NURS 5051/ NURS 6051 week 1 Discussion: The Application of Data to Problem-Solving

     Medications that are used to treat mental health disorders have not changed much since the first psychiatric medications came out, and little is known about how different the adverse drug reactions (ADR) are with the handful of newer psych medications. WellSpan Psychiatry should utilize the data in the EHR system of EPIC to compare the prevalence of ADR when using newer medications in comparison to the older and more traditional medication management. Al Zaabi, Sridhar, & Tadross (2020) explain how reporting and monitoring ADR in psychiatric patients leads to a better management of patients and can help in medication rational. The data recorded in EPIC for patients who take any antipsychotic or antidepression medication(s) should be pulled and studied. The key information to pull would be demographics (age, sex, other diagnosis, etc.), medication regimen, and any ADR reported for every patient prescribed these psychiatric medications.  According to Ejeta, et al. (2021), “ADRs can reduce quality of life, induce poor drug adherence, cause physical morbidity, generate shame, and, in the worst-case scenario, be lethal.” The knowledge gained from looking at ADRs in both traditional psychiatric medication and new-generation antipsychotic medication can help providers to determine the efficacy to ADR ratio in these patients. It will help to determine what medications have the lowest ADRs in targeted demographics. A nurse leader could use clinical reasoning and judgment to assess a mental health patient’s likelihood for ADRs and help to monitor and educate the patient regarding possible effects. Medication “non-compliance increases the risk of relapse, hospitalization and suicide attempts” (du Plessis et al. 2021). As a nurse leader, studying the EHR data to help monitor and predict ADRs could lead to greater patient outcomes and medication compliance.

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References

Al Zaabi, M. S. R., Sridhar, S. B., & Tadross, T. M. (2020). Assessment of Incidence,

Causality, Severity, and Preventability of Suspected Adverse Drug Reactions to Antidepressant Medications in a Psychiatry Outpatient Setting of a Secondary Care Hospital. Journal of Pharmacy & Bioallied Sciences, 12(2), 131–138. https://doi.org/10.4103/jpbs.JPBS_196_19

du Plessis, J. M., Poggenpoel, M., Myburgh, C., & Temane, A. (2021). Family members’

lived experiences of non-compliance to psychiatric medication given to female adults living with depression. Curationis, 44(1), 1–9. https://doi.org/10.4102/curationis.v44i1.2105

Ejeta F, Aferu T, Feyisa D, Kebede O, Siraj J, Hammeso WW, Tadesse E, & Tinishku A.

(2021). Adverse Drug Reaction and Its Predictors Among Psychiatric Patients Taking Psychotropic Medications at the Mizan-Tepi University Teaching Hospital. Neuropsychiatric Disease and Treatment, ume 17, 3827–3835.

Jacqueline,

You bring up a great point. I work in home health and an important part of our job is medication reconciliation. Many medications, including over the counters, are omitted, or forgotten by patients during their doctor visits. We are the eyes of the provider in patient’s home. Our EMR system flags any potential drug interactions during any medication reconciliation. Drug to drug interactions can lead to medication adverse reactions, decreased medication efficacy, or unintended high medication levels (Morales-Ríos et al., 2018). Therefore, having access to this information is invaluable for patient safety and succession of the provider ordered plan of care (Hochheiser et al., 2021).

Hochheiser, H., Jing, X., Garcia, E. A., Ayvaz, S., Sahay, R., Dumontier, M., Banda, J. M., Beyan, O., Brochhausen, M., Draper, E., Habiel, S., Hassanzadeh, O., Herrero-Zazo, M., Hocum, B., Horn, J., LeBaron, B., Malone, D. C., Nytrø, Ø., Reese, T., & Romagnoli, K. (2021). A Minimal Information Model for Potential Drug-Drug Interactions. Frontiers in Pharmacology, 11. https://doi.org/10.3389/fphar.2020.608068

 

Morales-Ríos, O., Jasso-Gutiérrez, L., Reyes-López, A., Garduño-Espinosa, J., & Muñoz-Hernández, O. (2018). Potential drug-drug interactions and their risk factors in pediatric patients admitted to the emergency department of a tertiary care hospital in Mexico. PLOS ONE, 13(1), e0190882. https://doi.org/10.1371/journal.pone.0190882

Hello Jacqueline,

Thanks for sharing your thoughts and this great example. Considering the example you gave, what knowledge might be derived from that data? How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience?

