A relevant scenario in nursing that benefits from data access is the transition from traditional methods of passing off patient information during shift changes to an electronic nursing pass-off system. Additionally, upgrading the current Meditech charting system at our hospital to a new system that integrates Patient Keeper for mobile access to patient data is a significant change for our hospital. Currently, nurses in my facility are only able to access patient information through desktop computers, and the process involves double documentation, which takes valuable time away from patient care. With the new system, nurses will have access to real-time patient data on their mobile devices, making it easier to access up-to-date information at the point of care. This access allows for more efficient communication between nurses during shift changes and streamlines the documentation process, ultimately improving patient care.

In this scenario, data access helps solve problems by enabling quicker decision-making and reducing time spent on administrative tasks. By eliminating redundant documentation, nurses can spend more time with their patients, focusing on critical care rather than spending time inputting data in multiple places. Moreover, having mobile access to data improves the timeliness and accuracy of the information nurses use to make decisions, leading to better outcomes for patients.

The role of nurse leaders is crucial in implementing these changes, as they are often the ones working directly with hospital executives to gain approval and secure the resources necessary for technology upgrades. Nurse leaders also have the responsibility to advocate for their teams, pointing out any flaws or inefficiencies in the current system that affect patient care. In our facility, we have discussed how important it is to improve the handoff process and streamline access to patient data. This is where nursing informatics comes into play, allowing nurse leaders to ensure that the systems in place are not only efficient but also contribute to safe and high-quality care (Booth, Strudwick, McBride, O’Connor, & Solano López, 2021).

The integration of nursing informatics into practice also has broader implications for knowledge formation. Access to better data allows nurses to track patient outcomes more effectively and identify trends that inform evidence-based practices. For example, with the adoption of telehealth programs, nurses can remotely monitor patients with chronic conditions, providing daily coaching and triage that help prevent unnecessary emergency department visits. The data gathered from these programs can be analyzed to determine the effectiveness of certain interventions and improve practices across the healthcare system. Additionally, when nurses are able to aggregate and analyze data from multiple patients, they can contribute to the development of new best practices for managing various health conditions, which ultimately adds to the collective knowledge of the nursing profession.

In conclusion, access to data in nursing, through tools such as electronic handoffs, mobile technologies, and telehealth programs, not only facilitates problem-solving by improving efficiency and communication but also contributes to the advancement of knowledge in the field (Melnyk & Fineout-Overholt, 2019). By embracing nursing informatics, nurse leaders can ensure that their teams have the resources and tools necessary to provide safe, effective, and high-quality care, while also contributing to the ongoing development of nursing practices and patient outcomes.

Resources :

Booth, R. G., Strudwick, G., McBride, S., O’Connor, S., & Solano López, A. L. (2021). How the nursing profession should adapt for a digital future. The BMJ, 373, n1190. https://doi.org/10.1136/bmj.n1190

Melnyk, B. M., & Fineout-Overholt, E. (2019). Evidence-based practice in nursing & healthcare: A guide to best practice (4th ed.). Lippincott Williams & Wilkins.

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A scenario in the role of a Post Anesthesia Care Unit (PACU) nurse, improving pain management for post-surgical patients during recovery. Pain management in the recovery room is crucial for recovery outcomes for those recuperating from surgery. According to McGonigle, Mastrian, awareness and comprehension of information, as well as its application in supporting specific tasks or making decisions, can help understanding of what to do.

Gathering data to implement clinical enhancements can result in improved outcomes and improve patient satisfaction in pain management. Collecting patient feedback through surveys can document experiences with pain management, noting the effectiveness and side effects of medications. Pain scores, based on numeric rating scales, can track fluctuations in pain levels and identify trends. Additionally, Electronic Health Records (EHRs) can be used to monitor prescribed pain medications, their dosages, and administration details.

A nurse leader can utilize this data to develop and execute plans for improvement.  A leader would be able to provide ongoing education and training for the PACU staff on the best practices in pain management. The gathered data can also help identify trends in pain levels and the efficacy of medications, allowing for the formulation of effective strategies. Moreover, a nurse leader can work in collaboration with interdisciplinary teams to apply and assess the impact of these interventions. Additionally, they can advocate for resources and the establishment of policies that guide efficient pain management (ASPAN, n.d.). This comprehensive strategy guarantees that pain management is not only efficacious but also centered around the patient and subject to ongoing enhancement

References

American Society of Peri Anesthesia Nurses: Identifying and managing critical situations in the pacu. (n.d.). https://www.aspan.org/Portals/88/Conference/2022/Handouts/Sunday/107_Manage_Critical_Event.pdf?ver=AyEahXMUXLvpuhSGj_5scQ%3D%3D

McGonigle, D., & Mastrian, K. G. (2022). Nursing Informatics and the foundation of knowledge. Jones & Bartlett Learning, LLC.

Consider a hypothetical scenario based on your own the hospital emergency room that would require or benefit from the access/collection and application of data. Your scenario may involve a patient, staff, or management problem or gap.

Scenario: Improving Emergency Room Efficiency and Patient Care Through Data Collection and Analysis

Background:

The hospital emergency room is facing issues with high patient wait times, overcrowding, and not using resources appropriately. The hospital staff is overwhelmed, and patients sometimes experience delays in receiving care, leading to increased stress for patients and staff. The patients are leaving (AMA) against medical advice before being seen by a physician.  The management is concerned about the overall quality of care being provided and the operational efficiency of the ER.

Data Collection Needs:

Patient Arrival Data:

Collect real-time data on when patients arrive at the ER, the reason for their visit, and their priority level (based on triage).

Track patient demographics, including age, gender, and underlying conditions, to identify why patients are coming to the ER.

1. Wait Time Data:

Monitor the time each patient spends in different        stages of the ER process, including waiting to be triaged, waiting to see a doctor, waiting for diagnostic tests, and waiting for medication/treatment.

Track the number of patients in the ER at any given time and their current status in the treatment process.

2. Staffing and Resource Data:

Track the availability and workload of ER staff, including doctors, nurses, and technicians.

Monitor the utilization of critical resources, such as beds, medical equipment, and diagnostic tools (e.g., X-ray machines).

