SciELO - Scientific Electronic Library Online

 
vol.56 número4Tratamientos que provocan un alto número de reacciones adversas a la medicación y sus síntomasEstabilidad de las sales de quitosana, obtenidas por secado de aspersión, derivadas de quitina de langosta índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • No hay articulos similaresSimilares en SciELO
  • En proceso de indezaciónSimilares en Google

Compartir


Ars Pharmaceutica (Internet)

versión On-line ISSN 2340-9894

Ars Pharm vol.56 no.4 Granada oct./dic. 2015

https://dx.doi.org/10.4321/S2340-98942015000400004 

ORIGINAL ARTICLE

 

Factors related to missing and rescheduling pharmaceutical care appointments by aged outpatients in a Brazilian public health setting

Factores relacionados con la pérdida y reprogramación de citas de atención farmacéutica por los pacientes ambulatorios mayores en un servicio brasileño de salud pública

 

 

Thiago Vinícius Nadaleto Didone1; Maristela Ferreira Catão Carvalho2; Graziela Luppi2; Daniela Oliveira de Melo3 and Eliane Ribeiro4

1. Pharmacy Department. UBS Ponte Rasa - Basic Health Unit. São Paulo, Brazil.
2. Pharmacy Department. Paulista Institute of Geriatrics and Gerontology. São Paulo, Brazil.
3. Department of Biological Sciences of the Institute of Environmental, Chemical and Pharmaceutical Sciences. Federal University of São Paulo. São Paulo, Brazil.
4. Department of Pharmacy of the Faculty of Pharmaceutical Sciences. University of São Paulo. São Paulo, Brazil.

Correspondence

 

 


ABSTRACT

Objectives: To uncover reasons why patients missed pharmaceutical care (PC) appointments, identify predictive factors to miss at least one appointment and to reschedule after a miss, and compare the rescheduling behavior of patients receiving different types of PC.
Methods: All elderly patients who had at least one scheduled appointment in the PC service of a health setting of São Paulo city, Brazil, from January to December/2011 were included. Chi-square analysis compared categorical data between groups; multivariate logistic regression models predicted attendance and rescheduling behavior.
Results: We identified 421 patients, being 221 (52.5%) non-attenders. Forgetting the appointment was the most common patient-related reason (56.3%). Illiteracy was a risk factor to be a non-attender [OR(95%CI)=2.27(1.17:4.40), p=0.015]. Patients having previous knowledge of the pharmacist presented more chance to rescheduled an appointment after the first miss compared to those who had not [OR(95%CI)=3.57(1.90:6.71), p<0.001]. Further, non-attenders who had knowledge of the pharmacist and received Medication Review with Follow-up rescheduled more than the ones receiving other types of PC (p=0.035).
Conclusion: Illiteracy predicted non-attendance in PC to aged outpatients and forgetfulness was the main reason for that. The previous acquaintance of the pharmacist and the provision of pharmaceotherapeutic follow-up explained the rescheduling behavior, which indicates the establishment of a patient-centered patient-pharmacist relationship plays a pivotal role in the continuity of the PC.

Key words: Health Services for the Aged, Appointments and Schedules, Pharmaceutical Services, Professional-Patient Relations.


