I think it would be important to have a clinical spreadsheet over the course of 12-week intervals documenting the most frequent treatments needed along with the least treatments needed. Understanding factors take place such as age, and second therapy medications to determine if there is any data available to determine if other forms of treatment can aid is resolution of depression faster. “The vast majority of depressed patients referred for ECT will have severe, debilitating illness that has not responded to multiple medications and psychotherapy. The urgency of the clinical situation, often marked by intense suicidal preoccupation and drive, is commonly what compels the recommendation for ECT.” (Kellner, Obbels, & Sienaert, 2020).
As a nurse leader, one can use this information and relay the data on to the psychiatrist to help facility best practice measures along with safety of treatments for patients.
References
Kellner, C. H., Obbels, J., & Sienaert, P. (2020). When to consider electroconvulsive therapy (ECT). Acta Psychiatrica Scandinavica, 141(4), 304-315. doi:https://doi-org.ezp.waldenulibrary.org/10.1111/acps.13134
Sweeney, J. (2017). Healthcare Informatics. Online Journal of Nursing Informatics, 4-1.
I have spent the last 10 years working in emergency rooms as a staff nurse. One of the biggest challenges that my department faces regularly is delays with getting admitted patients out of the ED and onto their assigned units. These delays negatively impact the patients waiting for emergency treatment in the lobby and hallway stretchers. There are a number of factors that can prolong ED length of stay. Some of these include lack of bed availability due to hospital overcrowding, treatment delays such as loss of IV access, and delays caused by hospital personnel during the handoff report process (Paling et. al, 2020). Some of these factors, such as hospital overcrowding, are unavoidable and difficult to work around, which is why it is important for hospitals to assess which factors they can control to expedite patient flow out of the emergency room.
For my hospital’s scenario, the emergency department would collect data about admission delays that are specifically caused by disruptions in the nursing telephone report process. In my current workplace, there is not a standardized electronic handoff form, despite the fact that several studies have demonstrated the efficiency and increased patient safety outcomes associated with the transition to standardized electronic nursing report (Wolak et al., 2020). Instead, the ED nurse calls the receiving unit on the telephone, gives a verbal patient care handoff, and then transfers the patient to their hospital room. By collecting data about where in the handoff process delays are occurring, the ED could try to streamline the handoff process with the medical floors.
The emergency department nurses would collect quantitative data about the length of time between the first attempt to call report to the medical floor, and the time of the patient’s actual departure from the ED. The data would be recorded in the section of the EMR called “time to disposition” for each patient that is admitted. The ED leadership team could then pull a certain number of charts per month (or all the admission charts, if time allowed) and assess how long it takes on average for patient transfer to happen after report. Generally, most hospitals set their goals for disposition time for handoff and transfer within a 30-minute window (Potts et. al., 2018). If there are frequent delays causing transfer time to take greater than 30 minutes, the ED leadership team or unit-based council could meet with leadership from the floors where patient transfer takes the longest. By demonstrating the hard numbers associated with patient care delays, the teams could better understand the factors that lead to admission delays and work together to find solutions that expedite the admissions process.
References:
Paling, S., Lambert, J., Clouting, J., González-Esquerré, J., & Auterson, T. (2020). Waiting times in emergency departments: Exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data. Emergency Medicine Journal. https://doi.org/10.1136/emermed-2019-208849
Potts, L., Ryan, C., Diegel-Vacek, L., & Murchek, A. (2018). Improving patient flow from the emergency department utilizing a standardized electronic nursing handoff process. JONA: The Journal of Nursing Administration, 48(9), 432–436. https://doi.org/10.1097/nna.0000000000000645
Wolak, E., Jones, C., Leeman, J., & Madigan, C. (2020). Improving throughput for patients admitted from the Emergency Department. Journal of Nursing Care Quality, 35(4), 380–385. https://doi.org/10.1097/ncq.0000000000000462
Response
This is insightful Andrea; admission delays often lead to adverse treatment outcomes. The delays in patients’ admission to different hospitals are attributed to the increased number of patients or overcrowding. The impacts of delayed admission can be severe, including longer hospital stays, the inability of patients to access appropriate beds, and experienced healthcare experts (Goertz et al., 2020). Most patients leave without getting treatment due to delayed admissions to different healthcare facilities (Paling et al., 2020). There is a need for quality improvement to facilitate improvements in admission rates. The quality improvements should rely on the data collected in the course of operation. The application of the EMR system is one of the best methods of data collection in healthcare (Pastorino et al., 2019). Measuring and recording the time taken during hospital admission is necessary for determining areas that require adjustments. Through the analysis of the collected data or information, healthcare institutions are able to initiate quality improvement processes and ensure effective outcomes in the management of patients. One of the questions that I would ask is: What variables ought to be involved in the data collection processes?
