Hospital settings are known to have complex care actions, which can lead to confusion and medical errors. For instance, patients admitted for delivery may be transferred to the surgery department for a cesarean. Patients are transferred between numerous units, which might increase the chances of medical errors (Ting et al., 2017). In this fragmented environment, the reduction of medical errors is critical in enhancing patient safety and outcomes. Patients need to be given their newest therapeutic information as they are transferred from one unit to another. Patient handoff is a necessary procedure that involves passing valuable information between various hospitals or departments. Health care professions in busy environments may, however, neglect such critical patient information during shift change.
The origin of the standard handoff structure, Situation, Background, Assessment, and Recommendations (SBAR) is traced to be United States Navy. SBAR protocol was first introduced in the healthcare setting at Kaiser Permanente in 2003 to structure communications between nurses and doctors on situations that call for the immediate medication (Shahid & Thomas, 2018). Implementing SBAR in the health care setting was to improve nurse-physician communication in acute care situations (MacDonald et al., 2017). SBAR tool has also been of critical help during a handoff in specialties such as obstetrics, emergency medicine, postoperative medicine, and perioperative medicine, among others (Manias, 2018). This study seeks to examine the impact of using SBAR during shift reports to improve communication and reduce medication errors in the Temple University Health System (TUHS). The clinical evaluation exercise (CEX) tool will be used to evaluate communication improvement.
Figure 1: SBAR framework
|S||Situation||This is the patient’s problem, the diagnosis, treatment plan, and patient needs|
|B||Background||What is the cause of the problem, vital signs, mental, lab results, and code status|
|A||Assessment||Provide current assessment on the patient’s problem|
|R||Recommendation||What actions need to be taken or what needs to be done and identify any pending lab or treatment.|
A quantitative study design was used, which involved 30 participants from TUHS obstetric care. Questionnaires were delivered with questions about the care and medical errors during handoffs. The obstetric nurse-physician questionnaire was employed to measure communications between different professionals, and the safety attitude questionnaire was used to measure staff members’ attitudes towards obstetric care patients. A Spritzer’s scale was used to measure the attitude of nurses towards patients. Questionnaires come with the benefit of collecting data from a large sample within a short time. Additionally, they are eradicated with ambiguities that come with the language and attitude of respondents.
Can the use of the SBAR communication tool be effective in reducing medical errors?
Statistical Analysis Method
Data analysis involved descriptive statistics such as percentages, means, Standard Deviation (SD), and absolute numbers. To evaluate change within the discussed problem, descriptive analysis, such as a t-test, would involve the statistical analysis method. The study will employ all prognostic variables that may impact a patient’s outcome in the maternity wing.
Prognostic Variables or Demographics Variables
The main aim of collecting data on medical errors was to provide useful, empirical data to the obstetric unit regarding errors and methods used to reduce their occurrence. To determine the error criteria, the American society of health pharmacists (ASHP) severity index and guidelines were used.
Table 2: Key Variables for the Study
|Communication between doctors and nurses||Ratio|
|Drug name confusion||Nominal|
|Medical obstetric level||Ratio|
|Poor Data entry||Nominal|
|Communication between nurses and patients||Ratio|
|Misinterpreted handwriting and incorrect dosing||Nominal|
Other Key Variables
Communication errors are vital in fueling the variables related to late delivery and maternity mortality rates. Initially, pregnancy outcomes were determined by such factors as late delivery, neonatal and maternal mortality rates. Even though such events have become rare, they are still appropriate variables to determine obstetric care. Morbidity will also be used to become a critical measure of pregnancy outcomes nowadays (Izadpanah et al., 2015). Types of morbidity cases, such as birth injuries, neonatal asphyxia, and infections, will also be used to reflect the quality of obstetric care. The data on these variables will be harvested from the medical health records maternity unit.
Table 3: Prognostic variables
|Maternity mortality rates||Ratio|
The selection of these variables is critical in analyzing obstetrician care since they feature communications between nurses and patients, and nurses and doctors. The TUHS maternity wing has been known to offer quality maternity services following smooth communications. Some of the key performance indicators (KPIs) to be used include the communication between nurses and nurses, nurses and doctors, nurses and patients, drug names, and data entry. Other KPIs include neonatal, maternity mortality rates, and late delivery. To set benchmarks for performance, current clinical practices within TUHS will be evaluated.
The Maternal Newborn Dashboard (MND) implementation is marred by a set of challenges associated with the indicator estimates’ stability with time. Later, when adequate data has been set to populate the dashboard, it will be launched every time the authorized staff log into the system (Ibrahim, 2020). This means that hospitals should acknowledge their monthly data to submit to the system to make it relevant and current on a timely basis (Chappell et al., 2020). Stable indicator estimates are relatively anticipated for a period in hospitals with large delivery volumes (Vanbrabant et al., 2019). Nevertheless, the reverse is true for small hospitals as there will be significant variations from month to month due to communication errors. For instance, in a center with only 100 births in a year, communication errors are expected to be few.