RESUMEN

Objetivos: Descubrir razones por las que los pacientes perdieron la cita en atención farmacéutica (AF), identificar los factores predictivos de perder al menos una cita y reprogramar después de una ausencia, y comparar el comportamiento de reprogramación de los pacientes recibiendo diferentes tipos de AF.
Métodos: Se incluyeron todos los pacientes mayores que tenían al menos una cita programada en el servicio de AF de un establecimiento de salud de la ciudad de São Paulo, Brasil, de enero a diciembre/2011. Análisis chi-cuadrado comparó datos categóricos entre los grupos; modelos de regresión logística multivariante predijeron el comportamiento de presencia y de reprogramación.
Resultados: Se identificaron 421 pacientes, siendo 221 (52,5%) ausentes. El olvido fue el motivo relacionado con el paciente más frecuente (56,3%). El analfabetismo fue un factor de riesgo para ser un ausente [OR (IC95%)=2,27(1,17:4,40), p=0,015]. Los pacientes que tenían conocimiento previo del farmacéutico presentaron más chance de reprogramar una cita después de la primera ausencia en comparación con los que no tenía [OR (IC95%)=3,57(1,90:6,71), p<0,001]. Además, ausentes que tenían conocimiento del farmacéutico y recibieron seguimiento farmacoterapéutico reprogramaron más de los que reciben otros tipos de PC (p=0,035).
Conclusión: El analfabetismo predijo ausencia en pacientes ambulatorios mayores bajo PC y el olvido fue la principal razón para eso. El conocimiento previo del farmacéutico y la provisión del seguimiento farmacoterapéutico explicaron el comportamiento de reprogramación, lo que indica que el establecimiento de una relación con el farmacéutico centrada en el paciente juega un papel fundamental en la continuidad de la AF.

Palabras clave: Servicios de Salud para Ancianos, Citas y Horarios, Servicios Farmacéuticos, Relaciones Profesional-Paciente.


 

Introduction

Missed appointments are an important problem for administrators, staff and patients1. Non-attendance aftermath comprise misused staff time, equipment and ward capacity, and patients spending longer time in waiting rooms and waiting longer for health services. The latter may worsen clinical outcomes2, especially in patients with chronic diseases who generally need long-term ambulatory care3.

McLeod et al.. (2005) showed that non-attenders with coronary artery disease receiving care at a cardiac outpatient clinic had higher mean total cholesterol levels and fewer of them achieved target lipid profile when compared to attenders4. In addition, non-attenders seem to have lower perceptions of symptoms and controllability and, or curability of their illness5.

There is a wide range of determinants of missing appointments3,6,7, which can be separated between patient- or health system-related factors. The proper identification of these factors helps administrators to develop effective strategies to reduce non-attendance, which might have a greater impact in elderly populations. That is because older patients usually need long-term care and represent the fastest growing population. It is estimated that by 2050 there will be 400 million people aged 60 or over, 4 out of 5 of them living in low- and middle-income countries8.

Medication Review with Follow-up (MRF) is a modality of pharmaceutical care (PC) by which the pharmacist can assess patient's drug-related needs, identify, solve and prevent drug related problems, develop a rational care plan and conduct follow-up evaluations to ensure effectiveness and safety of drug therapy9,10. Since the achievement of this goal relies on patient's cooperation, the coordination of the pharmacist with other health care providers9, and regular patient evaluation through repetitive appointments, one or more patient misses may compromise successful outcomes and waste pharmacist's work and health resources. The Dáder Method is a tool to perform MRF with any patient by a documented, systematic, and continuous manner10.

Here we assessed reasons why patients missed their last appointment in a PC service, and identified predictive factors to miss at least one appointment and to reschedule after a miss. We also compared the rescheduling behavior of patients receiving different types of PC.

 

Methods

This study was carried out in the Department of Pharmaceutical Care of the Paulista Institute of Geriatrics and Gerontology (PIGG). This is a public health setting which belongs to the Brazilian Unified Health System (Sistema Único de Saúde - SUS) and is run by the Department of Health of the State of São Paulo. It is located at east zone of São Paulo city, Brazil, and offers primary and specialized multidisciplinary care for aged patients (60 years and over). Near 16,800 appointments are performed every month.

The PC service was created in 2001. It assists patients mostly presenting hypertension, dyslipidemias, and/or type 2 diabetes mellitus who spontaneously seek care or are referred by PIGG physicians. Three clinical pharmacists are in charge of PC service, which has about 100 scheduled appointments every month and a waiting list of 2 weeks. The average time between appointments is about 1 month. Clinical pharmacists perform 3 types of PC: MRF by means of the Dáder Method, recipe loss follow-up, and health education follow-up. In the last 2 types the goal is, respectively, to refill lost prescriptions monthly until the prescribing doctor make a new one and to educate patients on medication knowledge.

