The North Dakota Nurse - July 2022

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Page 6 The North Dakota Nurse July, August, September 2022 Telehealth Interventions in the Management of Diabetes Appraised by: Amanda Papke SN Alyssa Dailey SN, Tayla Gange SN, Tierrany Trudell SN, , Wallen Masiasa SN, Jonathan Gallagher SN Allison Sadowsky MSN RN Assistant Professor of Practice (Faculty) (NDSU School of Nursing at Sanford Health Bismarck) Clinical Question: In diabetic patients, what is the effect of telehealth interventions on patient health outcomes? Sources of Evidence: Anderson, A., O’Connell, S.S., Thomas, C., & Chimmanamada, R. (2021). Telehealth Interventions to Improve Diabetes Management Among Black and Hispanic Patients: A Systematic Review and Meta-Analysis. Journal of Racial and Ethnic Health Disparities (2022). https://doi.org/10.1007/s40615- 021-01174-6 Baron, J. S., Hirani, S., & Newman, S. P. (2016). A randomized, controlled trial of the effects of a mobile telehealth intervention on clinical and patient-reported outcomes in people with poorly controlled diabetes. Journal of Telemedicine and Telecare, 23(2), 207–216. https://doi. org/10.1177/1357633x16631628 Eberle, C., & Stichling, S. (2021). Clinical Improvements by Telemedicine Interventions Managing Type 1 and Type 2 Diabetes: Systematic Metareview. Journal of medical Internet research, 23(2), 23244. https://doi. org/10.2196/23244 Wild, S. H., Hanley, J., Lewis, S. C., McKnight, J. A., McCloughan, L. B., Padfield, P. L., Parker, R. A., Paterson, M., Pinnock, H., Sheikh, A., & McKinstry, B. (2016). Correction: Supported telemonitoring and glycemic control in people with type 2 diabetes: The Telescot diabetes pragmatic multicenter randomized controlled trial. PLOS Medicine, 13(10). https://doi.org/10.1371/journal. pmed.1002163 Wong, V. W., Wang, A., & Manoharan, M. (2021). Utilization of telehealth for outpatient diabetes management during COVID-19 pandemic: How did the patients fare? Internal Medicine Journal, 51(12), 2021–2026. https://doi. org/10.1111/imj.15441 Zhai, Y. K., Zhu, W. J., Cai, Y. L., Sun, D. X., & Zhao, J. (2014). Clinical- and costeffectiveness of telemedicine in type 2 diabetes mellitus: a systematic review and meta-analysis. Medicine, 93(28), e312. https://doi.org/10.1097/ MD.0000000000000312 Synthesis of Evidence: Six articles were reviewed as evidence in this report. The articles included three systematic reviews, a quasi-experimental, and two single randomized controlled trials. In patients with chronic conditions, there are various barriers that inhibit management of their disease. Determining these barriers and researching alternative interventions is necessary in improving clinical and patient-reported outcomes. Baron and Newman (2016) conducted a randomized controlled trial to examine the effects of mobile telehealth on a range of clinical and patient reported outcomes. The study included 81 participants who were patients diagnosed with type 1 or type 2 diabetes. The participants exchanged medical information daily, including blood glucose and blood pressure, with the telehealth nurse using a mobile device that transmitted the data to a web server where it was accessed and reviewed by the telehealth nurse. Feedback was provided to the patient based on the transmitted data. Variables such as hemoglobin A1c, blood pressure, daily insulin doses, and patient-reported outcomes were studied. The results conclude that participants who received access to the mobile telehealth intervention reported a significant increase in quality of life, improvement in hemoglobin A1c and a decrease in both systolic and diastolic blood pressures. Hemoglobin A1c decreased in the intervention group from an average of 9.07 at baseline to 8.56 at nine months. Average hemoglobin A1c increased in the control group from 8.88 at baseline to 8.93 at nine months. Eberle and Stichling (2021) conducted a systematic meta-review of 31 studies. Including 21 SR & MA, 8 RCT, 1 non-RCT, and one qualitative study. These studies used the intervention of telehealth in various ways such as text messaging, telephone calls, video conferences, to email. Patients with type 1 and type 2 diabetes were studied with these clinical outcomes in mind: HbA1C, FBG (fasting blood glucose), BP, body weight, BMI, health-related quality of life (HRQoL), diabetes-related quality of life (DRQoL), cost effectiveness, and time saving. The results concluded a significant improvement in HbA1C. There were also noted positive effects on BP, FBG, body