Shared decision-making underutilisation at an outpatient clinic during BMI monitoring in rural Uganda

doi:10.21203/rs.3.rs-3333567/v1

Download PDF

Research Article

Shared decision-making underutilisation at an outpatient clinic during BMI monitoring in rural Uganda

https://doi.org/10.21203/rs.3.rs-3333567/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction:

Recall of medical instructions is associated with improved adherence to treatment. The recall of the Body Mass Index (BMI) is important for the improvement of adherence to treatments related to obesity. The burden of obesity all over the world has increased, including in many sub-Saharan African countries.

Objective

This study measured the recall of BMI, shared decision-making (SDM), and patients’ demographics to determine if they were associated.

Methods

The study was conducted at a rural hospital outpatient clinic in South Western Uganda. Data were collected using a questionnaire and collaboRate-5 tool for measuring shared decision-making. Data were analysed using SPSS version 20. The chi-square test and Pearson coefficient test were done.

Results

Out of the 92 participants in this study, the median age was 36 years, in an age range of 18 to 87 years, the majority were male 54 (58.7%), most had a background of attending formal education 75 (81.5%), 79 (85.9%) were not able to recall their BMI; 13 (14.1%) were able to recall their BMI. Only gender among demographic factors studied was associated with recall of BMI (p = 0.027). Shared decision-making was associated with the recall of BMI (P < 0.001).

Interpretation:

The prevalence of patients who are not able to recall their BMI after the outpatient clinic visit was high. Gender and the use of SDM were associated with recall of BMI. Measured items describing the use of SDM were only reported among 21(22.8%) participants. SDM was underutilised during the study period at the outpatient clinic. There is a need to have continuous medical education on SDM.

Shared decision making

Demographic factors

Body mass index

recall

As the world population faces challenges peculiar to their locations, some challenges cut across the globe’s diversity. The global challenge of morbid obesity has had an increase in the past 3 decades (Stenholm et al., 2017). This led to the mandatory inclusion of the measurement of the body mass index (BMI) for adult patients during their hospital visits (Ministry Of Health, 2016). Patients need to know and remember information regarding their BMI for their use. The way the information is shared at hospitals may hinder the recall of instructions (McHale et al., 2016).

Shared decision-making has been defined by several bodies that deal with health and related matters. The American Medical Association (AMA) defines it as a formal process or tool that helps physicians and patients work together to choose the treatment option that best reflects both medical evidence and the individual patient’s priorities and goals for his or her care” (Godolphin, 2009).“Shared decision-making is a collaborative process through which a healthcare professional supports a person to decide on their care, now or in the future.” The healthcare provider has to work with the patient to arrive at the best options (Bae, 2017). This study used the three-talk model of shared decision-making, which describes a three-step approach to applying shared decision-making. The steps are divided into: "choice talk, option talk, and decision talk, where the clinician supports deliberation throughout the process (Mathews et al., 2016). Patients are served with various categories of information at hospitals and the hospital may give customised information or follow general guidelines. The information given will either be remembered by the patients or not (Jansen et al., 2008). The concern for the health care team is the communication to be used effectively. The effective use may also mean an effective recall that the patients base on to act (Bae, 2017). During the routine outpatient visits, the patients get their anthropometry measurements taken. The procedure is never complete without feedback from the patients because the feedback is what patients recall (Jansen et al., 2008). “Effective treatments can be rendered useless by poor patient recall of treatment instructions” (Mathews et al., 2016).

Health professionals are thus the task of providing information in a way that it can be 'encoded' and retrieved when needed (Bae, 2017). This is a challenge faced when health workers attempt to teach new information. The process of communication will thus not only be affected using the health care provider’s technique; but also by the personal factors of the patient that influence the process (Selic et al., 2011).