Dashboard KPIs for the Maternal Newborn Dashboard
Key performance issues within the maternity wing to reduce medical errors will be tied to patient handoffs, correct data entry, reduced mortality rates, and patient satisfaction rate. The number of live births is used from the KPIs to identify the level of mortality rate de (De Mendonça Lima et al., 2019). High amounts will, therefore, mean that low mortality rates are experienced and hence good communication. A 2% and below indicates the target, while 2% to 3% reflect the need for caution, and more than 3% indicate a red alert (Uğurlu & Vural, 2020). The number of women who receive spontaneous vaginal births with episiotomy will also be used as a KPI to indicate treatment type. All these KPIs are one way or another connected to mortality rates.
Projected Number Participants
The Data will be obtained via individual sample questionnaires, including 30 maternity unit participants from TUHS obstetric care. Of the 30 participants, 10 will be patients recruited via quota sampling. Initially, letters shall be drafted to the maternity and surgery departments to ask for the research to be conducted. The CEO of the surgery department will also make part of the participants. The rest of the participants will be staff from the maternity unit, including doctors and nurses. Since nurses are the primary caregivers, they will be considered as the chief participants.
The sample of 30 participants will be appropriate as it features the patients, nurses, and doctors, among other key staff in the maternity wing. The Quota sampling method is essential to know which staff is available as well as a patient. The recruitment process will involve agreeing with the participants, after which mails will be sent to them one week prior to the research. The second and last mail will be sent, notifying them of participation in the research. Involving patients in the study will also be vital as it will grant first-hand study information. Nurses perform critical roles when it comes to delivery of care in hospitals and, hence, they are conversant with the KPIs tested in this DNP study.
Implementing the SBAR communication tool in the obstetric unit was associated with reduced medical errors and improved communication between nurses and doctors. Patient handoff is a necessary procedure that involves passing valuable information between various hospitals or departments. Since handoffs are inevitable, smooth communications have to be laid from one department to another. All these variables are critical in measuring fundamental communication errors. Therefore, mortality rates at obstetric care can be reduced by effectively employing the SBAR communication tool, which reduces medical errors. These KPIs and the implementation of a new dashboard will guide the hospital to enhance obstetric care. SBAR facilitates smooth communication between professionals while simultaneously increasing safety and reducing medical errors. Additionally, the adverse effects manifested in a professional hierarchy are also lowered. SBAR communication tool is essential when doing handoffs in healthcare settings as it provides a smooth communication framework.
Chappell, D., Neuhaus, C., & Kranke, P. (2020). Optimal care for mother and child: Safety in obstetric anaesthesia. Best Practice & Research Clinical Anaesthesiology, 2(1), 34-53. Web.
De Mendonça Lima, T., Aguiar, P. M., & Storpirtis, S. (2019). Development and validation of key performance indicators for medication management services provided for outpatients. Research in Social and Administrative Pharmacy, 15(9), 1080-1087.
Ibrahim, N. (2020). How to improve clinical pharmacy practice using key performance indicators. Global Journal of Medical Therapeutics, 2(1), 10-14.
Izadpanah, F., Kashani, H. H., & Sharif, M. R. (2015). Preventing medicine mistakes in pediatric and neonatal patients. Journal of Medicine and Life, 8(3), 6.
MacDonald, K., Cusack, M., Liang, S., & Rinco, K. (2017). Care gaps in the electronic discharge medication reconciliation process at an acute care facility. The Canadian Journal of Hospital Pharmacy, 70(6). Web.
Manias, E. (2018). Effects of interdisciplinary collaboration in hospitals on medication errors: An integrative review. Expert Opinion on Drug Safety, 17(3), 259-275.
Shahid, S., & Thomas, S. (2018). The situation, background, assessment, recommendation (SBAR) communication tool for handoff in health care – A narrative review. Safety in Health, 4(1). Web.
Ting, W., Peng, F., Lin, H., & Hsiao, S. (2017). The impact of situation-background-assessment-recommendation (SBAR) on safety attitudes in the obstetrics department. Taiwanese Journal of Obstetrics and Gynecology, 56(2), 171-174. Web.
Uğurlu, M., & Vural, G. (2020). Medical error status of nurses and midwives work in gynecology and obstetrics clinics and their opinions about the reasons. Bezmialem Science, 8(4), 403-410. Web.
Vanbrabant, L., Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2019). Simulation of emergency department operations: A comprehensive review of KPIs and operational improvements. Computers & Industrial Engineering, 131, 356-381.