3. Patient Outcome Data:

Collect data on patient outcomes, including recovery times, complications, or transfers to other departments ( ICU, floor, or surgery)

One of the biggest benefits is gather feedback from patients about their experience, wait times, and perceived quality of care.

Application of Data:

1. Predictive Analytics for Patient Flow Management:

Use historical information on patient arrivals, diagnoses, and treatments to predict busy periods (during flu season, holidays, or other peak times). This will help the ER management schedule staff more effectively, ensuring that there are enough doctors, nurses, and support staff during high-demand times.

Use predictive charts and graphs to estimate wait times for patients based on current ER capacity and patient acuity levels. This allows for better communication with patients about wait times and expectations.

2. Triage Optimization:

Data on patient acuity levels can help improve the triage process.  Quickly  assessing patient symptoms and recommending the appropriate triage level for treatment. This would ensure that critical patients receive immediate care while minimizing delays for less severe cases.

3. Resource Allocation:

By analyzing old and new data, ER management can identify patterns in resource usage (peak demand for specific diagnostic equipment or high rates of certain injuries) and allocate resources more effectively. For instance, more X-ray machines can be made available during high trauma times or additional beds could be set up during a flu outbreak.

Track staff workload and shift patterns to ensure that the right number of healthcare professionals are available when needed. This can also help reduce staff burnout and improve retention rates.

4. Improving Patient Satisfaction:

Data from patient surveys and experience metrics (feedback on wait times, communication, staff professionalism) can be analyzed to identify areas for improvement in patient care. This feedback could be used to train staff and improve the overall patient experience.

Analyzing wait time data allows management to identify bottlenecks in the process, so they can address issues like long waiting periods for triage or test results.

Benefits of Data Use:

Reduced Wait Times: Predictive analytics allows for better scheduling and optimized resource allocation, reducing patient wait times and improving care efficiency.

Improved Patient Outcomes: Data-driven decisions based on patient history and real-time diagnostics lead to better treatment outcomes, fewer complications, and a quicker recovery.

Enhanced Staff Efficiency: Tracking staffing levels and resource availability ensures that the ER runs smoothly even during peak hours, preventing burnout and reducing the chance of errors.

Increased Patient Satisfaction: By addressing the common pain points of long waits and communication issues, the hospital can provide a better overall patient experience, increasing patient satisfaction and trust in the ER.

Conclusion:

By effectively collecting and analyzing data related to patient arrivals, wait times, staffing, and resources, the hospital ER can optimize operations, improve care quality, and enhance patient satisfaction. This data-driven approach leads to a more efficient, responsive, and compassionate ER environment.

References:

Hoot, N. R., & Aronsky, D. (2008). The impact of emergency department crowding on clinical outcomes. Academic Emergency Medicine, 15(11), 1267-1273. https://doi.org/10.1111/j.1553-2712.2008.00295.x

Kastner, M., & Straus, S. E. (2014). Improving patient care using data and analytics: Challenges and opportunities. Healthcare Management Forum, 27(3), 123-127. https://doi.org/10.1016/j.hcmf.2014.06.003

Currently, I am a kidney transplant coordinator. As volume has grown at our transplant center, there has been an increase in transplant rejection episodes. Although there are many reasons rejection can occur, the most common but avoidable cause is medication noncompliance (Cleveland Clinic, n.d.). My healthcare practice would benefit from collecting and applying data to proactively identify patients with potential adherence issues to decrease rejection episodes and improve patient outcomes by finding the root cause of noncompliance.

According to McGonigle and Mastrian (2022), the foundation of nursing science practice is “using information, applying knowledge to a problem, and acting with wisdom”.  As such, noncompliant patients within the past year would be identified through Epic charting system by report.  Once non-compliant patients are identified, we would need to find commonalities in reasons for noncompliance. From clinical experience I have found that the lack of social support, transportation, adequate funds, and sometimes insurance is to blame for medication noncompliance.  However, to determine the true cause it would be prudent to ask the patients directly in a phone interview or face to face interaction. Once reasoning is identified, we could be more proactive in our approach and apply early interventions to decrease rejection episodes and improve patient outcomes.

Reference

Clevland Clinic. (n.d.). Kidney transplant rejection. https://my.clevelandclinic.org/health/diseases/21134-kidney-transplant-rejection#symptoms-and-causes

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning

In considering a hypothetical scenario, my practice focuses on high patient readmission rate as a key issue. There is a gap in medication adherence and follow-care from the patient discharged after treatment (Ayabakan et al., 2021). To collect data, I would focus on comorbidities, demographics, compliance with discharge instructions and post-discharge follow-up. They can be collected through patient surveys, electronic health records (EHR) and pharmacy databases (Qiu et al., 2022). The analysis will confirm underlying patterns like limited access to medications prescribed (for the socio-economically disadvantaged groups) and inadequate education upon discharge.

The data will present insights like risk factors for readmissions and potential mitigation strategies like telehealth follow-ups or home health visits. When responding to the scenario, a nurse leader will use clinical reasoning through data trends synthesis in developing focused interventions, including improved care coordination and enhanced protocols of discharge planning (Famure et al., 2021). It is an evidence-based approach whose outcomes will ensure individualized and patient-centered strategies for reducing readmissions and improving overall patient outcomes.

Ayabakan, S., Bardhan, I., & Zheng, Z. (2021). Triple aim and the hospital readmission reduction program. Health Services Research and Managerial Epidemiology, 8, 2333392821993704. https://doi.org/10.1177/2333392821993704

Famure, O., Kim, E. D., Au, M., Zyla, R. E., Huang, J. W., Chen, P. X., … & Kim, S. J. (2021). What are the burden, causes, and costs of early hospital readmissions after kidney transplantation?. Progress in Transplantation, 31(2), 160-167. https://doi.org/10.1177/15269248211003563

Qiu, L., Kumar, S., Sen, A., & Sinha, A. P. (2022). Impact of the Hospital Readmission Reduction Program on hospital readmission and mortality: An economic analysis. Production and Operations Management

DISCUSSION 1

Concept of informatics and knowledge work

According to the American Nurses Association (2023), nursing informatics is the management and dissemination of data, information, knowledge, and wisdom in nursing practice via the combination of nursing science, computer science, and information science. Both patients and healthcare professionals benefit from this data-driven approach to patient care. To give patients excellent clinical care, nurses are cognizant of when information is required. Therein lies the function of nursing informatics. The clinical and technological viewpoints of health care are connected by nursing informatics.

