Abstract
Introduction
In recent decades, HIV has become a chronic disease with which the HIV specialist pharmacist plays a fundamental role. The traditional pharmaceutical care model followed to date relied excessively on the medication, obviating the uniqueness of each patient. The purpose of this study was to determine the influence and acceptance of a Capacity-Motivation-Opportunity (CMO)-based structured pharmaceutical care (PC) intervention in a multidisciplinary team for improving healthcare results.
Methods
Prospective single-centre study of a structured health intervention with patients living with HIV who attended hospital between January 2017 and June 2018 for any cause. Pharmacotherapeutic follow-up was applied according to the CMO PC model based on three key elements, namely stratification, motivational interview and new technologies. To assess differences in the variables collected before and after the intervention, Student's t-test or Wilcoxon test, and McNemar’s test were used for quantitative and dichotomous variables, respectively.
Results
A total of 349 patients were included, 76.1% of which were men. The acceptance of pharmacist intervention by both doctors and patients was high [336 (97.7%) and 321 (93.3%)] and the adherence rate to antiretroviral therapy before intervention was lower than that observed afterwards (85.6%±33.7% vs 96.4%±17.7%; p<0.001). No differences were found between median viral load pre- versus post-intervention [1175 (62.75–26 050) copies/mL vs 274 (76.75–5542) copies/mL], although the undetectability rate was recorded as higher after intervention compared with the previous period [294 (85.5%) vs 274 (79.7%); p<0.001].
Conclusions
Our results could help HIV pharmacy clinic specialists to recognise high-risk patients and to develop personalised follow-up care, thereby ensuring good adherence and response to treatments.
Keywords: clinical pharmacy, health services administration & management, HIV & AIDS, medical education & training, pharmacy management (organisation, financial), HIV
Introduction
In recent decades, the life expectancy of people living with HIV (PLWHIV) has increased considerably, and now the disease can be considered as a chronic one.1 This is a victory on a worldwide scale but is also a challenge, since it is necessary to maintain patients’ autonomy and independence as they grow older.
In this context of progressive ageing, HIV-infected people have a greater prevalence of comorbidities that appear earlier than in the general population.2–4 Consequently, they usually take more non-antiretroviral drugs, and their drug therapy is more complex. In addition, clinical management is complicated by the greater risk of drug interactions and adverse events, adherence problems, falls, and also a greater risk of hospitalisation.5 6
The multidisciplinary approach to these patients is ideal in such cases and the HIV specialist pharmacist plays a fundamental role in the care of people living with HIV.7 The traditional pharmaceutical care (PC) model relied excessively on the medication, obviating the uniqueness of each patient.8 For that, this concept focused implicitly on the pharmaceutical activity in the search for individual and transversal intervention.9 A redefined model of care must be constructed in which the orientation to the individual and population needs, efficiency, technical quality, involvement and co-responsibility, accessibility, and professional integration are the key elements.10 In fact, factors such as educational, cognitive-functional, demographic, or use of health resources, among others, should be taken into account when focusing on providing increased value to those patients in greater need. Therefore, there is a need to stratify our population in order to be able to organise and prioritise resources. Additionally, a pharmacotherapy-based relationship must be established with patients, in which the motivational interview should be used as a key work tool.
Lastly, we should move away from the idea of PC being carried out in the presence of the patient, as we can then carry out our activities not only in the hospital scenario, and not in an episodic way but continuously and in accordance with patients' needs. In addition, real-time decision-making with the support of technology will allow us to be much more efficient than previously.
Based on this, Morillo-Verdugo et al 11 have defined a new PC model denoted by the acronym “CMO” (for Capacity-Motivation-Opportunity), according to three key elements (namely stratification, motivational interview and new technologies) that has already been applied successfully in ambulatory HIV patients.12 13 The CMO model has been used to date to improve health outcomes in the cardiovascular field.
This study contributes a more in-depth description of the different interventions carried out in the HIV field and other concomitant pathologies, and also demonstrates the acceptance of these interventions by both the patients and the rest of the multidisciplinary team, in a routine clinical practice setting.
The purpose of this study was to determine the influence and acceptance of a CMO-based structured pharmaceutical care intervention within a multidisciplinary team for improving healthcare outcomes in PLWHIV patients.
Methods
A prospective single-centre study of a structured pharmacist-led health intervention among PLWHIV patients who attended hospital between January 2017 and June 2018 for any cause was conducted.
