Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity-associated clinical complexity using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate ( < P80) to high/very high ( ≥ P80) complexity. Machine learning models identified predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of the individuals who remained alive throughout the analysis period transitioned to high/very high complexity. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression.

Despite decades of health-care digitalisation efforts worldwide, health information systems remain highly fragmented, with multiple vendor-specific silos that communicate through incomplete solutions. This fragmentation prevents the creation of real-time, lifelong patient health records and becomes increasingly problematic as demand grows for person-centred care, data-driven clinical practice, and greater patient involvement in health-care decisions. To address these challenges and establish a foundation for a nationwide electronic health record (EHR), the Spanish Ministry of Health commissioned a steering committee to develop recommendations based on a comprehensive national consensus. The committee conducted a Delphi study comprising 45 items across four domains, which was distributed to 220 experts from June 23, 2023, to Sept 26, 2023. With a response rate of 69·1% (152/220), the study achieved consensus in a single round, with all items reaching the pre-established threshold of greater than or equal to 70% agreement (scores 7-9 on a 9-point Likert scale), and consensus ranging from 118 (77·6%) to 151 (99·3%) of 152 responses (44 items ≥80%). The resulting recommendations were externally validated by an international advisory board, which assessed their consistency and alignment with global best practices and standards. The final set included 20 recommendations across four domains: justification of need (2 items), functional characteristics (7 items), technical characteristics (6 items), and governance (5 items). These recommendations provide a roadmap for developing a robust, integrated national health information system centred on a standardised, longitudinal EHR. The proposed approach moves beyond generic calls for interoperability by embedding clinical knowledge into open, standardised EHR architectures through ontology-driven semantic integration, supported by federated governance and citizen-controlled data use. This roadmap equips Spain to implement a longitudinal, knowledge-driven national record while providing a scalable model for other countries transforming fragmented health information systems.

Background Indoor air quality (IAQ) is a well demonstrated actionable determinant of health status. Low-cost sensors (LCS) could enable patient-centred assessments, but real-world clinical utility is uncertain.Objective Evaluate the feasibility, usability and clinical indications of home IAQ monitoring with LCS.Methods We conducted a cohort study involving household continuous IAQ monitoring with LCS of 205 adults with chronic obstructive pulmonary disease, bronchiectasis or asthma. Household IAQ was profiled prospectively over a 2-month period registering concentrations of carbon dioxide, particulate matter 2.5 µm (PM2.5) and formaldehyde every 10 min. For each pollutant, dwellings were classified as good, moderate or unhealthy according to the Global Open Air Quality Standards thresholds. Pulmonary exacerbations requiring unplanned hospitalisations and all-cause emergency department (ED) visits over the preceding 12 months were registered and potential relationships with household IAQ results were explored.Results Of the 205 participants, 178 were included in the final analysis after excluding dropouts and cases with insufficient monitoring data. More than half of homes (51.7%) had at least one pollutant in an at-risk category. The burden was mostly generated by PM2.5: 40.1% of dwellings were classified as at risk (32.8% moderate; 7.3% unhealthy). Formaldehyde exceeded the low-risk threshold in 22 homes (12.4%). Tobacco smoking, either active or passive, was significantly associated with PM2.5 levels (p<0.001). No relationships were found between IAQ categories and hospitalisations nor with all-cause ED visits.Conclusions LCS are useful tools for short-term, targeted household IAQ screening in chronic respiratory patients. Indoor pollution is highly prevalent and largely PM2.5 driven. Further research is needed to assess the short-term health impacts of these exposures.Trial registration number NCT06421402.