Healthcare scenario that will benefit from access/collection of data.

Informatics has improved patient record retrieval through wearable technology, which has benefited nursing. Enhancing the storage and retrieval of medical information through informatics can help guarantee that patients receive high-quality treatment tailored to their specific needs (McGonigle & Mastrian, 2022). For instance, a patient was recently admitted to my unit for Atrial fibrillation (Afib) with rapid ventricular response (RVR). He came to the emergency because his smartwatch alerted him multiple times that he was in Afib, and even though he voiced he was asymptomatic and never suffered from any cardiac arrhythmias in the past, he still wanted it checked out and was glad he did. Electrocardiogram results confirmed Afib with RVR. After getting treated for Afib, the patient was discharged on a monitoring device called Ziopatch.

The role of data in improving healthcare

The Zio patch is a continuous, uninterrupted, long-term cardiac monitoring device.  The computer systems of iRhythm Technologies evaluate the data from the patch and provide a technical report to the patient’s physician (Yenikonishian, et al., 2019). Wear time, arrhythmia episodes, and any events that were recorded by the patient are all included in the report. A variety of methods and factors can lead to cardiac arrhythmias, which include any slow, rapid, irregular, or abnormal heart beats. Atrioventricular (AV) block, sinus bradycardia/pauses, sustained ventricular tachycardia (VT), ventricular fibrillation, atrial fibrillation (AF), and supraventricular tachycardia (SVT) are among the serious and potentially fatal cardiac arrhythmias that can cause heart failure and stroke, even though not all of them are symptomatic or have prognostic significance (Yenikonishian, et al., 2019). The electrocardiogram (ECG) is a key diagnostic tool for cardiac arrhythmias, the typical 12-lead ECG does not permit patient movement. The only devices available for ambulatory ECG are wearable multi-lead Holter recorders and wearable event recorders, both of which can be challenging to operate and impede everyday living activities.

For patients who are asymptomatic or experiencing temporary symptoms, the FDA-approved Ziopatch cardiac arrhythmia recording device is a new, single-lead, continuously recording ECG recorder that can be used for up to 14 days. After patients have finished recording for up to 14 days, the ECG data is processed and analyzed by iRhythm’s Zio ECG Utilization Service (ZEUS). A certified cardiographic technician (CCT) then reviews the data, and a comprehensive summary report is shared with the patients’ doctors via an electronic health record system or uploaded to a physician web portal. Research results show that doctors were able to make timely decisions  and treat a diagnosis 90% of the time (Xintarakou et al., 2022). This technology is an example of how rapid data collection can lead to important health intervention and overall improve patient care.

Conclusion

Registered nurses have consistently embraced the latest technological advancements, including electronic health records, electronic drug administration records, and educational simulations. In this journey, wearable technology is the next phase, with endless possible uses. The influence of technology on nursing practice is one of the largest shifts that nurses encounter in the always changing and expanding fields of nursing and healthcare (McGonigle & Mastrian, 2022). Every day brings new technology that expands the realm of what is possible. Through an elaborate network of interlinked technologies, new gadgets are being created to help individuals live healthier lives and gain a deeper understanding of their bodies. In the rapidly changing digital healthcare landscape, nursing informatics is essential to preparing nurses to engage with and care for patients.

References

American Nurses Association. (2023). https://www.nursingworld.org/content-hub/resources/nursing-resources/nursing-informatics/

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Yenikomshian, M., Jarvis, J., Patton, C., Yee, C., Mortimer, R., Birnbaum, H., & Topash, M. (2019). Cardiac arrhythmia detection outcomes among patients monitored with the Zio patch system: a systematic literature review. Current Medical Research and Opinion, 35(10), 1659-1670.

Xintarakou, A., Sousonis, V., Asvestas, D., Vardas, P. E., & Tzeis, S. (2022). Remote cardiac rhythm monitoring in the era of smart wearables: present assets and future perspectives. Frontiers in Cardiovascular Medicine, 9, 853614.

In a mental health and psychiatric nursing practice, managing patient outcomes related to medication adherence and symptom tracking presents a common challenge. This scenario focuses on addressing gaps in monitoring medication adherence among patients diagnosed with bipolar disorder. Medication nonadherence can lead to symptom exacerbation, increased hospitalizations, and reduced quality of life (Semahegn et al., 2020).

Data Collection and Access

Key data points would include medication adherence rates, frequency and severity of mood episodes, patient-reported side effects, and socio-environmental factors such as support systems. This data could be collected through electronic health records (EHR), patient self-reports via mobile apps, and wearable devices that track daily activity and sleep patterns. Aggregated data from these sources would be accessed by the care team through an integrated informatics platform, ensuring real-time visibility into patient progress.

Knowledge Derived

Analyzing the data could reveal patterns in nonadherence, such as correlations between mood episodes and missed doses, or identify patients at high risk of nonadherence based on their side effect profiles. Additionally, the data may help identify system-level gaps, such as the need for more robust follow-up protocols.

Role of Clinical Reasoning and Judgment

A nurse leader would use clinical reasoning to interpret this data, considering individual patient contexts and the broader clinical evidence base. For example, a pattern of nonadherence linked to side effects might prompt the nurse leader to advocate for a medication change or enhanced patient education (Berardinelli et al., 2024). By applying clinical judgment, the nurse leader can translate raw data into actionable knowledge, such as personalized care plans or quality improvement initiatives that address systemic barriers to adherence (Baryakova et al., 2023). This approach ensures that informatics tools enhance both individual patient outcomes and broader organizational goals.