Patients remember as little as a fifth of the information discussed and immediately forget 40%- 80% of the content of their medical encounters.” (Dawson et al., 2014). This fact emphasizes the need to improve several approaches to how doctor-patient discussions should have been held a decade ago(Gionfriddo et al., 2013). Subsequent studies have continued to report little improvement in patient-doctor discussion techniques producing poor treatment adherence as the negative outcome (Lewis et al., 2016).

Patients’ recall of medical instructions requires further study to allow improvement on the already existing approaches to clinician-patient discussion. In this study, patient factors were studied to facilitate a patient-based approach that keeps the consumer of health services at the centre of improvements (Ong et al., 1995).

The current way of communicating has not efficiently led to teaching patients the basics of the outcomes of their visits to the hospital. Several patients may not recall what the healthcare providers found out about their health and thus cannot actively participate in the interventions, which leaves them as followers and not active participants (Skinner et al., 2012). The status thus given to patients makes them powerless or with little power to control the likely outcome of the health care intervention (Hoffmann et al., 2014).

A cross-sectional study design was employed and data was collected by a survey approach. The study was carried out during September 2019 at Comboni Hospital Kyamuhunga a rural hospital in southwestern Uganda. The hospital serves a population of mostly peasant farmers and staff of neighbouring tea estates. The hospital on average attends to about t 50 outpatient adults per day. All these patients routinely undergo anthropometry measurements at the outpatient department. Therefore, the outpatient department was used for data collection.

Sampling and Sample Size Determination

The sample was collected following a consecutive sampling technique (Geldsetzer et al., 2018). The sample size was determined using the formula below.

n = [Z²P (1-P)] / d², where

n = sample size,

Z = Z statistic for a level of confidence,

P = expected prevalence,

d = precision

“For the level of confidence of 95%, which is conventional, the Z value is 1.96” P = 0.94 Using the prevalence of 94% for immediate recall of weight (Dawson et al., 2014).

d = 0.05

Thus:

n = [1.96² x 0.94 x 0.06]/0.05², n = 86.664, n = 87

The targeted sample was 87 adults attending services at the hospital. Data Collection Procedures: The activities of the hospital staff were not interrupted nor interfered with, but an interaction was initiated at the point that the respondent was meant to leave the outpatient department. The respondent was then allowed to enrol on the study. All respondents were given the same privilege. They were enrolled by consecutive sampling until the target sample size was obtained. Before enrolment written informed consent was obtained from each participant. The questionnaire was administered to obtain demographic data, recall of BMI, and the use of shared decision-making by the collaboRate-5 tool (Forcino et al., 2018). Only participants who had completed their visit to the outpatient clinic were enrolled.

Data Management and Analysis: Data from the questionnaires were entered into Microsoft Excel and exported to SPSS version 20. Descriptive statistics were calculated and then bivariate analysis was carried out for measures of association.

The majority of the study participants were male (58.7%) and aged between 18–37 years 55.43%. with only 17.6% illiterate while the rest were able to read and write. Although most participants were educated only 14.1% of all participants could recall their BMI after attending the outpatient department. This is shown in Table 1 below.

Table 1

Participant social demographic characteristics
Variable	Category	Frequency	Percentage (%)
Gender	Male	54	58.7
Gender	Female	38	41.3
Age	18–27	26	28.26
	28–37	25	27.17
	38–47	17	18.48
	48–57	13	14.13
	58–67	7	7.61
	68–77	3	3.26
	78–87	1	1.09
Education	None	7	17.6
	Only able to read & write	10	10.9
	Primary	30	32.6
	Secondary	27	29.3
	Tertiary	18	19.6
Medical History; Chronic illness	Yes	15	16.3
Medical History; Chronic illness	No	77	83.7
Recall BMI	Yes	13	14.1
Recall BMI	No	79	85.9

Shared decision-making utilisation during the outpatient visit

The participants were attended to at the outpatient clinic but only a few confirmed having the health workers use Shared Decision Making. The items that were measured by the collaboRate-5 tool are shown in the table that follows. These items captured by the collaboRate-5 tool show evidence of shared decision-making use.