weight, BMI, DRQoL, HRQoL, time saving, and cost effectiveness using an intervention of telemedicine to find these outcomes. Wild & Hanley (2016) conducted a randomized, parallel, investigatorblind controlled trial with centralized randomization of 321 people diagnosed with type 2 diabetes and glycated hemoglobin (HbA1c) greater than 58 mmol/mol. Patients self-monitored and transmitted their blood sugar levels to a secure website twice weekly during the morning and evening. Individuals in the intervention group had a decrease in HbA1c and Blood Pressure compared to the control group. The results showed that the monitored group HbA1c decreased by 5.60 mmol/mol / 0.51% lower compared with the control group. The systolic BP decreased by 3.06 mmHg & diastolic BP decreased by 2.17 mmHg in the interventional group compared to the control group. This study showed that telemonitoring and supported self-management of blood glucose can result in clinically meaningful improvements in blood glucose among people with poorly controlled type 2 diabetes. Anderson, O’Connell, Thomas, and Chimmanamada (2021) conducted a systematic review and meta-analysis of randomized controlled trials including nine studies. The review and analysis of the nine studies were used to evaluate the effectiveness of telehealth interventions aimed at improving HbA1c values among Black and Hispanic patients with type 2 diabetes connected to primary care. Telehealth interventions were health care, health education, and health information services by remote technologies. The clinical effectiveness was aimed at evaluating the hemoglobin A1c pre- and post-telehealth intervention. The major findings indicated a net change decreased by -0.47%, this is a significant change in this case. These findings suggest telehealth interventions can be effective at improving glycemic control among Black and Hispanic diabetes patients. Zhai, Zhu, Cai, Sun, & Zhao (2014) conducted a systemic Review and Meta-analysis of 35 randomized controlled studies tied to patients with type 2 diabetes who were 18 years or older and receiving insulin or oral diabetic drugs. The sample size ranged from 13-844 individuals from various health care settings. The purpose of the study was to evaluate the clinical effectiveness and cost effectiveness of telemedicine approaches on glycemic control in patients with type 2 diabetes mellitus. After implementing the telehealth interventions which included virtual visits, telephone calls, and website check ins, A1C improved from 6.4- 11.2 to 6.4-8.7. The intervention also revealed institute for clinical and economic review (ICER) of $491 and $29,869 per capita for each unit reduction in HbA1c, for the telephone and internet base interventions. Wong, Wang, and Manoharan (2021) conducted a quasiexperimental, retrospective review of electronic medical records (EMRs) of 629 outpatient diabetic patients from two major hospitals in Sydney, Australia for a five-month period. Years reviewed were 2019 and 2020. Methods used for telehealth consultations consisted of voice calls, video calls, and sending blood glucose level results electronically. The review found that the attendance rate improved from 85.2% to 88.9%, HbA1c improved from 8.2 to 7.8. There was no statistical significance in unplanned admissions from 75 readmissions to 58 admissions after the telehealth interventions were implemented, albeit it improved. HbA1c was collected one year prior to 2019, one visit prior to the COVID-19 pandemic, and one visit a year after their pre-virtual management period. Conclusion: All six articles supported the use of mobile telehealth interventions in the care management of patients with diabetes. Five of the six articles showed clinically significant changes in the hemoglobin A1c (HbA1c), while one article showed a marginal change in the HbA1c. Across the studies, the most commonly used modes of telehealth include video conferencing, telephone calls, texting, and internet programs. The evidence suggests these interventions lead to effective management of diabetes. Implications for Nursing Practice: Diabetes is a chronic condition that can have long-lasting effects on patients. The effects the interventions have on diabetic patients would be beneficial in effectively improving hemoglobin A1c. Care surrounding the management of diabetes should aim to implement mobile telehealth interventions as adjunct means to managing diabetes. Patients should also be encouraged to follow-up with their diabetes providers for optimal outcomes.