Dr. Mattis

Hi Jacqueline,

You bring up a very good point, with how the medical field is continually evolving. The research on how old medications used to treat mental and the new medications being introduced should be studied to see what are the possible lasting effects. i agree that relapses are happening more often since the pandemic

Hi Jacqueline,

Thank you for your discussion post this week on nursing informatics. I thought you did a really good job explaining how nursing informatics could potentially help providers and patients avoid adverse drug reactions. “Nursing informatics is a specialty that integrates nursing, science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice” (Sweeney, 2017). I know, as a soon-to-be provider, I would love a better way to ensure I have the most pertinent data to avoid prescribing patients the wrong antipsychotic medications. “Several interventions involving information systems have been shown to reduce medication errors considerably” (Bates, 2000, p. 3).

 

References

Bates, D. W. (2000). Using information technology to reduce rates of medication errors in hospitals. BMJ, 320(7237), 788-791. https://doi.org/10.1136/bmj.320.7237.788

 

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

Hello, Jacqueline. You present a highly valuable position on the use of informatics. As much as the use of electronic health records goes a long way in facilitating medication reconciliation, physician competency also goes a long way. In that sense, using informatics can help increase physician competency on medications that present a high risk of adverse conditions. Ribeiro-Vaz et al. (2016) indicate that it is possible to create pharmacovigilance around adverse drug reactions through informatics.

References

Ribeiro-Vaz, I., Silva, F. A. B., Silva, A. M. M., Alves, D., & Cruz-Correia, R. (2016).

Pharmacovigilance Informatics. In Encyclopedia of E-Health and Telemedicine (pp. 299-315). IGI Global.

Hi Angel, I enjoyed reading your post. I love case management and I am also looking into bridging the gap better so

seriously mentally ill people can stop going inpatient and the door does not continue to revolve. The capabilities of

technology are endless. Like anything in nursing, information must be valuable and meaningful to be of good quality

(McGonigle & Garver Mastrian, 2022). The value of the information or data correlates to critical thinking and decision

making (McGonigle & Garver Mastrian, 2022). I am planning on collecting data from our interface on number of

admissions, diagnosis, and if they followed through with their aftercare plan. This is the processing or analysis of data

(Nelson, PhD, BC-RN, ANEF, FAAN, 2018, p. 30).

References

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

Nelson, PhD, BC-RN, ANEF, FAAN, R. (2018). Informatics: evolution of the Nelson data, information, knowledge, and wisdom model: part 1. The Online Journal of Issues in Nursing, 23(3).

Angel,

I enjoyed reading your post. I work closely with case managers in my current position, and I agree that it is vital to have access to patient information to facilitate the best collaboration and services. This decreases the risk of readmission and helps loved ones find adequate services that produce a better patient-care outcome. Nursing informatics is based not only on the technology used for proper documentation and communication among healthcare personnel but also on a primary mission of patient care and safety. Without integrating nursing informatics in healthcare, there would probably be an increase in adverse reactions or incidents and higher death rates. Again, great post.

Veinot, T. C., Ancker, J. S., & Bakken, S. (2019). Health informatics and health equity: Improving our reach and impact. Journal of the American Medical Informatics Association, 26(8-9), 689–695. Retrieved March 4, 2022, from https://doi.org/10.1093/jamia/ocz132

Professor and classmates,

Data mining is defined as the process of using software to sort through data to discover patterns and ascertain or establish relationship (McGonigle & Mastrian, 2022). Nursing Informatics can help us learn about any data that might be related to each other without any prior knowledge (Sweeney, 2018). Healthcare providers often use data mining and analysis to find best practices and the most effective treatments (Nagle et al., 2018). These tools compare symptoms, causes, treatments and negative effects. After such information is obtained, the data is analyzed and determined which action will prove most effective for a group of patients (Li et al, 2018). This is also a way for providers to develop the best standards of care and best clinical practices; an example of that is flu vaccination.