Table 2

Shared Decision Making CollaboRate-5 Tool Patient Response Statistics
Item Responses		Frequency	Per cent
How much effort was made to help you understand BMI?	No effort was made	71	77.2%
	A little effort was made	6	6.5%
	Some effort was made	10	10.9%
	A lot of effort was made	4	4.3%
	Every effort was made	1	1.1%
How much effort was made to listen to the things that matter most to you about BMI	No effort was made	71	77.2%
	A little effort was made	5	5.4%
	Some effort was made	14	15.2%
	A lot of effort was made	0	0%
	Every effort was made	2	2.2%
How much effort was made to include what matters most to you in choosing what to do next?	No effort was made	71	77.2%
	A little effort was made	9	9.8%
	Some effort was made	9	9.8%
	A lot of effort was made	1	1.1%
	Every effort was made	2	2.2%

Association between Participant variables and Recall of BMI

The chi-square test Table 3 gave a Pearson Chi-Square value for gender of 4.870 with p = 0.027, indicating that there is a statistically significant association between patient gender and their recall of BMI.

However, the p-values of age and education status were 0.163 and 0.232, respectively. There was thus no association between age, education status, and recall of BMI.

Table 3
VARIABLE		RECALL BMI		X²	P-VALUE
		YES (%)	NO (%)
GENDER:	Male	4 (7.4%)	50 (92.6%)	4.870	0.027
GENDER:	Female	9 (23.7%)	29 (76.3%)	4.870	0.027
EDUCATION:	None	0 (0%)	7 (100%)	5.587	0.232
	Only Able To Read And Write	0 (0%)	10 (100%)
	Primary	4 (13.3%)	26 (86.7%)
	Secondary	4 (14.8%)	23 (85.2%)
	Tertiary	5 (27.8%)	13 (72.2%)
AGE	18–24 Years	4 (30.8%)	9 (69.2%)	9.199	0.163
	25–35 Years	7 (22.6%)	24 (77.4%)
	36–45 YEARS	1 (4.2%)	23 (95.8%)
	46–55 YEARS	1 (10.0%)	9 (90.0%)
	56–65 YEARS	0 (0.0%)	9 (100%)
	66–75 YEARS	0 (0.0%)	4 (100%)
	ABOVE 75	0 (0.0%)	1 (100.0%)
Statistical significance is P < 0.05

Shared Decision Making and Recall Cross tabulation

A compound variable, shared decision making was computed from the variables of whether the patient was helped to understand their BMI, the effort was made to listen to things that mattered most to them about BMI, and if an effort was also made to include what matters to them in choosing what to do next.

Table 4 illustrates that; at the lowest level of reported shared decision-making, the majority of respondents (85.9%) cannot recall their BMI; as shared decision-making grows in magnitude, there is an increase in reported recall of BMI. This indicates a relationship between shared decision-making and the ability to recall BMI.

Table 4

Recall of BMI and Shared Decision Making
			Shared Decision Making								Total
			.00	1.00	1.33	1.67	2.00	2.33	3.67	4.00	Total
Recall BMI	No	Participants	71	1	3	1	1	1	1	0	79
	No	% of Total	77.2%	1.1%	3.3%	1.1%	1.1%	1.1%	1.1%	0.0%	85.9%
	Yes	Participants	0	1	1	4	4	2	0	1	13
	Yes	% of Total	0.0%	1.1%	1.1%	4.3%	4.3%	2.2%	0.0%	1.1%	14.1%
Total		Participants	71	2	4	5	5	3	1	1	92
Total		% of Total	77.2%	2.2%	4.3%	5.4%	5.4%	3.3%	1.1%	1.1%	100.0%

Chi-Square Test for shared decision making

The chi-square test gave a Pearson Chi-Square value of 63.017 with p < 0.001. The p-value < 0.001 is significant. This indicates that there is a statistically significant and positive association between Shared Decision Making and the recall of BMI. This suggests that improved Shared Decision Making could lead to an increase in the number of patients who recall their BMI.

