IntroductionPatient progress, the movement of patients through a hospital system from admission to discharge, is a foundational component of operational effectiveness in healthcare institutions. Optimal patient progress is a key to delivering safe, high-quality and high-value clinical care. The Baystate Patient Progress Initiative (BPPI), a cross-disciplinary, multifaceted quality and process improvement project, was launched on March 1, 2014, with the primary goal of optimizing patient progress for adult patients.MethodsThe BPPI was implemented at our system’s tertiary care, academic medical center, a high-volume, high-acuity hospital that serves as a regional referral center for western Massachusetts. The BPPI was structured as a 24-month initiative with an oversight group that ensured collaborative goal alignment and communication of operational teams. It was organized to address critical aspects of a patient’s progress through his hospital stay and to create additional inpatient capacity. The specific goal of the BPPI was to decrease length of stay (LOS) on the inpatient adult Hospital Medicine service by optimizing an interdisciplinary plan of care and promoting earlier departure of discharged patients. Concurrently, we measured the effects on emergency department (ED) boarding hours per patient and walkout rates.ResultsThe BPPI engaged over 300 employed clinicians and non-clinicians in the work. We created increased inpatient capacity by implementing daily interdisciplinary bedside rounds to proactively address patient progress; during the 24 months, this resulted in a sustained rate of discharge orders written before noon of more than 50% and a decrease in inpatient LOS of 0.30 days (coefficient: −0.014, 95% CI [−0.023, −0.005] P< 0.005). Despite the increase in ED patient volumes and severity of illness over the same time period, ED boarding hours per patient decreased by approximately 2.1 hours (coefficient: −0.09; 95% CI [−0.15, −0.02] P = 0.007). Concurrently, ED walkout rates decreased by nearly 32% to a monthly mean of 0.4 patients (coefficient: 0.4; 95% CI [−0.7, −0.1] P= 0.01).ConclusionThe BPPI realized significant gains in patient progress for adult patients by promoting earlier discharges before noon and decreasing overall inpatient LOS. Concurrently, ED boarding hours per patient and walkout rates decreased.
Congruence between sources of information on medication throughout patient courses cannot be obtained with separate medication charts. Discrepancies among patient, general practitioner and hospital give rise to a definitive risk of serious untoward effects.
Introduction Delays in patient flow in the emergency department (ED) result in patients leaving without being seen (LWBS). This compromises patient experience and quality of care. Our primary goal was to develop a predictive model by evaluating associations between patients LWBS and ED process measures and patient characteristics. Methods This was a cross-sectional study in a 95,000 annual visit adult ED comparing patients LWBS, with controls. Data were drawn from four seasonally adjusted four-week periods (30,679 total visits). Process measures included 1) arrivals per hour; 2) “door-to-provider” time; and the numbers of 3) patients in the waiting room; 4) boarding ED patients waiting for an inpatient bed; 5) providers and nurses (RN); and 6) patients per RN. Patient characteristics collected included 1) age; 2) gender; 3) race/ethnicity; 4) arrival mode (walk-in or via emergency medical services [EMS]); and 5) acuity based on Emergency Severity Index (ESI). Univariable analyses included t-tests and Pearson’s chi-square tests. We split the data randomly into derivation and validation cohorts. We used backward selection to develop the final derivation model, and factors with a p-value ≤ 0.05 were retained. Estimates were applied to the validation cohort and measures of discrimination (receiver operating characteristic) and model fit were assessed. Results In the final model, the odds of LWBS increased with the number of patients in the waiting room (odds ratio [OR] 1.05; 95% confidence interval [CI], 1.03 to 1.06); number of boarding patients (OR 1.02; 95% CI, 1.01 to 1.03); arrival rate (OR 1.04; 95% CI, 1.02 to 1.05) and longer “door-to-provider” times (test of linear trend in the adjusted OR was p = 0.002). Patient characteristics associated with LWBS included younger age (OR 0.98; 95% CI, 0.98 to 0.99), and lower acuity (higher ESI category) (OR 2.01; 95% CI, 1.84 to 2.20). Arrival by EMS was inversely associated with LWBS (OR 0.29; 0.23 to 0.36). The area under the curve for the final model in the validation cohort was 0.85 (95% CI, 0.84 to 0.86). There was good agreement between the observed and predicted risk. Conclusion Arrival rate, “door-to-provider time,” and the numbers of patients in the waiting room and ED boarders are all associated with patients LWBS.
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