Nursing Informatics Trends Report

Information technology has revolutionized the way healthcare is delivered and managed in recent years. This field of health informatics uses technology to organize and analyze health records leading to improved healthcare outcomes. As the future of healthcare changes, the use of informatics is expanding to address the evolving needs of patients and providers. As a result, there are evolving trends in information technology that are currently defining the field and the future of healthcare. The three key trends that are forecast for informatics include artificial intelligence, remote monitoring systems, and big data analytics. This discussion focuses on these three trends, how they can be used to address specific health challenges, and steps to implement these technologies in the healthcare industry.

Artificial intelligence in Healthcare

Artificial intelligence (AI) has impacted nearly every sector with healthcare included. This technology is transforming workflow in organizations by replacing repetitive manual processes. In simple terms, AI involves the science and engineering of making intelligent machines that mimic human cognitive functioning. These machines or technologies have the potential to anticipate problems and deal in an intentional, intelligent, and adaptive manner (Bajwa et al., 2021). Artificial intelligence has been used to streamline processes in healthcare across all departments. Decades ago, almost every aspect of healthcare delivery was documented in papers and communication done through phone calls. AI has changed this narrative and today healthcare is moving towards cloud databases and custom applications, thanks to AI technology.

Artificial intelligence has been used to push the boundaries of healthcare and make operations smooth leading to better patient outcomes. AI technology can be used to improve patient outcomes by ensuring diagnostic accuracy. For example, AI algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, with high accuracy and speed. By detecting abnormalities or patterns indicative of diseases, AI can assist healthcare providers in making more accurate and timely diagnoses, a challenge that has long existed in the healthcare industry. According to research, AI can be used to improve the accuracy of patient diagnosis, reduce costs by up to 50%, and improve health outcomes by 40% (Bajwa et al., 2021). Other studies have also indicated that the use of personalized health and treatment plans improves patient outcomes. By considering individual patient characteristics and medical histories, AI can help healthcare providers tailor treatment strategies to each patient’s specific needs. Apart from these aspects of care delivery, AI can predict patient outcomes using historical data and current trends in healthcare. This analysis can be used by providers to make proactive interventions and implement plans to improve patient outcomes.

Using Advancements in Technology to Drive Change in Nursing Healthcare

The use of artificial intelligence in healthcare has the potential to improve patient outcomes through remote monitoring. Studies indicate that AI-powered remote devices can alert providers when the patient’s health is in jeopardy (Bajwa et al., 2021). From another perspective, AI can enable drug discovery and development. AI algorithms can analyze vast datasets to identify potential drug candidates more efficiently than traditional methods. Lastly, workflow optimization by AI is observed to improve patient outcomes by reducing errors and time required to act. AI can streamline administrative tasks, such as appointment scheduling, billing, and medical coding, allowing healthcare providers to focus more on patient care (Bajwa et al., 2021). In the long term, AI systems are likely to become intelligent and enable organizations to achieve precision medicine and improved patient outcomes.

Remote Patient Monitoring (RPM)

Telehealth is another emerging trend in healthcare that has gained significant attention, especially remote monitoring technology. This digital communication technology allows monitoring of patient’s healthcare status outside the traditional clinical setting. The Agency for healthcare research and Quality (AHRQ) explains that this technology uses medical devices like weight scales, blood pressure monitors, pulse oximeters, and blood glucose meters to monitor patient data. The technology also utilizes automated feedback that enables providers to know when prompt intervention is needed. Traditionally, remote patient monitoring has been used for chronic conditions like asthma, hypertension, and diabetes. The technology is now projected to help in monitoring other patient populations like COPD and congestive heart failure (AHRQ, 2023). The use of remote monitoring can help to address key issues that have affected the healthcare industry like hospital readmissions, increased emergency department visits, and hospital length of stay. Remote patient monitoring technology has the potential to be used in other acute conditions to improve outcomes and safety.

Remote monitoring technologies can improve patient outcomes in many ways, depending on which condition they monitor. RPM could affect patient outcomes through continuous monitoring and tracking of patient data. This continuous monitoring allows healthcare providers to detect subtle changes in patient’s health status early, enabling prompt intervention and preventing complications (Makutonin et al., 2023). Secondly, RPM enables early detection of health issues like deterioration of patient conditions or disease progression. For instance, changes in heart variability for patients with cardiac disease can indicate imminent danger which should be addressed by physicians. Early detection of these issues allows healthcare providers to intervene promptly, potentially preventing further deterioration and improving patient outcomes. A recent study conducted among patients with hypertension found that RPM technology helped to achieve blood pressure control among patients and reduced all-cause clinic visits among patients with hypertension (Makutonin et al., 2023). This technology can be used to achieve the same results in patients with other conditions that require continuous monitoring outside the hospital setting.