The majority of the respondents 79 (85.9%) could not recall their BMI while only 13 (14%) could remember their BMI]. Most times the patients leave the doctor’s room when they don’t know what the doctor said. Patients need cooperative decision-making during their visits. Enhanced care with patient satisfaction may rise as a result of adherence that results from group decision-making (Fernandez-Lazaro et al., 2019). The study found that demographic factors except gender did not have a significant association with the recall of BMI, while shared decision-making was associated with the recall of BMI. The findings were thus pointing at a situation that is modifiable or can be learnt (shared decision-making). The discussion that follows explains the relationships that were observed during the findings.

Regarding demographic factors, the majority of the patients, 33.7% (31 patients) belonged to the 25 to 35-year category in the study. The findings demonstrated the challenges that youth seeking medical assistance could be facing in terms of recalling medical instructions. However, the age distribution of the sample still relevantly showed that all age groups had difficulty recalling their BMI. Participants’ age was not protective against failing to recall BMI.

The study also measured the level of education for each participant. The findings differed from those of a study in New Zealand which found that mothers who had a university education were more able to recall instructions than those with a lower level of education. This further outlines the differences in communities that may hinder the recall process (Dawson et al., 2014). The current study thus revealed the limitations of the level of education to independently improve recall in the study population.

The third item that was studied was gender and its association with the recall of BMI. It was found to be associated with the recall of BMI. The findings did not match with other studies. Gender may not influence recall (Shiber et al., 2018). Shiber and colleagues (2018) related their findings to the improved communication by healthcare providers, overshadowing gender differences. Our current study's observation associates gender with recall. This may underscore the likely poor shared decision-making practices among the health workers at the time of the study. The exact description of gender quality needs further study if it will be used to improve recall of BMI.

This study observed an association between recall of BMI and shared decision-making, as an indicator of varied communication with each patient despite the hospital being the same. Some of the models used in health care include the paternalistic model, informed decision-making model, and shared decision-making model. Poor adherence to treatment due to poor communication is common among the sick. Although these concepts have been known for a long time to improve recall and adherence, decades later they are still measured to be under-utilised such as our study reports. Ley gives us a model decades earlier among other authors that shows that recall follows understanding (Kessels, 2003; Ley, 1989).

Patients’ understanding is crucial since it is the pathway for recall resulting in adherence. A model of decision-making in communication would have to give the patient the most in terms of improving the understanding of therapies. Shared decision-making (SDM) was associated with a better ability to recall BMI (P < 0.001) [Table 3]. This showed that regardless of the demographic factors, there was a need for clinician-patient interaction to be done through SDM. The discussions that the health care provider facilitates involving patient, aid recall and understanding of the health instructions. The more the interaction gives preference to the patient for understanding, the better it becomes. 77.2% of the 92 participants reported that no effort was made to help them understand BMI. Patients who had poor recall may not have been exposed to explanations that would aid their understanding and recall of BMI. The explanations may not have been tailored to the patient's preferences. BMI needs to be explained to the patients. The information that is delivered to the patients about their BMI needs to be provided in a manner that the patients can understand. SDM has been in use for decades but has not implied its vast usage in medical practice in Uganda (Nuwagaba et al., 2021). This calls for more efforts towards its utilisation.

The prevalence of not recalling BMI after the outpatient consultation was high. Patient demographic factors were not associated with the recall of BMI except gender. SDM is likely to improve patients’ recall of BMI when used. The findings point out the role of shared decision-making in monitoring BMI and thus should be considered for continuous medical education at the facility. The country needs more studies at different healthcare facilities to assess the utilisation of shared decision-making.

RECOMMENDATIONS

This study recommends intensified communication between healthcare workers and clients. All possible methods should be employed, especially the use of shared decision-making.

Continuous medical education on communication needs to be need-driven to tackle the lack of shared decision-making at hospitals.

