Elisabeth Mack, MBA, BSN, BA, RN
Sherri Mack, BSN, RN

ABSTRACT

Cannabinoid-based therapeutics are increasingly utilized in the management of cognitive fatigue, mood dysregulation, and functional impairment; however, integration into telehealth care models remains inconsistent and frequently lacks structured, longitudinal clinical oversight. This gap limits the ability to deliver standardized, outcomes-driven cannabinoid care.

This white paper evaluates real-world evidence derived from a 21-day observational study (n ≈ 100) examining a 6:1:1 multi-cannabinoid formulation containing cannabidiol (CBD), tetrahydrocannabivarin (THCV), and cannabigerol (CBG). Outcomes were assessed using validated instruments, including the Fatigue Assessment Scale (FAS) and the World Health Organization Well-Being Index (WHO-5), in addition to self-reported measures of productivity, energy, and psychological well-being. Participants were not managed within a structured clinical care model; no nurse coaching or ongoing clinical oversight was provided, and data collection was limited to questionnaire-based and self-reported outcomes over the study period.

Participants reported improvements across multiple functional domains, including reductions in fatigue, increased perceived energy, enhanced focus, and improved overall well-being. A majority also reported decreased perceived stress. No serious adverse events were reported. These findings are consistent with existing literature suggesting that cannabinoids may influence cognitive, emotional, and physiological processes, though outcomes remain dependent on formulation, dosing, and individual variability (Arkell et al., 2019; Hergenrather et al., 2020; Sherman et al., 2016).

Findings observed in this unstructured context highlight both the potential of multi-cannabinoid formulations and the limitations of unsupervised use. These results support the need for structured, protocol-driven cannabinoid care models. Integration into telehealth-based frameworks, including clinical oversight and longitudinal patient-reported outcome tracking, may improve consistency, safety, and overall clinical effectiveness. Further controlled studies are warranted to validate these findings and inform standardized clinical protocols.

Introduction

Cannabinoid therapeutics have emerged as a rapidly expanding area of interest within integrative and digital healthcare, driven in part by increasing patient demand for alternatives to conventional pharmacologic interventions. Patients frequently seek cannabinoid-based therapies for overlapping symptom clusters, including cognitive fatigue, impaired concentration, anxiety, sleep disturbance, and metabolic dysregulation. These symptom patterns often reflect complex interactions among neurobiological, inflammatory, and psychosocial processes, many of which are influenced by the endocannabinoid system (Lu & Mackie, 2016).

Despite increased utilization, cannabinoid care delivery remains fragmented and inconsistently integrated into clinical practice. Telehealth platforms have expanded access to cannabinoid-based therapies; however, many current models emphasize certification or episodic consultation rather than longitudinal, outcomes-driven management. As a result, patients are frequently left to self-direct formulation selection, dosing, and evaluation of response without consistent clinical guidance.

Emerging evidence suggests that while telehealth improves access, it does not inherently ensure continuity of care or clinical standardization. Gaps persist in longitudinal monitoring, protocol-driven care, and real-world outcome tracking (Ngo et al., 2022; Mahmoud et al., 2022). These limitations are particularly relevant in cannabinoid therapeutics, where treatment response is highly individualized and influenced by formulation, dosing, and patient-specific physiology.

In parallel, evidence aggregation platforms such as CannaKeys demonstrate that cannabinoids are being investigated across a broad range of clinical conditions, including attention-related disorders, metabolic syndrome, and obesity, with varying levels of evidence depending on study design and cannabinoid composition (CannaKeys, n.d.). While this expanding body of research supports potential therapeutic applications, it also highlights variability in outcomes and the challenges of translating heterogeneous evidence into consistent clinical practice.

Real-world evidence (RWE) has emerged as an important tool for evaluating interventions in complex and evolving therapeutic areas where randomized controlled trials remain limited (Sherman et al., 2016). Within cannabinoid therapeutics, RWE offers insight into patient-reported outcomes, functional changes, and patterns of use under conditions that more closely reflect routine practice.

This white paper examines real-world cannabinoid use in an unstructured setting, where participants received no clinical guidance or nurse support and outcomes were assessed using validated questionnaires and self-reported measures. Within this context, the paper evaluates functional outcomes associated with a multi-cannabinoid formulation and uses these observations to propose a structured, telehealth-based cannabinoid care model designed to improve consistency, safety, and clinical effectiveness.

The Endocannabinoid System and Functional Regulation

The endocannabinoid system (ECS) is a widely distributed neuromodulatory network involved in the regulation of multiple physiological processes, including mood, cognition, energy balance, immune function, and sleep. Its role in maintaining homeostasis has made it central to understanding complex, overlapping symptom patterns such as fatigue, impaired concentration, and mood disturbance, which are frequently reported by individuals seeking cannabinoid-based therapies (Lu & Mackie, 2016).

