: Healthcare Information Technology Trends
Throughout history, technological advancements have appeared for one purpose before finding applications elsewhere that lead to spikes in its usage and development. The internet, for example, was originally developed to share research before becoming a staple of work and entertainment. But technology—new and repurposed—will undoubtedly continue to be a driver of healthcare information. Informaticists often stay tuned to trends to monitor what the next new technology will be or how the next new idea for applying existing technology can benefit outcomes.In this Discussion, you will reflect on your healthcare organization’s use of technology and offer a technology trend you observe in your environment.To Prepare:Reflect on the Resources related to digital information tools and technologies.Consider your healthcare organization’s use of healthcare technologies to manage and distribute information.Reflect on current and potential future trends, such as use of social media and mobile applications/telehealth, Internet of Things (IoT)-enabled asset tracking, or expert systems/artificial intelligence, and how they may impact nursing practice and healthcare delivery.BELOW IS THE QUESTIONPost a brief description of general healthcare technology trends, particularly related to data/information you have observed in use in your healthcare organization or nursing practice. Describe any potential challenges or risks that may be inherent in the technologies associated with these trends you described. Then, describe at least one potential benefit and one potential risk associated with data safety, legislation, and patient care for the technologies you described. Next, explain which healthcare technology trends you believe are most promising for impacting healthcare technology in nursing practice and explain why. Describe whether this promise will contribute to improvements in patient care outcomes, efficiencies, or data management. Be specific and provide examples.BELOW IS THE RESOURCESLearning ResourcesRequired ReadingsMcGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.Chapter 14, “The Electronic Health Record and Clinical Informatics” (pp. 267-287)Chapter 15, “Informatics Tools to Promote Patient Safety and Quality Outcomes” (pp. 293-317)Chapter 16, “Patient Engagement and Connected Health” (pp. 323-338)Chapter 17, “Using Informatics to Promote Community/Population Health” (pp. 341-355)Chapter 18, “Telenursing and Remote Access Telehealth” (pp. 359-388)Dykes, P. C., Rozenblum, R., Dalal, A., Massaro, A., Chang, F., Clements, M., Collins, S. …Bates, D. W. (2017). Prospective evaluation of a multifaceted intervention to improve outcomes in intensive care: The Promoting Respect and Ongoing Safety Through Patient Engagement Communication and Technology Study. Critical Care Medicine, 45(8), e806-e813. doi:10.1097/CCM.0000000000002449HealthIT.gov. (2018c). What is an electronic health record (EHR)? Retrieved fromhttps://www.healthit.gov/faq/what-electronic-health-record-ehrRao-Gupta, S., Kruger, D. Leak, L. D., Tieman, L. A., & Manworren, R. C. B. (2018). Leveraging interactive patient care technology to Improve pain management engagement. Pain Management Nursing, 19(3), 212-221.Skiba, D. (2017). Evaluation tools to appraise social media and mobile applications. Informatics, 4(3), 32-40.PLEASE DONT FORGET TO INCLUDE 4 REFERENCIES NOT MORE THAN 5 YEARS OLD WITH 7TH EDITION APA FORMAT: CORE SKILL: evaluating an emerging technology on BOTH its promise and its risks, and doing so with enough specificity that the analysis could not have been written about any other technology.
CURRENT TRENDS WORTH ANALYZING (pick one and go deep rather than surveying all): ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING in clinical decision support and diagnostics; AMBIENT DOCUMENTATION / AI SCRIBES (a genuinely significant recent development, aimed squarely at documentation burden); TELEHEALTH and remote patient monitoring; wearables and patient-generated health data; INTEROPERABILITY (FHIR APIs, TEFCA, the information-blocking rules); patient portals and open notes; predictive analytics and risk stratification; blockchain for health records; precision medicine and genomics; robotics; and virtual/augmented reality in education and therapy.
THE ANALYTICAL STRUCTURE THE RUBRIC WANTS: describe the trend → identify the CHALLENGE OR RISK it creates → identify the BENEFIT or promise → then evaluate.
THE RISKS — be specific and mechanistic, because “privacy concerns” as a bare phrase earns nothing:
— ALGORITHMIC BIAS: cite Obermeyer et al. (2019), which found a widely used commercial risk algorithm systematically under-referred Black patients because it used HEALTHCARE COST as a proxy for HEALTH NEED — and less is historically spent on Black patients at equivalent illness severity. The model was not maliciously designed; it faithfully learned a real inequity and then perpetuated it at scale. This example demonstrates the deep point: bias enters through the choice of TARGET VARIABLE, not through bad code.
— MODEL DEGRADATION ON TRANSFER: models routinely perform far worse when deployed at a site other than the one they were trained at (the Epic sepsis model’s disappointing external validation is the standard citation). External validation in the LOCAL population before go-live is therefore not optional.
— ALERT FATIGUE and the Bayesian trap: in a low-prevalence setting, even a highly specific model produces mostly FALSE POSITIVES. A predictive tool with poor positive predictive value trains clinicians to ignore it — and then it is worse than nothing, because it also consumes attention.
— AUTOMATION BIAS and deskilling; the BLACK BOX explainability problem; accountability (who is liable when the model is wrong — the clinician who followed it, or the one who overrode it?); data security and ransomware (health systems are a prime target, and attacks have measurably disrupted care and been associated with patient harm); commercial ownership of patient data; and the DIGITAL DIVIDE — telehealth expands access for those with broadband and devices and can WIDEN disparities for those without, which is the most important equity point in the whole topic.
THE BENEFITS — equally specific: earlier detection, reduced documentation burden (returning cognitive attention to the patient), expanded access in rural and underserved areas, chronic disease management outside the clinic, reduced diagnostic error.
THE MOST PROMISING FOR NURSING and WHY: make an argument. Ambient documentation is a strong candidate precisely because the burden it targets — documentation time and “pajama time” — is a leading driver of burnout, and burnout drives turnover, error, and cost. Tie it to the QUADRUPLE AIM’s fourth aim and the argument becomes structural rather than merely enthusiastic.
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