NURS FPX 8030 Assessment 2

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NURS FPX 8030 Assessment 2 Evidenced-Based Literature: Search and Organization

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Sequelae of the complications of medical errors include inadequate or mixed response to therapy, aggravation of the patient’s condition. In order to minimize medical errors, healthcare personnel should be given access to the latest and trustworthy information. Evidence-based research is also an integral part of this process in that it provides the clinicians with the knowledge and study results data to use as a reference in making their decisions. However, the information is so vast that knowing what and how to organize it becomes crucial. Yao et al. (2020) concludes that with evidence-based book search and organizing techniques, healthcare providers acquire more accurate diagnoses, make patients safer, and provide better care.

Statement of Patient Safety Issue

Diagnostic errors occur when a health problem is misdiagnosed or undiagnosed, and this can harm the patient or lead to suboptimal outcomes (Albahri et al., 2023) It is now a concern for patient safety. Research has indicated that medical mishaps are a major cause of what goes wrong in the health care sector. Diagnostic errors may lead to many bad things to occur especially injury to the patient even the death. In order to address this challenge, we have to find a way of enhancing the evaluation process and reducing errors. The focus of this PICOT is electronic decision support systems (I), which are software tools that support doctors in making evidence-based decisions (Zajac et al., 2021). The objective of the PICOT study is to collect data that could demonstrate the effectiveness of computer-based decision support systems in reducing the rate of diagnostic errors. The goal is to address the current issue or gap in healthcare, where diagnostic errors are still a huge threat to patient safety. Computer decision support systems impact on the diagnosis accuracy, patient’s outcomes and quality of care within the adult primary care context can be traced by healthcare workers in the most recent study (Yao et al., 2020).

PICOT Question

The PICOT question that was made to deal with this problem is this: If computer decision support systems (I) are used in primary care settings for adults (P) instead of normal clinical practice (C), do diagnostic mistakes (O) happen less often during the diagnostic process (T)?

Narrative of Search Strategy

To identify the optimal evidence for the PICOT question concerning the application of electronic decision support systems in reducing medical errors in adult primary care settings, a well-designed research plan could be used. Selecting appropriate sources is the beginning of research. In such instances, sources such as PubMed, Embase, CINAHL, and Cochrane Library are commonly applied for healthcare research. Most important is to search databases or sources of the so-called “gray literature” that are experts in a specific field and could provide you useful data (Freynhagen et al., 2019). The second stage is to create a search query by means of buzzwords and a number of sentences in one particular language. These words can be used with this PICOT question: “diagnostic errors,” “primary care,” “electronic decision support systems and “reduction”. The ideas can be appropriately combined using the Boolean operators, “AND” and “OR”. Cuts and wildcards are used for selecting different versions of keywords. You may use the search terms “diagnosis errors” OR “diagnosis errors” AND “primary care” OR “general practice” AND “electronic decision support systems” OR “clinical decision support systems” AND “reduce” OR “decrease” OR “minimize” and retrieve studies on a diagnostic error, primary care settings, computer decision support systems, and error reduction (Lockwood et al., 2019).

Once the study question has been defined, inclusion and exclusion criteria will be helpful to avoid misunderstandings of the results. Studies from general care settings for adults, research using computer decision support systems, and findings of how frequent are diagnosis errors could all be contributors. Researches in infant or critical care areas may not qualify. Limiting the search to a certain type of publications, such as systematic reviews, randomized controlled trials, or cohort studies (Miao et al., 2019), could also help in locating the best data. This method is useful for researchers to sift through the papers and choose the strongest supporting data to resolve the PICOT problem. This approach prevents missing critical data and ensures that the related studies are incorporated (Greenhalgh et al., 2019).