This work was divided into 2 phases and was performed from January/2011 to December/2011. Subjects for study phase 1 included all elderly patients who had at least one scheduled appointment in PC service. Phase 2 included phase 1 patients who missed at least one scheduled appointment.

During phase 1, data were collected from the PIGG patient database and included sociodemographics, available health service, and attendance behaviour. The first comprised age, gender, self-reported skin color, literacy status, and family arrangement. The second represented the type of health care to which the patient had access at PIGG. The third classified patients between non-attenders (who missed at least one scheduled PC appointment in the period) and attenders (who did not miss any). Still, we assembled the number of scheduled appointments, and number and type of missed appointments. The latter included initial (first patient visit) and return type (subsequent patient visits). We calculated the non-attendance rate (NAR) by dividing the number of missed appointments by the number of scheduled appointments in the period.

In study phase 2 we collected supplementary data: number of prescribed drugs, previous knowledge of the pharmacist, rescheduling behaviour, and type of PC. The first was accounted for the last medical prescription made by all medical specialties the patient attended in the period. The second corresponded to weather the patient was already attending PC service before the first miss of the period. The third divided non-attenders into two groups according to weather they rescheduled a PC appointment after the first miss. The fourth variable split patients into two groups (receiving MRF or receiving other types of PC) and was available only for non-attenders who had previous acquaintance of the pharmacist before the first miss.

Also in phase 2, non-attenders were telephone interviewed by trained staff. Interviewers asked patients the question: For what reason did you miss the last scheduled pharmaceutical care appointment? The following patients were excluded from the study: patients whose telephone number were missing, not reached by interviewers after 5 calls in five different days and time of the day, with hearing loss, who had died, and who did not sign a Free and Informed Consent Term.

Data analyses were performed using IBM SPSS version 20 (IBM Corporation, Armonk, NY, USA). Descriptive statistics were presented as mean values and standard deviation (SD), or as absolute and relative frequencies depending on variable nature. Bivariate Pearson chi-square analysis compared categorical data between groups. Odds Ratios (OR) and 95% Confidence Intervals (CI) were calculated by logistic regression models. Univariate logistic regression analysis identified interactions between dependent and independent variables. Those with p-values<0,200 were included by backward method based on likelihood ratio statistic in a multivariate analysis. P-values<0,050 were considered statistically significant.

The study was approved by the Committee of Ethics in Human Research of the Institute of Health of the Department of Health of São Paulo State prior to its initiation.

 

Results

Phase 1 revealed 421 patients who scheduled at least one appointment in PC service, being 221 (52.5%) non-attenders and 200 (47.5%) attenders. The mean age was 73.5(SD=6.8) years (range 60-94). Regarding the available health service, 59.1% had access only to specialized care at PIGG, while the remaining had access to specialized and primary care. Prevalence of females (70.8%), patients self-identified as whites (52.7%), and literates (84.1%) were found in this sample. In regards to family arrangement, 47.2% of the patients were living with spouse (with or without other relatives and non-relatives), 37.9% without spouse and children (with at least another relative or non-relative), and 14.9% alone. Information about self-reported skin color, literacy and family arrangement was not available for, respectively, 44, 16 and 25 patients. Difference in frequencies between non-attenders and attenders was found only in literacy groups; in brief, the frequency of illiterates was higher in non-attenders compared to attenders (χ²=9.32, p=0.002).

All 421 PC patients scheduled 941 appointments of which 291 were missed. The median and mean number of overall scheduled appointments was, respectively, 1.0 and 2.2(SD=1.9) (range 1-13). NAR was 30.9%, being 17.2% accounted for initial misses and 13.7% for return misses.

