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Venous thromboembolism risk increases in hospitals due to reduced patient mobility. However, initial mobility evaluations for thromboembolism risk are often subjective and lack standardization, potentially leading to inaccurate risk assessments and insufficient prevention. A retrospective study at a quaternary academic hospital analyzed patients using the Padua risk tool, which includes a mobility question, and the Johns Hopkins-Highest Level of Mobility (JH-HLM) scores to objectively measure mobility. Reduced mobility was defined as JH-HLM scores ≤3 over ≥3 consecutive days. The study evaluated the association between reduced mobility and hospital-acquired venous thromboembolism using multivariable logistic regression, comparing admitting health care professional assessments with JH-HLM scores. Symptomatic, hospital-acquired thromboembolisms were diagnosed radiographically by treating providers. Of 1715 patients, 33 (1.9%) developed venous thromboembolism. Reduced mobility, as determined by the JH-HLM scores, showed a significant association with thromboembolic events (adjusted OR: 2.53, 95%CI:1.23-5.22, P =.012). In contrast, the initial Padua assessment of expected reduced mobility at admission did not. The JH-HLM identified 19.1% of patients as having reduced mobility versus 6.5% by admitting health care professionals, suggesting 37 high-risk patients were misclassified as low risk and were not prescribed thrombosis prophylaxis; 4 patients developed thromboembolic events. JH-HLM detected reduced mobility in 36% of thromboembolic cases, compared to 9% by admitting health care professionals. Initial mobility evaluations by admitting health care professionals during venous thromboembolism risk assessment may not reflect patient mobility over their hospital stay. This highlights the need for objective measures like JH-HLM in risk assessments to improve accuracy and potentially reduce thromboembolism incidents. [ABSTRACT FROM AUTHOR]

Compared to the non-ARISE hospitals, patients in the ARISE hospital were younger, lived in more socioeconomically disadvantaged areas, were more often admitted from another acute facility, were more frequently admitted to the ICU or ventilated within 48 h, had higher admission SOFA scores, and had more days with delirium (Table 1). Improving hospital outcomes using an acute hospital rehabilitation intensive service (ARISE) for patients with COVID-19 Dear Editor, The initial surge of COVID-19 in 2020 placed an unprecedented strain on the hospital healthcare system [[1]]. [Extracted from the article]

Background: Post-hospitalization transition interventions remain a priority in preventing rehospitalization. However, not all patients referred for readmission prevention interventions receive them. We sought to 1) define patient characteristics associated with non-receipt of readmission prevention interventions (among those eligible for them), and 2) determine whether these same patient characteristics are associated with hospital readmission at the state level.Methods: We used state-wide data from the Maryland Health Services Cost Review Commission to determine patient-level factors associated with state-wide readmissions. Concurrently, we conducted a retrospective analysis of discharged patients referred to receive 1 of 3 post-discharge interventions between January 2013 and July 2019-a nurse transition guide, post-discharge phone call, or follow-up appointment in our post-discharge clinic-to determine patient-level factors associated with not receiving the intervention. Multivariable generalized estimating equation logistic regression models were used to calculate the odds of not accepting or not receiving the interventions.Results: Older age, male gender, black race, higher expected readmission rate, and lower socioeconomic status were significantly associated with 30-day readmission in hospitalized Maryland patients. Most of these variables (age, sex, race, payer type [Medicaid or non-Medicaid], and socioeconomic status) were also associated with non-receipt of intervention.Conclusions: We found that many of the same patient-level characteristics associated with the highest readmission risk are also associated with non-receipt of readmission reduction interventions. This highlights the paradox that patients at high risk of readmission are least likely to accept or receive interventions for preventing readmission. Identifying strategies to engage hard-to-reach high-risk patients continues to be an unmet challenge in readmission prevention. [ABSTRACT FROM AUTHOR]