The ECS consists of cannabinoid receptors, endogenous ligands, and the enzymes responsible for their synthesis and degradation. CB1 receptors are expressed predominantly in the central nervous system, where they influence neurotransmitter release and synaptic plasticity. Through these mechanisms, CB1 activity affects dopaminergic, glutamatergic, and GABAergic signaling pathways relevant to attention, motivation, and executive functioning. CB2 receptors are more commonly associated with peripheral tissues and immune cells, where they contribute to inflammatory regulation and immune signaling.

Disruptions in endocannabinoid signaling have been associated with a range of neuropsychiatric and metabolic conditions. These disruptions often do not present as isolated symptoms, but rather as clusters that include low energy, reduced cognitive clarity, increased stress reactivity, and difficulty sustaining attention. These domains align with the functional outcomes assessed in the present observational analysis, including fatigue, productivity, focus, and overall well-being.

Phytocannabinoids interact with the ECS through a combination of direct receptor activity and indirect modulation of endogenous signaling pathways. Cannabidiol (CBD), for example, has relatively low affinity for CB1 and CB2 receptors but influences the ECS through inhibition of endocannabinoid degradation and interaction with non-cannabinoid systems, including serotonergic pathways. These mechanisms are consistent with reported effects on anxiety, stress perception, and emotional regulation (Blessing et al., 2015).

Cannabigerol (CBG) exhibits a distinct pharmacological profile. Preclinical research suggests interaction with cannabinoid receptors as well as α2-adrenergic and serotonergic targets, indicating a potential role in modulating arousal and cognitive processing (Cascio et al., 2010). These mechanisms provide a plausible biological basis for reported changes in focus and task engagement.

Tetrahydrocannabivarin (THCV) demonstrates dose-dependent activity at CB1 receptors, functioning as an antagonist at lower doses and a partial agonist at higher doses. This dual activity has been associated with effects on appetite regulation, glycemic control, and energy metabolism, suggesting potential relevance for individuals experiencing fatigue or metabolic dysregulation (Englund et al., 2016).

Importantly, cannabinoid effects are not uniform. Outcomes vary based on formulation, dose, route of administration, and individual patient characteristics. Aggregated evidence from CannaKeys highlights variability across conditions and cannabinoid profiles, reinforcing the need for approaches that extend beyond single-compound use toward more targeted, formulation-specific strategies (CannaKeys, n.d.-a; CannaKeys, n.d.-b; CannaKeys, n.d.-c; CannaKeys, n.d.-d).

The Advanced Focus formulation evaluated in this analysis, combining CBD, THCV, and CBG in a 6:1:1 ratio, reflects an approach designed to engage multiple physiological pathways simultaneously. Rather than targeting a single receptor or symptom, this combination may influence interconnected domains such as energy regulation, cognitive performance, and emotional stability. In the observational context of this study, participants reported changes across these domains, as assessed through validated instruments and self-reported measures.

These findings are best understood within the context of both ECS complexity and the variability of real-world cannabinoid use. In the absence of structured clinical guidance, patient response may remain inconsistent, reflecting differences in dosing, adherence, and individual physiology. Within this context, the use of patient-reported outcomes provides a practical method for capturing changes over time, particularly in telehealth settings where continuous physiological monitoring may not be feasible.

Taken together, the ECS provides a biologically plausible framework for interpreting the functional changes observed in this analysis. At the same time, the variability inherent in cannabinoid response underscores the need for structured, longitudinal care models capable of aligning pharmacological mechanisms with consistent clinical application.

Cannabinoids and Target Conditions: Evidence Synthesis

Research on cannabinoid therapeutics has expanded across a range of clinical domains, particularly in conditions characterized by cognitive dysfunction, metabolic dysregulation, and overlapping neuropsychiatric symptoms. While the volume of literature has increased, the strength and consistency of findings vary considerably depending on study design, cannabinoid composition, dosing strategies, and patient population.

In the context of attention and cognitive function, cannabinoids have been explored for their potential effects on executive functioning, impulsivity, and attentional control. Observational data suggest that some individuals using cannabinoid-based therapies report improvements in focus and reduced reliance on conventional pharmacologic treatments; however, controlled evidence remains limited, and findings are not uniform across studies (Hergenrather et al., 2020). Systematic reviews of cannabinoid use in neurodevelopmental and neuropsychiatric conditions similarly report diversity in outcomes, with variability driven by differences in cannabinoid ratios, dosing, and duration of use (Rice et al., 2024). These inconsistencies limit generalizability and highlight the need for more targeted, formulation-specific approaches.

