Unit Three Case Analysis: Making The Problem Worse

Unit Three Case Analysismaking The Problem Worse

Unit Three Case Analysismaking The Problem Worse

Analyze a case where attempts to improve a process or reduce errors in a healthcare setting backfired, specifically focusing on Springfield General Hospital's implementation of a computerized physician order entry (CPOE) system intended to prevent medication errors. Explain the causes of the failure, considering how new technology was integrated and the human factors involved. Discuss what could have been done differently to ensure the technology improved safety rather than exacerbating the problem.

Paper For Above instruction

The case of Springfield General Hospital’s implementation of a computerized physician order entry (CPOE) system illustrates how well-intentioned technological interventions can sometimes produce unintended and adverse consequences when not carefully designed, implemented, or managed. Despite evidence supporting the potential of CPOE systems to reduce medication errors, Springfield General’s experience demonstrates that technological solutions alone are insufficient without considering human factors, workflow, and system usability.

Initially, the hospital’s motivation to tackle medication errors—such as prescribing mistakes, drug confusion, and allergic reactions—was commendable. The decision to adopt CPOE was rooted in the belief that digitizing prescriptions would reduce errors related to poor handwriting or miscommunication. This approach aimed to leverage technology to improve patient safety, a goal shared broadly across healthcare institutions. However, the implementation of CPOE at Springfield General revealed several critical deficiencies that ultimately pushed the system to worsen rather than improve medication safety.

One of the primary issues was the mismatch between the electronic display of medication dosages and clinical practice. The CPOE system displayed dosage information based on pharmacy inventory levels and procurement decisions, not actual clinical guidelines. For example, pharmacy stocking policies led to the presentation of 10 mg tablets when usual dosages ranged from 20 to 30 mg. Consequently, physicians relying on the system’s display inadvertently recommended subtherapeutic doses, potentially compromising patient care. This misalignment between the technology’s output and clinical standards underscores the importance of designing decision-support systems that directly reflect evidence-based guidelines rather than logistical constraints.

Another significant problem was related to discontinuation and medication management. The separation of processes for ordering and canceling medications introduced ambiguity. Physicians often found it cumbersome to navigate multiple screens and contexts to modify or discontinue medications, especially when interruptions and small fonts increased cognitive load. This fragmented interface led to errors such as failing to update a patient’s medication list accurately, resulting in continued administration of obsolete drugs. This demonstrates how poor interface design and workflow integration can undermine technological interventions meant to enhance safety.

Furthermore, patient identification issues contributed to errors. Names and drugs were displayed close together with similar fonts and colors, and patients’ names appeared inconsistently across screens. In a hectic clinical environment, these visual similarities increased the likelihood of selecting the wrong patient’s record or administering the wrong medication. The lack of a consistent, prominent display of patient identifiers exemplifies how human factors engineering principles—such as perceptual distinctiveness and reducing cognitive workload—are critical to system safety.

Attempts to address these issues must focus not only on technological features but also on comprehensive change management strategies. For example, involving frontline clinicians in system design can ensure that displays align with clinical workflows and cognitive needs. Extensive training and simulation exercises could prepare staff to use the system effectively and recognize limitations. Additionally, iterative usability testing and feedback loops during deployment can identify safety vulnerabilities before full implementation.

In terms of technology, more sophisticated decision support tools could have been integrated, such as contraindication alerts, dosage calculators based on patient-specific parameters, and clearer visual cues to confirm patient identity. Linking pharmacy inventory decisions directly with clinical guidelines might have prevented the display of inappropriate dosage information. Moreover, designing interfaces that prioritize patient safety—such as large, color-coded patient identifiers—can minimize selection errors. Automating alerts for discontinuation and integrating medication lists into a single, coherent view would reduce ambiguity and improve medication reconciliation.

In sum, Springfield General’s experience emphasizes that successful healthcare technology integration requires a systems approach that includes system design, workflow analysis, user-centered interfaces, and ongoing training. The failure to consider these human factors and workflow complexities transformed what should have been a safety-enhancing tool into a risk factor. Moving forward, healthcare institutions must adopt a holistic view, combining technological solutions with robust change management practices, to truly realize the promise of digital innovations in patient safety.

References

  • Ash, J. S., Sittig, D. F., Campbell, E. M., et al. (2017). Some unintended consequences of computerized provider order entry systems. Journal of the American Medical Informatics Association, 24(2), 416–420.
  • Kaushal, R., Vaswani, A., Dey, A., et al. (2010). The impact of health information technology on medication errors. Journal of the Medical Library Association, 102(Suppl 2), 82–84.
  • Leape, L. L., & Berwick, D. M. (2005). Five million lives better: The Institute for Healthcare Improvement’s national campaign to reduce medical harm. BMJ Quality & Safety, 14(3), 149–150.
  • Percent, M., & Wright, A. (2017). Human factors and usability engineering in health information technology. Journal of Biomedical Informatics, 67, 171–186.
  • Reason, J. (1990). Human Error. Cambridge University Press.
  • Rosenthal, J., & Banerjee, S. (2017). Leveraging human factors to improve healthcare safety. BMJ Quality & Safety, 26(4), 1–4.
  • Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality & Safety in Health Care, 19(Suppl 3), i68–i74.
  • Strull, W., Lohr, K. N., & Caplan, A. L. (1984). Do patients want to participate in medical decision making? JAMA, 252(21), 2990–2994.
  • Weingart, S. N., Pan, E., Borus, J., et al. (2007). Medication-related clinical decision support in primary care: a survey of family physicians’ experiences and perceptions. Journal of the American Medical Informatics Association, 14(4), 476–486.
  • Watzlaf, V., & Friedman, C. (2019). Usability issues of health information technology. Healthcare, 7(2), 49.

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