whose work is low frequency were considered to fall outside of the definition. Diagnosis specific teams  could fall under ICCS; however, such services are beyond the scope of this paper.Beyond the overarching definition, the expert group discussed and agreed factors relating to the appropriate or ideal organisational boundaries of ICCS teams. The first point discussed related to admission and intake procedures. Like many services, ICCS teams should serve a pre-determined population and use measurable and operationally defined criteria to screen out referrals that do not meet the intake criteria for the service. The group agreed that CYPs admitted to ICCS should comply with the explicit admission criteria  pre-determined by the service and the team should aim to be resilient against organisational pressure, such as waiting lists, to admit CYPs who do not meet the pre-agreed criteria. The responsibility and scope of ICCS teams was also discussed by the expert group; alongside case management, ICCS should seek to take a broad psychosocial approach to addressing the varied needs of the CYPFs they support. Areas that ICCS teams support with, may include, but are not limited to, the provision of psychiatric services, psychological therapies, housing support, educational support, substance related treatment, social care and safeguarding, employment and rehabilitative services. Formulations and care plans which support CYPS to have agency in their treatment should also be in place; these should include evidence-based interventions and incorporate a systemic approach.safely return patients to regular community clinic care in the shortest time possible, should be a key focus of care planning reviews and subsequent discharge decisions.The appropriate provision of after-hours support was also discussed. It was agreed that ICCS should provide, at least, some provision beyond Monday to Friday 9am–5pm; such provision ensures that ICCS are able to respond to and care for psychiatric crises as and when they arise. Although ICCS are predominately community-based treatment models, the responsibility of teams in relation to potential hospitalisation was discussed. The expert group determined that, whilst it might be a long-term goal for ICCS to lead on decisions to admit CYPs who require inpatient treatment, it may be enough, at this stage in ICCS’ development, to aim for involvement in such decisions in collaboration with CYPFs. The importance of maintaining a close relationship between ICCS and inpatient services was confirmed by the ex-service user involved in this study. Discharge procedures from ICCS teams were also discussed and, in particular, the nature of ICCS as a time-limited goal-oriented intervention was noted. ICCS should regularly review the needs of CYPFs and check the eligibility of CYPFs for their service. The group determined that ICCS should aim to form a treatment pathway. The goal, to The group determined that, in order to deliver the treatment model detailed above, a range of staffing and personnel issues related to ICCS teams and co-ordinating institutions must also be considered. The number of staff, the roles required, caseload and structure were amongst the areas that were debated and agreed by the expert group. The caseload per clinician is a critical factor in determining the capacity and quality of care that can be provided by each staff member in an ICCS team. The maximum caseload agreed by the group was 5 clients per full-time equivalent (FTE). Although this figure was agreed, the group recognised that great variation currently exists with regards to caseload in ICCS. As such, and if a caseload of 5 clients or fewer per FTE is inappropriate or unattainable, ICCS should ensure that caseloads are small enough to allow for intensive community work and which proffer the flexibility required in order to meet with CYPFs when, and how frequently, they need to be seen. This flexibility was, in particular, supported by the ex-service user involved in this project; they reported that flexibility to meet when and where required was key to their successful involvement with ICCS. Alongside caseload, the minimum clinician contact for each CYPF was also discussed and agreed upon by the group. It was determined that in order to qualify as ICCS, 90% or more of the CYPFs on caseload should have a minimum of two episodes of direct clinical contact with at least one clinician each week. Regular contact was deemed important by the young person involved in this paper; they noted that regularity enabled them to build trusting relationships faster which, ultimately, supported their treatment. The type of contact offered by ICCS teams may vary  dependent on the need and preference of the CYPF. In addition to supporting the CYPFs on the caseload, on at least 1 day per week, the ICCS team should meet and discuss each CYPF on the caseload; the discussion may be brief or detailed depending on each CYPF’s immediate needs or crisis status.The following points relate to the staffing structure of ICCS teams and the minimum capacity with which they can operate as agreed by the expert group. The first point relates to the role and practice of the ICCS lead; whilst the lead may not have a caseload themselves, they should maintain connection to, and knowledge of, the caseload as a whole. The expert group also determined that ICCS teams should, as a minimum, have access to MDT resources including to one professional who is legally able to provide diagnoses, prescribe medication and who is also able to apply mental health legislation . The importance of regular clinical supervision for ICCS team members was also considered by the group. As a minimum, ICCS should aim to provide individual clinical supervision at least once per month. A separate space that provides reflective practice for the whole team is also recommended. The final point of discussion with regards to human resources related to the minimum size of ICCS teams. In order to consistently provide the necessary staffing diversity and coverage required in the ICCS model, the minimum size of a team should be at least four FTE staff members.The group engaged in detailed conversation about the nature and scope of services that should be provided by ICCS teams described above. A primary point of discussion related to the location of ICCS interventions. As community-based services which seek to support CYPs to cope with community re-integration following psychiatric emergency, it was agreed that a minimum of 80% of face-to-face contacts for ICCS teams should take place in the community. It was also agreed that ICCS team members should proactively promote a range of community resources, such as sport and activity clubs, to CYPFs. Beyond promotion of such services, ICCS team members should also directly support CYPS to engage in these. Support may include, but is not limited to, travelling with CYPs, attending early sessions or supporting with the booking or organisation of such activities. The second point of discussion related to levels of engagement required from CYPs on the caseload. Primarily, ICCS teams should take a considered, flexible and persistent approach to attempting to engage with CYPs; however, CYP’s consent is critical in such an approach. The need for flexibility around appointments was confirmed by the ex-service user