Further studies to explore the association between demographic factors and recall of patients’ BMI are needed. This will improve the understanding of their role.

ETHICAL CONSIDERATIONS

The study protocol was approved by Nexus International University and Comboni Hospital’s management before data collection. All participants were contacted voluntarily and only participants who consented through writing were enrolled for the study.

CONFLICT OF INTEREST

The authors have no conflict of interest to disclose.

Data availability statement

Data is availed as part of the submitted manuscript.

Author contribution

A.B. and D.K. prepared the manuscript and did data analysis and presentation. P.T. reviewed the manuscript.

Funding

There was no funding availed for the study by either private or public sources.

Bae, J.-M. (2017). Shared decision making: Relevant concepts and facilitating strategies. Epidemiology and Health, 39, e2017048. https://doi.org/10.4178/epih.e2017048
Dawson, A. M., Taylor, R. W., Williams, S. M., Taylor, B. J., & Brown, D. A. (2014). Do parents recall and understand children’s weight status information after BMI screening? A randomised controlled trial. BMJ Open, 4(7), e004481–e004481. https://doi.org/10.1136/bmjopen-2013-004481
Fernandez-Lazaro, C. I., García-González, J. M., Adams, D. P., Fernandez-Lazaro, D., Mielgo-Ayuso, J., Caballero-Garcia, A., Moreno Racionero, F., Córdova, A., & Miron-Canelo, J. A. (2019). Adherence to treatment and related factors among patients with chronic conditions in primary care: A cross-sectional study. BMC Family Practice, 20(1), 132. https://doi.org/10.1186/s12875-019-1019-3
Forcino, R. C., Barr, P. J., O’Malley, A. J., Arend, R., Castaldo, M. G., Ozanne, E. M., Percac-Lima, S., Stults, C. D., Tai-Seale, M., Thompson, R., & Elwyn, G. (2018). Using CollaboRATE, a brief patient-reported measure of shared decision making: Results from three clinical settings in the United States. Health Expectations, 21(1), 82–89. https://doi.org/10.1111/hex.12588
Geldsetzer, P., Fink, G., Vaikath, M., & Bärnighausen, T. (2018). Sampling for Patient Exit Interviews: Assessment of Methods Using Mathematical Derivation and Computer Simulations. Health Services Research, 53(1), 256–272. https://doi.org/10.1111/1475-6773.12611
Gionfriddo, M. R., Leppin, A. L., Brito, J. P., LeBlanc, A., Shah, N. D., & Montori, V. M. (2013). Shared decision-making and comparative effectiveness research for patients with chronic conditions: An urgent synergy for better health. Journal of Comparative Effectiveness Research, 2(6), 595–603. https://doi.org/10.2217/cer.13.69
Godolphin, W. (2009). Shared Decision-Making. Healthcare Quarterly. https://www.longwoods.com/content/20947
Hoffmann, T. C., Légaré, F., Simmons, M. B., McNamara, K., McCaffery, K., Trevena, L. J., Hudson, B., Glasziou, P. P., & Del Mar, C. B. (2014). Shared decision making: What do clinicians need to know and why should they bother? The Medical Journal of Australia, 201(1), 35–39. https://doi.org/10.5694/mja14.00002
Jansen, J., van Weert, J., van der Meulen, N., van Dulmen, S., Heeren, T., & Bensing, J. (2008). Recall in Older Cancer Patients: Measuring Memory for Medical Information. The Gerontologist, 48(2), 149–157. https://doi.org/10.1093/geront/48.2.149
Kessels, R. P. C. (2003). Patients’ memory for medical information. Journal of the Royal Society of Medicine, 96(5), 219–222.
