The CMO System Model: A Dynamic Framework to Operationalize Adaptive Pharmaceutical Care and Drive Health-System Sustainability

The CMO System Model: A Dynamic Framework to Operationalize Adaptive Pharmaceutical Care and Drive Health-System Sustainability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The CMO System Model: A Dynamic Framework to Operationalize Adaptive Pharmaceutical Care and Drive Health-System Sustainability Ramón Morillo-Verdugo, María Romero-Murillo, Cecilia Solís-Martín, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8334732/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Pharmaceutical care (PC) models are evolving from episodic interactions to complex, continuous systems managing multimorbidity and polypharmacy. The Capacity–Motivation–Opportunity (CMO) framework is widely used to stratify patients, but current applications often treat these dimensions as static and independent. This study aims to reconceptualise the CMO framework as a dynamic system in which these constructs influence one another through feedback loops, and to formalise this architecture into a model that supports adaptive, sustainable pharmacist-led care. Methods We conducted a conceptual theory-building study integrating systems thinking, abductive reasoning, and cross-impact analysis. Purposive conceptual sampling was used to identify relevant literature across pharmaceutical care, behavioural science, complexity science, and implementation frameworks. Interdependencies among capacity, motivation, and opportunity were iteratively mapped and synthesised into a Conceptual Interdependence Matrix (CIM). The resulting model and a derived Theoretical Taxonomy of Pharmaceutical Interventions (TTPI) underwent external expert audit to assess conceptual coherence and mechanistic plausibility. Results Six directional interdependencies were identified, forming a closed set of reinforcing and balancing feedback loops. Two mechanisms emerged as dominant regulators: a balancing sustainability loop where motivation reduces pharmacotherapeutic complexity, and a reinforcing value-generation loop where opportunity enhances early detection and stabilises trajectories. These loops organise the system into a fast-timescale behavioural subsystem and a slower structural subsystem. The derived TTPI links interventions to specific leverage points, providing a decision structure for workload planning. Conclusions The CMO System Model offers a coherent mid-range theory explaining how pharmacist-led care produces both patient-level behavioural change and system-level organisational effects. By articulating the feedback mechanisms that generate value, it provides a theoretical foundation for the redesign and digital augmentation of personalised pharmaceutical care. Pharmaceutical care Systems thinking Capacity-Motivation-Opportunity Complexity Telepharmacy Model Health services research Figures Figure 1 Figure 2 Background Pharmaceutical care (PC) has evolved from a medication-focused, episodic activity to a complex clinical, behavioural, and organisational practice aimed at optimising patient outcomes and contributing to the performance of health systems [1, 2]. Contemporary PC increasingly addresses patients with multimorbidity, polypharmacy, and high therapeutic burden, often managed across multiple providers and care settings. In this context, pharmacists are expected not only to optimise pharmacotherapy but also to influence behaviour, coordination, and continuity of care within resource-constrained systems. Health services research has highlighted that care delivery in real-world systems is inherently complex, characterised by non-linear interactions, feedback loops, and emergent behaviours that challenge linear, encounter-based models of practice [3–5]. Parallel shifts towards value-based healthcare and the “quadruple aim” have underscored the need to link clinical outcomes with cost, patient experience, and provider well-being in a coherent way [6, 7]. Furthermore, the rapid expansion of telemedicine and hybrid models of care has exposed both the potential and limitations of traditional models regarding access, continuity, and workload distribution [8–10]. Within this changing landscape, the Capacity–Motivation–Opportunity (CMO) framework has emerged as a reference structure for personalised PC, developed under the MAPEX ( Mapa Estratégico de Atención al Paciente Externo ) initiative of the Spanish Society of Hospital Pharmacy [11–13]. The framework stratifies patients along three core dimensions: clinical and pharmacotherapeutic complexity (Capacity), level of engagement and self-management (Motivation), and continuity or ease of maintaining contact (Opportunity). While empirical applications suggest CMO-based stratification improves patient experience [12, 13], current uses tend to treat these three dimensions as independent descriptors rather than interdependent elements of a dynamic system. It is crucial to note that while the terms capacity, motivation, and opportunity resemble constructs in behavioural models such as COM-B, the MAPEX-CMO framework has a distinct clinical and organisational origin focused on pharmacotherapeutic complexity and continuity needs [11]. Clinical practice suggests these dimensions co-evolve: increased opportunity via telepharmacy may enhance motivation, which in turn reduces complexity through better adherence. However, existing PC models rarely explicate the feedback mechanisms through which individual-level changes propagate to system-level effects like sustainability and workload. Addressing these challenges requires reconceptualising the CMO framework as a dynamic system. This study aims to: (1) reconceptualise the CMO framework as a dynamic system of reinforcing and balancing feedback loops; (2) formalise this architecture in a Conceptual Interdependence Matrix (CIM); and (3) translate it into a Theoretical Taxonomy of Pharmaceutical Interventions (TTPI) that supports adaptive, sustainable pharmacist-led care. Methods Aim and Design This study employed a conceptual theory-building design, aimed at developing explanatory mid-range theories in health services research [16, 17]. The objective was to verbalize the dynamic interactions linking capacity, motivation, and opportunity within pharmacist-led care. Our approach integrated systems thinking, abductive reasoning, and cross-impact conceptual analysis [20–23]. Conceptual Sampling and Sources We used purposive conceptual