involved in this project; they stated that in ICCS, “I wasn’t given up on or made to feel guilty,  we would come up with different ways to move forward and [to] stop me from withdrawing.” Alongside the amount of engagement, the methods of engagement with CYPs on the caseload were also discussed. ICCS teams should utilise a range of mechanisms for engagement with CYPs, and they should have a well-considered engagement strategy in place to enable this. The final points of discussion related to the frequency and intensity of face-to-face contact that should be provided by ICCS teams. As an intensive treatment model, it was agreed that at least 2 h of direct clinical contact should be provided to each CYPF on the caseload per week. The rate of contact with CYPs families or informal support systems was also considered; as ICCS should seek to work holistically, engagement with CYPs network  is encouraged. In response, the expert group agreed that ICCS should meet with the wider network of CYPs, with or without the CYP present, on a regular basis; an example of such holistic practice is the Care Programme Approach .The importance of robust evaluation and monitoring within ICCS was also discussed by the expert group and confirmed by the young person involved in this process. ICCS should consistently monitor CYPs progress using a variety of pre-determined outcome measures related to mental health and psychological wellbeing such as the Children’s Global Assessment Scale (CGAS)  or the HThis paper, and the discussion to which it pertains, has sought to provide clarity and consensus regarding the nature and scope of ICCS. Alongside determining the key features of ICCS , consensus was reached amongst the group in relation to four key areas: organisational boundaries, human resources, nature of services and evaluation and outcome. Key findings were that ICCS should provide at least some out-of-hours support and a minimum of 90% CYPs should receive direct clinical contact at least twice per week. The maximum caseload should be approximately 5 clients per FTE, and the minimum number of staff required to effectively facilitate ICCS is 4 FTE. The importance of supporting CYPs connection with their community and of regular meetings with CYPs wider family and informal support network were also found. Finally, the need for regular and robust evaluation for ICCS was confirmed.Whilst this paper sets out the minimum requirements of ICCS, the expert group also discussed the future aims of ICCS and what ‘best practice’ may come to entail. Access to education was a key focus of this discussion. ICCS should, at this stage, support CYPs to safely return to school following crisis; however, in the future, the routine provision of day schooling in the ICCS model may bridge the gap between crisis and return. The critical importance of creating and maintaining CYPs connection to their communities is highlighted throughout this paper. In future, ICCS may wish to develop this further and employ a model wherein mental health treatment methods take the form of social and community activities. Finally, the inclusion of support workers in the staffing composition of ICCS should also be considered. Support workers are often young and are anecdotally very good at engaging with CYPs in mental health crisis. In addition, they are a low-cost resource and the value they may bring to the ICCS process should be further explored.Although the minimum requirements for ICCS were agreed upon by the expert group, an enduring theme of discussion was the sheer variety of models that ICCS may entail. Alongside varied levels of resources and organisational structures, differences in relation to the geography of ICCS were also discussed. The inner-city model of an ICCS, for example, is likely to vary significantly from that of a rural one and such variety should be considered when designing and delivering ICCS. On a practical level, services which offer intensive community-based care may differ in their delivery; however, the shared philosophy of ICCS, of seeking to co-create mental health care alongside CYPFs and stakeholders, is, and should always remain, a key commonality of the practice.To our knowledge, this paper was the first of its kind in applying a consensus seeking model to ICCS. As such, this paper provides novel guidelines which can be used to develop services to effectively support CYPFs in crisis. The variety of professionals involved in the production of this paper is a particular strength. A wide variety of multi-disciplinary professionals including psychiatrists, psychologists, nurses and allied health professionals were involved in the process. In addition, and perhaps an even greater strength, was the inclusion of a young person (now 18+) who had previously been a service user of ICCS; their expertise via experience was of great help and their perspective on guidelines was warmly welcomed. Another strength of this paper was the range of countries represented in the consensus process; professionals from Austria, Germany, Spain, France and the UK were involved. Notwithstanding this diversity, many countries were not represented in the process despite great efforts otherwise, the paper remains UK-centric. Similarly, as participants in the meeting were largely identified via pre-existing research and clinical collaborations, leading ICCS clinicians and researchers from around the world may have not been involved; we look forward to working widely and collaboratively in future. This paper also did not have the capacity to explore culturally specific elements of ICCS, and future work in this area should do so. Due to its capacity, this work did not explore aetiologic or systemic factors of mental illness and mental health crisis in young people; researchers may wish to consider these in future development of ICCS models. A final limitation relates to the methodology employed in this paper; whilst a free-flowing and open group discussion was facilitated to gain consensus on the points discussed, a more structured model, such as the Delphi method, may have provided increased methodological robustness. Despite the potential benefit, such methods seek to reach consensus with group members operating in isolation. The varied clinical experiences of group members, and the ranging scope of ICCS across the UK and more widely, meant that active, live and collaborative group discussion was deemed necessary for the creation of these guidelines.This paper, which is the product of a round-table meeting of experts, has defined the minimum requirements of ICCS. Despite noting great variety in the current provision of ICCS, areas of agreement, as detailed throughout this paper relate to organisational boundaries, staffing concerns, the scope of an appropriate treatment model and to the evaluation of ICCS. It is hoped that this paper may now come to serve as the basis of clinical guidelines and associated policy documents both in the UK and around the world. In addition, by using this paper, clinicians, academics and related colleagues working in ICCS may seek to further develop the evidence base for this treatment model. Not only should the efficacy of ICCS be further explored in robust and methodologically sound trials, the comparison of ICCS to inpatient and non-intensive community services should also be investigated.