Abstract Background Community-based management of exacerbations in high-risk patients with chronic obstructive respiratory diagnoses remains a major challenge. Hybrid care interventions, combining digital support with in-person, patient-centered care, have shown efficacy to reduce unplanned hospitalizations in controlled trials. However, an efficacy-effectiveness gap remains, indicating the complexities of its deployment and sustainable adoption in real-world scenarios. Objective This study aimed to co-design the core components of a hybrid care intervention for the preventive management of exacerbations in high-risk patients with chronic obstructive respiratory conditions, generating insights to guide sustainable adoption in routine clinical practice. Methods Four plan-do-study-act (PDSA) co-design cycles were conducted, using a convergent mixed methods approach, during the 2-year follow-up (2024‐2025) of a cohort of 205 high-risk patients pertaining to two different clinical programs: (1) the community-based program included multimorbid patients with chronic obstructive pulmonary disease (COPD), asthma, or bronchiectasis from the Integrated Health District of Barcelona-Esquerra (AISBE, 520 k citizens) and (2) the Severe Asthma Program included patients with severe asthma. In all cases, patients were managed following the corresponding disease-specific consensus guidelines. The specific aims of each PDSA cycle were: PDSA-1, patients’ profiling and applicability of technological tools; PDSA-2, definition of clinical aspects of the hybrid care intervention and refinement of technology; PDSA-3, evaluation of a consolidated version of the hybrid care intervention; and PDSA-4, final refinements. Results At the end of PDSA-3 (August 2025), the operationalization of the three core components of hybrid care—(1) nurse-led in-person care, (2) personalization of the intervention, and (3) advanced digital support—was achieved. The main study outcome was established through consensus among all key stakeholders on two aspects: (1) applicability of the hybrid care intervention for management of these patients in clinically stable conditions and during exacerbations in the real-world setting and (2) a well-defined strategy for its short-term deployment and sustainable site adoption. Conclusions The intervention rollout requires emphasis on (1) alignment with local care pathways and information systems, (2) clear role definition and escalation procedures across care tiers, and (3) adaptation of the digital layer to patients’ capabilities, including pragmatic support for those with limited digital literacy. The co-design process enabled the operationalization of a hybrid care intervention integrating nurse-led management, personalization of care, and advanced digital support. Stakeholders reached consensus regarding its applicability and implementation strategy. Future real-world implementation studies are needed to evaluate its effects on clinical outcomes, health care use, patient experience, and health care value generation.

Community-based management of exacerbations in high-risk patients with chronic obstructive respiratory diagnoses remains a major challenge. Hybrid care interventions, combining digital support with in-person, patient-centered care, have shown potential to reduce unplanned hospitalizations. However, an efficacy-effectiveness gap has been observed. To co-design the core components of a hybrid care intervention for the preventive management of exacerbations in high-risk patients with chronic obstructive respiratory conditions, generating insights to guide sustainable adoption in routine clinical practice. In the Integrated Health District of Barcelona-Esquerra (AISBE, 520 k citizens), four Plan-Do-Study-Act (PDSA) co-design cycles, each lasting six months, were conducted using a mixed-methods approach during the two-year follow-up (2024–2025) of a cohort of 205 high-risk patients with chronic obstructive pulmonary disease (COPD) or severe asthma, following the corresponding disease-specific consensus guidelines. The operationalization of the three core components of hybrid care: i) nurse-led in person care, ii) personalization of the intervention, and iii) advanced digital support was achieved. Moreover, clinical applicability and stakeholders’ acceptance of the approach for management of these patients in clinically stable conditions and during exacerbations were achieved. The intervention rollout requires emphasis on: (i) alignment with local care pathways and information systems, (ii) clear role definition and escalation procedures across care tiers, and (iii) adaptation of the digital layer to patients’ capabilities, including pragmatic support for those with limited digital literacy. The study provides guidance for site deployment. Its large-scale adoption still requires implementation tailoring and demonstration of healthcare value generation. NCT06421402

Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity burden using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate (< percentile 80) to high/very high (≥ P80) burden. Machine learning and statistical models (including random forest, neural networks, and gradient boosting) were used to assess predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of individuals transitioned to high/very high burden. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression. Clinical trial number : Not applicable.