sampling to identify literature informing the architecture and mechanisms of the CMO system. Sources were drawn from four domains: Pharmaceutical care frameworks: PCNE definition, MAPEX-derived CMO literature, and telepharmacy models [1, 11–13]. Behavioural science: COM-B, Behaviour Change Wheel, and engagement literature [3, 14, 15]. Systems thinking: Non-linear dynamics, feedback loops, and complexity science [5, 18, 19]. Implementation frameworks: Integrated care theories and co-production [24, 25]. Inclusion criteria focused on conceptual clarity, relevance to mechanism explication, and applicability to complex health systems. Analytic Strategy The methodological workflow is summarized in Fig. 1 . Construct Clarification : We refined the operational boundaries of the constructs: Capacity (clinical/pharmacotherapeutic complexity), Motivation (engagement/self-regulation), and Opportunity (continuity/accessibility). Abductive Reasoning We used abductive reasoning to infer directional relationships. We identified plausible causal narratives observed in practice (e.g., motivation reducing complexity) and mapped them to theoretical evidence. Six directional interactions (C→M, M→C, C→O, O→C, M→O, O→M) were examined for directionality, underlying mechanism, and expected system-level effect. Cross-Impact Conceptual Analysis We applied cross-impact analysis to evaluate the coherence of interdependencies, identifying complementarity and conflict to define loop types (reinforcing vs. balancing) [22, 23]. This produced the Conceptual Interdependence Matrix (CIM). Synthesis and Validation The interdependencies were synthesised into the CMO System Model and visualized in a Causal Loop Diagram (CLD). Concurrently, the model was translated into the Theoretical Taxonomy of Pharmaceutical Interventions (TTPI). The model underwent external expert audit by three experts in PC, behavioural modelling, and systems science to ensure conceptual validity and robustness. Results The analysis revealed that the CMO framework operates as a minimal dynamic system where each construct acts as both determinant and consequence of the others. Conceptual Interdependence Matrix (CIM) The CIM (Table 1 ) formalises six directional interdependencies. Key propositions derived include: Table 1 Conceptual Interdependence Matrix (CIM) of the Capacity–Motivation–Opportunity (CMO) System Directional Interaction Loop Type Underlying Mechanism Expected System Behaviour Illustrative Indicators C → M (Capacity influences Motivation) Reinforcing Higher clinical/pharmacotherapeutic complexity increases perceived need for support, prompting greater engagement and reliance on pharmacist input. Progressive enhancement of motivational readiness in patients with higher complexity; increased acceptance of clinical recommendations. Self-reported engagement; PROMs reflecting perceived support; frequency of voluntary contacts. M → C (Motivation influences Capacity) Balancing (Stabilising) Motivated patients adhere more consistently, detect issues earlier, and self-regulate treatment decisions—reducing medication burden and avoiding preventable escalation. Downward pressure on treatment complexity; reduced pharmacotherapeutic burden over time; stabilisation of follow-up needs. Adherence metrics; number of medication-related problems; deprescribing opportunities; regimen simplification events. M → O (Motivation influences Opportunity) Reinforcing Higher motivation increases willingness to use available communication channels, attend follow-ups, and maintain contact proactively. Increased interaction density; improved continuity; reduced risk of disengagement or loss to follow-up. Follow-up attendance rates; inbound patient-initiated contacts; use of telepharmacy tools. O → M (Opportunity influences Motivation) Reinforcing Accessibility, continuity, and timely support enhance relational trust and perceived support, reinforcing engagement and adherence. Strengthened motivational trajectory; improved self-management; reduced behavioural variability. PREMs on accessibility; missed-appointment rates; persistence with follow-up plans. C → O (Capacity influences Opportunity) Reinforcing Higher complexity/risk triggers increased monitoring intensity and broader use of available channels by clinicians. Increased monitoring frequency; closer follow-up; greater system attention allocated to high-risk patients. Assigned follow-up intensity; number of scheduled vs. unscheduled contacts; telemonitoring activation. O → C (Opportunity influences Capacity) Reinforcing Expanded monitoring and continuous contact enable earlier identification and resolution of medication-related problems, reducing complexity over time. Reduced preventable deterioration; fewer acute events; improved therapeutic stability. Number of detected MRPs; time-to-detection; preventable ED visits; treatment adjustments prompted by monitoring. P1 (C→M): Higher complexity increases the perceived need for support, stimulating engagement (Reinforcing). P2 (M→C): Higher motivation improves adherence and self-management, reducing pharmacotherapeutic complexity (Balancing/Stabilising). P3 & P4 (M↔O): Motivation and Opportunity mutually reinforce each other; motivated patients seek contact, while accessibility builds trust. P5 & P6 (C↔O): Complexity triggers increased monitoring (C→O), while expanded monitoring enables early problem resolution, reducing complexity (O→C). System Dynamics and Feedback Loops The dynamic architecture is visualised in Fig. 2 . Two dominant regulatory loops emerged: Balancing Loop B1 (Sustainability): Motivation reduces pharmacotherapeutic complexity, acting as a stabilising force on system load. Reinforcing Loop R5 (Value-Generation): Opportunity amplifies long-term value through earlier detection and correction of medication-related problems (O→C). These loops organise the system into two coupled subsystems: a fast-timescale behavioural subsystem (engagement, communication) and a slower structural subsystem (monitoring capacity, workload, clinical value). Theoretical Taxonomy of Pharmaceutical Interventions (TTPI) The TTPI (Table 2 ) maps intervention categories to the system loops they activate: Table 2 Theoretical Taxonomy of Pharmaceutical Interventions (TTPI) derived from the CMO System Model Intervention Category Activated Loop(s) Underlying Mechanism System Consequence Potential Unintended Effects Capacity-focused interventions (e.g., medication review, deprescribing, regimen