Table 1 shows univariate logistic regression results for sociodemographics and available health service between non-attenders and attenders. Illiterates had increased risk to miss at least one appointment (OR=2.67 [95%CI 1.40:5.11], p=0.003) compared to literate patients.

 

 

We split non-attenders between the ones who did and did not reschedule after the first miss of the period. Difference of frequencies between categories was seen only in the variable that accounted the knowledge of the clinical pharmacist the patient had before the miss (χ²=16.30, p<0.001). The univariate logistic regression results for phase 2 patients are shown in table 2. A point of interest is that patients who had had previous acquaintance of the pharmacist presented greater risk of rescheduling than patients who had not known the pharmacist (OR=3.57 [95%CI 1.90:6.71], p<0.001).

 

 

We pushed the analysis even further and investigated the 73 patients who had previous acquaintance of the pharmacist before the first miss (figure 1); fifty-seven received MRF and 16 received other types of care. Results showed that the majority of patients on MRF rescheduled after a miss comparatively with patients receiving other types of PC (χ²=4.44, p=0.035).

 

 

Before the telephone interviews, 18 patients (8.1%) were excluded because their phone numbers were missing at PIGG database and 10 patients (4.5%) did not answer the phone after all attempts. Out of the 193 patients contacted over the phone, 2 (1.0%) had hearing loss and 9 (4.7%) had died. Of the remaining, five (2.7%) refused to participate and sign the Free and Informed Consent Term. As a result, 177 patients were interviewed to assess their reasons to miss. Seventeen of them (9.6%) did not want to uncover their motives so they said answers such as "because other things". The remaining 160 patients gave 8 different reasons in total, being forgetfulness the most frequent one (44.4%). We divided the reasons into 2 groups (figure 2): patient-related (78.8%) and health system-related (21.2%). In the first group, "forgetting the appointment" (56.3%) was the most common reason, while in the second, "not informed about the appointment date" (82.3%) was the pivotal motive.

 

 

In the multivariate analysis, gender, self-reported skin color, and literacy were included as independent variables. Due to missing data, the number of included cases was 371. As a result, literacy was the only variable that remained in the model (model χ²=6.33, p=0.012; Nagelkerke's R²=0.023), presenting a OR of 2.27 (95%CI 1.17:4.40, p=0.015). In the same fashion, we selected self-reported skin color, number of prescribed drugs, and previous knowledge of the pharmacist as independent variables for multivariate logistic regression aiming to predict whether a non-attender rescheduled after the first miss. The only variable kept in the model was the knowledge of the pharmacist (model χ²=16.43, p<0.001; Nagelkerke's R²=0.119). Results showed no change compared to the univariate model (OR=3.57 [95%CI 1.90:6.71], p<0.001).

 

Discussion

To the best of our knowledge, this work was the first to assess the rate, causes, and related factors of elderly outpatient non-attendance in a PC service. NAR in PC was 30.9% and forgetfulness was the major reason patients gave to explain why they missed the last appointment. In addition, illiteracy and previous knowledge of the pharmacist were risk factors for, respectively, missing at least one appointment and rescheduling after the first miss of the period. Still, we evidenced that more patients receiving MRF rescheduled after the first miss compared to patients receiving other types of PC.

Our NAR is higher than recent work. Non-attendance in general practice appointments in England showed rates of 6.5%11 and 7.7%12; in Malaysia, general practice NAR was 16.7%13; and Murdock et al.. (2002) found a NAR of 14.0% in a gastroenterology clinic in Ireland14. This was somehow expected since advanced age seems to predict non-attendance pattern15. Despite that, we found 30.9% a high figure that needs immediate intervention.

Brazilian work addressing the study of non-attendance is wanting and has been conducted so far mainly in surgery services. In João Pessoa city, Paraíba, Brazil, it was found NAR from 24.1% to 41.2% of scheduled general practice appointments in health settings managed by SUS16. That is another example why health administrators in Brazil should address the issue of non-attendance, especially in services managed by SUS, which has been experiencing a burden of healthcare attendance and costs.