References

Baryakova, T. H., Pogostin, B. H., Langer, R., & McHugh, K. J. (2023). Overcoming barriers to patient adherence: the case for developing innovative drug delivery systems. Nature Reviews Drug Discovery, 22(5), 387-409. https://doi.org/10.1038/s41573-023-00670-0

Berardinelli, D., Conti, A., Hasnaoui, A., Casabona, E., Martin, B., Campagna, S., & Dimonte, V. (2024, November). Nurse-led interventions for improving medication adherence in chronic diseases: A systematic review. In Healthcare (Vol. 12, No. 23, p. 2337). MDPI. https://doi.org/10.3390/healthcare12232337

Semahegn, A., Torpey, K., Manu, A., Assefa, N., Tesfaye, G., & Ankomah, A. (2020). Psychotropic medication non-adherence and its associated factors among patients with major psychiatric disorders: a systematic review and meta-analysis. Systematic reviews, 9, 1-18. https://doi.org/10.1186/s13643-020-1274-3 

A regular dialysis patient presents with low blood pressure. This is not his normal. He usually runs in the 140s over 80s and 90s. The world of informatics would be essential to diagnosing and managing his care.

An advanced practice nurse would need to know what is normal for this patient. What medications he is prescribed, and how he is taking them. It would be necessary to see his recent blood pressure trends. How he has been doing his treatments and whether he is fluid overloaded.

Every place that would hold this information would need to be accessed. Gathering all this data would need to be efficient and current. Has fluid surrounded his heart effecting how it pumps? Is he taking too much medication for the blood pressure he is having. Is he consistently checking his blood pressure before he takes his medication? Is he dehydrated? Has he been prescribed the right medications for his needs. Do any of his medications conflict or react to each other and cause him to have hypotension? Has he had any recent changes?

Not having the proper information could lead to incorrect diagnosis and the wrong treatment for a patient that already has comorbidities that could lead to further health problems. (NLM Definition, n.d.) The merging of healthcare and information technology can be the catalyst for change in many patients’ lives. Healthcare outcomes will improve as technology advances. (University of Cincinnati 2024)

As an advanced practice nurse, I will rely on the data I can find about the patient’s past health treatment and the progression of his disease process. I will need to be able search, correlate, and consult with other providers if needed. (Curran, 2003) Along with my assessment skills and diagnostic test to treat and restore my patient to their norm. (Ortiz & Clancy, 2003)

Bibliography

Curran, C. R. (2003). Informatics Competencies for Nurse Practitioners. AACN Advanced Critical Care, 14(3), 320-330.  https://ncbi.nlm.nih.gov/pubmed/12909800

NLM Definition. (n.d.).  https://www.nlm.nih.gov/hsrinfo/informatics.html

Ortiz, E., & Clancy, C. M. (2003). Use of information technology to improve the quality of health care in the United States. Health Services Research, 38(2).  https://ncbi.nlm.nih.gov/pmc/articles/pmc1360897

“What Is Health Informatics? A Complete Guide – University of Cincinnati ….” https://online.uc.edu/blog/what-is-health-informatics/

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One situation where data access would be of significant value is for the management of patients with CHF. CHF is a disease that requires constant surveillance and personalized interventions to alleviate symptoms, avert flares and avoid hospital readmissions. Real-time data based on a range of sources, including EHRs, PROs, and remote monitoring apps, could enable physicians to provide better and faster care, which in turn would enhance patient outcomes.

The information collected to treat CHF is drawn from EHRs, where the patient’s history, comorbidity, medication and hospitalizations are recorded in great detail. Weight, blood pressure, heart rate and oxygen level are all recorded on a regular basis for early indicators of decline. Infrared monitoring — wearable ECG monitors, or exercise monitors — give you even more immediate information about your patient’s state. These points are collected through a mix of in-person visits and remote monitoring, and are available to clinicians via connected healthcare platforms for interventions that can be made quickly on the basis of current data.

Knowing what is based on this data can detect early warning signs of heart failure exacerbations (eg, weight fluctuations or blood pressure). Based on patterns in these data points, physicians can see if a patient is likely to develop new symptoms, and they can take actionable actions to modify the treatment regimen. Further, gathered information from many patients can be used to tailor clinical regimens, decision-making, and ultimately outcomes for people with CHF (Gurung et al, 2022; Kasper et al, 2023).

Data-based clinical practice is the job of nurse leaders. They do so through clinical reasoning and judgement, making sense of patterns and advising patients on what to do. It’s also the job of nurse leaders to help ensure the data is used efficiently and that clinicians are empowered to make decisions based on real-time data. They also advocate for the use of data systems that improve processes, care quality and patient safety. With an enabling culture of data-based practice, nurse leaders are making healthcare for chronic conditions such as CHF more proactive and data-driven (Gurung et al., 2022; Kasper et al., 2023; Lippi et al., 2022).

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I am a registered nurse circulator in an operating room. Working in the operation room and the data gathered and evaluated to ensure the safety of circulating nurses are the primary focus of the scenario and information I will provide. Among the information that operating room nurses are taught are air circulation revolutions and elevated CO2 levels. In an operating room with 20 air exchanges per hour and a positive pressure of 0.03 in. H2O, we demonstrate that non-dispersive infrared CO2 sensors may be employed to detect CO2 (Carroll et al., 2022). To maintain a low quantity of microorganisms, the operating room’s airflow must be revolutionized more quickly. In the operating room, respiratory CO2 byproducts may be expelled as we move and breathe. The CO2 sensor data can be used to quantify the risks to outpatients and a possible elevated risk of airborne illness. According to Carroll et al. (2022), the amount of CO2 detected rises with an increase in the number of residents, activity level, duration of residency, and ventilation level. According to the research, the CO2 level will drop if there are fewer people in the operating room. We discussed the CO2 data collected in this situation and how reducing the number of people in the room and boosting ventilation speeds can help maintain the CO2 levels.

The information gained from this data helps shield operating room nurses from elevated CO2 levels. The risk of airborne infectious pathogens for the surgical patient would increase if the operating room did not have 20 air exchanges every hour. Even though surgical site infections are a frequent issue, Ogce et al. (2022) showed that perioperative staff adherence to evidence-based guidelines can avoid 70% of them. To guarantee patient safety in the operating room, nurses with master’s degrees in nursing employ evidence-based procedures.