simplification) C→M, O→C Reduction of pharmacotherapeutic burden and removal of drivers of complexity; earlier correction of treatment problems through enhanced monitoring. Lower long-term workload; reduced preventable utilisation; stabilisation of therapeutic regimens; improved clinical safety. Short-term increase in monitoring needs; potential patient uncertainty after medication changes. Motivation-directed interventions (e.g., motivational interviewing, goal-setting, PROM/PREM feedback) M→C, M→O Strengthening of self-regulation, engagement and proactive follow-up behaviours; enhancement of patient perceived support and internal motivation. Reduction in long-term complexity trajectories; improved adherence; more stable follow-up patterns; fewer avoidable deteriorations. Saturation or fatigue if intensity is excessive; over-reliance on patient activation without parallel structural support. Opportunity-enhancing interventions (e.g., telepharmacy, proactive outreach, remote monitoring, interprofessional coordination) O→M, O→C Expansion of continuity, accessibility and detection capacity; increased frequency and quality of pharmacist–patient interactions. Earlier identification of medication-related problems; improved engagement; enhanced system responsiveness and safety. Short-term workload increase due to higher interaction density; risk of dependence on digital channels for complex patients. Capacity-focused interventions (e.g., deprescribing) activate R1 and R5, reducing structural burden. Motivation-directed interventions (e.g., motivational interviewing) activate B1 and R2, stabilising demand. Opportunity-enhancing interventions (e.g., telepharmacy) activate R3, R4, and R5, expanding detection capacity. Discussion This study reconceptualises the CMO framework as an adaptive system. Unlike static stratification tools, the CMO System Model explains how pharmacist-led interventions generate both patient-level behavioural change and system-level organisational effects. A key contribution is the differentiation from behavioural models like COM-B. While sharing terminology, the CMO System Model is rooted in hospital pharmacy practice [11]. It demonstrates that Opportunity is not merely an antecedent to behaviour but an outcome of repeated interactions: effective monitoring (Opportunity) builds trust (Motivation), which stabilises complexity (Capacity). The dual-subsystem architecture—behavioural and structural—offers actionable insights for service design. For instance, telepharmacy (Opportunity-enhancing) does not just improve access; it activates the value-generation loop (R5), allowing earlier detection of adverse events. However, the model also warns that expanding opportunity without managing the resulting workload can destabilise the system [30, 31]. Limitations As a conceptual model, this study does not quantify the strength or timing of feedback loops. Empirical research is needed to measure these interactions in specific clinical contexts. Additionally, exogenous factors (policy, staffing) were treated as external to focus on endogenous dynamics. Conclusions The CMO System Model reframes pharmaceutical care as a dynamic system where capacity, motivation, and opportunity continuously influence one another. By clarifying the feedback mechanisms that generate value and sustainability, the model and the derived TTPI provide a theoretical foundation for the redesign, evaluation, and digital augmentation of personalised pharmaceutical care. Abbreviations CMO: Capacity–Motivation–Opportunity PC: Pharmaceutical Care CIM: Conceptual Interdependence Matrix TTPI: Theoretical Taxonomy of Pharmaceutical Interventions CLD: Causal Loop Diagram MAPEX: Mapa Estratégico de Atención al Paciente Externo PROMs: Patient-Reported Outcome Measures PREMs: Patient-Reported Experience Measures Declarations Ethics approval and consent to participate Not applicable. This study is a conceptual theory-building analysis and did not involve human participants or animal subjects. Consent for publication Not applicable. Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Competing interests The authors declare that they have no competing interests. Funding No specific funding was received for this work. Authors' contributions RMV conceptualised the study and the initial model. MRM and CSM contributed to the literature search and cross-impact analysis. ECM contributed to the refinement of the causal loop diagram and drafting of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Allemann SS, van Mil JWF, Botermann L, Berger K, Griese N, Hersberger KE. Pharmaceutical care: the PCNE definition 2013. Int J Clin Pharm. 2014;36(3):544-55. Chisholm-Burns MA, Kim Lee J, Spivey CA, et al. US pharmacists’ effect as team members on patient care: systematic review and meta-analyses. Med Care. 2010;48(10):923-33. Michie S, van Stralen MM, West R. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8334732","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571697647,"identity":"a3e968a2-b7b1-457f-8017-36f691ac3467","order_by":0,"name":"Ramón Morillo-Verdugo","email":"","orcid":"","institution":"Hospital Universitario de Valme","correspondingAuthor":false,"prefix":"","firstName":"Ramón","middleName":"","lastName":"Morillo-Verdugo","suffix":""},{"id":571697648,"identity":"8c185d75-4bfa-488a-9863-e8993dfb6504","order_by":1,"name":"María Romero-Murillo","email":"","orcid":"","institution":"University of 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07:46:55","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49772,"visible":true,"origin":"","legend":"","description":"","filename":"6346556eab2c42ffa287757f9587e1d51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8334732/v1/8174e60ffe0ece9d4891bc0c.xml"},{"id":100013911,"identity":"c5524999-bc9b-4737-b48f-8ab31558f307","added_by":"auto","created_at":"2026-01-12 06:22:56","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55343,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8334732/v1/61270e654cddf7c4e8148276.html"},{"id":100013903,"identity":"26dd1966-bee4-48eb-a903-d352c78f966c","added_by":"auto","created_at":"2026-01-12 06:22:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":512997,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological workflow for the conceptual modeling of the Capacity–Motivation–Opportunity (CMO) system.