Patients received the pharmacotherapeutic follow-up routinely applied to ambulatory care patients according to the CMO PC model. Patients were excluded if they were participating in a clinical trial or did not give their written informed consent.
A flowchart illustrating the PC intervention is shown in figure 1 and the schedule of visits and procedures is outlined in figure 2.
Figure 1.
Flowchart illustrating the pharmaceutical care intervention. CRVF, cardiovascular risk factors.
Figure 2.
Chronology of study follow-up. PO, pharmacotherapeutical objectives.
Definitions
CMO PC model
This was a pharmacotherapeutic follow-up of all medication taken by the patient in order to detect and work towards the achievement of pharmacotherapeutic objectives related to their prescribed drugs as well as making recommendations, for example, for improving diet, exercise and smoking cessation. Patients were given information leaflets on non-adherence and healthy habits (including information regarding smoking cessation) and an individual motivational interview to enhance this particular aspect. Finally, patients were periodically contacted by sending text messages with content related to healthy living habits and health promotion. Patients who failed to attend two prearranged pharmacotherapeutic follow-up visits were withdrawn from the study and considered as dropouts and were not replaced by new participants.
Adherence
Adherence to antiretroviral therapy (ART) and concomitant medication were measured with the Simplified Medication Adherence Questionnaire (SMAQ) and the Morisky–Green questionnaire, respectively. In addition, pharmacy dispensing records were also consulted. In both cases, patients were considered adherent if they obtained a positive score using the appropriate measurement instrument.
The SMAQ is a questionnaire based on the Morisky–Green–Levine questionnaire and developed in our setting that consists of six items that evaluate forgetfulness, routine, adverse events and missing doses.14 The Morisky–Green–Levine questionnaire consists of four items that evaluate forgetfulness, routine, adverse events and, in contrast to the SMAQ, evaluates the impact of feeling better and does not evaluate missing doses; we used the Spanish validated version.15
Adherence rate was quantified as the proportion of days covered (PDC) during the 6 months prior to the study according to filled e-prescriptions. We estimated the total days of supplies from the first to the last refill during the 6-month observation period divided by the total days of the treatment interval, defined as the time elapsed from the date of the first refilled prescription until the end of the observation period. A PLWHIV was considered as adherent if the PDC was >95% and not positive on the SMAQ (where positive means that there was a positive response to any of the qualitative questions), no more than two doses were missed over the past week, or they had fewer than 2 days of total non-medication during the past 3 months.
To evaluate adherence to concomitant medication, we only considered disease-modifying medications (eg, treatment for diabetes, cardiovascular disease, etc.) but not symptomatic treatments (eg, analgesics, medications for gastro-oesophageal reflux, etc.). Adherence to concomitant medication was defined as a PDC >90% and also a Morisky–Green–Levine questionnaire score ≥4.
Patient’s stratification
PC variables like stratification of patients was performed according to the risk-stratified model for PC in HIV patients of the Spanish Society of Hospital Pharmacy.16 Interventions were classified according to the taxonomy for pharmaceutical interventions in HIV+ patients based on the CMO model.17
Polypharmacy
Polypharmacy was defined as the use of six or more different drugs, including antiretroviral medication; major polypharmacy was restricted to the use of ≥11 different drugs. To describe the patterns of polypharmacy we used the categorisation proposed by Calderón-Larrañaga et al 18 who classified those patterns according to the type of disease they were intended to treat (cardiovascular, depression-anxiety, acute respiratory infection, chronic pulmonary disease, rhinitis-asthma, pain and menopause). After categorising a drug according to the anatomical therapeutic chemical classification system (ATC) up to the first three levels, a patient was categorised to a specific pattern when he/she was dispensed at least three drugs included in the pattern.
Health outcomes
The consequences of pharmaceutical intervention on health outcomes were established with the measure of compliance with certain objectives, such as those related to dyslipidaemia, hypertension, diabetes and hepatitis C treatment, and defined according to the corresponding scientific societies' criteria.19
All information was recorded from the patient’s clinical records except for the evaluation of therapy compliance that was determined by patient interview.
We recorded demographic data (age, sex), HIV infection control variables as viral load (copies/mL) and CD4 count at the time of inclusion (cells/μL), as well as comorbidities and pharmacological therapy.
In addition, the latter included ART and other medication for complications and/or comorbidities. Clinical variables and pharmacotherapeutics, such as type of ART therapy, concomitant medications prescribed and adherence (pre- and post-intervention), switching treatment and polypharmacy, were also recorded.