Metabolic health represents another area of growing interest. Preclinical and early clinical studies suggest that cannabinoids may influence pathways related to appetite regulation, insulin sensitivity, and energy balance. In particular, THCV has been associated with modulation of glycemic control and potential effects on metabolic parameters, although available evidence remains preliminary and, in some cases, inconsistent (Englund et al., 2016; Caprioglio et al., 2022). Broader analyses of cannabinoid effects on endocrine and metabolic systems further emphasize that outcomes are highly dependent on cannabinoid type, dose, and context of use, with both beneficial and adverse effects reported (Meah et al., 2022).

Emerging research on minor cannabinoids, including CBG, illustrates both the promise and the current limitations of the evidence base. Experimental and preclinical studies suggest potential roles in neuroprotection, inflammatory modulation, and cardiovascular and metabolic regulation; however, clinical data remain limited (Nachnani & Raup-Konsavage, 2021; Krzyżewska et al., 2025). As a result, much of the current understanding is derived from early-stage or small-scale studies, limiting the ability to establish definitive clinical guidance.

Across these domains, a consistent finding is the context-dependent nature of cannabinoid effects. Differences in formulation, chemotype, dosing, and individual patient characteristics contribute to variability in outcomes. Evidence aggregation platforms such as CannaKeys further reflect this diversity, demonstrating that even within a single condition, findings may differ substantially based on cannabinoid composition and study methodology (CannaKeys, n.d.-e; CannaKeys, n.d.-f; CannaKeys, n.d.-g).

From a clinical perspective, this variability presents a central challenge. Traditional models of care, which often rely on standardized dosing or single-compound approaches, may not be well suited to cannabinoid therapeutics. Increasingly, there is recognition that multi-cannabinoid formulations and individualized dosing strategies may be necessary to address complex symptom profiles that span cognitive, metabolic, and emotional domains.

Within this context, real-world evidence provides an important complement to controlled trials. Observational data capture patterns of use, patient-reported outcomes, and functional changes under conditions that more closely reflect routine practice (Sherman et al., 2016). While such data do not establish causality, they offer practical insight into how cannabinoid therapies are experienced outside of controlled environments.

The present analysis contributes to this evolving evidence base by examining a defined multi-cannabinoid formulation within an unstructured, real-world setting and evaluating outcomes using validated patient-reported measures. The observed variability in both outcomes and response patterns further supports the need for structured, protocol-driven approaches capable of translating heterogeneous evidence into more consistent clinical application.

Real-World Evidence: Observational Study Outcomes

Real-world evidence (RWE) provides insight into how interventions perform in routine settings, where patient populations are more heterogeneous and conditions of use are less controlled than in randomized trials. In emerging areas such as cannabinoid therapeutics, RWE is particularly relevant, as it captures patterns of use, patient experience, and functional outcomes that may not yet be fully represented in controlled studies (Sherman et al., 2016; Dang, 2023).

The present analysis is based on a 21-day observational study involving approximately 100 participants who used a standardized multi-cannabinoid formulation containing cannabidiol (CBD), tetrahydrocannabivarin (THCV), and cannabigerol (CBG) in a 6:1:1 ratio. The study was designed to evaluate changes in functional outcomes over time, with a focus on domains commonly reported in clinical settings, including fatigue, energy, cognitive performance, and overall well-being.

The observational dataset analyzed in this study was derived from a product-specific outcomes report developed by Bloom Hemp CBD, which utilized structured patient questionnaires to evaluate functional changes over a 21-day period (Bloom Hemp, 2024).

Participants were not managed within a structured clinical care model. No nurse coaching, individualized dosing guidance, or ongoing clinical oversight was provided. Data collection was limited to App-based validated patient-reported outcome measures and structured self-report questionnaires over the 21-day period. As such, findings reflect real-world use in an unstructured context.

Outcome assessment included the Fatigue Assessment Scale (FAS) to evaluate perceived fatigue and the World Health Organization Well-Being Index (WHO-5) to assess psychological well-being. Participants also reported on productivity, focus, energy, and perceived stress, providing a broader view of day-to-day functional change.

Across the study period, participants reported improvements in multiple domains. Reductions in fatigue were accompanied by increases in perceived energy and self-reported ability to complete daily tasks. Improvements in focus and productivity were also reported, suggesting changes in cognitive and functional capacity rather than isolated symptom relief. WHO-5 scores indicated an overall improvement in well-being, with many participants reporting reduced stress and improved mood.

Although the magnitude of change varied across individuals, the overall pattern of response was consistent. A majority of participants reported improvement in at least one functional domain, and many reported changes across multiple domains simultaneously. This pattern reflects the interconnected nature of fatigue, cognitive performance, and emotional well-being and suggests that observed effects may extend beyond single-symptom changes.