\u003c/p\u003e\n\u003cp\u003eThe figure depicts the four iterative stages of the study—construct definition, identification of causal interactions, synthesis into the Conceptual Interdependence Matrix (CIM), and validation through theoretical triangulation and expert audit. The circular structure reflects the abductive and systems-thinking logic of the process, culminating in the final outputs: the CIM and the Theoretical Taxonomy of Pharmaceutical Interventions (TTPI).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8334732/v1/4e729bcd44006675e2954a17.png"},{"id":100362500,"identity":"8628bde7-7e12-4298-9e79-e83cbd45b173","added_by":"auto","created_at":"2026-01-16 07:46:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":983505,"visible":true,"origin":"","legend":"\u003cp\u003eCausal loop diagram of the Capacity–Motivation–Opportunity (CMO) system in pharmaceutical care.\u003c/p\u003e\n\u003cp\u003eThe diagram illustrates the dynamic feedback architecture underlying the CMO framework. Reinforcing loops (R) and the balancing sustainability loop (B1) describe how Capacity, Motivation, and Opportunity interact through pharmacist interventions. Motivation stabilizes system demand (B1: sustainability mechanism), while Opportunity amplifies value creation (R: value-generation mechanism), positioning the pharmacist as the central integrator of adaptive pharmaceutical care. The structure was iteratively validated through theoretical triangulation and expert review\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8334732/v1/67184e10d686aea92c1eacc4.png"},{"id":100380953,"identity":"ffdecfe9-6ee2-4f9e-9cbc-94879da2340c","added_by":"auto","created_at":"2026-01-16 10:36:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8334732/v1/4140349f-390c-4573-a782-d0533200974e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The CMO System Model: A Dynamic Framework to Operationalize Adaptive Pharmaceutical Care and Drive Health-System Sustainability","fulltext":[{"header":"Background","content":"\u003cp\u003ePharmaceutical care (PC) has evolved from a medication-focused, episodic activity to a complex clinical, behavioural, and organisational practice aimed at optimising patient outcomes and contributing to the performance of health systems [1, 2]. Contemporary PC increasingly addresses patients with multimorbidity, polypharmacy, and high therapeutic burden, often managed across multiple providers and care settings. In this context, pharmacists are expected not only to optimise pharmacotherapy but also to influence behaviour, coordination, and continuity of care within resource-constrained systems.\u003c/p\u003e \u003cp\u003eHealth services research has highlighted that care delivery in real-world systems is inherently complex, characterised by non-linear interactions, feedback loops, and emergent behaviours that challenge linear, encounter-based models of practice [3\u0026ndash;5]. Parallel shifts towards value-based healthcare and the \u0026ldquo;quadruple aim\u0026rdquo; have underscored the need to link clinical outcomes with cost, patient experience, and provider well-being in a coherent way [6, 7]. Furthermore, the rapid expansion of telemedicine and hybrid models of care has exposed both the potential and limitations of traditional models regarding access, continuity, and workload distribution [8\u0026ndash;10].\u003c/p\u003e \u003cp\u003eWithin this changing landscape, the Capacity\u0026ndash;Motivation\u0026ndash;Opportunity (CMO) framework has emerged as a reference structure for personalised PC, developed under the MAPEX (\u003cem\u003eMapa Estrat\u0026eacute;gico de Atenci\u0026oacute;n al Paciente Externo\u003c/em\u003e) initiative of the Spanish Society of Hospital Pharmacy [11\u0026ndash;13]. The framework stratifies patients along three core dimensions: clinical and pharmacotherapeutic complexity (Capacity), level of engagement and self-management (Motivation), and continuity or ease of maintaining contact (Opportunity). While empirical applications suggest CMO-based stratification improves patient experience [12, 13], current uses tend to treat these three dimensions as independent descriptors rather than interdependent elements of a dynamic system.\u003c/p\u003e \u003cp\u003eIt is crucial to note that while the terms capacity, motivation, and opportunity resemble constructs in behavioural models such as COM-B, the MAPEX-CMO framework has a distinct clinical and organisational origin focused on pharmacotherapeutic complexity and continuity needs [11]. Clinical practice suggests these dimensions co-evolve: increased opportunity via telepharmacy may enhance motivation, which in turn reduces complexity through better adherence. However, existing PC models rarely explicate the feedback mechanisms through which individual-level changes propagate to system-level effects like sustainability and workload.\u003c/p\u003e \u003cp\u003eAddressing these challenges requires reconceptualising the CMO framework as a dynamic system. This study aims to: (1) reconceptualise the CMO framework as a dynamic system of reinforcing and balancing feedback loops; (2) formalise this architecture in a Conceptual Interdependence Matrix (CIM); and (3) translate it into a Theoretical Taxonomy of Pharmaceutical Interventions (TTPI) that supports adaptive, sustainable pharmacist-led care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim and Design\u003c/h2\u003e \u003cp\u003eThis study employed a conceptual theory-building design, aimed at developing explanatory mid-range theories in health services research [16, 17]. The objective was to verbalize the dynamic interactions linking capacity, motivation, and opportunity within pharmacist-led care. Our approach integrated systems thinking, abductive reasoning, and cross-impact conceptual analysis [20\u0026ndash;23].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConceptual Sampling and Sources\u003c/h3\u003e\n\u003cp\u003eWe used purposive conceptual sampling to identify literature informing the architecture and mechanisms of the CMO system. Sources were drawn from four domains:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePharmaceutical care frameworks: PCNE definition, MAPEX-derived CMO literature, and telepharmacy models [1, 11\u0026ndash;13].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBehavioural science: COM-B, Behaviour Change Wheel, and engagement literature [3, 14, 15].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSystems thinking: Non-linear dynamics, feedback loops, and complexity science [5, 18, 19].