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A qualitative study exploring the factors influencing admission to hospital from the emergency department.

ABSTRACT: OBJECTIVE:The number of emergency admissions to hospital in England and Wales has risen sharply in recent years and is a matter of concern to clinicians, policy makers and patients alike. However, the factors that influence this decision are poorly understood. We aimed to ascertain how non-clinical factors can affect hospital admission rates. METHOD:We conducted semistructured interviews with 21 participants from three acute hospital trusts. Participants included 11 emergency department (ED) doctors, 3 ED nurses, 3 managers and 4 inpatient doctors. A range of seniority was represented among these roles. Interview questions were developed from key themes identified in a theoretical framework developed by the authors to explain admission decision-making. Interviews were recorded, transcribed and analysed by two independent researchers using framework analysis. FINDINGS:Departmental factors such as busyness, time of day and levels of senior support were identified as non-clinical influences on a decision to admit rather than discharge patients. The 4-hour waiting time target, while overall seen as positive, was described as influencing decisions around patient admission, independent of clinical need. Factors external to the hospital such as a patient's social support and community follow-up were universally considered powerful influences on admission. Lastly, the culture within the ED was described as having a strong influence (either negatively or positively) on the decision to admit patients. CONCLUSION:Multiple factors were identified which go some way to explaining marked variation in admission rates observed between different EDs. Many of these factors require further inquiry through quantitative research in order to understand their influence further.

SUBMITTER: Pope I

PROVIDER: S-EPMC5577896 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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A qualitative study exploring the factors influencing admission to hospital from the emergency department.

Pope Ian I Burn Helen H Ismail Sharif A SA Harris Tim T McCoy David D

BMJ open 20170829 8

<h4>Objective</h4>The number of emergency admissions to hospital in England and Wales has risen sharply in recent years and is a matter of concern to clinicians, policy makers and patients alike. However, the factors that influence this decision are poorly understood. We aimed to ascertain how non-clinical factors can affect hospital admission rates.<h4>Method</h4>We conducted semistructured interviews with 21 participants from three acute hospital trusts. Participants included 11 emergency depa ...[more]

PMID: 28851767

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Project description:BackgroundLittle is known about the factors that influence physicians' admission decisions, especially among lower acuity patients. For the purpose of our study, non-medical refers to all of the factors-other than the patient's clinical condition-that could potentially influence admission decisions.ObjectiveTo describe the influence of non-medical factors on physicians' decisions to admit non-critically ill patients presenting to the ED.DesignCross-sectional study of hospital admissions at a single academic medical center.ParticipantsNon-critically ill adult patients admitted to the hospital (n = 297) and the admitting emergency medicine physicians (n = 34).Main measuresA patient survey assessed non-medical factors, including primary care access and utilization. A physician survey assessed clinical and non-medical factors influencing the decision to admit. Based on physician responses, admissions were characterized as "strongly acuity-driven," "moderately acuity-driven," or "weakly acuity-driven." Among these admission types, we compared length of stay, cost, and readmission within 30 days to the hospital or ED.Key resultsBased on the admitting physician's assessment, we categorized the motivation for admission as strongly acuity-driven in 185 (62 %) admissions, moderately acuity-driven in 92 (31 %), and weakly acuity-driven in 20 (7 %). Per the physician surveys, 51 % of hospitalizations were strongly or moderately influenced by one or more non-medical factors, including lack of information about baseline conditions (23 %); inadequate access to outpatient specialty care (14 %); need for a diagnostic testing or procedure (12 %); a recent ED visit (11 %); and inadequate access to primary care (10 %). Compared with strongly-acuity driven admissions, admissions that were moderately or weakly acuity-driven were shorter and less costly but were associated with similar rates of ED (35 %) and hospital (27 %) readmission.ConclusionsNon-medical factors are influential in the admission decisions for many patients presenting to the emergency department. Moderately and weakly acuity-driven admissions may represent a feasible target for alternative care pathways.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:BackgroundEmergency Department attendance is increasing internationally, of which a significant proportion could be managed in general practice. In England, policies backed by substantial capital funding require such patients attending Emergency Departments be directed or 'streamed' to General Practitioners working in or parallel to Emergency Departments. However, evidence for streaming is limited and the processes of streaming patients attending Emergency Departments to General Practitioners lacks exploration.ObjectivesThis paper explores streaming to General Practitioners in and alongside Emergency Departments at ten sites across England. It highlights positive streaming practice, as well as issues that may contribute to poor streaming practice, in order to inform future service improvement.MethodsA longitudinal qualitative study was conducted with data collected between October 2017 and December 2019 across 10 case study sites throughout England as part of a broader mixed methods study. 186 non-participant observations and 226 semi-structured interviews with 191 health professionals working in Emergency Departments or related General Practitioner Services were thematically analysed in relation to streaming processes and experiences.ResultsSix interconnected themes influencing streaming were identified: implementing and maintaining structural support; developing and supporting streaming personnel; implementing workable and responsive streaming protocols; negotiating primary/secondary care boundaries; developing and maintaining interprofessional relationships and concerns for patient safety. Streaming was considered central to the success of General Practitioners in/parallel to Emergency Departments. The importance of the skills of streaming nurses in delivering an optimal and safety critical service was highlighted, as was the skillset of General Practitioners and interprofessional relationships between streamers and General Practitioners. There was no distinct streaming model or method associated with good streaming practice to General Practitioners in/alongside Emergency Departments, instead factors for success were identified and key recommendations suggested. 'Inappropriate' streaming was identified as a problem, where patients streamed to General Practitioners in or parallel to Emergency Departments required Emergency Department management, or patients suitable for General Practitioner care were kept in the Emergency Department.ConclusionDespite adopting differing methods, commonalities across case sites in the delivery of good streaming practice were identified, leading to identification of key recommendations which may inform development of streaming services.Study registrationISRCTN51780222. Tweetable abstract: Workplace culture and the skillset of streamers and General Practitioners is crucial to streaming of patients to General Practitioners in Emergency Departments.

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A qualitative study exploring the factors influencing admission to hospital from the emergency department.

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A qualitative study exploring the factors influencing admission to hospital from the emergency department.

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