Summary
Multiple searches across several databases may be required to find relevant peer-reviewed literature for population targeting, intervention evaluation, comparison, and understanding the outcome over anticipated time frames (PICOT). When conducting a literature search on a topic related to the PICOT claim, the search for systematic reviews and evidence-based studies should be the focus of attention in order to develop a solid theoretical base. The aim of this study is to search the literature corresponding to PICOT and find at least eight relevant journal articles to build an evidence base.
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Databases Utilized
The OVID/MEDLINE database was used to conduct this literature search. This database provides access to complete nursing and general health articles covering biological research (Rutgers University, 2020). Since the articles were relevant enough, no additional search in other databases was required. The high relevance of the article layout was ensured by the use of precise keywords, which made it possible to find several articles on the chosen topic and related areas.
APA-Formatted References, Abstract, and Level of Evidence
Acharya, A., Cheng, B., Koralkar, R., Olson, B., Lamster, I. B., Kunzel, C., & Lalla, E. (2018). Screening for diabetes risk using integrated dental and medical electronic health record data. JDR Clinical & Translational Research, 3(2), 188-194. Web.
Abstract
The study focuses on the ability of the iEHR to provide data for the detection of undiagnosed diabetes. The study used dental electronic records of patients over 21 with previously undiagnosed diabetes, the sample size was 4560 people (Acharya et al., 2018). The model achieved an area under the receiver performance curve of 0.71 (Acharya et al., 2018). This conclusion suggests that diagnostic accuracy improves when data is combined with information from a medical electronic health record. The study will allow future research to prove the efficacy of EHR integration in the treatment of T2DM.
Level of Evidence
The study is a broad database analysis based on a large sample, reaching II level of evidence. The study is based on data from electronic patient records; however, it has not conducted its own study of blood glucose levels. It can be considered that the detected period of 3 months is too short for a relevant observation, since the disease may manifest itself much later. To improve the study, it is necessary to conduct personal testing of patients from the sample and increase the intervention time.
Andersen, J. A., Scoggins, D., Michaud, T., Wan, N., Wen, M., & Su, D. (2021). Racial disparities in diabetes management outcomes: evidence from a remote patient monitoring program for type 2 diabetic patients. Telemedicine and e-Health, 27(1), 55-61. Web.
Abstract
This research focuses on the study of racial differences in the telemedicine treatment of diabetes. The study examines outcomes in the treatment of type 2 diabetes between white and black patients based on a remote monitoring program. The study sample included 914 white patients and 365 black patients with T2DM who completed 3 months of telemonitoring (Andersen et al., 2021). HbA1c among black patients at the end of the program was 0.23 points higher than among white patients (Andersen et al., 2021). Based on the data obtained, it can be concluded that access to the latest medical devices reduces the racial gap. For further investigation, the findings will help support the claim that new medical treatments are effective in treating black patients.
Level of Evidence
The study is a description of data based on a well-defined sample, reaching II level of evidence. Although the sample is quite large, it is based on a study in a particular area. In addition, the study involved almost half the number of black patients compared to whites. The study would be more conclusive if the sample were equal in the number of representatives, which would reduce possible inaccuracies.
Blonde, L., Meneghini, L., Peng, X. V., Boss, A., Rhee, K., Shaunik, A., & McCrimmon, R. J. (2018). Probability of achieving glycemic control with basal in sulin in patients with type 2 diabetes in real-world practice in the USA. Diabetes Therapy, 9(3), 1347-1358. Web.
Abstract
The research focuses on the role of basal insulin in the treatment of type 2 diabetes to achieve glycemic control. The study is retrospective and observational, using electronic health records to assess the likelihood of achieving glycemic control within 24 months (Blonde et al., 2018). The sample is 6597 patients with T2DM who started BI and had at least one significant HbA1c result (Blonde et al., 2018). Collectively, about 38% of patients achieved HbA1c < 7% in the first year; only about 8% more did so in the second year (Blonde et al., 2018). This study suggests that the intervention dramatically loses its effectiveness over time. For a follow-up study, this article provides evidence for the efficacy of electronic HbA1c monitoring resources and suggests a decline in efficacy results beyond the estimated intervention time of 6 months.
Level of Evidence
The study is a retrospective analysis of a large sample to determine the effect of BI on the treatment of T2DM and achieves I level of evidence. Firstly, a large sample of patients with pre-existing initial HbA1c results was selected for high relevance. A study period of more than two years contributes to the increased relevance of the results. The conclusions of the study, tracing the trend of decreasing efficiency over time, are fully proven.