Developing a charge nurse shadowing program to spark the interest of future leaders (2022) stated that becoming a charge nurse is a highly difficult job, and assuming a leadership role is entirely up to the individual. Nurses in leadership positions use their expertise to make sure that their subordinates in the operating room follow the guidelines set out by AORN (The Association of Perioperative Registered Nurses). A nurse manager can use this data to create valid knowledge-based evaluations. Their knowledge can help establish the optimal staffing levels for operating rooms, reducing the risk of infection. According to Moss et al. (2002), the charge nurse plays a crucial role in making sure that the surgical team, the patient, and the equipment all work together smoothly. As a registered nurse working as a circulator in the operating room, I am appreciative of the charge nurse and the coordination abilities that brings to the table.

References

Carroll, G. T., Kirschman, D. L., & Mammana, A. (2022). Increased CO2 levels in the operating room correlate with the number of healthcare workers present: an imperative for intentional crowd control. Patient Safety in Surgery, 16(1), 1–7. https://doi.org/10.1186/s13037-022-00343-8

Developing a charge nurse shadowing program to spark the interest of future leaders. (2022). AORN Journal, 115(2), P7–P9. https://doi.org/10.1002/aorn.13619

Moss J, Xiao Y, & Zubaidah S. (2002). The operating room charge nurse: coordinator and communicator…reprinted from the proceedings of the 2001 AMIA Annual Symposium, with permission. Journal of the American Medical Informatics Association, 9, S70-4. https://doi.org/10.1197/jamia.m1231

Ogce Aktaş, F., & Turhan Damar, H. (2022). Determining Operating Room Nurses’ Knowledge and Use of Evidence-Based Recommendations on Preventing Surgical Site Infections. Journal of PeriAnesthesia Nursing, 37(3), 404–410. https://doi.org/10.1016/j.jopan.2021.08.012

Nursing informatics is a specialized field that integrates nursing science with information management and analytical sciences to improve the health of individuals, families, and communities. It aims to ensure that nurses have access to the appropriate data by utilizing electronic health records (EHRs) and other digital tools that store and organize patient information (Darvish, A., et. al., 2014). This access to accurate and up-to-date data helps nurses make informed decisions in the interest of their patients, such as determining the best course of treatment or identifying potential health risks. Additionally, nursing informatics contributes to the broader body of knowledge by enabling the collection and analysis of large datasets, which can be used for research and to identify trends in patient care (Darvish, A., et. al., 2014). This ultimately leads to improved healthcare outcomes and more efficient healthcare delivery systems.

I currently work in the NICU. The scenario that I have created takes place in the Neonatal Intensive Care Unit (NICU) where a nurse is caring for a premature infant who is experiencing irregular heart rates and oxygen levels. With access to comprehensive data through the NICU’s electronic health record system, the nurse can quickly review the infant’s medical history, including birth details, previous vital signs, and any administered medications. This immediate access to data allows the nurse to identify patterns or changes in the infant’s condition that may indicate a developing issue, such as an infection or a reaction to medication. By having this information readily available, the nurse can make timely and informed decisions about interventions, such as adjusting oxygen levels, administering medication, or alerting the medical team for further evaluation. Additionally, the data collected from this and other similar cases can be analyzed to identify trends and improve care protocols. For example, if multiple infants with similar symptoms respond well to a specific treatment, this information can be used to update guidelines and training, ultimately enhancing the overall quality of care in the NICU. Analyzing patterns in vital signs and responses to treatments can lead to the identification of early warning signs for complications, such as sepsis or respiratory distress.

This knowledge can help in developing protocols for early intervention, which can be crucial for the survival of premature infants. The data can reveal the effectiveness of various treatment strategies for specific conditions. For instance, if certain medications or therapies consistently lead to better outcomes in similar cases, this information can guide best practices and improve clinical guidelines (Booth, R. G., et. al., 2021). Also, the demographic data combined with clinical outcomes can help identify disparities in care or outcomes based on factors like birth weight, gestational age, or socioeconomic status. This knowledge can inform targeted interventions to address these disparities and improve overall care quality. The aggregated data can contribute to research and the development of new treatment modalities or technologies in neonatal care, fostering continuous improvement in the field (Booth, R. G., et. al., 2021). This ongoing accumulation of knowledge not only benefits individual patients but also advances the entire discipline of neonatal nursing.

A nurse leader would use clinical reasoning and judgment to interpret the data collected from the NICU experience and transform it into actionable knowledge. By carefully analyzing the data, the nurse leader can identify trends and patterns that may not be immediately obvious. For instance, they might notice that infants with certain characteristics respond better to specific treatments or that certain interventions lead to quicker recovery times (Kennedy, M. A., & Moen, A., 2017). Using clinical reasoning, the nurse leader would consider the context of each piece of data, such as the infant’s overall health, the timing of interventions, and any other relevant factors. They would then use their judgment to determine which findings are significant and how they can be applied to improve care. This might involve developing new protocols, adjusting existing ones, or providing targeted training to staff based on the insights gained. The nurse leader can facilitate discussions and collaborative decision-making among the healthcare team, ensuring that the knowledge derived from the data is integrated into practice effectively (Kennedy, M. A., & Moen, A., 2017). By fostering a culture of continuous learning and evidence-based practice, the nurse leader helps to ensure that the NICU team is always working with the best possible information to provide high-quality care to their patients.

References

Booth, R. G., Strudwick, G., McBride, S., O’Connor, S., & Solano López, A. L. (2021). How the nursing profession should adapt for a digital future. The BMJ, 373, n1190. https://doi.org/10.1136/bmj.n1190

Darvish, A., Bahramnezhad, F., Keyhanian, S., & Navidhamidi, M. (2014). The role of nursing informatics on promoting quality of health care and the need for appropriate education. Global journal of health science, 6(6), 11–18. https://doi.org/10.5539/gjhs.v6n6p11

Kennedy, M. A., & Moen, A. (2017). Nurse Leadership and Informatics Competencies: Shaping Transformation of Professional Practice. Studies in health technology and informatics, 232, 197–206.