No serious adverse events were reported during the study period. Tolerability was generally favorable, although the absence of structured adverse event monitoring limits definitive conclusions regarding safety.

The interpretation of these findings should be considered within the context of the observational design. Without randomization or a control group, changes cannot be attributed solely to the intervention. Factors such as expectation effects, behavioral changes, or external influences may have contributed to reported outcomes. At the same time, observational data provide an important view of how interventions perform under real-world conditions, where such factors are inherently present (Sherman et al., 2016; Dang, 2023).

An important feature of this analysis is the use of structured patient-reported outcomes to track change over time. The integration of validated instruments such as the FAS and WHO-5 provides a more standardized approach to assessing response and improves the interpretability of real-world data. Patient-reported outcomes are increasingly recognized as central to evaluating treatment effectiveness, particularly in areas where functional status and quality of life are primary concerns (Rivera et al., 2019; Snyder et al., 2012).

Taken together, these findings suggest that use of a defined multi-cannabinoid formulation in an unstructured, real-world setting is associated with improvements in patient-reported functional outcomes. At the same time, the absence of clinical guidance highlights a key limitation: variability in dosing, adherence, and response remains unaddressed. This distinction is critical, as it underscores both the potential of cannabinoid-based interventions and the need for structured approaches to support more consistent and clinically meaningful outcomes.

Clinical Interpretation

The findings from this observational analysis should be interpreted within the context of both their clinical relevance and their methodological limitations. While the absence of a control group and the short duration of follow-up limit causal inference, the consistency of patient-reported improvements across multiple functional domains suggests a pattern that warrants consideration.

Patient-reported outcomes provide a direct measure of how individuals experience changes in symptoms and daily functioning. Instruments such as the Fatigue Assessment Scale (FAS) and the WHO-5 Well-Being Index capture dimensions of health that are not always reflected in traditional clinical metrics, including perceived energy, emotional state, and the ability to engage in routine activities. In real-world settings, these measures are particularly relevant, as they reflect changes that are meaningful to patients, even when underlying mechanisms are not fully defined (Snyder et al., 2012; Rivera et al., 2019).

Reported improvements in fatigue, energy, and productivity are best understood as indicators of functional change rather than isolated symptom relief. From a clinical perspective, these domains are interdependent. Reduced fatigue may support greater cognitive engagement, while improved focus may enhance task completion and perceived productivity. Similarly, changes in well-being scores may reflect broader shifts in stress perception, mood regulation, and overall resilience.

At the same time, the unstructured nature of cannabinoid use in this analysis is a critical factor in interpretation. Participants were not provided with individualized guidance, dosing adjustments, or ongoing clinical support. As a result, observed outcomes occurred in the context of self-directed use, where variability in dosing, adherence, and individual response remains largely unaddressed.

This distinction is important. Improvements observed under unstructured conditions suggest that cannabinoid-based interventions may have meaningful functional effects; however, the absence of clinical oversight introduces variability that limits consistency and predictability of outcomes. In practice, this variability often translates into trial-and-error use, where patients adjust formulations and dosing without a clear framework for evaluating response.

The role of structured monitoring is therefore central. The use of validated patient-reported outcomes in this analysis provides a model for how changes can be tracked over time, even in remote or telehealth settings. When such measures are applied systematically and interpreted within a clinical framework, they can support more informed decision-making, earlier identification of non-response, and more targeted adjustments in care (Howell et al., 2020; Maruszczyk et al., 2022).

From this perspective, the findings of this study reflect both the potential and the limitation of current cannabinoid use patterns. Functional improvements were observed, but without the benefit of structured clinical integration. This suggests that more consistent and clinically meaningful outcomes may be achievable through approaches that combine formulation-specific strategies with longitudinal monitoring and clinical oversight.

Taken together, these observations support the need for a shift in how cannabinoid therapeutics are delivered. Rather than relying on unstructured, self-directed use, there is a clear opportunity to develop models of care that incorporate assessment, monitoring, and iterative adjustment. Such models may help translate variability into consistency and support a more reliable standard of care within cannabinoid therapeutics.

Clinical Model for Structured Cannabinoid Care

Based on these findings, and in consideration of the variability observed in unstructured cannabinoid use, a structured care model is proposed. This model is designed to address a key limitation in current cannabinoid care delivery: the absence of consistent, longitudinal, and outcomes-driven clinical frameworks. Policymakers today are seeking these types of models to validate reimbursement for cannabinoids (CMS, 2026).

At its foundation, the model shifts focus from isolated symptoms or diagnoses to functional domains. In practice, patient presentation is organized across key areas of health, including cognitive function, emotional regulation, physical symptoms, metabolic balance, and behavioral patterns. These domains often overlap and influence one another, reflecting the interconnected nature of patient-reported concerns. Organizing care in this way allows for a more comprehensive understanding of patient needs and supports formulation and dosing strategies that align with broader patterns of dysfunction rather than isolated symptoms.