Some reviews3,6,7 listed factors leading to missed healthcare appointments assessed by a variety of papers. Any of them found association between illiteracy and attendance behavior, likely due to the lack of illiterates attended by the settings where studies were performed. Here, illiteracy was the only significant risk factor to non-attendance. Once in Brazil illiteracy is positively correlated with age17, our results reasserted the importance of assessing non-attendance in the elderly.

In our work, patients who had acquaintance of the pharmacist prior to the miss had a greater chance to reschedule an appointment. That is likely due to the establishment of a relationship between pharmacist and patient during PC. Although there are studies that investigates the effect doctor-patient relationship has on attendance behavior, work on pharmacist-patient relationship is lacking. Patients in primary care expressed the lack of empathy and understanding from the general practitioner as an obstacle to attend appointments18. Doctors, in the same manner, felt that patients were less likely to attend if a relationship was not established yet18.

Further, the comparison we made between different types of PC indicated that patients receiving MRF were more prone to reschedule an appointment compared to other types of care. We are of the opinion this was due to the use of the Dáder Method to provide MRF because it engages pharmacist to develop attitudes to make patients feel that they are open and responsive to their healthcare needs, ultimately creating a patient-centered relationship. The Dáder Method propose setting up a plan of interventions in order to enhance or preserve patient health status; when setting up the plan, the pharmacist must create a patient-centered communication environment of intimacy, confidentiality and consent, engage the patient to actively participate in setting priorities and making decisions, embrace patient concerns and expectations, and, as soon as the decisions were made, support the patient throughout the process10.

A study conducted with older adults in the United States showed that their perceptions of the patient-centeredness of the relationship with the pharmacist were positively correlated with their perceptions of their own participative behavior and with the frequency pharmacists communicated with them19. This endorses that the pharmacist attitudes recommended by the Dáder Method effectively establishes a patient-centered patient-pharmacist relationship.

Forgetfulness was the most frequent reason patients gave to have missed an appointment, ranging from 30.0 to 49.4%13,14,20, what are in line with our results. Qualitative studies investigating health professionals and staff perceptions on causes of patient non-attendance also presented forgetfulness as the most important issue related to missed appointments1,18. Forgetting was usually perceived by doctors to be a genuine, universal and justifiable one-off mistake1 what is reasonable since patients even described reasons that let them more prone to forget appointments such as illnesses, change of job, and location on holidays14.

These findings support the use of reminders in healthcare. They not only allow patients to confirm attendance or cancel an appointment, but also are effective in shortening NAR. A recent meta-analysis about mobile phone messaging reminders concluded this intervention was more effective than no reminder, and as effective as telephone call reminders, in decreasing NAR of healthcare appointments21. Reminders are conceptually very effective in aged individuals since they have in general a decrease in memory performance, especially in regard to learning new information22. Conversely, we feel this type of reminders would not be effective in our study setting for the reason that illiterates usually have lower incomes so they are less likely to own a telephone. Previous work showed that non-attenders generally do not have a telephone23; as a result, they are more likely to fail in cancelling the appointment23 and to benefit for newer and more complex appointment system7. The strategy of overbooking appointments to allow for anticipated non-attendance is pragmatic14 and we found to be the most cost-effective short-time intervention in our scenario, despite the pressure it may represent to both patient and staff7.

Some limitations should be pointed out. In the first place, the regression model did not account for diseases related to memory loss or cognitive decline, which may play a substantial role in the attendance behavior. Secondly, we feel some patients might have omitted the real reasons to miss appointments fearing to face treatment cessation, even though they were fully enlightened this would not happen no matter their answers. Finally, despite the fact that the PIGG population is very distinctive, we believe the employment of the Dáder Method, a validated and reliable tool, enhances external validity.

