Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management最新文献

Health Information Exchanges (HIEs) are revolutionizing healthcare by facilitating secure and timely patient data sharing across diverse organizations. However, their rapid expansion has introduced significant legal and ethical challenges, particularly regarding privacy, informed consent, and liability risks. This paper critically assesses the effectiveness of existing legal frameworks, including Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), in addressing these challenges, revealing gaps in their application within HIEs. It argues that current consent models fail to provide meaningful control for patients, while privacy protections are weakened by issues such as re-identification and jurisdictional inconsistencies. Moreover, liability in data breaches remains complex due to ambiguous responsibility among stakeholders. The study concludes that reforms are needed, including dynamic consent models, standardized liability frameworks, and enhanced data governance structures, to ensure secure, ethical, and effective data sharing. These changes are essential to fostering patient trust, improving healthcare delivery, and aligning with Sustainable Development Goal (SDG) 3-ensuring healthy lives and promoting well-being for all.

The incorporation of artificial intelligence (AI) in health care offers revolutionary enhancements in patient diagnostics, clinical processes, and overall access to services. Nevertheless, this technological transition brings forth various new, intricate risks that pose challenges to current safety and ethical norms. This research explores the ability of enterprise risk management as an all-encompassing framework to tackle these arising risks, providing both a forward-looking and responsive strategy designed for the health care industry. At the core of this method are instruments that together seek to proactively uncover and address AI-related weaknesses like algorithmic bias, system failures, and data privacy issues. On the reactive side, it incorporates incident reporting systems and root cause analysis, tools that enable health care providers to quickly address unexpected events and consistently improve AI implementation procedures. However, some application difficulties still exist. The unclear, "black box" characteristics of numerous AI models hinder transparency and responsibility, prompting inquiries about the clarity of AI-generated choices and their adherence to ethical benchmarks in patient treatment. The research highlights that with the progress of AI technologies, the enterprise risk management framework also needs to evolve, addressing these new complexities while promoting a culture focused on safety in health care settings.

Incident reporting in Emergency Medical Services (EMS) is vital for enhancing patient safety and system performance, but time constraints often impede efficient documentation. Hamad Medical Corporation Ambulance Service Group (HMCASG) implemented a streamlined “Occurrence, Variance, and Accident” (OVA) reporting system to address these challenges. This study evaluated the effectiveness of this system in reducing incident report completion time. A “Lean” approach was used to streamline the reporting process. Four-hundred eighty-two OVA reports (241 baseline, 241 post-intervention) submitted between September 13 and October 8, 2022, were analyzed. The time taken to complete an OVA report was measured. Statistical analyses included Student t-tests, bivariate regression, and a Shewhart control chart. The mean time to complete an OVA report decreased significantly from 328.9 to 145.09 seconds (p < 0.05). The Shewhart control chart visually demonstrated the intervention's impact, while regression analysis confirmed its significance (p = 0.007). The streamlined OVA reporting system significantly reduced reporting time, addressing the challenge of balancing incident reporting with emergency response availability. This lean-based approach enhanced operational efficiency, promoted risk reduction, and strengthened prehospital care's foundation for quality improvement.

This project aimed to (1) develop a multidisciplinary team to rapidly conduct event analysis, (2) create tools to standardize event communication, (3) expand resiliency support provided to staff, and (4) decrease cycle time between event occurrence and action implementation. A multidisciplinary team was created to investigate safety events. The team developed standard work including key stakeholder notification of the event, a huddle to facilitate immediate mitigation of risk, staff resiliency support, a consistent interview approach, analysis of investigation data, and an accountability meeting to ensure consensus on steps required to prevent future harm. Sustainability is hardwired through ongoing monitoring of metrics. The baseline data collection period was January 2020 through December 2022 (n = 41) and the intervention period was January 2023 through December 2023 (n = 25). First interview time was reduced from 2 days (SD = 2.38) to 1 day (SD = 1.20, p < 0.0001). Mean event finalization decreased from 31 (SD = 13.75) to 13 days (SD = 6.75, p < 0.001). Staff nervousness score decreased from 32.40 pre-interview to 13.96 post-interview (p < 0.001) on a 100-point analog scale. Non-fall related safety events decreased from an average of 10.5 per year between July 1, 2021–June 30, 2023 to a total of 6 between July 1, 2023–June 30, 2024 (p = 0.05).