Let’s say, during problem identification, data is collected that will define the problem in detail. In this case it would be the increasing number of patients with flu. Second phase is the exploration, which will determine the root of the problem. In this case weather it is an access to vaccination, financial issues or anything else that is causing it. Pattern discovery will show us any patterns like geographical area; financial status or weather patient is insured (Lodhi et al, 2017). Knowledge deployment, which is the final step, is put all together to test and see if a solution that is viable can be implemented.

In this case the knowledge deployment would be based on the data summary and decision outcome. If it’s determined that population is not being vaccinated due to cost, state or town clinic could be set up to help with the cost of flu vaccinations which in a long run will decrease the cost of flu treatment if no treatment would be necessary due to having been vaccinated (Bhati et al, 2018). Data mining could be applied almost to every research that is aimed at reducing healthcare cost, improving patient satisfaction and finding the most effective treatments through out healthcare.

References:

Bhatti, U. A., Huang, M., Wang, H., Zhang, Y., Mehmood, A., & Di, W. (2018). Recommendation system for immunization coverage and monitoring. Human vaccines & immunotherapeutics, 14(1), 165-171.

Li, R., Weintraub, E., McNeil, M. M., Kulldorff, M., Lewis, E. M., Nelson, J., … & Destefano, F. (2018). Meningococcal conjugate vaccine safety surveillance in the Vaccine Safety Datalink using a tree‐temporal scan data mining method. Pharmacoepidemiology and drug safety, 27(4), 391-397.

Lodhi, M. K., Ansari, R., Yao, Y., Keenan, G. M., Wilkie, D., & Khokhar, A. A. (2017, July). Predicting hospital re-admissions from nursing care data of hospitalized patients. In Industrial Conference on Data Mining (pp. 181-193). Springer, Cham.

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

Nagle, L., Sermeus, W., & Junger, A. (2017).  Evolving Role of the Nursing Informatics Specialist. 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.

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

Hello Vanessa

Very informative and detailed post. I was not familiar with the terminology data mining in healthcare. However, after reading your post and seeking information, I see how important it is to our healthcare system and safe patient care. This requires a team effort from all personnel involved in patient care. Nursing informatics is just as vital as the pharmacy making sure they have the correct dosage and the right drug is mixed correctly. I also believe that data mining is an excellent opportunity for education to students and new staff and as a refresher or CEU for other experienced healthcare professionals. Again, great post.

Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data processing and text mining technologies on electronic medical records: A review. Journal of Healthcare Engineering, 2018, 1–9. https://doi.org/10.1155/2018/4302425

Hi Madeline, I agree that there is a rising nursing shortage of nursing staff. Also, there is a constant rise of nurses leaving direct patient center care. I believe that due to patient acuity, leadership and politics, and new healthcare policies that are in place today, many nurses are removing themselves to find other nursing professions that allow for more autonomy and less stressful situations. I am sure that due to the current pandemic and other healthcare issues. There will still be more of a demand for nurses. Also, due to technology, which is constantly changing, there are numerous nursing informatics jobs that allow nurses to still provide the care, education, and compassion that lead most individuals to become a nurse. “ EMR and other technology advances can also affect nurses to stay in the profession”(Harer, 2022). Nursing informatics causes availability for nurses to still do what they love, but from a different role. However, this can lead to a shortage of direct nurse’s patient care occupations.

Harer, W. B. (2022). Nursing shortage. Archives of Otolaryngology-Head and Neck Surgery, 82(3), 326–326. https://doi.org/10.1001/archotol.1965.00760010328026

Hyun, S., Bakken, S., Douglas, K., & Stone, P. W. (2008). Evidence-based staffing: potential roles for informatics. Nursing economic$, 26(3), 151–173.

Megan,

It is not often that nurses stay at one facility for more than a couple of years let alone the same department. I applaud you for working in the emergency department for the last seven years. I have been at my current facility for fourteen years and even though I have worked in ICU, Cath Lab and now as a Forensic Nurse, I can count on one hand the nurses that I completed the nursing residency with that are still there. We have both witnessed throughout the years firsthand the nursing turnover rate and the significance of staffing shortages and the important role it plays in nursing burnout.