Finally, we believe our findings may contribute to the improvement of PC quality in other settings and encourage further research in this topic.

 

Conclusion

Illiteracy predicted non-attendance in PC to aged outpatients and forgetfulness was the main reason they gave to justify being absent in the last appointment. Besides, whether the patient rescheduled a PC appointment after a miss was explained by the acquaintance of the clinical pharmacist prior to it. On top of that, the provision of MRF by means of the Dáder Method, more than other types of PC, was positively related to the rescheduling behavior, indicating that the establishment of a patient-centered patient-pharmacist relationship plays a pivotal role in the continuity of the care.

 

Fundings

Without funding.

 

Acknowledgments

The authors thank Adilmo Henrique do Nascimento, Celina Marques Vichinsky and Sandra Cristina Neto for helping in collecting data.

 

Competing interest

All authors certify that there was no potential conflicts of interest related to the subject of this article.

 

References

1. Husain-Gambles M, Neal RD, Dempsey O, Lawlor DA, Hodgson J. Missed appointments in primary care: questionnaire and focus group study of health professionals. Br J Gen Pract. 2004;54(499):108-113. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1314803/pdf/14965389.pdf. Accessed April 16, 2014.         [ Links ]

2. Bech M. The economics of non-attendance and the expected effect of charging a fine on non-attendees. Health Policy. 2005;74(2):181-191.         [ Links ]

3. Deyo RA, Inui TS. Dropouts and broken appointments. A literature review and agenda for future research. Med Care. 1980;18(11):1146-1157.         [ Links ]

4. McLeod AL, Brooks L, Taylor V, Wylie A, Currie PF, Dewhurst NG. Non-attendance at secondary prevention clinics: the effect on lipid management. Scott Med J. 2005;50(2):54-56.         [ Links ]

5. Whitmarsh A, Koutantji M, Sidell K. Illness perceptions, mood and coping in predicting attendance at cardiac rehabilitation. Br J Health Psychol. 2003;8(Pt 2):209-221. Available at: http://web.b.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=36a06b91-7a4f-462c-882a-6fa035961971@sessionmgr111&vid=2&hid=123. Accessed April 16, 2014.         [ Links ]

6. George A, Rubin G. Non-attendance in general practice: a systematic review and its implications for access to primary health care. Fam Pract. 2003;20(2):178-184. Available at: http://fampra.oxfordjournals.org/content/20/2/178.full.pdf+html. Accessed April 16, 2014.         [ Links ]

7. Sharp DJ, Hamilton W. Non-attendance at general practices and outpatient clinics. BMJ. 2001;323(7321):1081-1082. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1121587/pdf/1081.pdf. Accessed April 16, 2014.         [ Links ]

8. Good Health adds Life to Years: Global Brief for World Health Day 2012. Geneva, World Health Organization, 2012. Available at: http://whqlibdoc.who.int/hq/2012/WHO_DCO_WHD_2012.2_eng.pdf. Accessed April 16, 2014.         [ Links ]

9. Cipolle RJ, Strand LM, Morley PC. Pharmaceutical care practice: the clinicians guide. New York, NY: McGraw-Hill; 2004.         [ Links ]

10. Hernández DS, de Castro MS, Dáder MJF. Método Dáder: Guía de Seguimiento Farmacoterapéutico. Tercera edición, 2007. Grupo de Investigación en Atención Farmacéutica de la Universidad de Granada. Available at: http://www.ugr.es/~cts131/esp/guias/GUIA FINAL DADER.pdf. Accessed April 16, 2014. Spanish.         [ Links ]

11. Waller J, Hodgkin P. Defaulters in general practice: who are they and what can be done about them? Fam Pract. 2000;17(3):252-253. Available at: http://fampra.oxfordjournals.org/content/17/3/252.full.pdf+html. Accessed April 16, 2014.         [ Links ]