References
Goertz, L., Pflaeging, M., Hamisch, C., Kabbasch, C., Pennig, L., von Spreckelsen, N., … & Krischek, B. (2020). Delayed hospital admission of patients with aneurysmal subarachnoid hemorrhage: clinical presentation, treatment strategies, and outcome. Journal of neurosurgery, 134(4), 1182-1189. https://doi.org/10.3171/2020.2.JNS20148
Paling, S., Lambert, J., Clouting, J., González-Esquerré, J., & Auterson, T. (2020). Waiting times in emergency departments: Exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data. Emergency Medicine Journal. https://doi.org/10.1136/emermed-2019-208849
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health, 29(Supplement_3), 23-27. https://doi.org/10.1093/eurpub/ckz168
The phone rings on a busy Saturday afternoon and the pleasant voice of a registered nurse answers professionally, greeting the caller seeking advice and care. This could be a day for a typical for an ambulatory telephone triage nurse. The concept of telephone triage and consultation can be one of a registered nurse using evidence-based algorithms from electronic databases. The nurses, like most nurses, working in a progressive health care industry are using technology to counsel patients. According to McGonigue & Mastrian, (2022), “For information to be valuable, it must be accessible, accurate, timely, complete, cost-effective, flexible, reliable, relevant, simple, verifiable, and secure.” p.9.
This information could be valuable to many leaders in the healthcare team. Accessibility would be easiest in form of electronic records and telephone recordings. McGonigue & Mastrian (2022), argue, “Computer science offers extremely valuable tools that when used skillfully, can facilitate the acquisition and manipulation of data and information by nurses, who then can synthesize the data into an evolving knowledge and wisdom base ”p. 35). Accurate and timely information could be an interest in nursing quality and control. One argument on how telephone triage could be cost-effective is that paying nurses to man the phone lines is cheaper than using inappropriate resources such as the emergency room to care that can be directed elsewhere. Flexibility, reliability, simple, verifiable and secure would require a more in-depth look into the nature of telephone triage and program development within a system, but the concept of triage nursing seems to be malleable to the interest of how the data would be used.
An additional source of centralized evidence-based algorithm software program could also be used and from my research is being used in assisting the nurses to effectively triage the caller and ensure best practice standards. Documentation done by triage nurses would have data from the callers that are subjective and objective, the nursing assessment, and recommendations based on the call.
From this data collection, multiple departments within healthcare could use this or would have an interest in this data collection. Intradisciplinary teams have an opportunity to look at how to retrieve data from electronic retrieval of health records or from recorded lines if those are being used. An ambulatory nurse manager might be interested in using the data as a system educator of staff development and improvement strategy to support the training needs within their triage staff. A quality nurse might want to use this data to help in creating of protocol development and safety improvements for effective triage and outcomes. Ambulatory providers could use data to see the patient population’s interests and barriers to care and from there use it to modify their practices. Health information technology departments within health care organizations could be supportive of this nursing department in implementing programs in making documentation more time efficient and detailed. Nursing leadership could use this as a cost-effective strategy.
All departments could build off one another and become temporary team members to gain knowledge and benefit in patient care and satisfaction. Emerging roles could be created as, “Teams are working across boundaries of organizations and will be organized around a particular patient.” (Nagale et al, 2017, p. 215). Within most healthcare systems the mission and visions of these organizations are built on patient outcomes and patient centered care. An informatics nurse specialist could support patients, nurses, providers, and leaders with the interpretation of data analytics and therefore participate in applying new knowledge from data to wisdom. (Nauright et al., 1999)
This hypothetical scenario of a nurse working at a telephone triage call center would benefit immensely from data access, problem-solving and the process of knowledge formation. In a real-time, scenario, I could see how this could impact patient care and outcomes on a global level and be a perfect role for a nurse informatics specialist to pilot.