Databases and Keywords

Several sources would provide the best evidence for the PICOT question on electronic decision support systems implementation in reducing diagnostic errors in primary care settings in the population of adults. One of such is PubMed, which is a popular database for biological research (Wang et al., 2022). It has a big number of papers from a lot of medical journals that can educate you a lot. Other databases like Embase, CINAHL, and Cochrane Library may also be beneficial, as they present multiple perspectives and contain a great deal of medical literature. Another approach to obtaining research material is to search sources for that field of study, such as PsycINFO or Scopus (Fàbregues et al., 2022).

In preparing the search plan, you need to employ appropriate terms and short language sentences. This PICOT question revolves around “diagnostic errors,” “primary care,” “electronic decision support systems,” and “reduction.” Similar and opposite words should be taken into account. You can utilize Boolean operators such as “AND” and “OR” in order to appropriate the words correctly. One would use a search term such as “diagnostic errors” OR “misdiagnosis” AND “primary care” OR “general practice” AND “electronic decision support systems” OR “clinical decision support systems” AND “reduction” OR “decrease” OR “minimize”. This search strategy ensures that articles discussing primary care diagnostic errors, computer decision support systems and error reduction are located (Bell et al., 2020).

Inclusion and Exclusion Criteria

The clear specifics regarding which articles are to be included and excluded is critical during the searching phase to determine which ones will be used for further review. This PICOT question could be informed by research conducted in general care settings for adults, treatments that utilize computer decision support systems and information from how frequent diagnosis errors are. Studies focusing on the use of computer decision support systems to reduce primary care diagnostic errors, studies conducted in pediatric or specialized care settings, or papers not written in English may not be considered (Richens et al., 2020). After the sources and the search methods have been chosen, a given number of articles is found. The number of pieces that are retained is determined by the level of thoroughness of the research and how clear the guidelines are on what should be included and what should not.

By conducting the research, repeatability rate may increase (Mariano et al., 2021). However, most of the time, several hundreds or even thousands of pieces must be gathered first. At the beginning, 78 records were identified in the quest for information on the use of computerized decision support systems to reduce medical errors in primary care settings for adults. Only twelve of them were eliminated due to duplication, which gave 66 special books for further research. The inclusion criteria were used to the titles and the abstracts of these articles and this resulted to the exclusion of 20 papers that should have met the criteria. The last 46 works were then subjected to full-text review after this sorting process. All twelve were selected due to their relevance to PICOT (Greenhalgh et al., 2019).

These studies clarified that they were on medical errors and the use of computer decision support systems in the primary care settings for adults. They demonstrated that these approaches effectively reduce medical errors and enhance patient outcomes (Greenhalgh et al., 2019). Ultimately, the papers which are retained are selected since they are in favour of the study objectives, contain valuable information, and offer quality evidence. They are to address the PICOT question and to demonstrate the level of performance of computer decision support tools used to reduce diagnostic errors in primary care for adults. The publications selected can serve as base for researchers’ ideas and study because the selection process attempts to keep the information collected accurate and consistent (Miao et al., 2019).

Relatability with Issue

The 12 pieces directly address the problem of reducing diagnostic errors in preventive care of adults using a computer-based decision support system. These works presented that medical errors occur, and they demonstrate how computer decision support systems could improve precision in diagnosis and patient outcomes (Salvador et al., 2019). The papers discuss the effectiveness of these systems in aiding doctors to make diagnoses by providing them assistance in analyzing clinical data and prompting them to base decisions on facts. Many good studies addressed the characteristics and functions of electronic decision support systems, how they are used in primary care, and how this reduces the number of diagnostic errors made. These papers contain the entire essence of the problem and provide a large amount of information that can be used for supporting the usage of electronic decision support systems, which made diagnoses more accurate, and patients safer in general care settings for adults (Richens et al., 2020).

Conclusion

To reduce medical errors that pose serious risks to patients, healthcare workers should get access to the most recent and precise information. A comprehensive search with reliable key words and relevant sources would help to locate useful articles. The guidelines imply what to include and what not to include so that only the most pertinent study is preserved. Twelve papers were selected as they specifically discussed the PICOT question of utilizing electronic decision support systems to reduce diagnostics errors in adults within primary care settings.