Within this framework, multi-cannabinoid formulations are selected with intention. The combination of CBD, THCV, and CBG in a 6:1:1 ratio represents one approach to addressing overlapping domains such as low energy, impaired focus, and reduced functional capacity. Rather than relying on single-compound strategies, formulation selection is aligned with the complexity of patient-reported symptom patterns.

Dosing is approached as an iterative process. Patients begin with a defined starting point, followed by gradual adjustment based on response and tolerability. Changes are guided by patient-reported outcomes, including validated measures such as the Fatigue Assessment Scale (FAS) and the WHO-5 Well-Being Index, as well as self-reported changes in energy, focus, productivity, and perceived stress. This allows dosing to be refined over time in response to observed patterns rather than fixed at initiation.

Ongoing monitoring is central to the model. Patients complete structured assessments at defined intervals, creating a longitudinal record of functional change. This approach allows for identification of trends over time rather than reliance on isolated observations. It also provides a framework for evaluating whether a given formulation or dosing strategy is producing meaningful change. The integration of patient-reported outcomes into routine care has been shown to support more responsive and individualized clinical decision-making (Porter & Gonçalves-Bradley, 2016; Howell et al., 2020).

Clinical oversight is incorporated through a tiered model of care. Routine monitoring and follow-up may be supported through standardized workflows, while escalation pathways are defined for individuals who do not respond as expected or who require more complex management. This structure supports scalability while maintaining clinical accountability and aligns with emerging telehealth care models that combine protocol-driven processes with clinician oversight (Haddad et al., 2021; Govindaraj et al., 2023).

A key component of the model is the integration of patient-reported data into clinical decision-making. Outcome measures are not collected solely for documentation but are used to guide adjustments in formulation, dosing, and follow-up frequency. Over time, this creates a feedback loop in which care is continuously refined based on individual response. Such approaches are increasingly recognized as essential for translating real-world data into actionable clinical insight (Dang, 2023).

This model does not eliminate variability in cannabinoid response. Rather, it provides a structured framework within which variability can be observed, interpreted, and managed. By combining formulation-specific strategies with longitudinal monitoring and clinical oversight, the model offers an approach to translating real-world variability into more consistent and clinically meaningful outcomes.

Telehealth Implementation and Operational Integration

The clinical model described above is intended to function within a telehealth environment, where care delivery depends on structured workflows, consistent data capture, and clearly defined roles across clinical and support teams. Effective implementation requires more than digital access; it depends on the integration of assessment tools, communication systems, and decision-making processes into a cohesive and repeatable care pathway.

Patient onboarding begins with a structured digital intake process. This includes collection of baseline demographic and clinical information, as well as initial assessment using validated patient-reported outcome measures such as the Fatigue Assessment Scale (FAS) and the WHO-5 Well-Being Index. These baseline data establish a reference point for evaluating change over time and inform initial formulation selection and dosing approach. Standardized intake processes also reduce variability in how patient information is collected and interpreted.

Following intake, patients enter a defined care pathway with scheduled follow-up intervals and ongoing symptom monitoring. Rather than relying on ad hoc communication, the model emphasizes consistent assessment at predetermined time points, such as one-week, two-week, and four-week intervals. At each interval, patients complete repeat outcome measures along with brief structured check-ins focused on tolerability, adherence, and perceived functional changes. This allows for longitudinal tracking of response and supports early identification of non-response or adverse effects.

Clinical decision-making is supported through the integration of patient-reported data into a centralized system, allowing providers to evaluate trends over time rather than isolated data points. Changes in FAS and WHO-5 scores, along with self-reported measures of focus, productivity, and stress, inform adjustments in dosing or formulation. The integration of patient-reported outcomes into routine care has been shown to improve symptom monitoring and facilitate more timely clinical intervention (Knapp et al., 2021; Govindaraj et al., 2023).

Care delivery is structured through a tiered model that supports scalability. Initial intake and routine follow-up may be conducted using standardized protocols, while escalation pathways are defined for individuals who require more complex evaluation or management. This allows for efficient use of clinical resources while maintaining appropriate oversight and aligns with established telehealth frameworks that combine protocol-driven workflows with clinician involvement (Haddad et al., 2021).

Technology infrastructure plays a central role in supporting this model. Key components include secure telehealth platforms for synchronous and asynchronous communication, digital tools for administering and tracking patient-reported outcomes, and systems for documenting clinical decisions and treatment adjustments. Where available, integration with electronic health records further supports continuity of care and accessibility of patient data. The absence of such integration has been identified as a barrier in telehealth implementation and may limit the clinical utility of collected information (Talal et al., 2020).

