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Background: Greater mobility and activity among hospitalized patients has been linked to key outcomes, including decreased length of stay, increased odds of home discharge, and fewer hospital-acquired morbidities. Systematic approaches to increasing patient mobility and activity are needed to improve patient outcomes during and following hospitalization. Problem: While studies have found the Johns Hopkins Activity and Mobility Promotion (JH-AMP) program improves patient mobility and associated outcomes, program details and implementation methods are not published. Approach: JH-AMP is a systematic approach that includes 8 steps, described in this article: (1) organizational prioritization; (2) systematic measurement and daily mobility goal; (3) barrier mitigation; (4) local interdisciplinary roles; (5) sustainable education and training; (6) workflow integration; (7) data feedback; and (8) promotion and awareness. Conclusions: Hospitals and health care systems can use this information to guide implementation of JH-AMP at their institutions. [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]

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]

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]

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]