According to Lamp (2018), “We need ratios that ensure we have enough staff with the required skills to care for our patients. The current system gives us staff numbers only, inadequate staffing often causes delays in non-clinical care and management of patients”. This statement speaks to your point of considering patient acuity and not just the daily patient census.

Shift-by-shift measurement of patient demand can guide flexible staff deployment, but the baseline number of staff rostered must be sufficient. Higher baseline rosters are more resilient in the face of variation and appear cost-effective. Staffing plans that minimize the number of nurses rostered in advance are likely to harm patients because temporary staff may not be available at short notice. Such plans, which rely heavily on flexible deployments, do not represent an efficient or effective use of nurses (Griffiths et al., 2021). You mentioned utilizing the Emergency Severity Index (ESI) for the triage nurse. I think this would be a great tool to incorporate. I am sure there have been many shifts where you have witnessed several critical patients arriving at your ER all at once and let’s not forget the stable but at the blink of an eye crashing patient.

I enjoyed reading your post. You made some great suggestions as to how your facility could investigate handling staffing shortages to provide better patient-centered care.

 

References

Griffiths, P., Saville, C., Ball, J. E., Jones, J., & Monks, T. (2021). Beyond ratios – flexible and resilient nurse staffing options to deliver cost-effective hospital care and address staff shortages: A simulation and economic modelling study. International Journal of Nursing Studies, 117. https://doi.org/10.1016/j.ijnurstu.2021.103901

 

Lamp. (2018). Hours-based staffing needs reform: Our public hospitals need a simpler, more accountable ratios system. 75(6), 11.

 

Hi Paige,

The emerging trend in healthcare has powered the role of nursing informatics greatly. According to Shah et al, 2016, barcode medication administration (BCMA) technology is a health information technology credited for preventing medication errors and promoting patient safety when used accurately. Medication safety is the responsibility of all members of the healthcare team. It is evident from several studies that medication error is one of the leading causes of death in hospitalized patients (Naidu & Alicia, 2019).

References

Naidu, M., & Alicia, Y.L. (2019). Impact of Bar-Code Medication Administration and Electronic

Medication Administration Record System in Clinical Practice for an Effective Medication Administration Process. Health.

Shah, K., Lo, C., Babich, M., Tsao, N. W., & Bansback, N. J. (2016). Bar Code Medication

Administration Technology: A Systematic Review of Impact on Patient Safety When Used with Computerized Prescriber Order Entry and Automated Dispensing Devices. The Canadian journal of hospital pharmacy, 69(5), 394–402. https://doi.org/10.4212/cjhp.v69i5.1594A

 

Discussion #1

Diabetes is the 7^th leading cause of death in the United States and more than 37 million Americans have been diagnosed with diabetes (Diabetes Quick Facts, 2022). Data collection for diabetes is extremely important to provide the best outcomes for our patients. Primary care offices are responsible for about 90% of all diabetic care in the United States NURS 5051/ NURS 6051 week 1 Discussion: The Application of Data to Problem-Solving. This makes it vital that the primary care teams have diabetic programs to gather data and interpret how to improve patient outcomes (Shrivastav et al., 2018, para. 5).

Diabetic patients in our primary office scenario will be monitored in the office every 3 months. The data collected at the 3 month appointment will include: glucose logs, nutrition logs, exercise logs, and current medications. We will perform a 3 month A1C check at each visit to determine diabetic control. The knowledge the healthcare team would gain from this data can help us better determine the individual needs of each diabetic patient. Diabetes is very complex and requires data to be collected and interpreted in order to adjust medications or establish a better exercise or nutrition plan. 

The nurse leader would gather this information from the patient and document in the patient’s chart. The data would be organized and interpreted to help the patient set goals to improve diabetic control. Collecting this data can help the healthcare team determine the needs of the patient. Less than 10% of diabetic patients in the United States simultaneously attain recommended goals for their diabetic care (Lester et al., 2008). This statistic helps to understand the need of a strict diabetic workflow in primary care to help our diabetic patients achieve their healthcare goals.