The scenario I experienced recently involves the hospital readmission rate of my dialysis patients diagnosed with fluid overload. During data gathering, I noticed that at least three patients whose target weights are below 90kg have intradialytic weights of more than 4kg for the last three treatments. They are also less than three months in dialysis, considered new patients. They are also not reaching their dry or target weight on the data. Their blood pressures were also greater than 160/90mmhg before dialysis, even when they reported taking their blood pressure medicine at home. Using my clinical reasoning and judgment, patients with high blood pressure, more than 4kg of intradialytic weight gain, and not reaching their target weight are collecting fluid in their bodies. When they come to treatment and have gained 4kg, we calculate the fluid removal goal of 4kg plus 0.5kg Normal Saline rinse when we start dialysis and return their blood. Removing 4.5 kilograms of liquid in a patient weighing 90kg below is hard on the patient’s heart. There is a high chance they will experience cramping, nausea, vomiting, and hypotension. The data on the three patients showed fluid removal of less than 4kg in every treatment. This showed that patients accumulate extra fluids in their bodies during every therapy. By the end of three treatments, their body can no longer handle the excess fluids which go to their organs, such as the lungs making them short of breath.
When admitted, they are dialyzed daily until the extra fluids in their body are removed. They will get discharged if no other complications are found. They will be back on their routine, which is dialysis treatment three times a week in our facility. Diet counseling is the best plan to help them avoid gaining too much when they return. Since they are new patients, they still struggle with what food is right for them. Together with the dietician, we conducted one-on-one counseling with the patients and allowed family members. While the dietitian counsels them on food, I incorporated my education on organs affected by fluid overloads, such as the heart and the lungs. Education such as limiting fluid intake to 32oz a day, foods low on sodium, and informing them that fruits contain fluids added to the 32oz day limit.
Healthcare is experiencing rapid transformation. Digital technologies are advancing and aiming to make healthcare safer and more effective. Health informatics is one way of assisting healthcare is evolving. Health Informatics “integrates nursing, information and communication technologies, and professional knowledge to improve patient outcomes (Reid et al., 2021).”
References:
Reid, L., Maeder, A., Button, D., Breaden, K., & Brommeyer, M. (2021). Defining Nursing Informatics: A Narrative Review. Studies in health technology and informatics, 284, 108–112. https://doi.org/10.3233/SHTI210680
My current healthcare organization attends to numerous patients diagnosed with chronic illnesses such as cardiovascular diseases, diabetes, and cancer. The risk factors for most of these conditions can be identified early through screening and mitigated or approached taken to reduce the impact of the disease. Healthcare data can be potentially useful in predicting a patient’s risk for a disease such as Type 2 Diabetes, which has been a major concern due to its associated morbidity and mortality. The Electronic Health Record (EHR) can be used to collect a patient’s data including, their past medical history, family, social history, and lifestyle practices (Dash et al., 2019). The data can be collected on the initial contact with a patient, and health providers should be advised to take a comprehensive patient history in the first contact.
The data can be used to predict a patient’s degree of risk to a particular chronic illness such as diabetes. For instance, health providers can identify risk factors for diabetes such as the history of overweight, obesity, or high blood pressure, positive family history of diabetes, sedentary lifestyle, smoking, and excessive alcohol consumption. The data can guide health providers to make data-driven decisions to enhance a patient’s outcomes such as, requesting additional screenings or providing patient education on weight management and adoption of healthy lifestyles (Dash et al., 2019). Furthermore, for patients diagnosed with diabetes, the health provider can access the data in the EHR to monitor their treatment plans and guide on pharmacological management to promote better outcomes.
A nurse leader can use patient data from EHR to strategically plan and lead the healthcare team in developing treatment plans for patients. Nurse leaders can also analyze patients’ data from different demographic groups and identify what factors limit patients from achieving the desired health outcomes (McGonigle & Mastrian, 2017). Furthermore, the data can be used to form knowledge on ways to enhance clinical practice and new ways to provide patient care, to enhance health outcomes.
References
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 54. https://doi.org/10.1186/s40537-019-0217-0
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