Background: Patients frequently experience suboptimal transitions from the hospital to the community, which can increase the likelihood of readmission. It is not known which care coordination services can lead to improvements in readmission rates.Objective: To evaluate the effects of two care coordination interventions on 30-day readmission rates.Design: Prospective multicenter observational study of hospitalized patients eligible for two care coordination services between January 1, 2013, and October 31, 2015. Readmission rates were compared for patients who received each care coordination intervention versus those who did not using multivariable generalized estimating equation logistic regression models.Participants: A total of 25,628 patients hospitalized in medicine, neurosciences, or surgical sciences units.Interventions: Patients discharged home and deemed to be at high risk for readmission were assigned a nurse Transition Guide (TG) for 30 days post-discharge. All other patients were assigned the Patient Access Line (PAL) intervention, which provided a post-discharge phone call from a registered nurse.Setting: Two large academic hospitals in Baltimore, MD.Main Measures: Thirty-day all-cause readmission to any Maryland hospital.Key Results: Among all patients, 14.2% (2409/16,993) of those referred for the PAL intervention and 22.8% (1973/8635) of those referred for the TG intervention were readmitted. PAL-referred patients who did not receive the intervention had an adjusted odds ratio (aOR) for readmission of 1.27 (95% confidence interval [95% CI] 1.12-1.44, p < 0.001) compared with patients who did. TG-referred patients who did not receive the TG intervention had an aOR of 1.83 (95% CI 1.60-2.10, p < 0.001) compared with patients who received the intervention. Younger age, male sex, having more comorbidities, and being discharged from a medicine unit were associated with not receiving an assigned intervention. These characteristics were also associated with higher readmission rates.Conclusions: PAL and TG care coordination interventions were associated with lower rates of 30-day readmission. Our findings underscore the importance of determining the appropriate intervention for the hardest-to-reach patients, who are also at the highest risk of being readmitted. [ABSTRACT FROM AUTHOR]

Background. The lack of common language among interprofessional inpatient clinical teams is an important barrier to achieving inpatient mobilization. In The Johns Hopkins Hospital, the Activity Measure for Post-Acute Care (AM-PAC) Inpatient Mobility Short Form (IMSF), also called "6-Clicks," and the Johns Hopkins Highest Level of Mobility (JH-HLM) are part of routine clinical practice. The measurement characteristics of these tools when used by both nurses and physical therapists for interprofessional communication or assessment are unknown. Objective. The purposes of this study were to evaluate the reliability and minimal detectable change of AM-PAC IMSF and JH-HLM when completed by nurses and physical therapists and to evaluate the construct validity of both measures when used by nurses. Design. A prospective evaluation of a convenience sample was used. Methods. The test-retest reliability and the interrater reliability of AM-PAC IMSF and JH-HLM for inpatients in the neuroscience department (n = 118) of an academic medical center were evaluated. Each participant was independently scored twice by a team of 2 nurses and 1 physical therapist; a total of 4 physical therapists and 8 nurses participated in reliability testing. In a separate inpatient study protocol (n = 69), construct validity was evaluated via an assessment of convergent validity with other measures of function (grip strength, Katz Activities of Daily Living Scale, 2-minute walk test, 5-times sit-to-stand test) used by 5 nurses. Results. The test-retest reliability values (intraclass correlation coefficients) for physical therapists and nurses were 0.91 and 0.97, respectively, for AM-PAC IMSF and 0.94 and 0.95, respectively, for JH-HLM. The interrater reliability values (intraclass correlation coef- ficients) between physical therapists and nurses were 0.96 for AM-PAC IMSF and 0.99 for JH-HLM. Construct validity (Spearman correlations) ranged from 0.25 between JH-HLM and right-hand grip strength to 0.80 between AM-PAC IMSF and the Katz Activities of Daily Living Scale. Limitations. The results were obtained from inpatients in the neuroscience department of a single hospital. Conclusions. The AM-PAC IMSF and JH-HLM had excellent interrater reliability and test-retest reliability for both physical therapists and nurses. The evaluation of convergent validity suggested that AM-PAC IMSF and JH-HLM measured constructs of patient mobility and physical functioning. [ABSTRACT FROM AUTHOR]