References

Diabetes quick facts. (2022, January). Centers for disease control and prevention. https://www.cdc.gov/diabetes/basics/quick-facts.html

Lester, W. T., Zai, A. H., Chueh, H. C., & Grant, R. W. (2008). Diabetes information technology: Designing informatics systems to catalyze change in clinical care. Journal of Diabetes Science and Technology, 2(2), 275–283. https://doi.org/10.1177/193229680800200218

Shrivastav, M., Gibson, W., Shrivastav, R., Elzea, K., Khambatta, C., Sonawane, R., Sierra, J., & Vigersky, R. (2018). Type 2 diabetes management in primary care: The role of retrospective, professional continuous glucose monitoring. Diabetes spectrum, 31(3). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092883/

 Hello Heather,

Great write up. According to McGonigle 2017, the information system such as the internet enables us to create, collect, analyze, and share useful and meaningful data or information. This goes a long way to facilitate the care delivered to people, the very essence of nursing.  Per Sweeney 2017, the Electronic Health Record (HER) is very important for the collection of data and building statistics vital for care coordination by the various health care teams.

 

Reference

McGonigle, D., & Mastrian, K. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Jones and Bartlett.

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

 Nursing informatics can be described as a specialty which integrates the science of nursing along with computer science, and information science which assists in managing communication data, knowledge, information, and wisdom in the nursing practice (McGonigle & Garver Mastrian, 2022).

In my current practice as a nurse in a cardiac unit, data collection and use of technology are essential in our daily practice in caring for heart failure (HF) patients. My scenario involves a HF patient who is readmitted to the unit after two weeks of discharge home from a sister hospital following a diagnosis of Congestive heart failure (CHF). Congestive heart failure is the most common cause of hospitalization in the US for people older than 65 years of age. It has the highest 30-day re-hospitalization rate among medical and surgical conditions, accounting for up to 26.9% of the total readmission rates (Nair et al, 2020). The doctor established through Electronic Health Records (EMR) that the patient had received CHF treatment and was sent home with appropriate medications. The reduction of 30-day readmission is an essential quality of care metric for my facility. The hospital implemented a quality improvement program designed to identify, track, and improve the transition of patient care from the inpatient to the outpatient setting to reduce 30-day all-cause readmissions. EMR based measures are employed to aid these efforts, and an electronic HF dashboard has been implemented.

Data is collected by screening patients admitted with HF diagnosis. EMR system is used to record the patient’s screening tests, appropriate medications, education before discharge using a teach-back process, and a telephone and early post-discharge clinic follow-up arranged in advance. The electronic HF dashboard has allowed the team to identify these patients more quickly and accurately.

The knowledge derived from these data enables the team to perform serial root cause analysis for readmitted patients and construct lists of patients with frequent readmissions. This helps identify patterns that can be prevented, such as patients not taking their HF medications due to affordability or having inadequate social support.

The nurse leader is responsible for ensuring the best and safe patient care delivery. The nurse leader coordinates physicians, therapists, and pharmacy information and updates patients during care and discharge. “Informatics improves the coordination of this information, allowing nurses to give their patients all of the information they need, improving both outcomes and the satisfaction with care” (Electronic Health Reporter, November 14, 2016). I am honored to be a part of this evolving process in my career and would join the team to improve the future of nursing as a profession.

References

Electronic Health Reporter, “How Nurses Are Using Health Informatics to Improve Patient

Care,” November 14, 2016, Retrieved at https://electronichealthreporter.com/nurses-using-health-informatics-improve-patient-care/.

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

knowledge (5th ed.). Jones & Bartlett Learning.

Nair, R., Lak, H., Hasan, S., Gunasekaran, D., Babar, A., & Gopalakrishna, K. V. (2020).

Reducing All-cause 30-day Hospital Readmissions for Patients Presenting with Acute Heart Failure Exacerbations: A Quality Improvement Initiative. Cureus, 12(3), e7420. https://doi.org/10.7759/cureus.7420

Hello Nellie,

Just to add some information to your post.    Data mining which is also referred to as knowledge discovery in databases (KDD) according to Liou and Chang (2015) is a process of automatically searching large volumes of data for patterns. Taking into considerations the clinical pattern of a patient who is hypertensive as well as diabetic, a probable indication that the patient might easily suffer from cardiovascular accident (CVA) in the nearest future thereby enabling researchers, healthcare providers to learn valuable knowledge from this process (Liou & Chang, 2015).