The rest of the required information was obtained during the interview, held at the pharmacy unit during the periodic ART medication dispensing visit in accordance with the methodology stipulated in the study protocol.
The main objective of the study was to assess the percentage of intervention acceptance both by the patient and by the rest of the multidisciplinary team, after 3 months of follow-up after study inclusion. As secondary objectives we considered the following: the percentage of patients who increased adherence to HIV and non-HIV treatments and who achieved optimal virological control. Additionally, we evaluated the influence on health outcomes such as dyslipidaemia, hypertension, diabetes and hepatitis C coinfection, will be determined for each patient. The study was approved by the ethics committee “Comité Ético de Investigación del Sur de Sevilla” (Sevilla, Spain) (Code FAR-VIH-2017–01).
Statistical analysis
The quantitative variables were expressed as means±standard deviations (SDs), or medians and interquartile ranges (IQRs) when appropriate, and qualitative variables as counts (percentages).
To assess the differences in the variables collected before and after the intervention, a Student's t-test or Wilcoxon test for related groups was carried out to compare quantitative variables. McNemar’s test was applied to analyse changes in the dichotomous variables.
For analysis purposes, the CD4 level was dichotomised with a cut-off point set at 350 cells/mL or more, the undetectability of the viral load as a serum level of fewer than 50 copies/mL and adherence was considered as achieved when it was at least 95%. Significant differences were quantified with 95% confidence intervals. The threshold for statistical significance was defined as p<0.05. Data analysis was performed with IBM SPSS 25.0 statistical software (IBM Corp., Armonk, NY, USA).
Results
A total of 349 patients were included in the study, of which 76.1% were men with a mean age of 48.3±10.7 years. Of all the patients included in the study, 69.3% had at least one comorbidity, the cardiometabolic type being the most frequently observed with a prevalance of 35.2%. At baseline, the percentage of patients with concomitant medication was 76.8% with an average of 2.8±2.9 drugs per patient. The most prescribed pharmacotherapeutic groups were hypoglycaemic agents (18, 5.2%), antihypertensives (82, 23.8%) and lipid-lowering drugs (85, 24.6%).
Regarding the primary endpoint, the level of acceptance of pharmacist intervention by the medical professionals and the patient was high [336 (97.7%) and 321 (93.3%), respectively]. The remaining demographic and clinical characteristics of the patients are shown in table 1.
Table 1.
Baseline features of patients included in the study
| Baseline feature | Total cohort (n=349) |
CI (95% CI) |
| Demographics | ||
| Age (years) (mean±SD) | 48.3±10.7 | 47.4 to 49.6 |
| Gender (male) | 265 (76.1) | |
| CDC classification (AIDS)* | 84 (24.3) | 19.8 to 28.8 |
| Acquisition risk factor* | ||
| Sexual habits | 202 (58.4) | 52.6 to 62.9 |
| ADVP‡ | 140 (40.5) | 35.1 to 45.3 |
| Vertical transmission | 4 (1.2) | 0.45 to 2.91 |
| Comorbidities | ||
| Comorbidities (mean±SD)† | 1.53±1.41 | 1.38 to 1.67 |
| Cardiometabolic | 121 (35.2) | 29.8 to 39.8 |
| Geriatric depressive | 41 (11.9) | 8.8 to 15.5 |
| Thyroid mechanic | 7 (2) | 0.9 to 4.1 |
| Various | 73 (21.2) | 16.9 to 25.4 |
| None | 102 (29.7) | 24.7 to 34.2 |
| Multipathological | 222 (64.5) | 58.4 to 68.4 |
| Medical acceptance | 336 (97.7) | 93.7 to 97.8 |
| Patient acceptance | 321 (93.3) | 88.6 to 94.3 |
| Category | ||
| Capability | 145 (41.5) | 36.5 to 46.8 |
| Opportunity | 107 (30.7) | 26.1 to 35.7 |
| Motivation | 97 (27.8) | 23.3 to 32.7 |
Unless otherwise stated, values are given as number (percentage).
*Missing values=3.
†Missing values=5.
‡ADPV denotes parenteral drug addiction.
CDC, Centers for Disease Control and Prevention.
The most common pharmacist interventions were: concomitant medication review and validation (121, 34.7%), direct communication (98, 28.1%), commitment (49, 14%) and adherence (28, 8%). The rest of the interventions are shown in figure 3.
Figure 3.
Types of pharmacist intervention. ART, antiretroviral therapy.