Background: Hospital performance on the 30-day hospital-wide readmission (HWR) metric as calculated by the Centers for Medicare and Medicaid Services (CMS) is currently reported as a quality measure. Focusing on patient-level factors may provide an incomplete picture of readmission risk at the hospital level to explain variations in hospital readmission rates.Objective: To evaluate and quantify hospital-level characteristics that track with hospital performance on the current HWR metric.Design: Retrospective cohort study.Setting/patients: A total of 4785 US hospitals.Metrics: We linked publically available data on individual hospitals published by CMS on patient-level adjusted 30-day HWR rates from July 1, 2011, through June 30, 2014, to the 2014 American Hospital Association annual survey. Primary outcome was performance in the worst CMS-calculated HWR quartile. Primary hospital-level exposure variables were defined as: size (total number of beds), safety net status (top quartile of disproportionate share), academic status [member of the Association of American Medical Colleges (AAMC)], National Cancer Institute Comprehensive Cancer Center (NCI-CCC) status, and hospital services offered (e.g., transplant, hospice, emergency department). Multilevel regression was used to evaluate the association between 30-day HWR and the hospital-level factors.Results: Hospital-level characteristics significantly associated with performing in the worst CMS-calculated HWR quartile included: safety net status [adjusted odds ratio (aOR) 1.99, 95% confidence interval (95% CI) 1.61-2.45, p < 0.001], large size (> 400 beds, aOR 1.42, 95% CI 1.07-1.90, p = 0.016), AAMC alone status (aOR 1.95, 95% CI 1.35-2.83, p < 0.001), and AAMC plus NCI-CCC status (aOR 5.16, 95% CI 2.58-10.31, p < 0.001). Hospitals with more critical care beds (aOR 1.26, 95% CI 1.02-1.56, p = 0.033), those with transplant services (aOR 2.80, 95% CI 1.48-5.31,p = 0.001), and those with emergency room services (aOR 3.37, 95% CI 1.12-10.15, p = 0.031) demonstrated significantly worse HWR performance. Hospice service (aOR 0.64, 95% CI 0.50-0.82, p < 0.001) and having a higher proportion of total discharges being surgical cases (aOR 0.62, 95% CI 0.50-0.76, p < 0.001) were associated with better performance.Limitation: The study approach was not intended to be an alternate readmission metric to compete with the existing CMS metric, which would require a re-examination of patient-level data combined with hospital-level data.Conclusion: A number of hospital-level characteristics (such as academic tertiary care center status) were significantly associated with worse performance on the CMS-calculated HWR metric, which may have important health policy implications. Until the reasons for readmission variability can be addressed, reporting the current HWR metric as an indicator of hospital quality should be reevaluated. [ABSTRACT FROM AUTHOR]

Hoyer EH, Brotman DJ, Chan KS, Needham DM: Barriers to early mobility of hospitalized general medicine patients: survey development and results. Am J Phys Med Rehabil 201 5 ;94 :3 0 4 -3 1 2. Objective: Functional status decline commonly accompanies hospitalization making patients vulnerable to complications. Such decline can be mitigated through hospital-based early mobility programs. Success in implementing patient mobility quality improvement processes requires evaluating providers' knowledge, attitudes, and behaviors. Design: A cross-sectional, self-administered survey in two different hospital settings was completed by 1 20 nurses and physical and occupational therapists (rehabilitation therapists, 38; nurses, 82) from six general medicine units. The survey was developed using published guidelines, literature review, and provider meetings and refined through pilot testing. Psychometric properties were assessed, and regression analyses were conducted to examine barriers to early mobility by hospital site, provider discipline, and years of experience. Results: Internal consistency reliability, item consistency, and discriminant validity psychometric characteristics were acceptable. In multivariable regression analysis, overall perceived barriers were similar between the two hospitals (P = 0.25) and significantly higher for staff with less experience (P = 0.02) and for nurses vs. rehabilitation therapists (P < 0.001 ).The survey identified specific barriers common to both nurses and rehabilitation therapists and other barriers that were discipline specific. Conclusions: This novel survey identified important barriers to mobilizing medical inpatients that were similar across two hospital settings. These results can assist with the implementation of quality improvement projects for increasing early hospital-based patient mobility. [ABSTRACT FROM AUTHOR]