Lewis, K. B., Stacey, D., Squires, J. E., & Carroll, S. (2016). Shared Decision-Making Models Acknowledging an Interprofessional Approach: A Theory Analysis to Inform Nursing Practice. Research and Theory for Nursing Practice, 30(1), 26–43. https://doi.org/10.1891/1541-6577.30.1.26
Ley, P. (1989). Improving patients’ understanding, recall, satisfaction and compliance. In A. K. Broome (Ed.), Health Psychology: Processes and Applications (pp. 74–102). Springer US. https://doi.org/10.1007/978-1-4899-3228-0_5
Mathews, A. L., Coleska, A., Burns, P. B., & Chung, K. C. (2016). Evolution of Patient Decision-Making Regarding Medical Treatment of Rheumatoid Arthritis: RA Patient Treatment Decision-Making. Arthritis Care & Research, 68(3), 318–324. https://doi.org/10.1002/acr.22688
McHale, C. T., Laidlaw, A. H., & Cecil, J. E. (2016). Direct observation of weight-related communication in primary care: A systematic review. Family Practice, 33(4), 327–345. https://doi.org/10.1093/fampra/cmw045
Ministry Of Health. (2016). Guidelines For Integrated Management Of Acute Malnutrition In Uganda. https://platform.who.int/docs/default-source/mca-documents/policy-documents/guideline/UGA-CH-38-03-GUIDELINE-2016-eng-IMAM-Guidelines-for-Uganda-Jan-2016.pdf
Nuwagaba, J., Olum, R., Bananyiza, A., Wekha, G., Rutayisire, M., Agaba, K. K., Chekwech, G., Nabukalu, J., Nanyonjo, G. G., Namagembe, R., Nantongo, S., Lubwama, M., Besigye, I., & Kiguli, S. (2021). Patients’ Involvement in Decision-Making During Healthcare in a Developing Country: A Cross-Sectional Study. Patient Preference and Adherence, 15, 1133–1140. https://doi.org/10.2147/PPA.S302784
Ong, L. M. L., de Haes, J. C. J. M., Hoos, A. M., & Lammes, F. B. (1995). Doctor-patient communication: A review of the literature. Social Science & Medicine, 40(7), 903–918. https://doi.org/10.1016/0277-9536(94)00155-M
Selic, P., Svab, I., Repolusk, M., & Gucek, N. K. (2011). What factors affect patients’ recall of general practitioners’ advice? BMC Family Practice, 12(1), 141. https://doi.org/10.1186/1471-2296-12-141
Shiber, S., Zuker-Herman, R., Drescher, M. J., & Glezerman, M. (2018). Gender differences in the comprehension of care plans in an emergency department setting. Israel Journal of Health Policy Research, 7(1), 50. https://doi.org/10.1186/s13584-018-0245-9
Skinner, A. C., Perrin, E. M., & Steiner, M. J. (2012). Parental Recall of Doctor Communication of Weight Status: National Trends From 1999 Through 2008. Archives of Pediatrics & Adolescent Medicine, 166(4), 317. https://doi.org/10.1001/archpediatrics.2011.1135
Stenholm, S., Head, J., Aalto, V., Kivimäki, M., Kawachi, I., Zins, M., Goldberg, M., Platts, L. G., Zaninotto, P., Magnusson Hanson, L. L., Westerlund, H., & Vahtera, J. (2017). Body mass index as a predictor of healthy and disease-free life expectancy between ages 50 and 75: A multicohort study. International Journal of Obesity, 41(5), 769–775. https://doi.org/10.1038/ijo.2017.29

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Shared decision-making underutilisation at an outpatient clinic during BMI monitoring in rural Uganda

Status:

Version 1

Abstract

Introduction:

Objective

Methods

Results

Interpretation:

Introduction

Methods

Sampling and Sample Size Determination

RESULTS

Shared decision-making utilisation during the outpatient visit

Association between Participant variables and Recall of BMI

Shared Decision Making and Recall Cross tabulation

Chi-Square Test for shared decision making

DISCUSSION OF FINDINGS

CONCLUSION

RECOMMENDATIONS

DECLARATIONS

REFERENCES

Additional Declarations

Status:

Version 1