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImplementation frameworks: Integrated care theories and co-production [24, 25].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eInclusion criteria focused on conceptual clarity, relevance to mechanism explication, and applicability to complex health systems.\u003c/p\u003e\n\u003ch3\u003eAnalytic Strategy\u003c/h3\u003e\n\u003cp\u003eThe methodological workflow is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eConstruct Clarification\u003c/em\u003e: We refined the operational boundaries of the constructs: \u003cem\u003eCapacity\u003c/em\u003e (clinical/pharmacotherapeutic complexity), \u003cem\u003eMotivation\u003c/em\u003e (engagement/self-regulation), and \u003cem\u003eOpportunity\u003c/em\u003e (continuity/accessibility).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAbductive Reasoning\u003c/strong\u003e \u003cp\u003eWe used abductive reasoning to infer directional relationships. We identified plausible causal narratives observed in practice (e.g., motivation reducing complexity) and mapped them to theoretical evidence. Six directional interactions (C\u0026rarr;M, M\u0026rarr;C, C\u0026rarr;O, O\u0026rarr;C, M\u0026rarr;O, O\u0026rarr;M) were examined for directionality, underlying mechanism, and expected system-level effect.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCross-Impact Conceptual Analysis\u003c/strong\u003e \u003cp\u003eWe applied cross-impact analysis to evaluate the coherence of interdependencies, identifying complementarity and conflict to define loop types (reinforcing vs. balancing) [22, 23]. This produced the Conceptual Interdependence Matrix (CIM).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSynthesis and Validation\u003c/strong\u003e \u003cp\u003eThe interdependencies were synthesised into the CMO System Model and visualized in a Causal Loop Diagram (CLD). Concurrently, the model was translated into the Theoretical Taxonomy of Pharmaceutical Interventions (TTPI). The model underwent external expert audit by three experts in PC, behavioural modelling, and systems science to ensure conceptual validity and robustness.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis revealed that the CMO framework operates as a minimal dynamic system where each construct acts as both determinant and consequence of the others.\u003c/p\u003e \u003cp\u003eConceptual Interdependence Matrix (CIM) The CIM (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) formalises six directional interdependencies. Key propositions derived include:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConceptual Interdependence Matrix (CIM) of the Capacity\u0026ndash;Motivation\u0026ndash;Opportunity (CMO) System\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirectional Interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoop Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderlying Mechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected System Behaviour\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIllustrative Indicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC \u0026rarr; M (Capacity influences Motivation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforcing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher clinical/pharmacotherapeutic complexity increases perceived need for support, prompting greater engagement and reliance on pharmacist input.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive enhancement of motivational readiness in patients with higher complexity; increased acceptance of clinical recommendations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSelf-reported engagement; PROMs reflecting perceived support; frequency of voluntary contacts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM \u0026rarr; C (Motivation influences Capacity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBalancing (Stabilising)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMotivated patients adhere more consistently, detect issues earlier, and self-regulate treatment decisions\u0026mdash;reducing medication burden and avoiding preventable escalation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDownward pressure on treatment complexity; reduced pharmacotherapeutic burden over time; stabilisation of follow-up needs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdherence metrics; number of medication-related problems; deprescribing opportunities; regimen simplification events.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM \u0026rarr; O (Motivation influences Opportunity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforcing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher motivation increases willingness to use available communication channels, attend follow-ups, and maintain contact proactively.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncreased interaction density; improved continuity; reduced risk of disengagement or loss to follow-up.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFollow-up attendance rates; inbound patient-initiated contacts; use of telepharmacy tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO \u0026rarr; M (Opportunity influences Motivation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforcing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccessibility, continuity, and timely support enhance relational trust and perceived support, reinforcing engagement and adherence.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrengthened motivational trajectory; improved self-management; reduced behavioural variability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePREMs on accessibility; missed-appointment rates; persistence with follow-up plans.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC \u0026rarr; O (Capacity influences Opportunity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforcing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher complexity/risk triggers increased monitoring intensity and broader use of available channels by clinicians.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncreased monitoring frequency; closer follow-up; greater system attention allocated to high-risk patients.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssigned follow-up intensity; number of scheduled vs. unscheduled contacts; telemonitoring activation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO \u0026rarr; C (Opportunity influences Capacity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforcing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpanded monitoring and continuous contact enable earlier identification and resolution of medication-related problems, reducing complexity over time.