July, August, September 2022 The North Dakota Nurse Page 7 Staffing Effect on Hospital Acquired Infections Appraised by: Kiana Schatz SN, Chelsie Shook SN, Dominic Sieve SN, Jada Kjonaas SN, Kathryn Dragseth SN, Kylee Utter SN Allison Sadowsky MSN RN Assistant Professor of Practice (Faculty) (NDSU School of Nursing at Sanford Bismarck) Clinical Question: In hospitalized patients, does staffing on the unit affect hospital acquired infections? Sources of Evidence: Daud-Gallotti, R. M., Costa, S. F., Guimarães, T., Padilha, K. G., Inoue, E. N., Vasconcelos, T. N., da Silva Cunha Rodrigues, F., Barbosa, E. V., Figueiredo, W. B., & Levin, A. S. (2012). Nursing workload as a risk factor for healthcare associated infections in ICU: A prospective study. PLoS ONE, 7(12). https://doi. org/10.1371/journal.pone.0052342 Kaier, K., Mutters, N. T., & Frank, U. (2014, December 13). Bed occupancy rates and hospital-acquired infections-should beds be kept empty? Clinical Microbiology and Infection. Retrieved March 20, 2022, from https://www.sciencedirect.com/ science/article/pii/S1198743X14610909 Kouatly, I. A., Nassar, N., Nizam, M., & Badr, L. K. (2018). Evidence on Nurse Staffing Ratios and Patient Outcomes in a Low-Income Country: Implications for Future Research and Practice. Worldviews on Evidence-Based Nursing, 353-360. Mitchell, B. G., Gardner, A., Stone, P. W., Hall, L., & Pogorzelska-Maziarz, M. (2018). Hospital staffing and health care–associated infections: A systematic review of the literature. The Joint Commission Journal on Quality and Patient Safety, 44(10), 613–622. https://doi.org/10.1016/j. jcjq.2018.02.002 Shang, J., Needleman, J., Liu, J., Larson, E., & Stone, P. W. (2019). Nurse Staffing and Healthcare- Associated Infection, Unit-Level Analysis. The Journal of nursing administration, 49(5), 260–265. https://doi.org/10.1097/NNA.0000000000000748 Tawfik, D., Profit, J., Lake, E., Liu, J., Sanders, L., & Phibbs, C. (2020). Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit. Health Services Research, 190-200. Synthesis of Evidence: Six articles were reviewed as evidence in this report including a prospective cross-sectional study, a prospective cohort study, a repeated measures observational study, two systematic reviews, and a quantitative study with cross sectional data analysis. The articles are reviews of studies conducted to determine the relationship between the occurrence of hospital acquired infections or patient outcomes, and how they are related to nurse workload and staffing ratios. The first article by Kouatly, Nassar, Nizam, & Badr (2018) was a prospective cross-sectional study. This study was conducted to describe the relationship between nurse staffing and nurse sensitive outcomes (NSOs) at a Magnet designated university hospital in a low-income country. This study included 68,000 patients who had a fall, an injury fall, hospital-acquired pressure injury (HAPI), catheter-associated urinary tract infection (CAUTI), central line bloodstream infection (CLABSI), and ventilator-associated pneumonia (VAP) after admission during July 2011 to June 2013. The results concluded that in the critical care unit (CCU), high nurse hours per patient days (NHPPD) compared to low NHPPD it reduced total patient falls by 79%, injury falls by 77%, HAPIs by 74%, CAUTIs by 23%, CLABSIs by 62%, and VAP by 38%. In the medical-surgical unit, the results concluded that when there was high NHPPD compared to low NHPPD it reduced falls by 84%, injury falls by 75%, HAPI by 74%, CAUTIs by 39%, CLABSIs by 79%, and VAP by 20%. These results imply that when more nurses are available, the likelihood of adverse NSOs decreases. The second article by Daud-Gallotti, Costa, Guimarães, Padilha, Inoue, Vasconcelos, da Silva Cunha Rodrigues, Barbosa, Figueiredo, & Levin (2012) was a prospective cohort study. The aim of the study was to evaluate the role of nursing workload in the occurrence of HAIs (hospital acquired infection) in medical ICUs (intensive care unit) using a nursing activities score (NAS). The study included 195 patient’s ages 12+ who were admitted to three medical ICUs and a step-down unit. The results concluded that out of the 195 patients’ being evaluated, 43 of them developed HAI and data analysis showed that excessive nursing workload was found to be the biggest contributing factor to these HAI. It was also found that of the 195 patients, 79 of them suffered from adverse effects during their stay in the ICU. The third article by Tawfik, Profit, Lake, Liu, Sanders, & Phibbs (2020) was a repeated measures observational study. The purpose of the study was to determine whether adjusted nurse staffing in hospitals contributed to patient outcomes. The study took place in 99 California neonatal intensive care units (NICUs) where they studied 276,054 infants born between January 2008-May 2016. The study aimed to evaluate staffing effects on HAIs, infant mortality, and length of stay (LOS). The results concluded that increased nurse staffing may result in reductions of patient-related events. Although the results showed there was no impact on staffing related to infant mortality and LOS, there was great evidence regarding staffing impacts on lower odds of HAIs. There was a total of 25,744 infants that fell into the California Perinatal Quality Care Collaborative (CPQCC) test cohort and of those infants, 52% fell into the low birth weight and 7.2% experienced a HAI. These numbers corresponded to lower odds of HAI with each additional nursing hours per patient-day (NHPPD). The results supported the idea that staffing on a hospital unit does indeed affect the rates of hospital acquired infections. The fourth article by Kaier, Mutters, and Frank (2014) was a systematic review. The study was conducted to look at the correlation of bed occupancy rates and the effects of overcrowding and understaffing on hospitalacquired