Introduction Reducing unplanned hospital admissions in chronic patients at risk is a key area for action due to the high healthcare and societal burden of the phenomenon. The inconclusive results of preventive strategies in patients with chronic obstructive respiratory disorders and comorbidities are explainable by multifactorial but actionable factors.The current protocol (January 2024–December 2025) relies on the hypothesis that intertwined actions in four dimensions: (1) management change, (2) personalisation of the interventions based on early detection/treatment of acute episodes and enhanced management of comorbidities, (3) mature digital support and (4) comprehensive assessment, can effectively overcome most of the limitations shown by previous preventive strategies. Accordingly, the main objective is to implement a novel integrated care preventive service for enhanced management of these patients, as well as to evaluate its potential for value generation.Methods and analysis At the end of 2024, the specifics of the novel service will be defined through the articulation of its four main components: (1) enhanced lung function testing through oscillometry, (2) continuous monitoring of indoor air quality as a potential triggering factor, (3) digital support with an adaptive case management (ACM) approach and (4) predictive modelling for early identification and management of exacerbations. During 2025, the novel service will be assessed using a Quintuple Aim approach. Moreover, the Consolidated Framework for Implementation Research will be applied to assess the implementation. The service components will be articulated through four sequential 6-month plan-do-study-act cycles. Each cycle involves a targeted cocreation process following a mixed-methods approach with the active participation of patients, health professionals, managers and digital experts.Ethics and dissemination The Ethics Committee for Human Research at Hospital Clinic de Barcelona approved the protocol on 29 June 2023 (HCB/2023/0126). Before any procedure, all patients in the study must sign an informed consent form.Trial registration number NCT06421402.

We previously identified seven distinct multimorbidity clusters associated with major depressive disorder through a comprehensive analysis of 1.2 million individuals of multiple cohorts. These clusters, characterized by unique clinical, genetic, and psychiatric and somatic illness risk profiles, implicate divergent treatment pathways and disease management strategies. This study aims to deepen the understanding of these clusters by analyzing drug prescriptions, evaluating the effectiveness of antidepressant treatment strategies, and identifying potential markers for personalized medicine.Utilizing drug prescription data in the format of ATC codes, we performed epidemiological assessments, including multimorbidity (number of diseases), polypharmacy (number of chemical substances), and drug burden (number of prescriptions) analyses across the clusters. We applied linear regression models to assess strength and predictive capability of cluster membership on various metrics, and logistic regression to explore associations with treatment-resistant depression. We also quantified and visualized common antidepressant treatment sequences within each cluster.Our findings indicate significant variations in polypharmacy and drug burden across clusters, with distinct patterns emerging that correlate with the clusters’ profiles. Clusters liable to multimorbidity have higher drug burden, even after correction for number of diseases. Furthermore, the three clusters with higher risk for MDD showed different antidepressant treatment profiles; two required significantly more antidepressant prescriptions and had a higher risk for TRD.The detailed pharmacological profiling presented in this study not only corroborates the initial cluster definitions but also enhances our predictive capabilities for treatment outcomes in MDD. By linking pharmacological data with comorbidity profiles, we pave the way for targeted therapeutic interventions.

Background: Community-based management of exacerbations in high-risk chronic obstructive respiratory patients remains a major challenge due to patients heterogeneities, co-morbidities and symptoms-based assessment of the episodes. Hybrid care interventions-combining digital tools with in-person, patient-centered care-have demonstrated efficacy in reducing unplanned hospitalizations. However, their effectiveness in real-world settings is less well established. Objectives: To report the co-design process aiming to (1) characterize target candidates; and (2) adapt the implementation of a hybrid care intervention to routine clinical practice. Methods: In the Integrated Health District of Barcelona-Esquerra (520 k citizens), three Plan-Do-Study-Act (PDSA) co-design cycles, each lasting six months, were conducted using a mixed-methods approach during a two-year follow-up (2024-2025) of a cohort of 205 patients with chronic obstructive pulmonary disease (COPD) and co-morbidities or severe asthma. Results: By the end of PDSA cycle 3 (August 2025), profiles of high-risk candidates for the hybrid care intervention were identified. The specificities of the three intertwined components of the intervention: i) health risk assessment, ii) advanced digital support, and iii) nurse-led in person care were defined. Home-based, patient self-administered Oscillometry proved useful for the objective assessment and management of exacerbations. Adapting the implementation of the hybrid care intervention to local clinical workflows was identified as a priority to enable its sustainable adoption. Conclusions: A personalized hybrid care intervention appears suitable for the management of heterogeneous high-risk respiratory patients. Ongoing tailored implementation during PDSA cycle 4, until February 2026, will be key for its scale up and sustainable adoption. Trial registration number: [NCT06421402][1]. ### Competing Interest Statement IC and JR hold shares in Health Circuit SL. JR contributes to the Astra-Zeneca Global Oscillometry Advisory Board. All other authors declare no conflicts of interest. ### Clinical Trial NCT06421402 ### Funding Statement The K-HEALTHinAIR project funded this study, Grant Agreement n 101057693, under a European Union Call on Environment and Health (HORIZON-HLTH-2021-ENVHLTH-02). This research was also supported by the Catalan Government and the Catalan Department of Research and Universities under contract 2021 SGR 00326. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval for the process evaluation was granted by the Ethical Committee for Human Research at the Hospital Clinic de Barcelona on June 29, 2023 (HCB/2023/0126) and registered at ClinicalTrials.gov ([NCT06421402][1]). The patients participating in the research were required to provide and sign a written informed consent indicating the purpose of the study, the nature, use and management of their data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets generated and/or analysed during the current study contain sensitive patient information and will not be openly distributed. However, anonymized data may be made available upon reasonable request to the corresponding author, subject to institutional data sharing agreements and ethical approval. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT06421402&atom=%2Fmedrxiv%2Fearly%2F2025%2F09%2F12%2F2025.09.11.25335555.atom

