PICO(T) Question And Evidence-Based Approach

PICO(T) Question and Evidence-Based Approach

Incorporating the best available evidence to improve clinical acumen and care necessitates a prodromal phase of location and identification of credible scholarly resources. The application of relevant evidence-based findings leads to better patient outcomes, more efficient healthcare systems, and more positive nursing practice outcomes. The PICO(T) question is one tool that care providers can use to help them find relevant scholarly sources.

When applying evidence-based models, a PICO(T) question is a valuable starting point for care providers. It necessitates devoting time to investigating and precisely defining each of its components, the PICO(T), as well as searching credible medical and nursing databases and sifting for relevant sources (Bermudez, 2021). While using a PICO(T) question, a care provider isolates a proposed intervention in the care or solution of a patient problem and compares it against a standard or an existing approach. The goal of this paper, with a focus on type 2 diabetes mellitus (T2DM), is to apply a PICO(T) process to develop a research question, followed by a discussion of the research materials obtained.

Definition of the Practice Issue to be Explained in the Format of a PICO(T) Question

Diabetes mellitus (DM) is one of the most common chronic diseases worldwide, with significant morbidity, mortality, and cost implications. The World Health Organization (2020) data supports this assertion, highlighting that diabetes was the ninth leading cause of death globally in 2019. Besides, it causes significant morbidity, affecting 1 in 11 adults globally, with the majority (more than 90%) being T2DM patients (Sapra & Bhandari, 2022); causes significant death rates, estimated to be 1.37 million in 2017 with a projected rise to 1.59 million deaths by 2025 (Lin et al., 2020); and incurs a massive cost in its treatment, which the ADA (2018) estimates to be $327 billion.

T2DM, the center of this discussion, affected approximately 425 million people worldwide by 2017, accounting for 9% of adults aged 20 to 79 years (Forouhi & Wareham, 2019). Diabetes, due to its chronic nature, necessitates a cost-effective long-term approach aimed at preventing complications in patients and the social and economic burden that patients or people closest to them may face.

The PICO(T) Question

In patients with T2DM (P), does remote patient monitoring (I) confer better control of Hemoglobin A1C (O) compared to patients receiving the usual or routine outpatient care (C)

Patient/Population/Problem (P): T2DM Patients

As aforementioned, T2DM affects the vast majority (more than 90%) of diabetic patients (Sapra & Bhandari, 2022). The patients, in collaboration with their primary care providers, design treatment goals, and in almost all cases, a Hemoglobin A1C (HbA1C) control is a single or part of the many goals. Despite interventions to ensure intensive glycemic control in T2DM patients, it is estimated that 43.2%-55.6% of T2DM adult patients do not meet the HBA1C reference target for glycemic control (Andrès et al., 2019).

Suboptimal glycemic control has been attributed to a variety of factors, including inadequate home blood glucose (BG) monitoring, non-adherence to medications and lifestyle changes, limited access to healthcare, and inadequate patient education (Andrès et al., 2019). Telemedical technologies can thus bridge the gap between the patient and the care provider, improve patient education and self-management practices, and address compliance and monitoring issues.

Intervention

The healthcare industry has undergone radical transformations as a result of technological advancements. Diabetes management is gradually shifting toward technology leverage, and several studies have been conducted to support the utility of technology in diabetes management. Andrès et al. (2019) examined studies on telemonitoring in diabetes from 1990 to 2019. Between 1990 and 2010, over 20 studies on structured telephone support and telemonitoring that were primarily designed to monitor BG were classified as Glycemia Reduction Approaches in Diabetes (GRADE) ‘Very low’ and ‘Low.’

These studies were unable to conclude the usefulness of telemedicine in terms of DM equilibrium and management. Numerous more mature telemedicine project studies were conducted from 2010 to 2015, with the main goal of evaluating the use of technology to implement medical cost-effective and large-scale diabetes management. These studies were classified as GRADE ‘moderate,’ with at least three of them showing positive effects on BG, HbA1C, and comorbidity reduction.