12. Neal RD, Lawlor DA, Allgar V, Colledge M, Ali S, Hassey A, Portz C, Wilson A. Missed appointments in general practice: retrospective data analysis from four practices. Br J Gen Pract. 2001;51(471):830-832. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1314130/pdf/11677708.pdf. Accessed April 16, 2014.         [ Links ]

13. Zailinawati AH, Ng CJ, Nik-Sherina H. Why do patients with chronic illnesses fail to keep their appointments? A telephone interview. Asia Pac J Public Health. 2006;18(1):10-15.         [ Links ]

14. Murdock A, Rodgers C, Lindsay H, Tham TC. Why do patients not keep their appointments? Prospective study in a gastroenterology outpatient clinic. J R Soc Med. 2002;95(6):284-286. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1279909/pdf/0950284.pdf. Accessed April 16, 2014.         [ Links ]

15. Worcester MU, Murphy BM, Mee VK, Roberts SB, Goble AJ. Cardiac rehabilitation programmes: predictors of non-attendance and drop-out. Eur J Cardiovasc Prev Rehabil. 2004;11(4):328-335.         [ Links ]

16. Cavalcanti RP, Cavalcanti JCM, Serrano RMSM, de Santana PR. Absenteísmo de consultas especializadas nos sistema de saúde público: relação entre causas e o processo de trabalho de equipes de saúde da família, João Pessoa - PB, Brasil. Rev Tempus Actas Saúde Col. 2013; 7(2):63-84. Available at: http://www.tempusactas.unb.br/index.php/tempus/article/view/1344/1146. Accessed April 16, 2014. Portuguese.         [ Links ]

17. Informações de Saúde (TABNET) (Internet). Brasília (DF): Ministério da Saúde, DATASUS - Departamento de Informática do SUS. 2008. Available at: http://www2.datasus.gov.br/DATASUS/index.php?area=0206. Accessed April 16, 2014. Portuguese.         [ Links ]

18. Martin C, Perfect T, Mantle G. Non-attendance in primary care: the views of patients and practices on its causes, impact and solutions. Fam Pract. 2005;22(6):638-643. Available at: http://fampra.oxfordjournals.org/content/22/6/638.full.pdf+html. Accessed April 16, 2014.         [ Links ]

19. Worley-Louis MM, Schommer JC, Finnegan JR. Construct identification and measure development for investigating pharmacist-patient relationships. Patient Educ Couns. 2003;51(3):229-238.         [ Links ]

20. Neal RD, Hussain-Gambles M, Allgar VL, Lawlor DA, Dempsey O. Reasons for and consequences of missed appointments in general practice in the UK: questionnaire survey and prospective review of medical records. BMC Fam Pract. 2005;6:47. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1291364/pdf/1471-2296-6-47.pdf. Accessed April 16, 2014.         [ Links ]

21. Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, Atun R, Car J. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev. 2013;5;12:CD007458.         [ Links ]

22. Craft S, Cholerton B, Reger M. Cognitive changes associated with normal and pathological aging. In: Halter JB, Ouslander, JG, Tinetti ME, Studenski S, High KP, Asthana S, eds. Hazzard's Geriatric Medicine and Gerontology. New York, NY: McGraw-Hill Professional; 2009:751-765.         [ Links ]

23. Cosgrove M. Defaulters in general practice: reasons for default and patterns of attendance. Br J Gen Pract. 1990;40(331):50-52. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1371138/?page=1. Accessed April 16, 2014.         [ Links ]

 

 

Correspondence:
Thiago Vinícius Nadaleto Didone
Laboratory of Pharmacotechnical Research
Room 2c
Faculty of Pharmaceutical Sciences
of University of São Paulo
580 Av. Prof. Lineu Prestes Ave.
Butantã, São Paulo
SP, Brazil, 05508-000
tdidone@gmail.com

Received: 18.08.2015
Accepted: 04.10.2015

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons