Analysis of Technology in Nursing Practice

While there are many procedural adaptations that patient care technology might require in this fast-developing field, processes for evidence preservation are needed to enhance its application (Al Baalharith et al., 2022). Long-term continuous glucose monitoring (CGM) systems – that offer data and alerts in real-time – have contributed to the management of diabetes. To get the maximum of these technologies, healthcare professionals are to apply for an evidence-based approach matching the state-of-art research and clinical guidelines. Consequently, as customization guarantees that consideration is given to the patient’s needs and circumstances, akin to Precision Medicine in multifaceted T2D, this mode would not only necessitate fully optimized outcomes in patients but also define the efficiency of technology (Crawford et al., 2020).  However, it is necessary to recall the other sides and real-world challenges that can become an obstacle to the application of these approaches. In this paper, I shall introduce how the use of evidence-based practices can assist with the optimization of the CGM system.

Analysis of How Patient Care Technology Affects Patient Care and Nursing Practice

Patient Care Technology: Continuous Glucose Monitoring (CGM) Systems

Impact on Patient Care

This has brought about a change in diabetic management through what is known as continuous glucose monitoring (CGM). Therefore, listed under numerous benefits of this technology is improved glycemic control. CGM devices provide daytime and nighttime blood glucose measurements and so are less dependent on intermittent fingerstick samples in contrast to traditional blood glucose monitoring practices (Crawford et al., 2020). This makes it possible to adjust insulin doses and other treatments more accurately Therefore, it is good for patients since it helps control blood sugar levels more effectively and reduces episodes of hyperglycemia.

The early recognition of glucose fluctuations plays a huge role too as we have seen: It is through CGM that users and medical personnel receive messages of change of blood sugar levels before they reach dangerous levels (Galindo et al., 2020). Early prevention means that one can control situations before they get out of hand as seen with a hypoglycemia episode or diabetic ketoacidosis. In addition, CGM devices make the treatment of diabetes more active since the patients can get instant results.

Impact on Nursing Practice

When considering the implementation of CGM systems into nursing practice, several components are central to that process. One has to wonder, however, whether it is the responsibility of those using these technologies—nurses foremost among them—to critically assess the continuously flooding data and integrate them into individual patient management (Al Baalharith et al., 2022). As such, for the best patient care, this requires an understanding of how to work with the glucose data and the various actions that follow. Moreover, nurses are crucial for patient education as they teach how to put sensors and how to read data as well as how to respond to caution on CGM systems.

The use of CGM systems can also simplify the nurses’ workflow. CGM technology takes less time for the patients and nursing staff so it proves to provide effective patient care by reducing the burden of frequent fingerstick tests. This minimizes time consumption hence nurses can focus on other essential aspects of their patients’ care (Galindo et al., 2020). However, some of these CGM devices have additional features that enable the healthcare provider to ‘see’ what the patient’s blood sugar levels are at a distance. The feature of making timely interventions without the need for other visits enhances follow-up treatment.

Assumptions

Three analytically basic assumptions underlie the analysis of CGM systems: First of all, it rests on the technical reliability of the technology: such a device has to be accurate in measuring glucose and free of any malfunction or mishap (Shorey et al., 2022). Knowing that patients with diabetes will participate in the interactions and behaviors associated with the technology and absorb the information provided to apply it within their diabetic routines. CGM systems are particularly effective where patients are willing and able to optimize the use of the technology in the device (Sichieri et al., 2022).  Furthermore, as with most analyses of patient-generated health data, this analysis assumes that nurses and other healthcare professionals have the training and understanding to interpret CGM data. It is based on the premise that funding and support are sufficient to ensure that medical staff can employ the technology. In addition, it is believed that CGM systems acknowledge the privacy and data security laws as the HIPAA that assure the patient data is protected from third-party revelation.

Analysis of Data Communication and Evaluation Criteria

Data Communication

Continuous Glucose Monitoring (CGM) yields important information that is transferred through several critical processes ensuring that glucose data is correctly and promptly relayed. CGM devices monitor the glucose level of the interstitial fluid all the time, with the help of sensors implanted just under the skin (Al Baalharith et al., 2022). After that, patients and healthcare professionals can get feedback on this data via a smartphone or a wearable receiver. Often, bluetooth or any other similar wireless technologies are employed to transmit this data which is quite seamless with digital solutions.

Often, the real-time data from the CGM devices are represented in graphical forms on the dashboard with patterns, trends, or warning symbols on the possibility of fluctuations in blood glucose levels. From this graphic presentation of glucose data, patients and healthcare employees can rapidly assess glucose records and make a decision (Galindo et al., 2020). Moreover, a considerable number of CGM systems with remote monitoring technologies facilitate direct data transmission to healthcare providers to enhance the capability to deliver prompt revisions and interferences to the treatment plans.

Criteria for Evaluating Data

Continuous glucose monitoring (CGM) systems must meet several important requirements. Accuracy is crucial; CGM results must closely resemble conventional blood glucose values. Timeliness is also essential since it enables quick response to glucose variations when data is available in real-time or almost real-time (Shang,  2021). Data completeness guarantees uninterrupted monitoring with no appreciable lapses, allowing for trustworthy trend analysis. Clear, simple data presentations that are easy to understand for patients and healthcare professionals are essential to the system’s usability (Tinôco et al., 2021).  EHR system integration is essential for a comprehensive understanding of patient health, and alert accuracy is critical to prevent false positives or negatives. The system’s ability to assist diabetes control in day-to-day living is ultimately determined by patient adherence and engagement with it.