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduced preventable deterioration; fewer acute events; improved therapeutic stability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of detected MRPs; time-to-detection; preventable ED visits; treatment adjustments prompted by monitoring.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eP1 (C\u0026rarr;M): Higher complexity increases the perceived need for support, stimulating engagement (Reinforcing).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eP2 (M\u0026rarr;C): Higher motivation improves adherence and self-management, reducing pharmacotherapeutic complexity (Balancing/Stabilising).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eP3 \u0026amp; P4 (M\u0026harr;O): Motivation and Opportunity mutually reinforce each other; motivated patients seek contact, while accessibility builds trust.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eP5 \u0026amp; P6 (C\u0026harr;O): Complexity triggers increased monitoring (C\u0026rarr;O), while expanded monitoring enables early problem resolution, reducing complexity (O\u0026rarr;C).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eSystem Dynamics and Feedback Loops\u003c/h3\u003e\n\u003cp\u003eThe dynamic architecture is visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Two dominant regulatory loops emerged:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBalancing Loop B1 (Sustainability): Motivation reduces pharmacotherapeutic complexity, acting as a stabilising force on system load.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eReinforcing Loop R5 (Value-Generation): Opportunity amplifies long-term value through earlier detection and correction of medication-related problems (O\u0026rarr;C).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThese loops organise the system into two coupled subsystems: a fast-timescale \u003cem\u003ebehavioural subsystem\u003c/em\u003e (engagement, communication) and a slower \u003cem\u003estructural subsystem\u003c/em\u003e (monitoring capacity, workload, clinical value).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Taxonomy of Pharmaceutical Interventions (TTPI)\u003c/h2\u003e \u003cp\u003eThe TTPI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) maps intervention categories to the system loops they activate:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTheoretical Taxonomy of Pharmaceutical Interventions (TTPI) derived from the CMO System Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivated Loop(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderlying Mechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystem Consequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePotential Unintended Effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapacity-focused interventions\u003c/p\u003e \u003cp\u003e(e.g., medication review, deprescribing, regimen simplification)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026rarr;M, O\u0026rarr;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduction of pharmacotherapeutic burden and removal of drivers of complexity; earlier correction of treatment problems through enhanced monitoring.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower long-term workload; reduced preventable utilisation; stabilisation of therapeutic regimens; improved clinical safety.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShort-term increase in monitoring needs; potential patient uncertainty after medication changes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation-directed interventions\u003c/p\u003e \u003cp\u003e(e.g., motivational interviewing, goal-setting, PROM/PREM feedback)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u0026rarr;C, M\u0026rarr;O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrengthening of self-regulation, engagement and proactive follow-up behaviours; enhancement of patient perceived support and internal motivation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduction in long-term complexity trajectories; improved adherence; more stable follow-up patterns; fewer avoidable deteriorations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaturation or fatigue if intensity is excessive; over-reliance on patient activation without parallel structural support.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpportunity-enhancing interventions\u003c/p\u003e \u003cp\u003e(e.g., telepharmacy, proactive outreach, remote monitoring, interprofessional coordination)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u0026rarr;M, O\u0026rarr;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpansion of continuity, accessibility and detection capacity; increased frequency and quality of pharmacist\u0026ndash;patient interactions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEarlier identification of medication-related problems; improved engagement; enhanced system responsiveness and safety.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShort-term workload increase due to higher interaction density; risk of dependence on digital channels for complex patients.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCapacity-focused interventions (e.g., deprescribing) activate R1 and R5, reducing structural burden.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMotivation-directed interventions (e.g., motivational interviewing) activate B1 and R2, stabilising demand.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOpportunity-enhancing interventions (e.g., telepharmacy) activate R3, R4, and R5, expanding detection capacity.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reconceptualises the CMO framework as an adaptive system. Unlike static stratification tools, the CMO System Model explains how pharmacist-led interventions generate both patient-level behavioural change and system-level organisational effects.\u003c/p\u003e \u003cp\u003eA key contribution is the differentiation from behavioural models like COM-B. While sharing terminology, the CMO System Model is rooted in hospital pharmacy practice [11]. It demonstrates that \u003cem\u003eOpportunity\u003c/em\u003e is not merely an antecedent to behaviour but an outcome of repeated interactions: effective monitoring (Opportunity) builds trust (Motivation), which stabilises complexity (Capacity).