From 2010 to 2019, new generation projects based on information and communication technologies (ICT) and Web 2.0 technologies were built and integrated with various connected tools such as Bluetooth and WI-FI. The findings of these studies revealed improved management of both T1DM and T2DM, as well as a reduction in complications, and were classified as GRADE ‘moderate.’ Even with the current increased advancement in healthcare technology, more advanced telemedical approaches have emerged, some of which are already in use. In contrast, others are still being studied, raising the hope of eventually reaching the most effective approaches to long-term management of T2DM.

Comparison and Outcome

The comparison group consists of patients who receive routine or standard outpatient care at regular intervals for follow-up and management. Based on discussions with their primary care physicians, these patients attend outpatient clinics for BG monitoring, HbA1C measurement, medication refill, and health education. HbA1C reflects average glycemia over approximately three months, and the reference target for glycemic control for most clinicians and patients is <7.0% (ADA, 2020). To avoid stringent glycemic control, which would result in hypoglycemia, the target HbA1C is usually individualized based on the clinician’s judgment and the patient’s reference.

Sources of Evidence Helpful in Answering the Question, (2) Explanation of Findings, and the (3) Relevance of the Findings

Lee, J. Y., Chan, C. K. Y., Chua, S. S., Ng, C. J., Paraidathathu, T., Lee, K. K. C., & Lee, S. W. H. (2020). Telemonitoring and team-based management of glycemic control on people with type 2 diabetes: A cluster-randomized controlled trial. Journal of General Internal Medicine, 35(1), 87–94. https://doi.org/10.1007/s11606-019-05316-9

To ascertain the impact of telemonitoring and a team-based management of T2DM patients, Lee et al. (2020) conducted a pragmatic 52-week cluster randomized controlled study among eleven primary care facilities in Malaysia. The study’s participants were adults (≥18 years old) with T2DM who had uncontrolled diabetes (≥7.5%≤11%) in the previous three months and lived in the state of Selangor.

The telemedicine group (TG) received a gluco-telemeter that automatically uploaded the BG reading and transmitted at least 6 BG readings weekly to the central server at the clinician’s end. Besides, they received automated feedback on BG and metabolic results, as well as monthly communication from the care team about self-management skills, BG monitoring, and medication and lifestyle adherence. The care team made treatment recommendations based on the results. At this time, the control group received routine care by visiting their doctors on time.

The results after 24 weeks showed a non-significant difference in the number of participants who achieved an HbA1C of <7%. However, at the end of the study, 23 (19.2%) of the 120 participants in the TG and 21 (17.5%) of the 120 participants in the usual care group achieved an HbA1C of <7%, demonstrating the usefulness of telemedicine in the care of T2DM patients. This study provides credible information because it was published recently (within the last five years), addresses a current health issue, is written by medical experts, and serves its evaluation purpose.

Amante, D. J., Harlan, D. M., Lemon, S. C., McManus, D. D., Olaitan, O. O., Pagoto, S. L., Gerber, B. S., & Thompson, M. J. (2021). Evaluation of a diabetes remote monitoring program facilitated by connected glucose meters for patients with poorly controlled type 2 diabetes: Randomized crossover trial. JMIR Diabetes, 6(1), e25574. https://doi.org/10.2196/25574

In cognizance that technological advancements in blood glucose meters, cellular-connected devices, and self-monitoring BG provide timely support to T2DM patients, Amante et al. (2021) conducted a 12-month randomized crossover trial study among 119 T2DM patients aged 23-84 years and with a mean HbA1C of 10.1%. The intervention included cellular-connected glucose meters and Livongo Health’s telephone-based DM coaching. When an abnormal glucose level was reported, the coach assisted in goal setting, answering questions, and providing additional advice. While one group received the intervention for six months before returning to usual care for another six months (IV/UC), the other group did the contrary (UC/IV).

After six months, there were differences in both groups, with the intervention improving mean HbA1C by 1.1% and usual care by 0.8%. After the crossover, the group that returned to usual care (IV/UC) had no significant changes in mean HbA1C, whereas the group that started receiving the intervention (UC/IV) had an additional 0.4% improvement in mean HbA1C, supporting the efficacy of the telemedical approach to T2DM. The currency of the article (published within the last five years), relevance to a current health issue, authored by medical experts, and serving its purpose, which was to evaluate, all contribute to the credibility of these findings.