Aims Design Results Conclusion Implication for the Profession and/or Patient Care Reporting Method Patient or Public Contribution Our study aimed to (1) validate the accuracy of nursing mobility documentation and (2) identify the most effective timings for behavioural mapping.We monitored the mobility of 55 inpatients using behavioural mapping throughout a nursing day shift, comparing the observed mobility levels with the nursing charting in the electronic health record during the same period.Our results showed a high level of agreement between nursing records and observed mobility, with improved accuracy observed particularly when documentation was at 12 PM or later. Behavioural mapping observations revealed that the most effective timeframe to observe the highest levels of patient mobility was between 10 AM AND 2 PM.To truly understand patient mobility, comparing nursing charting with methods like behavioural mapping is beneficial. This comparison helps evaluate how well nursing records reflect actual patient mobility and offers insights into the best times for charting to capture peak mobility. While behavioural mapping is a valuable tool for auditing patient mobility, its high resource demands limit its regular use. Thus, determining the most effective times and durations for observations is key for practical implementation in hospital mobility audits.Nurses are pivotal in ensuring patient mobility in hospitals, an essential element of quality care. Their role involves safely mobilizing patients and accurately charting their mobility levels during each shift. For nursing practice, this research underscores that nurse charting can accurately reflect patient mobility, and highlights that recording the patient's highest level of mobility later in the shift offers a more precise representation of their actual mobility.Strobe.No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Background Purpose Method Findings Discussion Nurses routinely perform multiple risk assessments related to patient mobility in the hospital. Use of a single mobility assessment for multiple risk assessment tools could improve clinical documentation efficiency, accuracy and lay the groundwork for automated risk evaluation tools.We tested how accurately Activity Measure for Post‐Acute Care (AM‐PAC) mobility scores predicted the mobility components of various fall and pressure injury risk assessment tools.AM‐PAC scores along with mobility and physical activity components on risk assessments (Braden Scale, Get Up and Go used within the Hendrich II Fall Risk Model®, Johns Hopkins Fall Risk Assessment Tool (JHFRAT) and Morse Fall Scale) were collected on a cohort of hospitalised patients. We predicted scores of risk assessments based on AM‐PAC scores by fitting of ordinal logistic regressions between AM‐PAC scores and risk assessments. STROBE checklist was used to report the present study.AM‐PAC scores predicted the observed mobility components of Braden, Get Up and Go and JHFRAT with high accuracy (≥85%), but with lower accuracy for the Morse Fall Scale (40%).These findings suggest that a single mobility assessment has the potential to be a good solution for the mobility components of several fall and pressure injury risk assessments. [ABSTRACT FROM AUTHOR]

Background: Using an inpatient fall risk assessment tool helps categorize patients into risk groups which can then be targeted with fall prevention strategies. While potentially important in preventing patient injury, fall risk assessment may unintentionally lead to reduced mobility among hospitalized patients. Here we examined the relationship between fall risk assessment and ambulatory status among hospitalized patients. Methods: We conducted a retrospective cohort study of consecutively admitted adult patients (n = 48,271) to a quaternary urban hospital that provides care for patients of broad socioeconomic and demographic backgrounds. Non‐ambulatory status, the primary outcome, was defined as a median Johns Hopkins Highest Level of Mobility <6 (i.e., patient walks less than 10 steps) throughout hospitalization. The primary exposure variable was the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) category (Low, Moderate, High). The capacity to ambulate was assessed using the Activity Measure for Post‐Acute Care (AM‐PAC). Multivariable regression analysis controlled for clinical demographics, JHFRAT items, AM‐PAC, comorbidity count, and length of stay. Results: 8% of patients at low risk for falls were non‐ambulatory, compared to 25% and 54% of patients at moderate and high risk for falls, respectively. Patients categorized as high risk and moderate risk for falls were 4.6 (95% CI: 3.9–5.5) and 2.6 (95% CI: 2.4–2.9) times more likely to be non‐ambulatory compared to patients categorized as low risk, respectively. For patients with high ambulatory potential (AM‐PAC 18–24), those categorized as high risk for falls were 4.3 (95% CI: 3.5–5.3) times more likely to be non‐ambulatory compared to patients categorized as low risk. Conclusions: Patients categorized into higher fall risk groups had decreased mobility throughout their hospitalization, even when they had the functional capacity to ambulate. [ABSTRACT FROM AUTHOR]