Introduction: Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020–2023). Description: The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied. Discussion: The evolution of the Catalan HRA strategy (2010–2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe. Conclusions: Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

BackgroundComprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions. ObjectiveThis study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions. MethodsIn a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland. ResultsThe results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases. ConclusionsThe MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Abstract Background Many advantages of hospital at home (HaH), as a modality of acute care, have been highlighted, but controversies exist regarding the cost-benefit trade-offs. The objective is to assess health outcomes and analytical costs of hospital avoidance (HaH-HA) in a consolidated service with over ten years of delivery of HaH in Barcelona (Spain). Methods A retrospective cost-consequence analysis of all first episodes of HaH-HA, directly admitted from the emergency room (ER) in 2017–2018, was carried out with a health system perspective. HaH-HA was compared with a propensity-score-matched group of contemporary patients admitted to conventional hospitalization (Controls). Mortality, re-admissions, ER visits, and direct healthcare costs were evaluated. Results HaH-HA and Controls (n = 441 each) were comparable in terms of age (73 [SD16] vs. 74 [SD16]), gender (male, 57% vs. 59%), multimorbidity, healthcare expenditure during the previous year, case mix index of the acute episode, and main diagnosis at discharge. HaH-HA presented lower mortality during the episode (0 vs. 19 (4.3%); p < 0.001). At 30 days post-discharge, HaH-HA and Controls showed similar re-admission rates; however, ER visits were lower in HaH-HA than in Controls (28 (6.3%) vs. 34 (8.1%); p = 0.044). Average costs per patient during the episode were lower in the HaH-HA group (€ 1,078) than in Controls (€ 2,171). Likewise, healthcare costs within the 30 days post-discharge were also lower in HaH-Ha than in Controls (p < 0.001). Conclusions The study showed higher performance and cost reductions of HaH-HA in a real-world setting. The identification of sources of savings facilitates scaling of hospital avoidance. Registration ClinicalTrials.gov (26/04/2017; NCT03130283).

Over the past decades, health care systems have significantly evolved due to aging populations, chronic diseases, and higher-quality care expectations. Concurrently with the added health care needs, information and communications technology advancements have transformed health care delivery. Technologies such as telemedicine, electronic health records, and mobile health apps promise enhanced accessibility, efficiency, and patient outcomes, leading to more personalized, data-driven care. However, organizational, political, and cultural barriers and the fragmented approach to health information management are challenging the integration of these technologies to effectively support health care delivery. This fragmentation collides with the need for integrated care pathways that focus on holistic health and wellness. Catalonia (northeast Spain), a region of 8 million people with universal health care coverage and a single public health insurer but highly heterogeneous health care service providers, has experienced outstanding digitalization and integration of health information over the past 25 years, when the first transition from paper to digital support occurred. This Viewpoint describes the implementation of health ITs at a system level, discusses the hits and misses encountered in this journey, and frames this regional implementation within the global context. We present the architectures and use trends of the health information platforms over time. This provides insightful information that can be used by other systems worldwide in the never-ending transformation of health care structure and services.