Also, data mining according to Oskouei, Kor, and Maleki (2017) has two major tasks, these are predictive and descriptive tasks,. Predictive tasks are various techniques or algorithms that include classification as well as association rules, when applied, estimations or decisions can be made for the unknown or future values of other variables. Descriptive tasks are those that explain information/data and present  the patterns and results in the forms of figures, tables, and diagrams that can be easily understood by users of the data/information (Oskouei et al., 2017).

I knew someone who used to work in the oncology unit of St. Vincent’s hospital in Bridgeport as a patient care technician (PCT), the rates of admissions of cancer patients especially breast cancer patients made learn about oncology nursing to care for these patients as well as to enable me study more on the various causative agents of this deadly disease. I think it would be interesting to data mine breast cancer in women in order learn and contribute my quota in the treatment and the potential prevention of this disease owing to its proliferation and aggressive spreading otherwise known as metastasis, and also determine the reason why breast cancer is deadlier than most other types of cancer. Being cognizant that women breasts are fleshy with lobes as well as reasonable amounts of fats and fatty tissues, there are also other parts of women’s body that are, or almost the same as their breasts. One should wonder why breast cancer still continues even after radical bilateral mastectomy, chemotherapy, and radiation treatments. What is really going on? I seek data mining breast cancer in order to study, learn and gain more knowledge about the root causes, and also whether there are hormones other than estrogen in women that support or facilitate the rapid as well as aggressive division and spreading of breast cancer cells in women’s breasts more than the same type of cancer in other parts of their body, and to develop a technique to nip it in the bud.

References

Liou, Y. M., & Chang, W. P. ( 2015). Applying Data for the Analysis of Breast Cancer Data. Doi: 10.1007/978-1-4939-1985-7_12.

Oskouei, R. J., Kor, N. M., & Maleki, S. A. (2017). Data Mining and Medical World. Breast Cancer Causes’ Diagnosis, Treatment, Prognosis and Challenges. Am J Cancer Res 7(3):610 – 627.

In January 2021, the COVID-19 multidisciplinary clinical team at our hospital noted a sharp rise in the number of COVID-19 patients developing ventilator-associated pneumonia 48-72 hours after admission. The nurse leader and nurse informaticist (NI) to applied clinical reasoning and judgment to make the choice of extracting data from the hospital’s electronic health records (EHR) for knowledge dissemination, generation, and processing (Nelson, 2018). The nurse manager and NI extracted data for all the patients hospitalized on 10^th January 2021, for severe acute respiratory syndrome (SARS) requiring artificial ventilation NURS 5051/ NURS 6051 week 1 Discussion: The Application of Data to Problem-Solving.

An analysis showed that a total of 11 COVID-19 patients were mechanically ventilated after their states deteriorated following severe respiratory failure. Seven of the 11 patients were male and four female, with a mean age of 61 years. Approximately 73% of the 11 patients developed VAP within 48 – 96 hours after ICU admission, resulting in a loss of 45.45% to VAP-related inpatient deaths. Six of the patients representing 54.54% recovered and were discharged from the ICU from 13^th – 20^th January 2021. Further analysis of the data revealed that age was a possible predictor for acquiring VAP, with all patients aged  developing VAP and none of the three patients aged ≤ 45 years developed VAP. Besides, all the patients who died from VAP complications were aged above 70 years, and the younger patients with no VAP cases spent at most 96 hours in the hospital. In line with findings in published literature, the multidisciplinary team confirmed that VAP not only increases the odds for inpatient mortality but also prolonged the length of ICU and hospital stay for at least 9 – 13 days (Maes et al., 2021; Ippolito et al., 2021).