Patient engagement remains a critical factor in successful implementation. Sustained participation in remote monitoring depends on ease of use, clarity of expectations, and perceived relevance of the data being collected. Short, structured assessments and clear communication regarding how responses inform care decisions can improve adherence and data quality. User-centered design principles have been shown to support higher levels of engagement and more reliable outcome tracking in telehealth settings (Duran et al., 2023).

From an operational perspective, this model supports both standardization and flexibility. Defined workflows, consistent assessment intervals, and protocol-based decision-making reduce variability across providers and patient populations, while individualized dosing and iterative adjustment preserve patient-specific care. This balance between structure and adaptability is central to effective telehealth-based delivery of cannabinoid therapeutics.

In summary, implementation of a structured cannabinoid care model within a telehealth environment requires alignment of clinical protocols, patient-reported outcome tracking, and digital infrastructure. When these elements are integrated, telehealth platforms can extend beyond access alone and support continuous, data-informed care. This approach addresses limitations of current models and provides a pathway toward more consistent and scalable delivery of cannabinoid-based interventions.

Strengths and Limitations

This analysis has several strengths that support its relevance to clinical practice. First, it is based on real-world data collected in a setting that reflects routine use rather than controlled experimental conditions. This allows for observation of patient response in a naturalistic context, capturing variability that is often excluded from randomized trials. In areas such as cannabinoid therapeutics, where clinical heterogeneity is high and standardized protocols are still evolving, such data provide meaningful insight into patterns of use and functional outcomes (Sherman et al., 2016).

A second strength is the use of validated patient-reported outcome measures, including the Fatigue Assessment Scale (FAS) and the WHO-5 Well-Being Index. These instruments support structured assessment of domains directly relevant to patients, including energy, mood, and daily functioning. Repeated measurement over time allows for evaluation of trends rather than reliance on single observations, improving interpretability.

The use of a defined multi-cannabinoid formulation within a consistent observational framework also supports clarity of interpretation. While variability in individual response remains, a standardized formulation reduces some of the confounding commonly seen in cannabinoid research, where differences in product composition and dosing complicate analysis.

Several limitations should also be considered. The observational design precludes causal inference, as there is no control group or randomization. Reported improvements may be influenced by expectation effects, behavioral changes, or external factors unrelated to the intervention. These considerations are inherent to real-world evidence studies and must be taken into account when interpreting findings (Sherman et al., 2016; Dang, 2023).

The absence of clinical oversight, including nurse coaching or structured care, represents both a limitation and a defining feature of the study. While it allows for observation of real-world use, it also introduces variability in dosing, adherence, and response that cannot be controlled within the current design.

The duration of follow-up was relatively short, limiting assessment of long-term outcomes, sustainability of response, and potential delayed adverse effects. In addition, reliance on self-reported data introduces the possibility of reporting bias and variability in interpretation of assessment measures.

Finally, while the use of a standardized formulation supports consistency, it may limit generalizability to other cannabinoid products, delivery methods, or patient populations.

Taken together, these strengths and limitations reflect the position of this analysis within an evolving evidence landscape. The findings provide directional insight into real-world cannabinoid use while highlighting the need for more structured and controlled approaches to care.

Future Directions

The findings of this analysis highlight several areas for further development. Controlled clinical studies are needed to evaluate multi-cannabinoid formulations within structured care models, allowing for more definitive assessment of efficacy, dose-response relationships, and comparative effectiveness.

Longer-term studies are also warranted to assess the durability of observed functional improvements and to better characterize safety over extended periods of use. Given the chronic nature of many of the symptom domains examined, including fatigue and cognitive dysfunction, understanding sustained response is essential for clinical application.

Further work is needed to refine patient stratification approaches. Variability in cannabinoid response suggests that improved alignment between patient characteristics and formulation strategies may enhance outcomes. Incorporating factors such as baseline symptom patterns and functional domains into future study designs may support more individualized approaches to care.

Advances in digital health technology present additional opportunities. Enhanced platforms for tracking patient-reported outcomes, along with integration of additional data streams such as wearables, may support more responsive and adaptive care models within telehealth environments.

From a systems perspective, evaluation of how structured cannabinoid care models can be implemented across diverse clinical settings remains an important area of focus. This includes consideration of workflow integration, provider training, and regulatory factors that may influence adoption.

Conclusion

This white paper has examined real-world cannabinoid use in an unstructured setting, with a focus on functional outcomes including fatigue, energy, cognitive performance, and well-being. Findings suggest that meaningful improvements in patient-reported outcomes may occur even in the absence of clinical guidance or structured care.

