Dataset Information

Escalation-related decision making in acute deterioration: a retrospective case note review.

ABSTRACT: AIM:To describe how decision making inter-relates with the sequence of events in individuals who die during admission and identify situations where formal treatment escalation plans (TEPs) may have utility. DESIGN AND METHODS:A retrospective case note review using stratified sampling. Two data analysis methods were applied concurrently: directed content analysis and care management process mapping via annotated timelines for each case. Analysis was followed by expert clinician review (n=7), contributing to data interpretation. SAMPLE:45 cases, age range 38-96 years, 23 females and 22 males. Length of admission ranged from <24?hours to 97 days. RESULTS:Process mapping led to a typology of care management, encompassing four trajectories: early de-escalation due to catastrophic event; treatment with curative intent throughout; treatment with curative intent until significant point; and early treatment limits set. Directed content analysis revealed a number of contextual issues influencing decision making. Three categories were identified: multiple clinician involvement, family involvement and lack of planning clarity; all framed by clinical complexity and uncertainty. CONCLUSIONS:The review highlighted the complex care management and related decision-making processes for individuals who face acute deterioration. These processes involved multiple clinicians, from numerous specialities, often within hierarchical teams. The review identified the need for visible and clear management plans, in spite of the frame of clinical uncertainty. Formal TEPs can be used to convey such a set of plans. Opportunities need to be created for patients and their families to request TEPs, in consultation with the clinicians who know them best, outside of the traumatic circumstances of acute deterioration.

SUBMITTER: Campling N

PROVIDER: S-EPMC6104759 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Escalation-related decision making in acute deterioration: a retrospective case note review.

Campling Natasha N Cummings Amanda A Myall Michelle M Lund Susi S May Carl R CR Pearce Neil W NW Richardson Alison A

BMJ open 20180817 8

<h4>Aim</h4>To describe how decision making inter-relates with the sequence of events in individuals who die during admission and identify situations where formal treatment escalation plans (TEPs) may have utility.<h4>Design and methods</h4>A retrospective case note review using stratified sampling. Two data analysis methods were applied concurrently: directed content analysis and care management process mapping via annotated timelines for each case. Analysis was followed by expert clinician rev ...[more]

PMID: 30121604

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Project description:The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.

Project description:Introduction Consumer escalation systems allow patients and families to escalate concerns about acute clinical deterioration. Hospital staff can impact upon the success of this process. As part of evaluation processes within a Local Health Network, where a consumer escalation system was introduced in accordance with National requirements, we sought to explore clinicians’ understanding and perceptions of consumer escalation. Methods Voluntary and anonymous staff surveys pre, and post, system introduction. Quantitative data was analysed using descriptive statistics, chi-square independence, and non-parametric independent samples median tests. Qualitative data was evaluated using content analysis and cross-referenced with quantitative responses. Results Respondent’s (pre: 215; post: 89) area of work varied significantly between survey periods. Most agreed that patients/families have a sound knowledge of a patient’s typical health status (pre: 192/215 (89.3%); post 82/88 (93.2%)) and that patients/families should be encouraged to escalate concerns of deterioration to ward staff (pre: 209/212 (98.6%); post: 85/89 (95.5%)). Respondent perceptions of patient/family ability to recognise clinical deterioration varied. Staff agreement towards local response expectations decreased as the degree of clinical requirement increased. Staff concerns of increased workloads (pre: 90/214 (42.1%); post 12/72 (16.7%), p<0.001) and conflict generation (pre: 71/213 (33.3%); post: 7/71 (9.9%), p = 0.001) decreased significantly following system introduction. However, clinician perceptions of positive system effects also decreased (patient-staff rapport pre: 163/213 (76.5%); post: 38/72 (52.8%), p = 0.001; patient centred care pre: 188/214 (87.9%); post: 53/72 (73.6%), p = 0.012; patient safety pre: 173/214 (80.8%); post: 49/72 (68.1%), p = 0.077). Only 53% of respondents (pre: 112/213 (52.6%); post: 48/88 (54.5%)) perceived that patient/family have sufficient confidence to escalate concerns. Conclusion Consumer escalation systems require staff support. Staff perceptions may indicate, and act as, barriers to the operation of consumer escalation processes. Further exploration in identifying and managing staff barriers is crucial to the success of consumer escalation.

Project description:BackgroundNursing care is increasingly supported by computerized information systems and decision support aids. Since the advent of handheld computer devices (HCDs), there has been limited exploration of their use in nursing practice.ObjectiveThe study aimed to understand the professional and clinical impacts of the use of mobile health apps in nursing to assist clinical decision-making in acute care settings. The study also aimed to explore the scope of published research and identify key nomenclature with respect to research in this emerging field within nursing practice.MethodsThis scoping review involved a tripartite search of electronic databases (CINAHL, Embase, MEDLINE, and Google Scholar) using preliminary, broad, and comprehensive search terms. The included studies were hand searched for additional citations. Two researchers independently screened the studies for inclusion and appraised quality using structured critical appraisal tools.ResultsOf the 2309 unique studies screened, 28 (1.21%) were included in the final analyses: randomized controlled trials (n=3, 11%) and quasi-experimental (n=9, 32%), observational (n=10, 36%), mixed methods (n=2, 7%), qualitative descriptive (n=2, 7%), and diagnostic accuracy (n=2, 7%) studies. Studies investigated the impact of HCDs on nursing decisions (n=12, 43%); the effectiveness, safety, and quality of care (n=9, 32%); and HCD usability, uptake, and acceptance (n=14, 50%) and were judged to contain moderate-to-high risk of bias. The terminology used to describe HCDs was heterogenous across studies, comprising 24 unique descriptors and 17 individual concepts that reflected 3 discrete technology platforms ("PDA technology," "Smartphone/tablet technology," and "Health care-specific technology"). Study findings varied, as did the range of decision-making modalities targeted by HCD interventions. Interventions varied according to the level of clinician versus algorithmic judgment: unstructured clinical judgment, structured clinical judgment, and computerized algorithmic judgment.ConclusionsThe extant literature is varied but suggests that HCDs can be used effectively to support aspects of acute nursing care. However, there is a dearth of high-level evidence regarding this phenomenon and studies exploring the degree to which HCD implementation may affect acute nursing care delivery workflow. Additional targeted research using rigorous experimental designs is needed in this emerging field to determine the true potential of HCDs in optimizing acute nursing care.

Dataset Information

Escalation-related decision making in acute deterioration: a retrospective case note review.

Publications

Escalation-related decision making in acute deterioration: a retrospective case note review.

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