Sayin Kasar, K., Duru Asiret, G., Kutmec Yilmaz, C., & Canlar, Ş. (2022). The effect of model-based telephone counseling on HbA1c and self-management for individuals with type 2 diabetes: A randomized controlled trial. Primary Care Diabetes, 16(1), 41–48. https://doi.org/10.1016/j.pcd.2021.09.005

Sayin Kasar et al. (2022) conducted a randomized control trial study to determine the effect of telephone-based counseling on information, behavioral skills, and motivation (IBM) in T2DM patients’ HbA1C. From January to September 2019, 63 patients (31 intervention and 32 control) were enrolled in the study. After a prodromal phase of 45-60 minutes of IBM training, the intervention group was followed for a total of 12 weeks.

They also got a weekly reminder message and a phone call every two weeks, whereas the control group received no intervention. The findings, which were derived from the diabetes self-management questionnaires, diabetes self-efficacy scale, and the glycemic control (HbA1C), were as follows: there was a statistically significant difference between the pre-post-test HbA1c (F:13.589; p < 0.001), weight (F:32.176; p < 0.001), systolic blood pressure (F:7.109; p = 0.01), self-management perceptions (F:71.132; p < 0.001), self-efficacy (F:26.632; p < 0.001), and self-management (F:44.487; p < 0.001) with the study concluding that the telephone-based counseling on IBM is suitable in improving HbA1C and self-management skills among T2DM patients. These findings are relevant and can be accounted for by other studies, and they provide a real-time solution to a current problem, lending credibility to the source.

Conclusion

Even though a care provider can gain knowledge and skills through practice, specific concepts of care are subject to change, necessitating the acquisition of new information. The fact that care is constantly changing benefits patients because new approaches to improving patient outcomes are discovered. As a result, healthcare providers must understand the significance of locating and identifying credible literature and how to apply it in practice.

This can be accomplished, for example, by using a PICO(T) question, as extensively discussed above. While telemedicine has provided hope in the quest for effective approaches to long-term T2DM management, its implementation in hospitals has been limited due to challenges with resistance and effectuation costs.

References

American Diabetes Association. (2018). Economic costs of diabetes in the U.s. in 2017. Diabetes Care, 41(5), 917–928. https://doi.org/10.2337/dci18-0007

American Diabetes Association. (2020). 8. Obesity management for the treatment of type 2 diabetes: Standards of Medical Care in diabetes-2020. Diabetes Care, 43(Suppl 1), S89–S97. https://doi.org/10.2337/dc20-S008

Andrès, E., Meyer, L., Zulfiqar, A.-A., Hajjam, M., Talha, S., Bahougne, T., Ervé, S., Hajjam, J., Doucet, J., Jeandidier, N., & Hajjam El Hassani, A. (2019). Telemonitoring in diabetes: evolution of concepts and technologies, with a focus on results of the more recent studies. Journal of Medicine and Life, 12(3), 203–214. https://doi.org/10.25122/jml-2019-0006

Bermudez, N. (2021). Formulating well-written clinical practice questions and research questions. Nursing & Health Sciences Research Journal, 4(1), 70–82. https://doi.org/10.55481/2578-3750.1113

Forouhi, N. G., & Wareham, N. J. (2019). Epidemiology of diabetes. Medicine (Abingdon, England: UK Ed.), 47(1), 22–27. https://doi.org/10.1016/j.mpmed.2018.10.004

Lin, X., Xu, Y., Pan, X., Xu, J., Ding, Y., Sun, X., Song, X., Ren, Y., & Shan, P.-F. (2020). Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Scientific Reports, 10(1), 14790. https://doi.org/10.1038/s41598-020-71908-9

Sapra, A., & Bhandari, P. (2022). Diabetes Mellitus. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK551501/

World Health Organization. (2020). The top 10 causes of death. Who.int. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-deat