Objectives: A decline in functional status is common during acute care hospitalization and may make patients vulnerable to further complications. This decline can be mitigated through hospital-based early activity and mobility programs. An important component of successful activity and mobility programs is systematic documentation of patient function, which can be an important barrier to program implementation in the acute care setting. Herein, we present a new nursing-based documentation system created as part of an early activity and mobility quality improvement (QI) project. Design: This project was undertaken, using a structured QI process, on two 24-bed general medicine units in an academic hospital. Our performance measure was based on a mobility scale currently-used in the ICU setting, known as "highest level of mobility" (HLM). HLM served as a daily "vital sign" and is an ordinal scale ranging from 1 to 8 (example scores: 1 = bed rest, 6 = ambulating >10 steps, 8 = ambulating >250 feet). We present prospective data on 440 consecutive patients with a length of stay (LOS) >2 days, admitted between 3/1/2013 and 6/7/2013. We analyzed the association between the average HLM during the first 2 days of admission for each patient and our two primary outcomes, hospital LOS and discharge to home. After logarithmic transformation, LOS was analyzed using linear regression. Discharge to home was analyzed using logistic regression. These models were adjusted for 7 relevant variables (age, gender, race, marital status, payer, expected LOS, and the AHRQ comorbidity index). Results: Mean (T standard deviation) LOS was 6.9 (±5.1), with 385 (88%) patients discharged home. Compared to non-ambulatory patients (HLM <6), patients who were able to ambulate >10 steps or more (HLM ≥6) during their first two days on admission had significantly shorter mean hospital LOS (6.4 (±5.3) versus 7.4 (±4.9) days, p=0.03), and higher rates of discharge home (98% versus 77%, p<0.001). In our adjusted analysis, a 1 point increase in HML was associated with a significantly shorter LOS by 0.6 (95% CI 0.3-0.8, p<0.001) days and an increased odds of discharge to home (OR 1.8, 95% CI 1.5-2.2, p<0.001). Conclusions: When implemented within a structured QI project, nursing-based documentation of patients' physical function shortly after acute care hospital admission may be helpful in identifying general medicine patients requiring a longer length of stay and institutionalization at discharge. [ABSTRACT FROM AUTHOR]

Individualized mobility goals created using a goal calculator have been shown to increase patient mobility on medical nursing units, but have not been studied among postoperative populations. This study aimed to examine the feasibility of an automated mobility goal calculator on a postoperative nursing unit. To examine this, we used the goal calculator to create goals for patients (N = 128) following surgery and mobilized each patient with either a nurse or physical therapist. Each patient's highest level of mobility was recorded and providers completed surveys on the appropriateness of calculated goals. Overall, 94% of patients achieved calculated goals. Patients with more pain achieved goals significantly less often than those with less pain. Those with higher mobility achieved their goals similarly with either provider. Providers reported 47% of goals were appropriate, with goals being set too low as the primary reason for goals being inappropriate. We conclude that the automated goal calculator can be used on postoperative nursing units to set realistic goals for patients after surgery. [ABSTRACT FROM AUTHOR]