\u003c/p\u003e \u003cp\u003eThe dual-subsystem architecture\u0026mdash;behavioural and structural\u0026mdash;offers actionable insights for service design. For instance, telepharmacy (Opportunity-enhancing) does not just improve access; it activates the value-generation loop (R5), allowing earlier detection of adverse events. However, the model also warns that expanding opportunity without managing the resulting workload can destabilise the system [30, 31].\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eAs a conceptual model, this study does not quantify the strength or timing of feedback loops. Empirical research is needed to measure these interactions in specific clinical contexts. Additionally, exogenous factors (policy, staffing) were treated as external to focus on endogenous dynamics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe CMO System Model reframes pharmaceutical care as a dynamic system where capacity, motivation, and opportunity continuously influence one another. By clarifying the feedback mechanisms that generate value and sustainability, the model and the derived TTPI provide a theoretical foundation for the redesign, evaluation, and digital augmentation of personalised pharmaceutical care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCMO:\u003c/strong\u003e Capacity–Motivation–Opportunity\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePC:\u003c/strong\u003e Pharmaceutical Care\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCIM:\u003c/strong\u003e Conceptual Interdependence Matrix\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTTPI:\u003c/strong\u003e Theoretical Taxonomy of Pharmaceutical Interventions\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCLD:\u003c/strong\u003e Causal Loop Diagram\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMAPEX:\u003c/strong\u003e \u003cem\u003eMapa Estratégico de Atención al Paciente Externo\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePROMs:\u003c/strong\u003e Patient-Reported Outcome Measures\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePREMs:\u003c/strong\u003e Patient-Reported Experience Measures\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable. This study is a conceptual theory-building analysis and did not involve human participants or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e No specific funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e RMV conceptualised the study and the initial model. MRM and CSM contributed to the literature search and cross-impact analysis. ECM contributed to the refinement of the causal loop diagram and drafting of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllemann SS, van Mil JWF, Botermann L, Berger K, Griese N, Hersberger KE. Pharmaceutical care: the PCNE definition 2013. Int J Clin Pharm. 2014;36(3):544-55.\u003c/li\u003e\n\u003cli\u003eChisholm-Burns MA, Kim Lee J, Spivey CA, et al. US pharmacists\u0026rsquo; effect as team members on patient care: systematic review and meta-analyses. Med Care. 2010;48(10):923-33.\u003c/li\u003e\n\u003cli\u003eMichie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.\u003c/li\u003e\n\u003cli\u003eGreenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2018;16:95.\u003c/li\u003e\n\u003cli\u003eHomer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. Am J Public Health. 2006;96(3):452-8.\u003c/li\u003e\n\u003cli\u003ePorter ME. What is value in health care? N Engl J Med. 2010;363(26):2477-81.\u003c/li\u003e\n\u003cli\u003eBodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12(6):573-6.\u003c/li\u003e\n\u003cli\u003eAlexander GC, Tajanlangit M, Heyward J, Mansour O, Qato DM, Stafford RS. Use and content of primary care office-based vs telemedicine care visits during the COVID-19 pandemic in the US. JAMA Netw Open. 2020;3(10):e2021476.\u003c/li\u003e\n\u003cli\u003ePatel SY, Mehrotra A, Huskamp HA, Usher-Pines L, Ganguli I, Barne ML. Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States. Health Aff (Millwood). 2021;40(2):349-58.\u003c/li\u003e\n\u003cli\u003eMargusino-Frami\u0026ntilde;\u0026aacute;n L, Illarro-Uranga A, Lorenzo-Llinares M, Monte-boquet E, Marquez Saavedra E et al. Pharmaceutical care to hospital outpatients during the COVID-19 pandemic. Telepharmacy. Farm Hosp. 2020;44(7):61-65.\u003c/li\u003e\n\u003cli\u003eMorillo-Verdugo R, Margusino-Frami\u0026ntilde;\u0026aacute;n L, Monte-Boquet E, Morell-Baladron A, Barrera Hernandez D, Rey-Pi\u0026ntilde;ero XM et al. Spanish Society of Hospital Pharmacy position statement on telepharmacy: recommendations for its implementation and development. Farm Hosp. 2020;44(4):174-81.\u003c/li\u003e\n\u003cli\u003eMorillo-Verdugo R, Morillo-Lisa R, Espolita-Suarez J, Delgado-S\u0026aacute;nchez O. Evaluation of patient experience with a model of coordinated telematic pharmaceutical care between hospital and rural pharmacies in Spain: a proof of concept. J Multidiscip Healthc. 2023;16:1037-46.\u003c/li\u003e\n\u003cli\u003eMorillo-Verdugo R, L\u0026aacute;zaro-L\u0026oacute;pez A, Alonso-Grandes E, Martin-Conde MT, Diaz Ruiz P, Molina Cuadrado E et al. Patient experience evaluation of the CMO-based pharmaceutical care model vs usual care in people living with HIV. J Multidiscip Healthc. 2022;15:2991-3003.\u003c/li\u003e\n\u003cli\u003eBandurska E, Staszewska A, Czerniak U. Using patient-reported outcomes and experiences (PROMs/PREMs) in integrated care\u0026mdash;A systematic review. Healthcare (Basel). 2022;11(1):98.\u003c/li\u003e\n\u003cli\u003eBlalock SJ, Roberts AW, Lauffenburger JC, Thompson T, O\u0026rsquo;Connor SK. The effect of community pharmacy\u0026ndash;based interventions on patient health outcomes: a systematic review. Med Care Res Rev. 2013;70(3):235-66.\u003c/li\u003e\n\u003cli\u003eCarpiano RM, Daley DM. A guide and glossary on post-positivist theory building for population health. J Epidemiol Community Health. 2006;60(7):564-70.\u003c/li\u003e\n\u003cli\u003eVarpio L, Paradis E, Uijtdehaage S, Young M. The Distinctions Between Theory, Theoretical Framework, and Conceptual Framework. Acad Med. 2020;95(7):989-994.\u003c/li\u003e\n\u003cli\u003eCassidy R, Singh NS, Schiratti PR, Semwanga A, Binyaruka P, Sachingongu N, et al. Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models. BMC Health Serv Res. 2019;19(1):845.