At the same time, the variability observed in this context highlights a central limitation in current cannabinoid use. While access to cannabinoid products has expanded, clinical models have not consistently evolved to support structured, longitudinal, and outcomes-driven care.

The model proposed in this paper represents an approach to addressing this gap. By integrating formulation-specific strategies, patient-reported outcome tracking, and protocol-driven care within a telehealth framework, it offers a pathway toward more consistent, scalable, and clinically accountable delivery of cannabinoid therapeutics.

These findings should be interpreted within the limitations of the observational design. However, they also point to a clear opportunity: to move beyond product access alone and toward structured systems of care that support measurable, reproducible clinical outcomes that advance the field of cannabinoid therapeutics.

 

References

Arkell, T. R., Lintzeris, N., Kevin, R. C., Ramaekers, J. G., Vandrey, R., Irwin, C., Haber, P. S., & McGregor, I. S. (2019). Cannabidiol (CBD) content in vaporized cannabis does not prevent tetrahydrocannabinol (THC)-induced impairment of driving and cognition. Psychopharmacology, 237(9), 2713–2724. https://doi.org/10.1007/s00213-019-05246-8

Blessing, E. M., Steenkamp, M. M., Manzanares, J., & Marmar, C. R. (2015). Cannabidiol as a potential treatment for anxiety disorders. Neurotherapeutics, 12(4), 825–836. https://doi.org/10.1007/s13311-015-0387-1

Bloom Hemp. (2024). Advanced Focus Data Report. https://bloomhemp.com/focus-study-bloom/

CannaKeys. (n.d.-a). Cannabis research by medical condition. https://cannakeys.com/cannabis-research-medical-condition/

CannaKeys. (n.d.-b). Cannabidiol (CBD) cannabinoid research. https://cannakeys.com/cannabidiol-cbd-cannabinoid-research/

CannaKeys. (n.d.-c). Cannabigerol (CBG) cannabinoid research. https://cannakeys.com/cannabigerol-cbg-cannabinoid-research/

CannaKeys. (n.d.-d). Tetrahydrocannabivarin (THCV) cannabinoid research. https://cannakeys.com/tetrahydrocannabivarin-thcv-cannabinoid-research/

CannaKeys. (n.d.-e). THC/CBD for attention deficit and hyperkinetic disorders. https://cannakeys.com/thc-cbd-for-attention-deficit-and-hyperkinetic-disorders/

CannaKeys. (n.d.-f). THC/CBD for obesity. https://cannakeys.com/thc-cbd-for-obesity/

CannaKeys. (n.d.-g). THC/CBD for metabolic syndrome. https://cannakeys.com/thc-cbd-for-metabolic-syndrome/

Caprioglio, D., Amin, H. I. M., & Taglialatela-Scafati, O. (2022). Minor phytocannabinoids: A misleading name but a promising opportunity for biomedical research. Biomolecules, 12(8), 1084. https://doi.org/10.3390/biom12081084

Cascio, M. G., Gauson, L. A., Stevenson, L. A., Ross, R. A., & Pertwee, R. G. (2010). Evidence that the plant cannabinoid cannabigerol is a highly potent α2-adrenoceptor agonist. British Journal of Pharmacology, 159(1), 129–141. https://doi.org/10.1111/j.1476-5381.2009.00515.x

CMS. Substance Access Beneficiary Engagement Incentive | CMS. (2026). https://www.cms.gov/priorities/innovation/substance-access-beneficiary-engagement-incentive

Dang, A. (2023). Real-world evidence: A primer. Pharmaceutical Medicine, 37, 25–33. https://pmc.ncbi.nlm.nih.gov/articles/PMC9815890/

Duran, A. T., Keener-DeNoia, A., & Stavrolakes, K. (2023). Applying user-centered design and implementation science to the early-stage development of a telehealth-enhanced hybrid cardiac rehabilitation program. JMIR Formative Research, 7, e47264. https://doi.org/10.2196/47264

Englund, A., Atakan, Z., Kralj, A., et al. (2016). The effect of tetrahydrocannabivarin (THCV) on THC-induced effects. Journal of Psychopharmacology, 30(2), 140–151. https://doi.org/10.1177/0269881115615104

Govindaraj, R., Agar, M., Currow, D., & Luckett, T. (2023). Assessing patient-reported outcomes in routine care using telehealth technologies. Journal of Medical Internet Research, 25, e48483. https://www.jmir.org/2023/1/e48483/

Haddad, T. C., Blegen, R. N., Prigge, J. E., Cox, D. L., Anthony, G. S., Leak, M. A., & Channer, D. D. (2021). A scalable framework for telehealth: The Mayo Clinic Center for Connected Care response to the COVID-19 pandemic. Telemedicine Reports, 2(1), 47–54. https://doi.org/10.1089/tmr.2020.0032