Use patient demographic and clinical characteristics at admission and time-varying in-hospital measures of patient mobility to predict patient post-acute care (PAC) discharge. Retrospective cohort analysis of electronic medical records. Patients admitted to the two participating Hospitals from November 2016 through December 2019 with ≥72 hours in a general medicine service. Discharge location (PAC vs home) was the primary outcome, and 2 time-varying measures of patient mobility, Activity Measure for Post-Acute Care (AM-PAC) Mobility "6-clicks" and Johns Hopkins Highest Level of Mobility, were the primary predictors. Other predictors included demographic and clinical characteristics. For each day of hospitalization, we predicted discharge to PAC using the demographic and clinical characteristics and most recent mobility data within a random forest (RF) for survival, longitudinal, and multivariate (RF-SLAM) data. A regression tree for the daily predicted probabilities of discharge to PAC was constructed to represent a global summary of the RF. There were 23,090 total patients and compared to PAC, those discharged home were younger (64 vs 71), had shorter length of stay (5 vs 8 days), higher AM-PAC at admission (43 vs 32), and average AM-PAC throughout hospitalization (45 vs 35). AM-PAC was the most important predictor, followed by age, and whether the patient lives alone. The area under the hospital day–specific receiver operating characteristic curve ranged from 0.76 to 0.79 during the first 5 days. The global summary tree explained 75% of the variation in predicted probabilities for PAC from the RF. Sensitivity (75%), specificity (70%), and accuracy (72%) were maximized at a PAC probability threshold of 40%. Daily assessment of patient mobility should be part of routine practice to help inform care planning by hospital teams. Our prediction model could be used as a valuable tool by multidisciplinary teams in the discharge planning process. [ABSTRACT FROM AUTHOR]

Objective. Physical function is associated with important outcomes, yet there is often a lack of continuity in routine assessment. The purpose of this study was to determine data elements and instruments for longitudinal measurement of physical function in routine care among patients transitioning from acute care hospital setting to home with home health care. Methods. A 4-round modified Delphi process was conducted with 13 participants with expertise in physical therapy, health care administration, health services research, physiatry/medicine, and health informatics. Three anonymous rounds identified important and feasible data elements. A fourth in-person round finalized the recommended list of individual data elements. Next, 2 focus groups independently provided additional perspectives from other stakeholders. Results. Response rates were 100% for online rounds 1, 3, and 4 and 92% for round 2. In round 1, 9 domains were identified: physical function, participation, adverse events, behavioral/emotional health, social support, cognition, complexity of illness/disease burden, health care utilization, and demographics. Following the fourth round, 27 individual data elements were recommended. Of these, 20 (74%) are "administrative" and available from most hospital electronic medical records. Additional focus groups confirmed these selections and provided input on standardizing collection methods. A website has been developed to share these results and invite other health care systems to participate in future data sharing of these identified data elements. Conclusion. A modified Delphi consensus process was used to identify critical data elements to track changes in patient physical function in routine care as they transition from acute hospital to home with home health. Impact. Expert consensus on comprehensive and feasible measurement of physical function in routine care provides health care professionals and institutions with guidance in establishing discrete medical records data that can improve patient care, discharge decisions, and future research. [ABSTRACT FROM AUTHOR]

Stroke is the leading cause of long-term disability. Understanding how people recover from stroke and other brain lesions remain one of the biggest conundrums in neuroscience. As a result, concerted efforts in recent years have focused on investigating the neurophysiological changes that occur in the brain after stroke, and in developing novel strategies to enhance motor recovery. In particular, transcranial magnetic stimulation (TMS) is a non-invasive tool that has been used to investigate the brain plasticity changes resulting from stroke and as a therapeutic modality to safely improve motor function. In this review, we discuss the contributions of TMS to understand how different motor areas, such as the ipsilesional hemisphere, secondary motor areas, and contralesional hemisphere are involved in motor recovery. We also consider recent studies using repetitive TMS (rTMS) in stroke patients to enhance upper extremity function. Although further studies are needed, these investigations provide an important starting point to understand the stimulation parameters and patient characteristics that may influence the optimal response to non-invasive brain stimulation. Future directions of rTMS are discussed in the context of post-stroke motor recovery. [ABSTRACT FROM AUTHOR]

