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Reply to the Case Study Questions upon review of the case study : Prevention Preventing High-Risk Safety Events Through Use of a HardStop Alert Effective implementation of an EHR requires that clinical and operational leaders understand effective clinical workflows and design care delivery 00_Nash (2382. The Healthcare Quality Book processes that facilitate safe, effective, and efficient care (Walker, Bieber, and Richards 2005). This case study describes the use of an automated alert system for a contraindicated drug. In one large academic health system, an active error-reporting process and surveillance system identified a patient safety event in which an elderly patient receiving levodopa to treat her Parkinson’s disease was admitted for treatment of a urinary tract infection. When she developed some behavioral disturbances, she was given Haldol for three days. She subsequently became less responsive, requiring an admission to the intensive care unit (ICU). Haldol was discontinued, and the patient recovered to baseline function after an extended ICU stay. However, the risk to her health was substantial. Haldol is an antipsychotic drug that blocks dopamine receptors in the brain, which can dramatically worsen the effects of Parkinson’s disease and reduce the effectiveness of treatment with levodopa (Derry et al. 2010; Magdalinou, Martin, and Kessel 2007). When administered to patients with Parkinson’s disease, it may result in serious mental status changes, a reduction in ability to respond, or even coma or death (Okun 2012, 2011). For these reasons, Haldol is contraindicated for elderly patients with Parkinson’s disease. In this case, a multidisciplinary team of clinicians, pharmacists, and health IT experts initially designed a soft-stop alert. However, the intervention did not significantly reduce the number of providers attempts to order Haldol for this patient population (suggesting alert fatigue). The team then designed a hard-stop alert that prevented providers from ordering Haldol for Parkinson’s disease patients and allowed continuation of the process only after consultation with an expert pharmacist or medical specialist. The results of this health IT intervention are shown in exhibit 7.8. The hard stop was initiated in the first quarter of 2014, during which 105 attempts were made to order Haldol for a patient with Parkinson’s disease, resulting in 105 alert firings to abort the ordering. After this period, no patient safety events involving administration of Haldol to patients with Parkinson’s disease were identified or reported. Thus, the hard stop proved extremely effective in preventing inadvertent unsafe care. In the months that followed, the health IT implementation substantially reduced the number of ordering attempts and subsequent alerts fired. We are not concerned that the alert firings have not dropped to zero; these results, combined with the lack of identified patient safety events, offer some reassurance that the alert is functional. This case highlights the usefulness of health IT interventions such as hard stops in appropriate settings, given the complexity of medicine, the volume of knowledge that exists, and the human factors that can lead to 00_Nash (2382). Health IT for Identification Health insurance providers are placing increased pressure on healthcare systems to reduce the cost of care delivery and to improve patient outcomes. This pressure is often applied through tiered reimbursement structures that beneft those systems that meet or exceed certain performance benchmarks, as well as through nonreimbursement for care determined by the payer to be unnecessary or in excess of “standard care.” Organizations can use health IT to identify problem areas and suggest changes that lead to better performance under these pressures. Health IT can help expose certain populations of patients, as segments of the overall inpatient population, that are strongly associated with a particular challenge or issue, for instance, length of stay. During the implementation of any new process, organizations must consider balancing measures. In other words, they need to answer the question, what unintended negative outcomes could arise from the errors. It also stresses the importance of systemwide provider education, competency testing, and ongoing surveillance. Case Study Discussion Questions 1. Describe the relationship between soft stops and hard stops as health IT interventions and ongoing surveillance. 2. In this situation, why was the hard stop more effective than the soft stop in preventing an error from reaching the patient? 3. What might be some pitfalls in using hard stops in a clinical setting? Might there be negative consequences that should be considered?

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