Lee, E. Y., Cha, S. A., Yun, J. S., Lim, S. Y., Lee, J. H., Ahn, Y. B., & Ko, S. H. (2022). Efficacy of personalized diabetes self-care using an electronic medical record–integrated mobile app in patients with Type 2 Diabetes: 6-Month randomized controlled trial. Journal of medical Internet research, 24(7), 1-20. Web.
Abstract
The study assesses the impact of a mobile application integrated into an electronic medical record for self-management for people with T2DM, with a particular focus on blood glucose control. The intervention was a 6.5-month controlled and randomized study, the sample was based on patients with T2DM and an HbA1c level above 7.5% (Lee et al., 2022). Using the iCareD system, glucose, diet, and physical activity, data were automatically transferred. In subgroup analysis, HbA1c levels showed a statistically significant decrease, with virtually all participants satisfied with the system (Lee et al., 2022). This study will make a significant contribution to proving the effectiveness of EHR-based applications for the self-management of patients with T2DM.
Level of Evidence
The study is an open-label, randomized and controlled intervention and achieves I level of evidence. The sample is large enough to provide relevant results, with a high rate of participants reaching the end of the study. Dividing the participants into three subgroups contributed to the drawing up of relevant conclusions. The openness and controllability of the intervention provided the study with a serious reasoning for the reliable results.
Singla, R., Gupta, G., & Gupta, Y. (2022). What drives glycemic control in a person living with diabetes?International Journal of Diabetes in Developing Countries, 42(2), 369-373. Web.
Abstract
The study is an analysis of electronic health records aimed at identifying factors that affect glycemic control. The researchers studied data from 4647 people who have been seeing a doctor for diabetes over the past five years (Singla et al., 2022). Glycemic control factors based on patient behavior were critically analyzed; factors included adherence to medications, exercise, and diet (Singla et al., 2022). The study found that patient behavioral factors affected glycemic control linearly, with statistically lower HbA1c in people with better adherence to diet, medication, and exercise (Singla et al., 2022). Thus, people who followed the recommendations for a healthy lifestyle and self-monitored blood glucose levels had better indicators. The article will be useful for further research to prove the benefits of HbA1 monitoring for treatment outcomes.
Level of Evidence
The article is a result of a retrospective, cross-sectional study of factors affecting glycemic control and reaches I level of evidence. The sample size is significant enough for drawing relevant conclusions. In addition, the sample is based on an effective narrowing of the study population, since it is based only on patients with long-term diabetes. These factors of the study contribute to the conclusion that self-monitoring is an effective adjunct to the treatment of patients who were diagnosed with diabetes a significant time ago.
Szymonifka, J., Conderino, S., Cigolle, C., Ha, J., Kabeto, M., Yu, J., & Zhong, J. (2020). Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data. JAMIA open, 3(4), 583-592. Web.
Abstract
The research is a cohort study of electronic health records as a source for predicting clinical risk. The sample was composed of patients with type 2 diabetes using the 2009-1017 EHR and the 1995-2014 Health and Retirement Survey to examine the risks of cardiovascular diseases (Szymonifka et al., 2020). The overall incidence of cardiovascular disease was 37.5 and 90.6 per 1000 person-years since the onset of T2DM (Szymonifka et al., 2020). Based on the findings of the study, EHR may be used to generate risk prediction. These findings reinforce the usefulness of the EHR to control T2DM.
Level of Evidence
The research is a cohort study of a sample of patients over a wide period of time and achieves II level evidence. The data obtained from the study of the bases can be contradictory because it does not take into account all the risk factors for the occurrence of cardiovascular diseases. To increase the reliability of the study, it was necessary to use several more indicators applicable to a wide sample.
Tchero, H., Kangambega, P., Briatte, C., Brunet-Houdard, S., Retali, G. R., & Rusch, E. (2019). Clinical effectiveness of telemedicine in diabetes mellitus: a meta-analysis of 42 randomized controlled trials. Telemedicine and e-Health, 25(7), 569-583. Web.