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References

Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., Santamaría, J., Ouyang, C., Gupta, A., Gu, Y., & Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion.

https://doi.org/10.1016/j.inffus.2023.03.008

Bell, S. K., Delbanco, T., Elmore, J. G., Fitzgerald, P. S., Fossa, A., Harcourt, K., Leveille, S. G., Payne, T. H., Steinmetz, R. A., Walker, J., & DesRoches, C. M. (2020). Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Network Open, 3(6).

https://doi.org/10.1001/jamanetworkopen.2020.5867

Fàbregues, S., Mumbardó, C.A., Escalante, E. L.B., Hong, Q. N., Edelstein, D., Vanderboll, K., & Fetters, M. D. (2022). Mixed methods intervention studies in children and adolescents with emotional and behavioral disorders: A methodological review. Research in Developmental Disabilities, 126.

https://doi.org/10.1016/j.ridd.2022.104239

Freynhagen, R., Parada, H. A., Ospina, C. A. C., Chen, J., Emril, D. R., Villacorta, F. J. F., Franco, H., Ho, K. Y., Solares, A. L., Li, C. C. F., Alvarado, A. M., Nimmaanrat, S., Santos, M. D., & Andrade, D. C. A. (2019). Current understanding of the mixed pain concept: A brief narrative review. Current Medical Research and Opinion, 35(6), 1011–1018.

https://doi.org/10.1080/03007995.2018.1552042

Greenhalgh, T., Hinton, L., Finlay, T., Macfarlane, A., Fahy, N., Clyde, B., & Chant, A. (2019). Frameworks for supporting patient and public involvement in research: Systematic review and co‐design pilot. Health Expectations, 22(4), 785–801.

https://doi.org/10.1111/hex.12888

Lockwood, C., Santos, K. B. D., & Pap, R. (2019). Practical guidance for knowledge synthesis: Scoping review methods. Asian Nursing Research, 13(5), 287–294.

https://doi.org/10.1016/j.anr.2019.11.002

Mariano, D. H., Hernández, L. C., Zorita, A. L., Duque, O. P., & Santos, F. G. (2021). A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detection & diagnosis. Journal of Building Engineering, 33.

https://doi.org/10.1016/j.jobe.2020.101692

Miao, M., Wang, J., Wen, S., & Ma, J. (2019). Publicly verifiable database scheme with efficient keyword search. Information Sciences, 475, 18–28.

https://doi.org/10.1016/j.ins.2018.09.067

Richens, J. G., Lee, C. M., & Johri, S. (2020). Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications, 11(1).

https://doi.org/10.1038/s41467-020-17419-7

Salvador, J. A. O., Marco, G. C., & Arquero, R. A. (2019). Errors in search strategies used in systematic reviews and their effects on information retrieval. Journal of the Medical Library Association, 107(2).

https://doi.org/10.5195/jmla.2019.567

Wang, B., Zhu, Y., Li, D., & Wu, C. (2022). Evidence-based accident prevention and its application to hazardous chemical storage accident prevention. Process Safety and Environmental Protection, 160, 274–285.

https://doi.org/10.1016/j.psep.2022.02.020

Yao, X., Florez, I. D., Zhang, P., Zhang, C., Zhang, Y., Wang, C., Liu, X., Nie, X., Wei, B., & Ghert, M. A. (2020). Clinical research methods for treatment, diagnosis, prognosis, etiology, screening, and prevention: A narrative review. Journal of Evidence-Based Medicine, 13(2), 130–136.

https://doi.org/10.1111/jebm.12384

Zajac, S., Woods, A., Tannenbaum, S., Salas, E., & Holladay, C. L. (2021). Overcoming challenges to teamwork in healthcare: A team effectiveness framework and evidence-based guidance. Frontiers in Communication, 6(1).

https://doi.org/10.3389/fcomm.2021.606445

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