The aim of this study was to identify hospital-based workplace violence (WPV) risk factors with the Haddon Matrix Model (HMM) to determine its potential utility to conceptualize multiple risks for WPV events. This descriptive study utilized two independent convenience samples Data from behavioral emergencies (2014–2015) for patient violence (N = 192) and from health care staff (N = 380) 12-month violence survey responses (2015) in a Midwestern academic hospital were analyzed. Logistic regression examined patient features associated with physical violence. Survey questions pertained to employee, environment, and cultural factors associated with WPV; responses were examined with Chi-square and two-sample t-tests. Violence risk factors populated the 4 Haddon Matrix domains at pre-event time frames as Host (worker)-age/demographics, Agent (patient)-age/gender, Physical Environment-door/window structure, and Social Environment-worker safety. Risks at event time frames populated for Agent—behavior/delirium, and Physical Environment—event medication/patient identification. The Haddon Matrix identification of hospital violence risks indicates its utility as a comprehensive approach to workplace violence.

Using pharmacy claims data from a single commercial health plan to identify opportunities for opioid use disorder (OUD) focused screening and interventions communicated via targeted prescriber messaging. Participants included members ≥18 years using more than 90 morphine milligram equivalents (MMED) daily based on all opioid claims, had >1 paid claim for an opioid product, and had ≥90 days of total opioid therapy. Members were excluded with ≥1 claims for an oral chemotherapy agent (except methotrexate). Intervention was completed with a secure communication to the primary outpatient opioid prescriber that included resources for diagnosis, treatment, and best practices for opioid prescribing. The main outcome measure was any documented change to opioid use following the intervention. Seven hundred forty-five members were identified; a subset (n = 20) was further assessed, and all had identified OUD risk factors; providers were subsequently sent a communication. Sixteen providers acknowledged receipt and 11 patients (55%) had at least one documented intervention following communication receipt. Provision of targeted, evidence-based recommendations to providers for patients identified to be at risk of OUD from pharmacy claims data can result in increased recognition and intervention. Future efforts to explore feasibility of provider education detailing efforts and continued evaluation of efficacy are needed.

The field of healthcare quality and safety has been informed by the study of Human Factors contributing to adverse events. Hitherto, much of the study of Human Factors has been focused on a narrow lens of human error, identifying cognitive-based or knowledge-based errors and cognitive processes such as loss of situational awareness contributing to error. While these factors are important, this narrow approach fails to consider the complexity of relational and systemic factors that also contribute to adverse events. We aimed to explore the relational and systemic human factors, including shared clinician attitudes and behavior, that contribute to serious adverse patient events in a public health setting. The study, set in a metropolitan local health district in New South Wales, Australia, was conducted using a retrospective qualitative multi-incident content analysis design. Serious adverse event reviews (SAER) over 6 months (2022–2023) were subject to qualitative content analysis until data saturation was reached. Data saturation reached at 20 reports. Emergent themes related to human factors in serious adverse events included: (i) delays and inertia—with a subtheme of inertia of ageism; (ii) “All-or-nothing” approach to end-of-life care and planning; (iii) communication lapses; and (iv) implementation gap between standards and practice. Error-based incidents accounted for only 35% of the serious adverse events examined. The sample studied involved mostly (65%) male patients, with a mean age of 69 (70% aged >65), managed across the gamut of specialties, with the most common incident being the management of acutely deteriorating patients. In conclusion, there is more to Human Factors in adverse events than cognitive or knowledge-based error. While identifying and correcting errors is absolutely essential, we need adjunctive “soft measures” to address clinical attitudes, behaviors, and relationships in health care, particularly in increasingly complex, fraught, and stressful health care environments.