I am currently a Clinical Specialist at a law firm that specializes in appealing denials of patient hospital claims. Gaines, Auleta, and Berwick (2020) emphasize that, irrespective of whether claim denials originate from the insurer, hospital, or physician, patients often face significant financial burdens, emotional distress, and psychological impacts, which frequently leave them with few avenues for recourse. They further emphasize that even when clinicians or hospitals assure patients that “insurance has approved everything,” insurers may later refuse payment, resulting in unexpected and hefty bills. In the intricate landscape of healthcare, this firm enjoys a significant advantage by strategically leveraging diverse types of data, which are essential for effective problem-solving and knowledge development. The firm draws upon various data sources, with claims data playing a central role. This includes information about submitted claims, reasons for denials, billing codes, and timelines. Patient data, comprising medical records that detail diagnoses, treatment plans, and outcomes, provides crucial context for each case. Additionally, the firm examines insurance policy data to clarify coverage limitations and specific governing language. Historical appeals data enriches resources by highlighting past outcomes and common reasons for acceptance or denial, while regulatory data offers insights into healthcare laws impacting claims at various levels.

To collect this information, the firm utilizes several methods. Electronic Health Records (EHR) systems provide access to detailed patient histories, while insurance provider portals are crucial for obtaining current claims statuses and reasons for denials. Legal databases compile important precedents and case law, enhancing the firm’s understanding of medical denials. Surveys and interviews with healthcare providers and patients yield valuable qualitative insights. From this data, the law firm extracts vital insights to inform its strategies. By recognizing patterns in denials related to specific diagnoses or insurance providers, the firm can develop informed strategies for future appeals. Poland and Harihara (2022) highlight that monitoring and analyzing trends in rejections and denials enables a clear distinction between the two, facilitating the identification of problem areas and allowing for prompt resolution. Analyzing historical data also reveals effective argument techniques, enabling the legal team to construct stronger cases. Furthermore, the firm can identify policy gaps that may lead to denials, empowering it to advocate for necessary changes and improve patient education.

In the context of appealing medical denials, nurse leaders are essential contributors. Their clinical expertise allows them to interpret medical data effectively and provide insights that legal teams might miss. This collaboration ensures that appeals are grounded in strong clinical evidence, enhancing their credibility. Furthermore, nurse leaders analyze data to identify the reasons behind denials and highlight potential systemic issues in healthcare delivery. Hughes (2023) underscores the significance of quality data in crafting compelling narratives for appeals. Analyzing this data involves recognizing patterns and correlations that can lead to informed conclusions about denials. When combined with historical context and other relevant factors, this data helps frame the current situation and supports evidence-based arguments in the appeals process.

References

Gaines, M.E., Auleta, A.D., and Berwick, D.M. (2020). Changing the Game of Prior Authorization: The Patient Perspective. JAMA, 323(8), 705–706. https://doi:10.1001/jama.2020.0070

Hughes, Ronda PhD, MHS, RN, FAAN. (2023, Mar). Recognizing the Value of Data. Nursing Management (Springhouse) 54(3):p 56. https://doi.10.1097/01.NUMA.0000919080.84084.ed

Poland, L., and Harihara, S. (2022, April 25). Claims Denials: A Step-by-Step Approach to Resolution. Journal of AHIMA. https://journal.ahima.org/page/claims-denials-a-step-by-step-approach-to-resolution

Main post:

Nursing informatics combines technology (computers, data, information, and knowledge) with the art of nursing science.  These two powerful entities mix and can produce meaningful information and help the population with health and knowledge, which can set people, patients, and healthcare systems up for success.  McGonigle & Mastrian (2022) explain that nurses are knowledge workers according to the “Foundation of Knowledge Model” they developed.  A knowledge worker is constantly learning. They continually take in knowledge and information and process it appropriately for the patient and the healthcare system.  This is also shown in how our practices are evidence-based.  We perform evidence-based services based on previously learned information to have the best outcomes.

A scenario I will use is protocols we follow in the emergency room when someone comes in with chest pain.  We follow evidence-based practice and research that allows for the best patient outcomes.  We have found the best patient outcomes when we follow these protocols promptly.  When a patient arrives with chest pain complaints, we must obtain an EKG within five minutes of arrival, blood work to obtain cardiac enzymes, IV placement, and cardiac monitoring; all must/should be done within this same timeframe if you are able.  The EKG at this point is most important.  If the EKG shows elevation of the ST segment, medications must be given and the patient must be transferred to the cardiac catheter lab immediately.  We prefer it as quickly as possible but try to obtain it within 30-45 minutes.  We say in the hospital “time is muscle.”  The more timely we accomplish this the less heart muscle is lost. Since I work in a small ER, time is of the essence because we must transfer the person to a higher level of care.  If we have done all this work, the patient can be transferred directly to the cardiac catheter lab, and the information they will need has already been accomplished, thus leading to an increased positive outcome for the patient.  According to Fry et al. (2023), the American College of Cardiology has developed these protocols most hospitals follow, improving patient outcomes.  I have seen it many times.

According to Sweeny (2017), healthcare informatics, continually seeking knowledge, and merging this data will lead to improved patient care.  This constant seeking of learning and improvement must include managers, staff, government, etc.  It is an absolute team of people who come together with each piece of data and meld it together, whether it is a computer, patient, medical records, medications, staffing, or how to obtain data; all of this information leads to knowledge, and that knowledge is necessary to improve outcomes.

References:

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Fry, C., Engel, J., Granger, B., Komada, M. & Lovins, J. (2023). Evidence-Based Clinical Decision Support to Improve Care for Patients Hospitalized With Acute Myocardial Infarction. CIN: Computers, Informatics, Nursing, 41 (5), 323-329. doi: 10.1097/CIN.0000000000000959.

Sweeney, J. (2017). Healthcare Informatics. Online Journal of Nursing Informatics, 21(1), 4–1.

A community-based healthcare organization implements a telehealth program to improve access to care for rural and underserved populations. This initiative requires the collection and application of patient data, including demographic information, medical histories, clinical measurements, and telehealth usage patterns. Data such as patients’ primary concerns, adherence to scheduled appointments, and follow-up outcomes can be collected through electronic health records (EHRs), patient surveys, and telehealth platforms. Administrative data, such as clinician availability and telehealth session efficiency, can also be tracked. By accessing these datasets, the organization can identify gaps in care, optimize scheduling, and tailor interventions to meet patients’ needs, ensuring equitable access and improved health outcomes (McGonigle & Mastrian, 2022, p.9).