At the time of the intervention and afterwards there were no differences between stratification levels, which were respectively: N3 [274 (78.5%) vs 272 (77.9%)], N2 [38 (10.9%) vs 35 (10.0%)] and N1 [34 (9.7%) vs 37 (10.6%)]. No differences were found between median viral load pre- versus post-intervention [1175 (62.75–26 050) copies/mL vs 274 (76.75–5542) copies/mL] although the undetectability rate was recorded as higher after the intervention compared with the previous period [294 (85.5%) vs 274 (79.7%); p<0.001]. The measurement of CD4 levels showed a lower level before pharmacist intervention (271, 78.8% vs 294, 85.5%; p<0.001).
In relation to ART (table 2), the initial status distribution was observed as follows: naive (40, 11.6%), rescue (119, 34.5%) and multi-failure (186, 53.9%).
Table 2.
Pharmacotherapy pre- and post-intervention
| Variable | Total (n=349) | P value | |
| Pre-intervention | Post-intervention | ||
| ART status* | |||
| Naive | 40 (11.6) | – | – |
| Rescue (≤2 ART agents) | 119 (34.5) | – | – |
| Multi-failure (>3 ART agents) | 186 (53.9) | – | – |
| ART type† | |||
| ITIAN+Inin | 95 (27.5) | 104 (30.2) | 0.078 |
| ITIAN+ITINN | 76 (22) | 73 (21.2) | 0.791 |
| ITIAN+PI | 50 (14.5) | 48 (14) | 1 |
| Others | 125 (36.1) | 119 (34.6) | 0.146 |
| Adherence to ART (%) (mean±SD) | 85.6±33.7 | 96.4±17.7 | <0.001 |
| Adherence to ART (>95%)* | 278 (80.6) | 330 (95.9)‡ | <0.001 |
| ART duration (unit) (mean±SD) | 89.5±6.8 | -- | – |
| Concomitant medication*§ | 265 (76.8) | – | – |
| Lipid-lowering drugs | 85 (24.6) | – | – |
| Hypoglycaemic agents | 18 (5.2) | – | – |
| Antihypertensives | 82 (23.8) | – | – |
| Others | 227 (65.8) | – | – |
| Polymedicated* | 111 (32.2) | ||
| Concomitant drugs (mean±SD) | 2.85±2.93 | ||
Unless otherwise stated, values are given as number (percentage).
*Missing values=4.
†Missing values=3.
‡Intragroup differences were assessed pre- versus post-intervention by McNemar test or Wilcoxon test for dichotomous and quantititative variables, respectively.
§59 patients had more than one concomitant medication.
ART, antiretroviral therapy; Inin, integrase inhibitors; ITIAN, nucleoside analogue reverse transcriptase inhibitors; ITINN, non-nucleoside reverse transcriptase inhibitors; PI, protease inhibitors.
The usage of different types of combinations were similar for both pre- and post-intervention periods, respectively: nucleoside analogue reverse transcriptase inhibitors plus integrase inhibitors (ITIAN+Inin), 95 (27.5%) versus 104 (30.2%); ITIAN plus non-nucleoside reverse transcriptase inhibitors (ITINN), 76 (22%) versus 73 (21.2%); ITIAN+protease inhibitors (PI), 50 (14.5%) versus 48 (14%); and others, 50 (14.5%) versus 48 (14%). The polymedication rate at the beginning was 32.2%.
In the whole group of patients, the adherence rate to ART before intervention was lower than that observed afterwards (85.6%±33.7% vs 96.4%±17.7%; p<0.001). In addition, the percentage of patients with an adequate adherence to ART (PDC >95%) increased significantly by 15.4% after pharmacist CMO-based intervention compared with the pre-intervention situation [330 (95.9%) vs 278 (80.6%); p<0.001].
The analysis of the relationship between patients’ objectives achievement rates prior to pharmacist intervention and afterwards is shown in table 3. The percentage of achievement of the dyslipidaemia objective was higher after pharmacist intervention than before (76.5% vs 58.8%: p<0.001) and the same results were recorded for the arterial hypertension (AHT) target achievement rate (48.7% vs 70.7%; p<0.001) and diabetes mellitus objective (50% vs 77.7%; p<0.001). No patients receiving hepatitis C virus (HCV) therapy were recorded.
Table 3.