The multidisciplinary team evaluated data-based quality improvement guidelines recommended by the IHI and AHRQ, as well as empirically tested interventions aimed at reducing the risk of VAP among mechanically-ventilated patients. The evaluation led to the recognition that, while the team implemented the IHI Ventilator Bundle, compliance with the bundle was substantially low, with a large share of the patients’ health records lacking documented daily goals sheets (IHI, n.d.). The hospital’s COVID-19 multidisciplinary team developed a digital signature (electronic search algorithms) to track the team’s adherence with the components of the Ventilator Bundle and assess the effectiveness of specific components in mitigating VAP (IHI, n.d.). The virtual signatures were incorporated with the patients’ medical records, such that in case one is not completed, the ICU nurse would be notified to complete all the four Ventilator Bundle components. Thus, nursing informatics facilitated not only the identification of the clinical problem but also the formulation of evidence-based solutions to VAP among COVID-19 patients.

References

Institute of Health Iimprovement. (n.d.). Ventilator-associated pneumonia. http://www.ihi.org/Topics/VAP/Pages/default.aspx

Ippolito, M., Misseri, G., Catalisano, G., Marino, C., Ingoglia, G., Alessi, M., Consiglio, E., Gregoretti, C., Giarratano, A., & Cortegiani, A. (2021). Ventilator-associated pneumonia in patients with covid-19: A systematic review and meta-analysis. Antibiotics, 10(5), 1–19. https://doi.org/10.3390/antibiotics10050545

Maes, M., Higginson, E., Pereira-Dias, J., Curran, M. D., Parmar, S., Khokhar, F., Cuchet-Lourenço, D., Lux, J., Sharma-Hajela, S., Ravenhill, B., Hamed, I., Heales, L., Mahroof, R., Solderholm, A., Forrest, S., Sridhar, S., Brown, N. M., Baker, S., Navapurkar, V., … Conway Morris, A. (2021). Ventilator-associated pneumonia in critically ill patients with COVID-19. Critical Care, 25(1), 1–11. https://doi.org/10.1186/s13054-021-03460-5

Nelson, R. (2018). Informatics: Evolution of the Nelson data, information, knowledge and wisdom model: part 1. OJIN: The Online Journal of Issues in Nursing, 23(3). https://doi.org/10.3912/OJIN.VOL23NO03INFOCOL01

 

 

 

Healthcare Informatics is defined as “the integration of healthcare sciences, computer science, information science, and cognitive science to assist in the management of healthcare information” (Sweeney, 2017). For this discussion, I chose the topic of nurse staffing ratios and how extremely important this topic is when it comes to nursing informatics. According to an article, there was a study done in Thailand on nurse-to-patient ratios, the findings of their study revealed that the ratio of nursing staff to patients was found to be the best predictor of in-hospital mortality rates (Sasichay-Akkadechanunt, 2013). They were able to come to this conclusion by utilizing nursing informatics in their local hospital by collecting data over a certain amount of time. They did a study on four nurses and the 2531 patients admitted throughout a six-month span. The findings provided information for hospital and nursing administrators to use when restructuring the clinical workforce, revising hospital policies, or making contractual decisions on behalf of nursing and public beneficiaries. This work can advance nursing practice to move beyond “on-the-job informatics training” to a more competency-based model of nursing informatics education and practice (Collins, 2017). As a nursing manager or leader, I can utilize the data analyzed to propose a solution and plan for my fellow colleagues to continue maintaining nursing excellence within the facility.

 

References

 

Collins, S. (2017, April 1). Nursing Informatics Competency Assessment for the Nurse. . . : JONA: The Journal of Nursing Administration. LWW. Retrieved March 2, 2022, from https://journals.lww.com/jonajournal/Abstract/2017/04000/Nursing_Informatics_Competency_Assessment_for_the.7.aspx

 

Sasichay-Akkadechanunt, T. (2013, September 1). The Relationship Between Nurse Staffing and Patient Outcomes : JONA: The Journal of Nursing Administration. LWW. Retrieved March 2, 2022, from https://journals.lww.com/jonajournal/Abstract/2003/09000/The_Relationship_Between_Nurse_Staffing_and.8.aspx

 

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

Hello Jordan,

Thanks for sharing your thoughts and this great example. Considering the example you gave, what knowledge might be derived from that data? How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience?

Dr. Mattis