According to McGonigle & Mastrian (2022) The Foundation of Knowledge Model serves as a framework for understanding how knowledge is created, processed, and applied in healthcare, particularly as it relates to patient data and patient care. The model is built on four essential elements: knowledge acquisition, knowledge dissemination, knowledge generation, and knowledge processing. These components interact within the domains of data, information, knowledge, and wisdom to inform and enhance clinical decision-making and patient care (p. 10).

Data represents raw, unprocessed facts collected from patient interactions, such as vital signs, lab results, or demographic details. For example, a patient’s blood pressure readings recorded during visits are raw data. Information emerges when this data is organized and analyzed to provide meaning, such as noting that the patient’s blood pressure has been consistently high over several months. Knowledge is developed when information is combined with clinical expertise and evidence-based practices, enabling healthcare providers to recognize patterns or predict outcomes, such as identifying a risk for hypertension-related complications. Wisdom occurs when knowledge is applied in practice to make informed clinical decisions, such as initiating a treatment plan to manage hypertension and prevent long-term adverse outcomes (McGonigle & Mastrian, 2022, p. 10).

In patient care, the model emphasizes the importance of using data systematically to generate actionable insights. For example, an EHR system can aggregate a patient’s historical data to inform care strategies. Nurses and providers utilize this data to understand trends in the patient’s condition, communicate findings to the care team, and educate the patient about necessary lifestyle changes. This structured approach to managing and applying patient data ensures that decisions are evidence-based and personalized to individual needs.

Ultimately, the Foundation of Knowledge Model highlights the dynamic and iterative process of transforming data into wisdom. Healthcare professionals, particularly nurse leaders, play a pivotal role in facilitating this process by applying clinical reasoning and leveraging informatics tools to optimize patient outcomes and drive continuous improvement in care delivery (McGonigle & Mastrian, 2022, p. 10).

The knowledge derived from this data is multifaceted. Patient data analytics could reveal trends such as frequent telehealth users requiring chronic disease management or the time of day when appointments are most in demand. Population health insights could identify social determinants impacting access, such as broadband availability or language barriers. Organizational data may highlight inefficiencies, such as prolonged waiting times for certain providers. By synthesizing this information, the organization can improve care coordination, allocate resources effectively, and provide targeted education or interventions to vulnerable populations. For example, identifying frequent no-show appointments may lead to implementing automated reminder systems or scheduling flexibility to accommodate patient preferences (Nagle n.d., 2017, p. 214).

A nurse leader would play a vital role in transforming data into actionable knowledge through clinical reasoning and judgment. They would evaluate patient trends and clinical outcomes to ensure telemedicine aligns with evidence-based practice while addressing quality improvement needs. For instance, if data shows poor adherence to chronic disease follow-ups, the nurse leader might recommend integrating case management services into virtual telehealth visits (Nagle n.d., 2017, p. 216). Additionally, by applying principles of informatics and leveraging decision-support tools, nurse leaders can guide staff on how to utilize data-driven insights to prioritize patient care and streamline workflows. These efforts emphasize the integration of informatics into clinical and organizational knowledge work to enhance healthcare delivery and outcomes (McGonigle & Mastrian, 2022, p.11).

References

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge

(5th ed.). Jones & Bartlett Learning. Retrieved November 25, 2024, from Walden

University

Nagle, L., Sermeus, W., & Junger, A. (2017). Evolving role of the nursing informatics

specialist. In J. Murphy, W. Goossen, & P. Weber (Eds.), Forecasting

Competencies for Nurses in the Future of Connected Health (212–221). Clifton,

VA: IMIA and IOS Press. Retrieved from

https://serval.unil.ch/resource/serval:BIB_4A0FEA56B8CB.P001/REF

Utilization of EHR

Since the beginning of time, healthcare workers have needed to keep tabs on their patients. They have done this by writing things down into a chart and have since evolved to a computerized medical record. Within my community, we have two different hospital systems, both of which use different EHRs. Our system uses Cerner, while theirs uses Epic. This is not a bad thing until it is. Frequently, our patients go from one hospital system to another making it difficult to track their medical history. These patients might have surgery or see a specialist within a different hospital system and when this happens, we are unable to obtain their records in a timely fashion. In an Emergency Room setting, sometimes we don’t have time to go through medical records to get their most up-to-date information which can make caring for these patients slightly more difficult.

“Nursing informatics (NI) has been traditionally defined as a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. (McGonigle & Mastrain, 2021)” I include this because nursing informatics is an important aspect of critical thinking about how we can care for these patients accurately and efficiently. As we navigate the charting systems, we eventually get the information we need through medical records. This often delays care if something is not emergent, but in the coming years, we are working to merge our EHR with that of the other hospital system.

As we merge these charting systems it will decrease the need for increased radiation, fewer lab sticks, and better patient care and outcomes. As a healthcare provider, I think it is important to note that the least restrictive and invasive measures are best. When we are unable to properly collaborate with providers that our patients frequent, we will have a harder time with good patient outcomes. I hope over the years that EHRs can all be merged as this would cut down on repeat visits. Cutting down repeat visits will cut costs down for not only the patient but the hospital system. I live in a rural tourist area that sees 1000s of tourists during peak times of the year and most of them don’t carry their medical information. We might get “I take blue pill for my heart”, or “I am allergic to an antibiotic, but I don’t know what it’s called”. These scenarios come up daily in the Emergency Department.

I am in a workgroup that helps to navigate the staff on the benefits of merging our charting systems. Nursing informatics will help guide me in this decision-making process as well as allow me to be an advocate for changing the lives of our patients. It is important that we always remain up to date on current healthcare practices, and it starts with our patients and their safety. I think about a patient we had. He was from out of town and had a stroke. Unable to answer any medical questions, we brought him to CT and did a CT contrast study. Upon arriving back to his room, he began having anaphylaxis, but also needed clot-busting medication because he had some clots that we found. Had we known that this patient was anaphylactic from contrast, we likely would not have given it and treated the stroke with the clot-buster without the CT perfusion study. Things like this happen across the world with unknown medical histories in emergent situations. Finding a solution will be key, but EHR systems that can be tracked will be helpful.