Pre- and post-intervention bivariate analysis
| Parameter | Total cohort (n=349) | P value | |
| Pre-intervention | Post-intervention | ||
| Dyslipidaemia target achievement* | 50/85 (58.8) | 65/85 (76.5) | <0.001 |
| AHT target achievement*† | 40/82 (48.7) | 52/82 (70.7) | <0.001 |
| Diabetes mellitus target achievement‡ | 9/18 (50.0) | 14/18 (77.7) | <0.001 |
| Stratification§ | |||
| N3 | 274 (78.5) | 272 (77.9) | NS |
| N2 | 38 (10.9) | 35 (10.0) | NS |
| N1 | 34 (9.7) | 37 (10.6) | NS |
| CD4 level (≥350 cells/mL) | 271 (78.8)†† | 294 (85.5) | <0.001 |
| Viral load (copies/mL) (median, IQR)¶ | 1175 (62.75–26 050)‡‡ | 274 (76.75–5542) | 0.211 |
| Undetectable viral load (<50 copies/mL) | 274 (79.7)** | 318 (92.4) | <0.001 |
Unless otherwise stated, values are given as number (percentage).
*Pre NA 248 (71.9%), post NA 245 (71.2%).
†Pre NA 279 (80.9%), post NA 278 (80.8%).
‡Pre NA 312 (90.4%), post NA 311 (90.4%).
§Missing values=3.
¶Patients with undetectable viral load were excluded from the calculation.
**Differences were found between pre- vs post-intervention undetectability rate (McNemar test=30.81, p<0.001).
††Differences were found between pre- vs post-intervention CD4 level (McNemar test=11.25, p<0.001).
‡‡No differences were found between pre- vs post-intervention viral load (Wilcoxon test p=0.211).
AHT, arterial hypertension; ART, antiretroviral therapy; IQR, interquartile range; NA, not applicable; NS, not significant; PDA, parenteral drug addiction.
Discussion
Our study found that the CMO PC model applied to PLWHIV has a positive influence on healthcare results and a high level of acceptance in a multidisciplinary HIV care team, and particularly impacts on adherence to ART and to concomitant medications.
The percentage of patients with undetectable viral load and with seric CD4 count >350 cells/mL increased significantly after the pharmaceutical intervention. Although the viroimmunological control of the patients is key, in recent years it is working, according to the specific guidelines, in increasing the therapeutic results beyond merely control of the infection.20 This is why it is important to have a comprehensive vision of pharmacotherapy and direct all efforts towards the control of the disease and its comorbidities.
Importantly, we also determined that polypharmacy in HIV patients is significantly related and that adherence among these patients might be particularly different. Thus, PLWHIV patients were more adherent to their ART drugs but less to comedication. It is known that patients receiving several concomitant drugs tended to have less adherence to other prescribed treatments.21
The intake of numerous medications for comorbidities is a frequent event in patients receiving HIV treatment. Since discontinuation of comedication may lead to major health problems, it is essential to determine whether longitudinal PC interventions in a multidisciplinary team might influence adherence to concomitant medications. It is particularly relevant to highlight the longitudinal, non-transverse and patient-centred aspect of this work model. In this sense, the clinical pharmacist is uniquely positioned to help patients manage their medications and provide adherence, motivational and new technologies skills support.
In our study, cardiometabolic disease was the most prevalent comorbidity. Accordingly, we found that the most commonly prescribed comedications were lipid-lowering drugs (85, 24.6%) and hypoglycaemic agents (18, 5.2%). This is consistent with previous studies in which an association was found between HIV infection and the appearance of vascular, cognitive and metabolic comorbidities with similar prevalence rates.22–24 In a previous multicentric study, with a very similar preliminary methodology study, it was shown that this PC intervention might lead to improved health outcomes in HIV+ patients at greater cardiovascular risk.12
In our study, since non-adherence may be related to polypharmacy, which may negatively impact therapeutic success, it is important to closely monitor patients at high risk for poor medication adherence, and to choose appropriate interventions to improve compliance. In this sense, the pharmacy clinic staff are uniquely positioned to help patients manage their medications and provide adherence support. Another important characteristic of this model is that by stratifying the population served and having defined what type of interventions to carry out specifically for each level of complexity, this new way of attending to patients allows more time to be dedicated to those who need it and enables better planning of the care bundle.