Controls and Safeguards for Patient Safety and Confidentiality in CGM Systems: Analysis and Knowledge Gaps

Some of the core controls and safeguards needed in CGM systems are as follows Safety and privacy of the patients are maintained at any cost (Tinôco et al., 2021). One of the crucial security measures is data encryption which ensures the possibility of transmitting sensitive information, such as glucose levels, between the CGM sensor, receiver, or the related smart application and other healthcare systems securely (Al Baalharith et al., 2022). Encryption ensures that data is protected from spying, and during transmission or storage, it is difficult to obtain or read it.

Restrictions using adherent authentication methods such as passwords and user identities form a critical component of EMR system security (Hovenga,  2024). While data storage security involves the use of safe servers and databases to store safely patient data, it confines patient data access to particular persons. Security check-outs help identify and correct holes and ensure that all security measures are up-to-date and functioning properly.
Hence the a need for informed patient consent and privacy regulations to ensure that the patients know how their data is going to be used and protected (Mrayyan et al., 2023). Another area of compliance has to do with legal requirements concerning data protection, which means that the content of these requirements has to be followed, for instance, HIPAA laws. Last but not least, the establishment of data integrity procedures ensures that the data collected by CGM systems are not manipulated or corrupted in any way hence maintaining the reliability and accurateness of the data obtained.

Knowledge Gaps, Unknowns, and Areas of Uncertainty

The analysis of CGM has to be enhanced by removing certain knowledge gaps and reducing areas of uncertainty. Sophisticated security threats are a major factor as there is always an emergence of new techniques of hacking and other related cyber crimes. For strong security to be maintained there is research necessary on how such a CGM system might react to different threats (Crawford et al., 2020). One of the areas that needs additional study is the concept of long-term data preservation particularly that of longevity of security systems currently deployed. Another area that requires research is the patient attitudes and perceptions of privacy, as this would provide insight into how patients feel about the use of their data and its impact on trust in technology (Galindo et al., 2020). This means that the risk relating to the integration of CGM data into EHRs must be investigated to ensure that there are no possible breaches or inaccuracies. Due to the likely concerns over patient privacy, more studies are required on the ethical and legal implications of data sharing particularly in conditions remotely supervised.

Improving the Application of Patient Care Technology through Evidence-Based Strategies

Evidence-Based Strategies

 The effective application of CGM systems can be informed by these evidence-based protocols and guidelines derived from clinical studies (Al Baalharith et al., 2022). Some examples of topics that can be discussed and improved include research related to glycemic control and targets, general care approaches within the current healthcare system that would allow for a better understanding of CGM data or to quickly change patients’ insulin dosage. Contraindications By applying the above guidelines in using CGM systems, modern findings in the application of the device are achieved.

Relaunching Patient Education

It was also agreed that through the use of evidence-based programs, there could be increased understanding and acceptance of CGM technology among diabetes patients (Mrayyan et al., 2023). The use of CGMs is more structured and having training about the results with participants getting alarms improves the use and hence diabetes management. Patients should get the best out of their CGM devices, and enhance self-care through adopting such teaching techniques.

Ongoing Monitoring and Feedback

It is possible to enhance the implementation of CGM systems to attain higher efficacy by tracking the proof of the tracking efficacy utilizing evidence-based practices and feedback provision  (Galindo et al., 2020). Therefore, Some of the measurements indicated to be based on factual data include; patient satisfaction, patient outcomes, and system performances, which can be used on a routine basis to determine what is effective out of the changes made in intended care delivery processes or technology usage.

Personalized Treatment Plans

Altogether, the included papers suggest that MDTs incorporating specific patient data, including demographic and disease characteristics, are supported by moderate evidence (Crawford et al., 2020). Combined with biometrics and other aspects of patient care including the dosage of insulin, CGM data can be used by healthcare professionals to create an individualized treatment plan for the patient. This implies that the care given is tailored care for the patient hence maximising the benefits that come with CGM devices.

Acknowledging Other Viewpoints

While techniques grounded in evidence provide a good foundation to improve the use of patient care technology, it is important to keep other viewpoints in mind:

Patient-Centered

Those who believe the patients’ preferences and experiences should be at the top of our list, despite running counter to evidence-based recommendations (Galindo et al., 2020). Patient happiness and adherence can be significantly improved if they feel like their use of technology was a choice, even when that decision breaks with accepted best practices.

Technical constraints

Some evidence-based techniques may not be compatible with all CGM systems as the types of CGMs are different and there can also be other technological issues (Al Baalharith et al., 2022). Some technologies may not support specific evidence-based features or functions, making these the subject of modification and alternative activity depending on what is possible.

Financial and Resource Constraints 

Employing evidence-based strategies requires additional costs or resources that may not be readily available. Factors such as resource constraints and budgets can affect how the impact of all these plans works out in practice.

Innovation and Emerging Technology

New inventions that can support the facilitation of patient care (Hovenga,  2024). The effectiveness of CGM systems and other patient care technologies must be continually innovated by integrating with new concepts that will enhance existing evidence-based practices.

Conclusion

The nursing technologies studied and learned from developers and users alike in detail below, have been reported to improve patient care considerably with attendant implications for a model specification that can simplify nursing practice. Electronic health records (EHRs) and technology like continuous glucose monitoring systems positively affect better data accuracy, patient care abilities, and real-time tracking (Crawford et al., 2020). These innovations not only optimize nurse workflow but also drive better and more personalized care. However important factors like levels of data security, mandatory integration complications, and those required for training, must also be considered to have a successful implementation (Galindo et al., 2020). Regular appraisals of these technologies against certain performance benchmarks ensure that healthcare practitioners can be assured that nursing technologies are being used to their fullest potential to streamline the healthcare delivery system and improve patient outcomes.

References

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