Abstract
The article is a meta-analysis, comparing the effectiveness of telemedicine intervention and conventional care for patients with diabetes. The study method is a randomized controlled trial based on the measurement of HbA1 levels before and after the intervention; the sample is 6170 people divided equally into two groups (Tchero et al., 2019). Patients with type 2 diabetes had a higher decrease in HbA1c compared to patients with type 1 diabetes; telemedicine programs lasting more than 6 months resulted in significantly greater reductions (Tchero et al., 2019). This article can be used for further research as evidence of the benefits of long-term intervention using new medical technologies for patients with T2DM.
Level of Evidence
The study is a meta-analysis of interventions based on randomized controlled trials reaching I level of evidence. The sample is large enough to draw relevant conclusions about the effectiveness of telemedicine for patients with T2DM. The extended age of the studied patients contributes to an increase in the reliability of the results. To further expand the study, it is possible to examine which specific telemedicine tools have better and worse effects on diabetes care outcomes.
Wright, A. K., Welsh, P., Gill, J. M., Kontopantelis, E., Emsley, R., Buchan, I., & Sattar, N. (2020). Age-, sex-and ethnicity-related differences in body weight, blood pressure, HbA1c and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes. Diabetologia, 63(8), 1542-1553. Web.
Abstract
The research presents a study of weight, glucose and blood pressure to determine how diabetes is diagnosed based on age, sex and ethnicity. Researchers are using the UK Clinical Practice Research Datalink to identify patients with type 2 diabetes to compare their scores over a period of 17 years (Wright et al., 2020). HbA1 levels are associated with earlier onset of diabetes in black patients (Wright et al., 2020). In addition, one of the strongest risk factors for black patients has becoming overweight (Wright et al., 2020). Findings from this study will help future research build on HbA1 levels as one of the most relevant markers for diagnosing type 2 diabetes in black patients.
Level of Evidence
The article examined is a database study and embodies II level of evidence. The sample size is large enough to provide relevant conclusions. The study covers a large set of factors that affect the diagnosis of type 2 diabetes. The reliability of the results can be reduced due to the subjectivity of the diagnosis in each case. Researchers cannot be reliably sure of the correctness of the diagnosis for each patient from the sample.
References
Acharya, A., Cheng, B., Koralkar, R., Olson, B., Lamster, I. B., Kunzel, C., & Lalla, E. (2018). Screening for diabetes risk using integrated dental and medical electronic health record data. JDR Clinical & Translational Research, 3(2), 188-194. Web.
Andersen, J. A., Scoggins, D., Michaud, T., Wan, N., Wen, M., & Su, D. (2021). Racial disparities in diabetes management outcomes: evidence from a remote patient monitoring program for type 2 diabetic patients. Telemedicine and e-Health, 27(1), 55-61. Web.
Blonde, L., Meneghini, L., Peng, X. V., Boss, A., Rhee, K., Shaunik, A., & McCrimmon, R. J. (2018). Probability of achieving glycemic control with basal insulin in patients with type 2 diabetes in real-world practice in the USA. Diabetes Therapy, 9(3), 1347-1358. Web.
Lee, E. Y., Cha, S. A., Yun, J. S., Lim, S. Y., Lee, J. H., Ahn, Y. B., & Ko, S. H. (2022). Efficacy of personalized diabetes self-care using an electronic medical record–integrated mobile app in patients with Type 2 Diabetes: 6-Month Randomized Controlled Trial. Journal of medical Internet research, 24(7), 1-20. Web.
Rutgers University. (2022). MEDLINE (Ovid). Web.
Singla, R., Gupta, G., & Gupta, Y. (2022). What drives glycemic control in a person living with diabetes?International Journal of Diabetes in Developing Countries, 42(2), 369-373. Web.
Szymonifka, J., Conderino, S., Cigolle, C., Ha, J., Kabeto, M., Yu, J., & Zhong, J. (2020). Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data. JAMIA open, 3(4), 583-592. Web.
Tchero, H., Kangambega, P., Briatte, C., Brunet-Houdard, S., Retali, G. R., & Rusch, E. (2019). Clinical effectiveness of telemedicine in diabetes mellitus: a meta-analysis of 42 randomized controlled trials. Telemedicine and e-Health, 25(7), 569-583. Web.
Wright, A. K., Welsh, P., Gill, J. M., Kontopantelis, E., Emsley, R., Buchan, I., & Sattar, N. (2020). Age-, sex-and ethnicity-related differences in body weight, blood pressure, HbA1c and lipid levels at the diagnosis of type 2 diabetes relative to people without diabetes. Diabetologia, 63(8), 1542-1553. Web.