The ED-inpatient dashboard: Uniting emergency and inpatient clinicians to improve the efficiency and quality of care for patients requiring emergency admission to hospital.

Patients who require emergency admission to hospital require complex care that can be fragmented, occurring in the ED, across the ED-inpatient interface (EDii) and subsequently, in their destination inpatient ward. Our hospital had poor process efficiency with slow transit times for patients requiring emergency care. ED clinicians alone were able to improve the processes and length of stay for the patients discharged directly from the ED. However, improving the efficiency of care for patients requiring emergency admission to true inpatient wards required collaboration with reluctant inpatient clinicians. The inpatient teams were uninterested in improving time-based measures of care in isolation, but they were motivated by improving patient outcomes. We developed a dashboard showing process measures such as 4 h rule compliance rate coupled with clinically important outcome measures such as inpatient mortality. The EDii dashboard helped unite both ED and inpatient teams in clinical redesign to improve both efficiencies of care and patient outcomes.

A hidden Markov model for decoding and the analysis of replay in spike trains.

We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.

A prospective trial of a clinical pharmacy intervention in a primary care practice in a capitated payment system.

BACKGROUND: There is evidence that pharmacist interventions improve clinical outcomes. The few studies that address economic outcomes (a) often report estimated instead of actual medical costs, (b) report only medication costs, or (c) have been conducted in settings that are not typical of community-based primary care. OBJECTIVES: To (a) determine whether a clinical pharmacist's recommendations to physicians regarding optimizing medication therapy are related to medical costs in capitated patients in an internal medicine practice, and (b) compare what primary care physicians (PCPs) in a comparison group actually did proactively to optimize medication therapy versus what a clinical pharmacist would have recommended to them. METHODS: This was a prospective, controlled study comparing 2 internal medicine practices. Study enrollment was performed using a screening process carried out every 1-2 weeks on a rolling basis for 1 year from July 2001 through June 2002. Eligibility criteria for prospective enrollment were (a) 1 or more risk factors: at least 1 chronic disease or an event (e.g., emergency room visit, adverse drug reaction, medication nonadherence) or aged 50 years or older, (b) a scheduled visit to see a PCP within 2 weeks from the screening date or a diagnosis of diabetes without a PCP visit during the first 6 months of the study, (c) need for optimization of medication therapy as determined by a clinical pharmacist on the screening date, and (d) 12 months of continuous insurance eligibility before enrollment in the study. For inclusion in the final study analyses, patients were also required to have continuous insurance eligibility through 12 months from study enrollment. One clinical pharmacist made recommendations to optimize medication therapy in the intervention group. For the comparison group, the same pharmacist proposed recommendations that remained concealed from the physicians. The primary outcome measure was per patient per year (PPPY) medical cost, based on plan liability (gross allowable costs minus patient costs), excluding prescription drug cost. Additional outcome measures included numbers of outpatient visits, hospital admissions, emergency room (ER) visits per 1,000 patients, and hospital days; and percent of recommendations that were accepted by the PCPs. Changes in outcome measures from the pre-intervention to postintervention period were compared across study groups in a difference-indifference analysis, using the Student's t-test for normally distributed data and the Mann-Whitney U-test (nonparametric) for skewed data. RESULTS: There were 127 and 216 adult patients in the intervention and comparison groups, respectively. The primary outcome, change in mean PPPY medical (excluding pharmacy) cost, did not differ significantly between the groups (P = 0.711). The between-group difference in the change in ER visits per 1,000 patients approached statistical significance (P = 0.054). Intervention group patients were more likely than comparison group patients to have the following issues addressed: medication nonadherence (85.7% vs. 40.0%, respectively; P = 0.032), untreated indication (72.6% vs. 11.5%, P < 0.001), suboptimal medication choice (60.0% vs. 5.9%, P < 0.001) and cost-ineffective drug therapies (72.1% vs. 6.5%, P < 0.001). Of the estimated number of actionable opportunities identified for the comparison group (but concealed from the physicians), 23.5% were adopted by comparison group physicians without any assistance from a clinical pharmacist. CONCLUSION: Compared with patients of PCPs who received no input from a clinical pharmacist, patients of PCPs who received clinical pharmacist recommendations were more likely to have several medication-related issues addressed, including medication nonadherence, untreated indications, suboptimal medication choices, and cost-ineffective drug therapies. However, total medical (excluding pharmacy) costs for the intervention and comparison groups were not significantly different.