Timing errors and temporal uncertainty in clinical databases-A narrative review.

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

Psychological Outcomes in Family Members of Patients With Acute Respiratory Failure: Does Inter-ICU Transfer Play a Role?

BACKGROUND: Family members of patients admitted to the ICU experience a constellation of sequelae described as postintensive care syndrome-family. The influence that an inter-ICU transfer has on psychological outcomes is unknown. RESEARCH QUESTION: Is inter-ICU transfer associated with poor psychological outcomes in families of patients with acute respiratory failure? STUDY DESIGN AND METHODS: Cross-sectional observational study of 82 families of patients admitted to adult ICUs (tertiary hospital). Data included demographics, admission source, and outcomes. Admission source was classified as inter-ICU transfer (n = 39) for patients admitted to the ICU from other hospitals and direct admit (n = 43) for patients admitted from the ED or the operating room of the same hospital. We used quantitative surveys to evaluate psychological distress (Hospital Anxiety and Depression Scale [HADS]) and posttraumatic stress (Post-Traumatic Stress Scale; PTSS) and examined clinical, family, and satisfaction factors associated with psychological outcomes. RESULTS: Families of transferred patients travelled longer distances (mean ± SD, 109 ± 106 miles) compared with those of patients directly admitted (mean ± SD, 65 ± 156 miles; P ≤ .0001). Transferred patients predominantly were admitted to the neuro-ICU (64%), had a longer length of stay (direct admits: mean ± SD, 12.7 ± 9.3 days; transferred patients: mean ± SD, 17.6 ± 9.3 days; P < .01), and a higher number of ventilator days (direct admits: mean ± SD, 6.9 ± 8.6 days; transferred: mean ± SD, 10.6 ± 9.0 days; P < .01). Additionally, they were less likely to be discharged home (direct admits, 63%; transferred, 33%; P = .08). In a fully adjusted model of psychological distress and posttraumatic stress, family members of transferred patients were found to have a 1.74-point (95% CI, -1.08 to 5.29; P = .30) higher HADS score and a 5.19-point (95% CI, 0.35-10.03; P = .03) higher PTSS score than those of directly admitted family members. INTERPRETATION: In this exploratory study, posttraumatic stress measured by the PTSS was higher in the transferred families, but these findings will need to be replicated to infer clinical significance.

Determinants of Evidence-based Practice Uptake in Rural Intensive Care Units. A Mixed Methods Study.

Rationale: Evidence-based practices promote quality care for intensive care unit patients but chronic evidence-to-practice gaps limit their reach.Objectives: To characterize key determinants of evidence-based practice uptake in the rural intensive care setting.Methods: A parallel convergent mixed methods design was used with six hospitals receiving a quality improvement intervention. Guided by implementation science principles, we identified barriers and facilitators to uptake using clinician surveys (N = 90), key informant interviews (N = 14), and an implementation tracking log. Uptake was defined as completion of eight practice change steps within 12 months. After completing qualitative and quantitative data analyses for each hospital, site, staff, and program delivery factors were summarized within and across hospitals to identify patterns by uptake status.Results: At the site level, although structural characteristics (hospital size, intensivist staffing) did not vary by uptake status, interviews highlighted variability in staffing patterns and culture that differed by uptake status. At the clinician team level, readiness and self-efficacy were consistently high across sites at baseline with time and financial resources endorsed as primary barriers. However, interviews highlighted that as initiatives progressed, differences across sites in attitudes and ownership of change were key uptake influences. At the program delivery level, mixed methods data highlighted program engagement and leadership variability by uptake status. Higher uptake sites had better training attendance; more program activities completed; and a stable, engaged, collaborative nurse and physician champion team.Conclusions: Results provide an understanding of the multiple dynamic influences on different patterns of evidence-based practice uptake and the importance of implementation support strategies to accelerate uptake in the intensive care setting.

Implementation of an Academic-to-Community Hospital Intensive Care Unit Quality Improvement Program. Qualitative Analysis of Multilevel Facilitators and Barriers.

Rationale: Implementation of evidence-based best practices is influenced by a variety of contextual factors. It is vital to characterize such factors to maintain high-quality care. Patients in the intensive care unit (ICU) are critically ill and require complex, interdisciplinary, evidence-based care to enable high-quality outcomes. Objectives: To identify facilitators and barriers to implementation of an academic-to-community hospital ICU interprofessional quality improvement program, "ICU Innovations." Methods: ICU Innovations is a multimodal quality improvement program implemented between 2014 and 2017 in six community ICUs in rural settings serving underserved patients in South Carolina. ICU Innovations includes quarterly on-site seminars and extensive behind the scenes facilitation to catalyze the implementation of evidence-based best practices. We use qualitative analysis to identify contextual factors related to program implementation processes. Guided by an implementation science framework, the Exploration, Adoption/Preparation, Implementation, Sustainment framework, we conducted semistructured key informant interviews with clinician champions at six community ICUs and six parallel interviews with ICU Innovations' leadership. We developed a qualitative coding template based on the framework and identified contextual factors associated with implementation. Standard data on hospital and ICU structure and processes of care were also collected. Results: Outer and inner factors interconnected dynamically to influence implementation of ICU Innovations. Collaborative engagement between the program developers and partner sites (outer context factor) and site program champion leadership and staff readiness for change (inner context factors) were key influences of implementation. Conclusions: This research focused on rural hospital ICUs with limited or nonexistent intensivist leadership. Although enthusiasm for the ICU Innovations program was initially high, implementation was challenging because of multiple contextual factors. Critical steps for implementation of evidence-based practice in rural hospitals include optimizing engagement with external collaborators, maximizing the role of a committed site champion, and conducting thorough site assessments to ensure staff and organizational readiness for change. Identifying barriers and facilitators to program implementation is an on-going process to tailor and improve program initiatives.