infections. The study included 12 studies that were reviewed for analysis. These studies included various hospital settings which were composed of general wards, surgical wards, ICU’s, and NICU’s. The results concluded that high bed occupancy rates and understaffing directly impacted the incidence and spread of hospitalacquired infections. This was evidenced by nine of the twelve studies finding a positive correlation, whereas three of the studies found no correlation or a negative correlation. Therefore, hospitalacquired infections are at risk of increasing if overcrowding of beds and understaffing continue to be issues within the hospital setting. The fifth article by Mitchell, Gardner, Stone, Hall, Pogozelska-Maziarz (2018) conducted a systematic review. The systematic review included Appraised by: Leah Nelson, RN, Danica Calderon, RN, Vanessa Poitra, RN, and Mikayla Kitsch, RN: students at Mayville State University RN-to-BSN Program Clinical Question: Within staff in a medical-surgical unit, does bedside hand-off report compared to traditional report impact the number of errors made on a given shift? Articles: Becker, Sherry, MSN-Ed, RN-BC, Hagle, Mary, PhD, RN-BC, Amrhein, Andra, BSN, RN, et al. (2021). Implementing and sustaining bedside shift report for quality patient-centered care. Journal of Nursing Care Quality, 36, 125-131. Gettis, M. A., Dye, B., Williams, C., Frankish, B., & Alvarez, B. (2019). Bedside report: Nursing handoffs impact outcomes for caregivers, healthcare providers, and organizations. Worldviews on Evidence-Based Nursing, 16(6), 495–497. https://doi.org/10.1111/wvn.12404 McAllen, E., Stephens, K., Swanson-Biearman, B., Kerr, K., & Whiteman, K. (2018). Moving shift report to the bedside: An evidence-based quality improvement project. OJIN: The Online Journal of Issues in Nursing, 23(2). https://doi.org/10.3912/ ojin.vol23no02ppt22 Small, D. and Fitzpatrick, J. (2017). Nurse perceptions of traditional and bedside shift report. Nursing Management, 48, 44-49. Synthesis of Evidence: This synthesis includes four studies that provide evidence supported to the research question. The Bedside Reporting 54 studies. Study designs included cohort, case control, cross-sectional, randomized controlled or case reports. The aim of the study was to examine the association between hospital staffing and patients’ risk of developing HAIs in hospital settings. The studies were further categorized into studies examining nurse staffing and single site-specific infection; studies examining nurse staffing and multiple HAI’s; studies examining nurse staffing and organism-specific HAI’s; studies examining nurse staffing and HAI’s (unspecified infection type; and lastly, studies examining nonnurse staffing and HAI’s. Major findings of this systematic review find that 42/54 of the studies, suggest that staffing levels are associated with HAI’s. Increased levels of staffing seem to be connected to a decrease in the risk of patients acquiring HAI’s. The sixth article by Shang, Needleman, Liu, Larson, and Stone (2019) was a quantitative study with cross sectional data analysis. The study was done between 2007-2012 to examine whether healthcare associated infections (HAIs) and nurse staffing are associated using unit-level staffing data. It included 100,264 patients in the ICU, medical, med/surg, and step-down units in three hospitals in a large metropolitan area. The results concluded the hazard rate of HAIs in patients on units with nursing staff (NS) understaffing on both shifts two days before infection onset were 11% higher than for those staying in units with both day and night shifts adequately staffed with NS. Conclusion: All six articles indicated that staffing on a nursing unit plays a role in the prevalence of hospital acquired infections. These articles support the same idea that either increasing staff on a hospital unit contributes to a decreased rate of infection, or in the same way, that by decreasing staff on a hospital unit it contributes to an increased rate of infection. Implications for Nursing Practice: Findings suggest that staffing is directly related to the rate of hospital acquired infections. By increasing nurse staffing, increasing nurse hours per patient day, eliminating excessive nurse workloads, and eliminating overcrowding, it leads to a decreased rate of adverse effects, with the main one being HAIs. Staffing nurses properly will allow for more compliance to care plans and adherence to infection control prevention. first study completed by S. Becker et al., (2021), identified the lack of bedside report increased the number of errors from miscommunication. The second study by Gettis et al., (2019), using the situation, background, assessment, recommendation (SBAR) method was used less inconsistent when report is given at the bedside. The third study by McAllen et al., (2018), identified less falls and improved satisfaction from the nurse and patient. The fourth study by Small and Fitzpatrick (2017), found that bedside report improves nurse accountability, decrease patient errors, and increase patient involvement. Bottom Line: Based on current research, there is evidence to show improvement in patient safety outcomes when using bedside report to help reduce the number of errors on shift. Bedside report can reduce errors by making sure drips, lines, and wounds are in order before the off-going nurse leaves. This bedside report helps reduce the patient’s hospital anxiety by providing them a better understanding of their plan of care and allow them to participate in the decision-making process. Bedside report can also reduce the risk of out of bed falls by making sure the patient is safe with visual observation until the new nurse comes in to complete the new shift assessment. Bedside Reporting continued on page 9
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