Given the known association between polypharmacy and low adherence rates to concomitant medications, the choice of ART treatment and concomitant drugs is another variable that nowadays should be considered, especially in patients with multiple comorbidities. Physicians should prescribe the antiviral treatment that includes the least number of pills whenever possible to increase the likelihood of patient adherence to the concomitant treatment. Pharmacists should guide physicians’ efforts to optimise pharmacotherapy. Conversely, it is essential that the patient be able to deal with the complexity of the prescribed pharmacotherapy since it is related to adherence and, therefore, the achievement of pharmacotherapeutic objectives.25
Nimarko et al recently demonstrated that pharmacists can decrease the frequency of antiretroviral (ARV) errors and the need to incorporate such reviews in well-established stewardship programmes.26 It also reveals the shift in the use of PI-based regimens to Inin-based regimens. With this shift, there remains a risk of harmful ARV errors during hospitalisations, with the most common of these being drug interactions. Other authors have also gone in the same direction.27 28 Even Robustillo et al showed how hospital admission is a factor associated with the increase in pharmacotherapeutic complexity and, therefore, a higher possibility of errors.29 Our PC model includes not only work on the outpatient, but also the admitted patient and their risk of readmission, as one of the assessments to be carried out within the multidisciplinary team.30
The taxonomy of interventions published for CMO does not specifically include a line on medication errors that is already included in others such as the review and validation of both ART and concomitant medication. Additionally, the enhancement of direct communication with patients, through new technologies, included in the concept of ‘opportunity’ of the model can prevent the appearance of errors, by the pharmacist intervening with patients before the error occurs.
This study has several strengths including a large sample size and the evaluation of multiple variables related to adherence, concomitant medications and other not previously assessed factors (motivational interview, coordination, etc.). However, it also has some limitations. Adherence rates were obtained using pharmacy dispensing records and the SMAQ and Morisky score which despite being widely used in clinical practice, are known to overestimate rates. Also the study was validated at a time when most patients were taking treatments based on protease inhibitors, the use of which has now been reduced. Another limitation of the study is its single-centre nature. Once the number of hospitals working with this methodology has been expanded, it will be interesting, as a future line of research, to develop multicentric research that allows us to contrast the data obtained and, in addition, to further profile the interventions and the type of patient that will potentially be a candidate for closer monitoring by the multidisciplinary team. In addition, we recognise that the information regarding the change of concomitant medication has not been recorded. However, when dealing with patients with a high average age, the change seems unlikely given that most are prescribed for chronic diseases.
Despite these limitations, our study has successfully identified the HIV pharmacy clinic specialist interventions to be carried out more frequently and intensively in current patients, with a methodology that takes into account their needs and individual characteristics and that is not focused on the prescribed medication, stressing the importance of an effective patient care model to closely monitor high-risk patients.
As PLWHIV are becoming increasingly complex, and include both young people and older patients, it is increasingly necessary to individualise healthcare by offering a work system more oriented to patients' individual needs. We have shown that the CMO PC model is a methodology that improves adherence and the achievement of pharmacotherapeutic objectives and has high acceptability to both patients and the rest of the multidisciplinary team.11 One of the most important pending challenges for the future is to adapt this methodology to the needs of the multidimensional approach needed by PLWHIV, especially those older patients, who are ageing. For this reason, it will be necessary to incorporate new concepts and strategies of joint work to carry out interventions of the type of deprescription.31 32
In conclusion, this knowledge will help HIV pharmacy clinic specialists to recognise high-risk patients and to develop personalised follow-up care, thereby ensuring good adherence and response to treatments, thus increasing the value of the contribution of the pharmacist within the multidisciplinary teams that care for the PLWHIV population.
What this paper adds.
What is already known on this subject
The multidisciplinary approach to HIV patients is undoubtedly the best approach for improving healthcare results. The traditional pharmaceutical care model of care followed to date relied excessively on the medication, obviating the uniqueness of each patient.
What this study adds
This is the first study specifically to investigate the use of Capacity-Motivation-Opportunity (CMO) methodology to determinate its positive influence on healthcare results in HIV patients.
The results of the study have clearly illustrated that CMO methodology has a high level of acceptance in a multidisciplinary HIV care team, particularly impacting on adherence to antiretroviral therapy and to concomitant medications.
Footnotes
Twitter: @awina87
Contributors: MGC-S, MAR-C and RAM-V substantially contributed to study conception and design. MGC-S and MAR-C contributed to data acquisition. RAM-V contributed to data acquisition, analysis and interpretation. AG-P contributed to study design and analysis as well as data interpretation. All the authors critically revised the manuscript and approved the final manuscript.
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests: None declared.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Not required.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available upon reasonable request.