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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]

Objective: To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs).Methods: The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients.Results: The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory.Discussion: The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients' risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature.Conclusion: Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support. [ABSTRACT FROM AUTHOR]