Patient outcomes are dependent on factors like adherence to medication and proper patient education which can all be achieved through RPM technology. Remote monitoring technology can help improve medication adherence by providing reminders and tracking patients’ medication intake (AHRQ, 2023). Some remote systems can also detect missed doses of medication and alert providers leading to immediate follow-up. Remote monitoring technology often includes patient-facing interfaces such as mobile apps or web portals which allow the delivery of health education. These systems empower patients to be active participants in their health leading to improved outcomes. Apart from these aspects that directly influence patient outcomes, RPM technology has the potential to reduce costs associated with healthcare and ensure remote management of patients.

Data Analytics

Improving efficiency and coordination in healthcare is a critical aspect of delivering quality care to patients today. To achieve these outcomes, data analytics technology is an emerging trend that is used to analyze patient information, pinpoint issues, and create a plan for optimal care. Big data analytics involves the process of analyzing and interpreting large and complex sets of healthcare-related data to identify trends, patterns, and insights that can be used to make more informed decisions. For example, analyzing past data of patients can help providers make informed decisions and uncover insights that can lead to more positive outcomes. Apart from these positives, big data analysis can be used to predict the future of healthcare through the analysis of numerous data from patients. For example, the COVID-19 pandemic awakened the healthcare sector’s need to analyze patient data and trends in healthcare. The availability of huge data amounts during this period made it difficult to come up with preventive measures and solutions to the problem. Big data analysis technology can serve to address such challenges and help the healthcare system in dealing with such future problems.

Data analytics technology can help to improve patient outcomes by providing clinicians with evidence-based insights and recommendations at the point of care. For example, technologies like clinical decision support systems powered by big data analysis can ensure providers make informed decisions leading to better patient outcomes. Secondly, data analytics can improve patient outcomes through predictive analytics. The systems can identify patients at risk of certain complications by identifying patterns and ensuring clinicians avoid these challenges. Through population health management, data analytics can identify trends, disparities, and opportunities for improvement. Healthcare organizations can identify high-risk patient groups, implement targeted interventions, and allocate resources effectively when using data analytics. Lastly, this technology can analyze healthcare quality metrics and dictate the implementation of evidence-based interventions to improve patient outcomes.

Steps for Implementation

Mullti-step, iterative approaches are required to build effective and reliable informatics technologies in healthcare. The first step to implementing these technologies will involve the assessment of needs and organizational objectives. This step will include strategies like stakeholder engagement to define goals and metrics for measuring performance. Key stakeholders that may be included are computer scientists, physicians, informatics nurse specialists, and subject matter experts like biomedical scientists (Bajwa et al., 2021). The use of a multidisciplinary team approach can help to define problems and milestones during technology implementation.

Technology selection and procurement is another crucial step to be taken. Factors such as functionality, interoperability, diagnostic accuracy, and cost should be considered. Capital will be required to purchase software and hardware for the selected technologies while considering important aspects like return on investment. Risk analysis should be done before purchasing these technologies to prevent losses and adverse patient outcomes (Bajwa et al., 2021). For example, each technology should be analyzed for technical issues, data privacy concerns, regulatory compliance, resistance from staff or patients, and financial constraints. Upon analysis of these key features, training, and education of users should proceed. Pilot testing, ongoing support, and continuous monitoring are among the last steps in implementing the technology (Bajwa et al., 2021). This step will involve the collection and analysis of data on key performance indicators (KPIs) to identify opportunities for optimization and continuous improvement.

References

Agency for Healthcare Research and Quality. (2023). Remote patient monitoring. https://psnet.ahrq.gov/perspective/remote-patient-monitoring#:~:text=Remote%20patient%20monitoring%20(RPM)%20is,oximeters%2C%20and%20blood%20glucose%20meters.

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095

Batko, K., & Ślęzak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1), 3. https://doi.org/10.1186/s40537-021-00553-4

Makutonin, M., Dare, J., Heekin, M., Salancy, A., Hood, C., & Dominguez, L. W. (2023). Remote patient monitoring for hypertension: Feasibility and outcomes of a clinic-based pilot in a minority population. Journal of Primary Care & Community Health, 14, 21501319231204586.

https://doi.org/10.1177/21501319231204586