Purpose: Early mobilization in the intensive care unit (ICU) can improve patient outcomes but has perceived barriers to implementation. As part of an ongoing structured quality improvement project to increase mobilization of medical ICU patients by nurses and clinical technicians, we adapted the existing, validated Patient Mobilization Attitudes & Beliefs Survey (PMABS) for the ICU setting and evaluated its performance characteristics and results. Materials and Methods: The 26-item PMABS adapted for the ICU (PMABS-ICU) was administered as an online survey to 163 nurses, clinical technicians, respiratory therapists, attending and fellow physicians, nurse practitioners, and physician assistants in one medical ICU. We evaluated the overall and subscale (knowledge, attitude, and behavior) scores and compared these scores by respondent characteristics (clinical role and years of work experience). Results: The survey response rate was 96% (155/163). The survey demonstrated acceptable discriminant validity and acceptable internal consistency for the overall scale (Cronbach a: 0.82, 95% confidence interval: 0.76-0.85), with weaker internal consistency for all subscales (Cronbach a: 0.62-0.69). Across all respondent groups, the overall barrier score (range: 1-100) was relatively low, with attending physicians perceiving the lowest barriers (median [interquartile range]: 30 [28-34]) and nurses perceiving the highest (37 [31-40]). Within the first 10 years of work experience, greater experience was associated with a lower overall barrier score (-0.8 for each additional year; P = 0.02). Conclusions: In our medical ICU, across 6 different clinical roles, there were relatively low perceived barriers to patient mobility, with greater work experience over the first 10 years being associated with lower perceived barriers. As part of a structured quality improvement project, the PMABSICU may be valuable in assisting to identify specific perceived barriers for consideration in designing mobility interventions for the ICU setting. [ABSTRACT FROM AUTHOR]

Aim: Characterize the relationship between patient ambulatory status and in‐hospital call bell use. Background: Although call bells are frequently used by patients to request help, the relationship between physical functioning and call bell use has not been evaluated. Methods: Retrospective cohort study of 944 neuroscience patients hospitalized in a large academic urban medical centre between April 1, 2014 and August 1, 2014. We conducted multiple linear regression analyses with number of daily call bells from each patient as the primary outcome and patients' average ambulation status as the primary exposure variable. Results: The mean number of daily call bell requests for all patients was 6.9 (6.1), for ambulatory patients 5.6 (4.8), and for non‐ambulatory patients, it was 7.7 (6.6). Compared with non‐ambulatory patients, ambulatory patients had a mean reduction in call bell use by 1.7 (95% CI 2.5 to −0.93, p <.001) calls per day. In a post hoc analysis, patients who could walk >250 feet had 5 fewer daily call bells than patients who were able to perform in‐bed mobility. Conclusion: Ambulatory patients use their call bells less frequently than non‐ambulatory patients. Implications for Nursing Management: Frequent use of call bells by non‐ambulatory patients can place additional demands on nursing staff; patient mobility status should be considered in nurse workload/patient assignment. [ABSTRACT FROM AUTHOR]

Aim: To characterize resources to safely mobilize different types of hospitalized patients. Background: Current approaches to determine nurse–patient ratios do not always include information regarding the specific demands of patients who require extra resources to mobilize. Workflows must be designed with knowledge of resource requirements to integrate patient mobility into the daily nursing team care plan. Methods: Nurse‐led mobility sessions were evaluated on two adult hospital units, which consisted of nurse–patient encounters focused on patient mobility only. The resources assessed for each session were time‐to‐mobilize patient, time‐to‐document, need for additional staff support, and the need for assistive devices. Mobility sessions were also categorized by patient ambulation status, level of mobility limitations (low, medium and high) and diagnosis. Results: In 212 total mobility sessions, the median time‐to‐mobilize and time‐to‐document were 7.75 and 1.27 min, respectively. Additional staff support was required for 87% and 92% of patients with medium and high mobility limitations, respectively. All patients with low mobility limitations ambulated, and only 14% required additional staff. Ambulating patients with high mobility limitations was the most time‐intensive (median 12.55 min). Ambulating stroke patients required one additional staff and an assistive device in 92% and 69% of the sessions, respectively. Conclusion: This study describes the resources associated with mobilizing inpatients with different levels of mobility impairments and diagnoses. Implications for Nursing Management: These results could assist nursing management with facilitating appropriate daily nurse–patient ratios and justify the need for assistive devices and staff support to safely mobilize patients. [ABSTRACT FROM AUTHOR]