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How Advanced Practice Nurses Make Sense of Complexity in Health Information Technology

Making Sense of Complexity in Health Information Technology

For this article’s discussions, we use two models to understand health information technology (HIT) systems. Multiple models help demystify complexity and technology. These models offer a foundational understanding and a starting point. While applying the models to technology may be new, the models’ generalizability to the many different settings where APRNs (Advanced Practice Registered Nurses) practice is starkly apparent. Moreover, using a scientific and evidence-based approach, APRN core competencies, keeping the patient in the center, and applying systems knowledge are critical to any present or future technology. Assessing technology is within the skills of the APRN, whether considering an existing technology system or problem or the purchase of new technology by using the tools of nursing.

Health care today is more reliant on technology than ever before. Information systems span across various departments, from patient bedside monitoring to large-scale data processing for electronic health records (EHRs). Yet, amidst these advancements, the complexity of health information technology often becomes overwhelming, leading to potential risks and errors. To err is human, and technology in healthcare, although meant to reduce errors, introduces layers of complexity that need to be understood and managed properly.


Bio Data and Professional Information

NameJames Reason
OccupationPsychologist, Expert in Human Error & Systems Safety
Major ContributionsDeveloped the Swiss Cheese Model of system errors
Fields of ExpertiseSystem safety in high-risk industries like healthcare, aviation, and nuclear energy
Notable Works“Human Error” (1990), “Managing the Risks of Organizational Accidents” (1997)
WebsiteJames Reason Profile

The Swiss Cheese Model in HIT

The James Reason Swiss Cheese Model (SCM) of system safety errors is a pivotal framework for understanding risks in high-stakes environments such as healthcare. In HIT, the layers of protection against errors, such as electronic health records (EHRs) and computerized order entry systems, act as the “slices” of Swiss cheese. However, each layer is not flawless—there are “holes” that allow errors to slip through if the circumstances align just right.

When it comes to healthcare technology, these gaps might not immediately be visible. Data from wearable devices, for instance, silently flows into hospital systems, passing through multiple layers, servers, and applications before reaching a decision point. Errors within this flow might remain unnoticed until they result in a critical issue with the patient’s treatment. The Swiss Cheese Model helps healthcare providers dissect these layers and understand where the vulnerabilities in the system might lie.

As healthcare becomes more technology-dependent, understanding and addressing these complexities is essential. From an APRN perspective, applying the Swiss Cheese Model helps to visualize how health information technology contributes to patient safety risks and how proactive risk mitigation can reduce errors.


The Challenge of Human Fallibility in HIT Systems

James Reason’s work emphasizes that human fallibility plays a significant role in system failures. In healthcare, this is further compounded by the variability inherent in patient conditions and healthcare delivery. Even the most advanced HIT systems cannot escape the unpredictability of human error.

For APRNs and other healthcare providers, recognizing that errors are often a result of systemic failures, not individual incompetence, is critical. This acknowledgment helps shift the focus towards improving technology systems and developing more reliable safety nets. The more healthcare workers understand the interplay between human fallibility and technology, the better they can anticipate and prevent errors before they reach the patient.


Technological Advancements and the Institute of Medicine’s Report

In 1999, the Institute of Medicine (IOM) published the landmark report To Err Is Human: Building a Safer Health System. This report estimated that up to 98,000 Americans die each year due to medical errors, many of which could be attributed to failures in technology and communication. The report sparked widespread efforts to improve patient safety through health information technology, leading to innovations such as computerized physician order entry (CPOE), electronic health records (EHRs), and clinical decision support (CDS) systems.

However, despite these technological advancements, the question remains—has HIT genuinely improved patient safety? Experts remain divided. While technology has reduced some errors, new risks have emerged. From software glitches to miscommunication between systems, HIT itself can introduce new layers of complexity and risk. It’s this ongoing challenge that makes the role of APRNs in managing and evaluating HIT systems so crucial.


Key Models to Demystify Complexity in HIT

  1. The Swiss Cheese Model (SCM): As discussed, this model highlights the multiple layers of defense that exist in a system and how errors can bypass these layers. In HIT, this is crucial for visualizing how different systems, from EHRs to pharmacy databases, interact and where vulnerabilities might emerge.

  2. Evidence-Based Systems Approach: In the APRN core competencies, an evidence-based approach is vital in assessing technology systems. Keeping the patient at the center of care and applying nursing tools to evaluate HIT systems helps mitigate risks and ensure better outcomes.


FAQs About Making Sense of Complexity in Health Information Technology

Q: What is the Swiss Cheese Model and how does it apply to health information technology?
A: The Swiss Cheese Model, developed by James Reason, describes how systems have multiple layers of defense to prevent errors. In health information technology, these layers might include EHRs, order entry systems, and decision support tools. However, each layer has its “holes,” and errors can slip through if all the holes align.

Q: How does technology introduce complexity into healthcare?
A: While technology aims to streamline processes and reduce errors, it also introduces new risks due to system failures, software issues, and communication breakdowns between different platforms.

Q: Can health information technology truly improve patient safety?
A: HIT has the potential to improve patient safety by reducing medication errors and improving decision-making. However, it also introduces new challenges that need constant evaluation and management.

Q: How can APRNs contribute to reducing errors in health information technology?
A: APRNs play a crucial role in assessing HIT systems, identifying potential risks, and implementing safety measures. By using an evidence-based approach, they ensure technology supports patient care without introducing additional risks.


Table with Related Information for Health Information Technology

SystemDescriptionPotential RisksMitigation Strategies
Electronic Health Records (EHRs)Digital version of patients’ paper charts that stores health data and historyData breaches, software glitches, incomplete recordsRegular audits, data encryption, staff training
Computerized Physician Order Entry (CPOE)Electronic entry of medical practitioner instructions for patient treatmentInput errors, duplicate ordersClinical decision support integration, alert systems
Clinical Decision Support (CDS)Tools that analyze data to help healthcare providers make clinical decisionsAlert fatigue, incorrect recommendationsOptimized alert thresholds, user feedback

This table can be inserted easily into WordPress and offers a breakdown of key HIT systems, their risks, and strategies to address these complexities.

For more detailed information on the Swiss Cheese Model and health system safety, visit James Reason Profile.


By using models like the Swiss Cheese Model and an evidence-based approach, healthcare providers can begin to make sense of the complexities in health information technology. Understanding and proactively managing these challenges will help prevent errors and ensure that technology remains a valuable tool in advancing patient care.

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