\u003c/li\u003e\n\u003cli\u003eSterman JD. Business dynamics and system dynamics modeling for complex systems. Syst Dyn Rev. 2001;17(1):5\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eMichie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.\u003c/li\u003e\n\u003cli\u003eMaxwell-Smith C, Breare H, Dominguez Garcia A, Sim TF, Blackford K, Chih HJ, et al. Pharmacists\u0026apos; perceptions and delivery of health behaviour change recommendations: Mapping the COM-B model. Res Social Adm Pharm. 2024;20(2):115-123.\u003c/li\u003e\n\u003cli\u003eGreenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2018;16:95.\u003c/li\u003e\n\u003cli\u003ePlsek PE, Greenhalgh T. Complexity science: The challenge of complexity in health care. BMJ. 2001;323(7313):625\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBatalden M, Batalden P, Margolis P, Seid M, Armstrong G, Opipari-Arrigan L, Hartung H. Coproduction of healthcare service. BMJ Qual Saf. 2016;25(7):509\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eValentijn PP, Schepman SM, Opheij W, Bruijnzeels MA. Understanding integrated care: a comprehensive conceptual framework based on the integrative functions of primary care. Int J Integr Care. 2013;13:e010.\u003c/li\u003e\n\u003cli\u003eGilson L, Barasa E, Nxumalo N, Cleary S, Goudge J, Molyneux S, et al. Everyday resilience in district health systems: emerging insights from the front lines in Kenya and South Africa. BMJ Glob Health. 2021;6(12):e007024.\u003c/li\u003e\n\u003cli\u003eNieuwlaat R, Wilczynski N, Navarro T, Hobson N, Jeffery R, Keepanasseril A, et al. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2014;2014(11):CD000011.\u003c/li\u003e\n\u003cli\u003eNazar H, Nazar Z. Adopting a systems thinking approach to investigate the implementation and provision of a pharmacist-led post-discharge domiciliary medicines review service. Res Social Adm Pharm. 2021;17(4):808-815.\u003c/li\u003e\n\u003cli\u003eThelen J, Sant Fruchtman C, Bilal M, Gabaake K, Iqbal S, Keakabetse T, et al. Development of the Systems Thinking for Health Actions framework: a literature review and a case study. BMJ Glob Health. 2023;8(3):e010191.\u003c/li\u003e\n\u003cli\u003eViegas R, Dineen-Griffin S, S\u0026ouml;derlund L\u0026Aring;, Acosta-G\u0026oacute;mez J, Maria Guiu J. Telepharmacy and pharmaceutical care: A narrative review by International Pharmaceutical Federation. Farm Hosp. 2022;46(7):86-91.\u003c/li\u003e\n\u003cli\u003eBaldoni S, Amenta F, Ricci G. Telepharmacy Services: Present Status and Future Perspectives: A Review. Medicina (Kaunas). 2019;55(7):327.\u003c/li\u003e\n\u003cli\u003eMcPhail SM. Multimorbidity in chronic disease: impact on health care resources and costs. Risk Manag Healthc Policy. 2016;9:143-56.\u003c/li\u003e\n\u003cli\u003eAl-Quteimat OM, Amer AM. Evidence-based pharmaceutical care: The next chapter in pharmacy practice. Saudi Pharm J. 2016;24(4):447-51.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pharmaceutical care, Systems thinking, Capacity-Motivation-Opportunity, Complexity, Telepharmacy, Model, Health services research","lastPublishedDoi":"10.21203/rs.3.rs-8334732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8334732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePharmaceutical care (PC) models are evolving from episodic interactions to complex, continuous systems managing multimorbidity and polypharmacy. The Capacity\u0026ndash;Motivation\u0026ndash;Opportunity (CMO) framework is widely used to stratify patients, but current applications often treat these dimensions as static and independent. This study aims to reconceptualise the CMO framework as a dynamic system in which these constructs influence one another through feedback loops, and to formalise this architecture into a model that supports adaptive, sustainable pharmacist-led care.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a conceptual theory-building study integrating systems thinking, abductive reasoning, and cross-impact analysis. Purposive conceptual sampling was used to identify relevant literature across pharmaceutical care, behavioural science, complexity science, and implementation frameworks. Interdependencies among capacity, motivation, and opportunity were iteratively mapped and synthesised into a Conceptual Interdependence Matrix (CIM). The resulting model and a derived Theoretical Taxonomy of Pharmaceutical Interventions (TTPI) underwent external expert audit to assess conceptual coherence and mechanistic plausibility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSix directional interdependencies were identified, forming a closed set of reinforcing and balancing feedback loops. Two mechanisms emerged as dominant regulators: a balancing \u003cem\u003esustainability loop\u003c/em\u003e where motivation reduces pharmacotherapeutic complexity, and a reinforcing \u003cem\u003evalue-generation loop\u003c/em\u003e where opportunity enhances early detection and stabilises trajectories. These loops organise the system into a fast-timescale behavioural subsystem and a slower structural subsystem. The derived TTPI links interventions to specific leverage points, providing a decision structure for workload planning.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe CMO System Model offers a coherent mid-range theory explaining how pharmacist-led care produces both patient-level behavioural change and system-level organisational effects. By articulating the feedback mechanisms that generate value, it provides a theoretical foundation for the redesign and digital augmentation of personalised pharmaceutical care.\u003c/p\u003e","manuscriptTitle":"The CMO System Model: A Dynamic Framework to Operationalize Adaptive Pharmaceutical Care and Drive Health-System Sustainability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:22:51","doi":"10.21203/rs.3.rs-8334732/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-07T17:00:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-16T12:20:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-13T12:19:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-13T12:15:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-12-11T08:45:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69527bec-6287-4873-af41-892500e4544e","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T06:22:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:22:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8334732","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8334732","identity":"rs-8334732","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}