Hergenrather, J. Y., Aviram, J., Vysotski, Y., Campisi-Pinto, S., Lewitus, G. M., & Meiri, D. (2020). Cannabinoid and terpenoid doses are associated with adult ADHD status of medical cannabis patients. Rambam Maimonides Medical Journal, 11(1), e0001. https://doi.org/10.5041/RMMJ.10384

Howell, D., Li, M., Sutradhar, R., Gu, S., Iqbal, J., et al. (2020). Integration of patient-reported outcomes for personalized symptom management in real-world clinical practice. Supportive Care in Cancer, 28, 593–603. https://doi.org/10.1007/s00520-020-05313-3

Knapp, A., Harst, L., Hager, S., & Schmitt, J. (2021). Patient-reported outcome measures in telemedicine evaluation studies: A systematic review. Journal of Medical Internet Research. https://www.jmir.org/2021/11/e30042/

Krzyżewska, A., Kloza, M., & Kozłowska, H. (2025). Comprehensive mini-review: Therapeutic potential of cannabigerol. Frontiers in Pharmacology. https://doi.org/10.3389/fphar.2025.1561385

Lu, H. C., & Mackie, K. (2016). An introduction to the endogenous cannabinoid system. Biological Psychiatry, 79(7), 516–525. https://doi.org/10.1016/j.biopsych.2015.07.028

Mahmoud, H., Naal, H., Whaibeh, E., & Smith, A. (2022). Telehealth-based delivery of medication-assisted treatment for opioid use disorder: A critical review of recent developments. Current Psychiatry Reports, 24(9), 493–502. https://doi.org/10.1007/s11920-022-01346-z

Maruszczyk, K., Aiyegbusi, O. L., Cardoso, V. R., Gkoutos, G. V., Slater, K., Collis, P., & Keeley, T. (2022). Implementation of patient-reported outcome measures in real-world evidence studies: Analysis of ClinicalTrials.gov records (1999–2021). Contemporary Clinical Trials, 121, 106882. https://doi.org/10.1016/j.cct.2022.106882

Meah, F., Lundholm, M., Emanuele, N., & Amjed, H. (2022). The effects of cannabis and cannabinoids on the endocrine system. Reviews in Endocrine and Metabolic Disorders. https://doi.org/10.1007/s11154-021-09682-w

Nachnani, R., & Raup-Konsavage, W. M. (2021). The pharmacological case for cannabigerol. Journal of Pharmacology and Experimental Therapeutics. https://www.sciencedirect.com/science/article/pii/S0022356524257438

Ngo, Q. M., Braughton, J. E., Gliske, K., & Waller, L. A. (2022). In-person versus telehealth setting for the delivery of substance use disorder treatment: Ecologically valid comparison study. JMIR Formative Research, 6(4), e34408. https://formative.jmir.org/2022/4/e34408

Porter, I., Gonçalves-Bradley, D., Ricci-Cabello, I., Gibbons, C., & Gangannagaripalli, J. (2016). Framework and guidance for implementing patient-reported outcomes in clinical practice: Evidence, challenges and opportunities. Journal of Comparative Effectiveness Research, 5(5), 507–519. https://doi.org/10.2217/cer-2015-0014

Rice, L. J., Cannon, L., Dadlani, N., & Cheung, M. M. Y. (2024). Efficacy of cannabinoids in neurodevelopmental and neuropsychiatric disorders among children and adolescents: A systematic review. European Child & Adolescent Psychiatry. https://doi.org/10.1007/s00787-023-02169-w

Rivera, S. C., Kyte, D. G., Aiyegbusi, O. L., & Slade, A. L. (2019). The impact of patient-reported outcome data from clinical trials: A systematic review. Health and Quality of Life Outcomes, 17, 156. https://doi.org/10.1186/s12955-019-1220-z

Sherman, R. E., Anderson, S. A., Dal Pan, G. J., et al. (2016). Real-world evidence—What is it and what can it tell us? New England Journal of Medicine, 375(23), 2293–2297. https://doi.org/10.1056/NEJMsb1609216

Snyder, C. F., Aaronson, N. K., Choucair, A. K., Elliott, T. E., Greenhalgh, J., Halyard, M. Y., et al. (2012). Implementing patient-reported outcomes assessment in clinical practice: A review of the options and considerations. Quality of Life Research, 21(8), 1305–1314. https://doi.org/10.1007/s11136-011-0054-x

Talal, A. H., Sofikitou, E. M., Jaanimägi, U., Zeremski, M., Tobin, J. N., & Markatou, M. (2020). A framework for patient-centered telemedicine: Application and lessons learned from vulnerable populations. Journal of Biomedical Informatics, 112, 103622. https://doi.org/10.1016/j.jbi.2020.103622