Laura M. Cascella, MA, CPHRM
The concept of bias in relation to artificial intelligence (AI) usually is discussed in terms of biased data and algorithms, which pose significant ethical and safety issues. However, another type of bias also raises concern. Automation bias occurs when “clinicians accept the guidance of an automated system and cease searching for confirmatory evidence . . . perhaps transferring responsibility for decision-making onto the machine . . .”1 Similarly, clinicians who use generally reliable technology systems might become complacent and miss potential errors, particularly if they are pressed for time or carrying a heavy workload.
Read more Laura M. Cascella, MA, CPHRM
Artificial intelligence (AI), much like other types of health information technology, raises concerns about data privacy and security — particularly in an era in which cyberattacks are rampant and patients’ protected health information (PHI) is highly valuable to identity thieves and cybercriminals.
Read more Laura M. Cascella, MA, CPHRM
At the heart of many innovations in healthcare are patients and finding ways to improve the quality of their care and experience. This is perhaps no truer than in the case of artificial intelligence (AI), which offers vast potential for improving patient outcomes through advances in population health management, risk identification and stratification, diagnosis, and treatment. Yet even with this promise, questions arise about how patients will interact with and react to these new technologies as well as how these advances will change the provider–patient relationship.
Read more Laura M. Cascella, MA, CPHRM
Training and education are imperative in many facets of healthcare — from understanding clinical systems, to improving technical skills, to understanding regulations and professional standards. Technology often presents unique training challenges because of the ways in which it disrupts existing workflow patterns, alters clinical practice, and creates both predictable and unforeseen challenges.
Read more Laura M. Cascella, MA, CPHRM
In any workplace, disruptive and negative behaviors can chip away at workers’ confidence, erode trust in leadership, and generally sour the working environment. Healthcare is no exception, and disruptive behavior among healthcare providers and staff is a well-documented problem in various practice settings.
Read more Marcy A. Metzgar
All healthcare organizations need robust systems and processes that help them provide quality care as part of their commitment to patient safety. When a system or process is being purchased, developed, reviewed, or reconstructed, healthcare organizations can be proactive about risk control and explore what may go wrong with a system or process by applying the Failure Mode and Effects Analysis (FMEA) method. After examining possible failures, a healthcare team can generate measures to prevent their occurrence.
Read more Laura M. Cascella, MA, CPHRM
Speaking up about risks, concerns, and errors in patient care is crucial for patient safety. Although voicing concerns may seem like a reflexive response for healthcare providers and staff, barriers can prevent it from happening. Fear, intimidation, lack of confidence, power differentials, and other factors can thwart individuals’ efforts to assert concerns. These issues can permeate healthcare organizations that permit or do not constructively address disruptive behavior, bullying, workplace incivility, retaliation, and blame.
Read more Laura M. Cascella, MA, CPHRM
Maintaining professional boundaries is essential in healthcare. When boundaries are crossed —or the patient perceives they are crossed — the provider–patient relationship can quickly deteriorate. As a result, the provider may face allegations of misconduct and/or malpractice.
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