References:

McGonigle, D., & Mastrian, K. (2021). Nursing Informatics and the Foundation of Knowledge (5th ed.). Jones & Bartlett Learning. https://bookshelf.vitalsource.com/books/9781284234701

Nagle, L., Sermeus, W., & Junger, A. (2017).  Evolving Role of the Nursing Informatics SpecialistLinks to an external site.. In J. Murphy, W. Goosen, &  P. Weber  (Eds.), Forecasting Competencies for Nurses in the Future of Connected Health (212-221). Clifton, VA: IMIA and IOS Press. Retrieved from https://serval.unil.ch/resource/serval:BIB_4A0FEA56B8CB.P001/REF

Syazana Binti Wan Hashim, W. N., Jislan, F. B., Binti Mohd Kodri, A. N., Akma Ahmad, S. N., Binti Eshak, E. S., Mohd Nor, K. B., & Binti Nor Azam, N. H. (2024). The Implementation of Electronic Medical Record (EMR) in Hospitals: A Conceptual Study. Global Business & Management Research, 16(2), 688–695.

Electronic Medical Record

Using my own scenario, I’ll talk about the different charting systems I’ve observed at two workplaces. While my present workplace utilizes an electronic system, one location continued to use paper charting. Over the past ten years, patient outcomes have been greatly impacted by the use of informatics to develop healthcare technologies. “The integration of healthcare sciences, computer science, information science, and cognitive science to assist in managing healthcare information” is the definition of informatics (Sweeney, 2017). Many places have used electronic health records in an effort to improve the standard of care. Smaller institutions, however, still frequently use paper charts. I was shocked to learn that the skilled nursing facility where I used to work still kept paper records. In contrast to my previous employment, which employed electronic medical records (EMR), it was difficult to obtain patient information promptly, and prescription errors were significantly more common. EMRs are more efficient than paper-based records, claim Lin et al. (2020).

In summary, because technology is always changing, informatics poses several difficulties. Informatics-focused nurses may already have out-of-date knowledge by the time they finish their degree (Nagle et al., 2017). However, keeping abreast with the most recent developments in technology is essential.

References

Honavar S. G. (2020). Electronic medical records – The good, the bad and the ugly. Indian journal of ophthalmology, 68(3), 417–418. https://doi.org/10.4103/ijo.IJO_278_20

Lin, H.-L., Wu, D.-C., Cheng, S.-M., Chen, C.-J., Wang, M.-C., & Cheng, C.-A. (2020). Association between Electronic Medical Records and healthcare quality. Medicine, 99(31). https://doi.org/10.1097/md.0000000000021182

Sweeney, J. (2017). Healthcare Informatics. Online Journal of Nursing Informatics, 21(1).

What is Nursing Informatics and why is it so important?. ANA. (2024, February 19). https://www.nursingworld.org/content-hub/resources/nursing-resources/nursing-informatics/#:~:text=This%20person%20may%20be%20in,security%20of%20health%20care%20data.

 Main Discussion 1: The Application of Data to Problem-Solving

Nursing informatics is a specialized field that combines nursing science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice. It involves the use of technology and information systems to improve patient care and support clinical decision-making (Reid, Breaden, & Brommeyer, 2024).

Nursing informatics may be used in implementing electronic health records (EHRs), data analysis, and developing health information systems tailored to nursing needs. Nursing leaders have an important role in educating their staff members on how to efficaciously use these tools (Hall et al., 2024).

In the hospital where I work, as a Medical Surgical Neuro/Spine nurse, a scenario involving patient falls on a specific unit could benefit from the access and application of data to improve safety and care quality. The unit has seen a noticeable increase in fall incidents over the past few months, particularly among elderly patients who are frequently post-surgical or suffering from chronic conditions like dementia. As a nurse leader, addressing this issue would require collecting data related to patient risk factors, environmental hazards, and staff intervention patterns. The primary goal would be to reduce the number of falls and enhance patient safety.

To gather this data, several types of information would be useful. First, patient-level data could be collected through electronic health records (EHR), including fall risk assessments, medical history, mobility status, medications, and recent surgical procedures. Data on previous falls, such as time of day, location, and specific causes, would also be crucial. Additionally, environmental data could be gathered through periodic audits of the unit, noting any hazards such as clutter, poor lighting, or wet floors. Staff data, including nurse staffing levels, shift assignments, and incident reports, could provide insight into patterns or gaps in patient care. This data could be accessed through the hospital’s EHR system, incident reporting tools, and environmental safety audits.

The knowledge derived from this data would focus on identifying key risk factors and trends contributing to falls, as well as determining the effectiveness of current interventions. For example, the data might reveal that falls tend to occur more frequently during night shifts when staffing levels are lower or in rooms with inadequate lighting. It could also highlight specific patient conditions, such as cognitive impairment or medication side effects, that increase fall risk. By analyzing this data, the team could develop targeted strategies, such as improving staff education on fall prevention, adjusting staffing during high-risk times, or modifying the physical environment to minimize hazards.

A nurse leader would use clinical reasoning and judgment to interpret this data and formulate effective interventions. For instance, they might prioritize interventions for patients with multiple fall risk factors, implement regular staff huddles to review patient safety concerns or advocate for environmental changes based on the findings. Clinical judgment would guide decision-making in balancing patient safety with resource constraints, while the integration of evidence-based practice would support the implementation of best practices for fall prevention. Ultimately, the nurse leader would use the data to foster a culture of safety, continually reassessing the situation as new data is collected to ensure ongoing improvements (Williams, 2019).

References

Reid L, Button D, Breaden K, & Brommeyer M. (2024). Nursing Informatics: Competency Challenges for Nursing Faculty. Studies in Health Technology and Informatics, 310, 1196–1200. https://doi.org/10.3233/SHTI231154

Hall, E. S., Holt, J. E., & Sengstack, P. (2024). Role of a Nursing Informatics Specialist in a Clinical Simulation Laboratory: The Catalytic Link. Nurse Educator, 49(5), E260–E264. https://doi.org/10.1097/NNE.0000000000001629

Williams A. (2019). Nursing Informaticians Address Patient Safety to Improve Usability of Health Information Technologies. Studies in Health Technology and Informatics, 257, 501–507.