Introduction: Complex chronic patients are prone to unplanned hospitalizations leading to a high burden on healthcare systems. To date, interventions to prevent unplanned admissions show inconclusive results. We report a qualitative analysis performed into the EU initiative JADECARE (2020–2023) to design a digitally enabled integrated care program aiming at preventing unplanned hospitalizations. Methods: A two-phase process with four design thinking (DT) sessions was conducted to analyse the management of complex chronic patients in the region of Catalonia (ES). In Phase I, Discovery, two DT sessions, October 2021 and February 2022, were done using as background information: i) the results of twenty structured interviews (five patients and fifteen professionals), ii) two governmental documents on regional deployment of integrated care and on the Catalan digital health strategy, respectively, and iii) the results of a cluster analysis of 761 hospitalizations. In Phase II, Confirmation, we examined the 30- and 90-day post-discharge periods of 49,604 hospitalizations as input for two additional DT sessions conducted in November and December 2022. Discussion: The qualitative analysis identified poor personalization of the interventions, the need for organizational changes, immature digitalization, and suboptimal services evaluation as main explanatory factors of the observed efficacy-effectiveness gap. Additionally, a program for prevention of unplanned hospitalizations, to be evaluated during the period 2024–2025, was generated. Conclusions: A digitally enabled adaptive case management approach to foster collaborative work and personalization of care, as well as organizational re-engineering, are endorsed for value-based prevention of unplanned hospitalizations. Resum Introducció: Els pacients crònics complexos són propensos a hospitalitzacions no planificades que comporten una gran càrrega per els sistemes sanitaris arreu del mon. Les intervencions per prevenir ingressos no planificats mostren resultats poc concloents pel que fa a efectivitat. Aquest estudi descriu una anàlisi qualitativa realitzada, en el marc de l’Acció Conjunta Europea JADECARE (2020–2023), per dissenyar un programa d’atenció integrada, amb suport digital, que te per objectiu prevenir les hospitalitzacions no planificades. Mètodes: L’estudi va consistir en un procés de co-disseny efectuat a partir de l’anàlisi de la gestió de pacients crònics complexos en la demarcació de Catalunya (ES). Es va efectuar en dues fases amb un total de quatre sessions de “Design Thinking (DT)”. A la Fase I, Discovery, es van fer dues sessions de DT, Octubre de 2021 i Febrer de 2022, utilitzant com a informació de fons: i) Els resultats de vint entrevistes estructurades (cinc pacients i quinze professionals), ii) Dos documents governamentals sobre el desplegament regional de l’atenció integrada i sobre l’estratègia de salut digital, respectivament, i iii) Els resultats d’una anàlisi de clústers de 761 hospitalitzacions. A la Fase II, Confirmació, vam examinar els resultats als 30 i 90 dies post-alta hospitalària de 49.604 casos en a dues sessions de DT addicionals realitzades durant els mesos de Novembre i Desembre de 2022. Discussió: L’anàlisi qualitativa va identificar: i) Insuficient personalització de les intervencions, ii) Necessitat de canvis organitzatius, iii) Immaduresa de la digitalització, i iv) Avaluació sub-óptima del desplegament dels serveis com a principals factors explicatius de la bretxa entre eficàcia i efectivitat observada en estudis previs. L’estudi proposa un programa de prevenció d’hospitalitzacions no planificades, a avaluar durant el període 2024–2025. Conclusions: Els resultats de l’anàlisi qualitativa donen suport a la modalitat de gestió adaptativa de casos, amb suport digital, per fomentar el treball col·laboratiu i la personalització de l’atenció. També plantegen la necessitat de reenginyeria dels processos clínics per assolir una prevenció eficient i basada en valors, de les hospitalitzacions no planificades en el mon real. Paraules Clau: Gestió adaptativa de casos; “Design Thinking”; Digitalització; Atenció personalitzada; Prevenció d’ingressos hospitalaris no planificats

Abstract Background Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system. Methods Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity. Results The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011–5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system. Conclusions The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

BackgroundMajor depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters.MethodsWe analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression.ResultsAt SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction.LimitationsCTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders.ConclusionsThe first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.