Faculty Bibliography

OBJECTIVE: The purpose of this study was to evaluate the association of physician continuity of care with length of stay, likelihood of weekend discharge, in-hospital mortality and 30-day readmission. DESIGN: A cohort study of hospitalized medical patients. The primary exposure was the weekend usual provider continuity (UPC) over the initial weekend of care. This metric was adapted from an outpatient continuity of care index. Regression models were developed to determine the association between UPC and outcomes. SETTING: An academic medical center. MAIN OUTCOME MEASURE: Length of stay which was calculated as the number of days from the first Saturday of the hospitalization to the day of discharge. RESULTS: Of the 3391 patients included in this study, the prevalence of low, moderate and high UPC for the initial weekend of hospitalization was 58.7, 22.3 and 19.1%, respectively. When compared with low continuity of care, both moderate and high continuity of care were associated with reduced length of stay, with adjusted rate ratios of 0.92 (95% CI 0.86-1.00) and 0.64 (95% CI 0.53-0.76), respectively. High continuity of care was associated with likelihood of weekend discharge (adjusted odds ratio 2.84, 95% CI 2.11-3.83) but was not significantly associated with mortality (adjusted odds ratio 0.72, 95% CI 0.29-1.80) or readmission (adjusted odds ratio 0.88, 95% CI 0.68-1.14) when compared with low continuity of care. CONCLUSIONS: Increased weekend continuity of care is associated with reduced length of stay. Improvement in weekend cross-coverage and patient handoffs may be useful to improve clinical outcomes.

BACKGROUND: Previous studies have suggested that weekend hospital care is inferior to weekday care and that this difference may be related to diminished care intensity. The purpose of this study was to determine whether a metric for measuring intensity of hospital care based on utilization of the electronic health record (EHR) was associated with patient-level outcomes. METHODS: We performed a cohort study of hospitalizations at an academic medical center. Intensity of care was defined as the hourly number of provider accessions of the electronic health record, termed "EHR interactions." Hospitalizations were categorized based on the mean difference in EHR interactions between the first Friday and Saturday of hospitalization. We used regression models to determine the association of these categories with patient outcomes after adjusting for covariates. RESULTS: EHR interactions decreased from Friday to Saturday in 77% of the 9,051 hospitalizations included in the study. As compared to hospitalizations with no change in Friday to Saturday EHR interactions, the relative lengths of stay for hospitalizations with a small, moderate, and large decrease in EHR interactions were 1.05 (95% CI 1.00-1.10), 1.11 (95% CI 1.05-1.17), and 1.25 (95% CI 1.15-1.35), respectively. Although a large decrease in EHR interactions was associated with in-hospital mortality, these findings were not significant after risk adjustment (odds ratio 1.74, 95% CI 0.93-3.25). CONCLUSIONS: Intensity of inpatient care, measured by EHR interactions, significantly diminished from Friday to Saturday, and this decrease was associated with length of stay. Hospitals should consider monitoring and correcting temporal fluctuations in care intensity.

BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems. Journal of Hospital Medicine 2013;8:513-518. (c) 2013 Society of Hospital Medicine.

BACKGROUND: Electronic order-sets increasingly ask clinicians to answer questions or follow algorithms. Cooperation with such requests has not been studied. SETTING: Internal Medicine service of an academic medical center. OBJECTIVE: We studied the accuracy of clinician responses to questions embedded in electronic admission and discharge order-sets. Embedded questions asked whether any of three "core" diagnoses was present; a response was required to submit orders. Endorsement of any diagnosis made available best-practice ordering screens for that diagnosis. DESIGN: Three reviewers examined 180 electronic records (8% of discharges), drawn equally (for each core diagnosis) from possible combinations of Yes/No responses on admission and discharge. In addition to noting responses, we identified whether the core diagnosis was coded, determined from notes whether the admitting clinician believed that diagnosis present, and sought clinical evidence of disease on admission. We also surveyed participating clinicians anonymously about practices in answering embedded questions. MEASUREMENTS: We measured occurrence of six admission and five discharge scenarios relating medical record evidence of disease to clinician responses about its presence. RESULTS: The commonest discordant pattern between response and evidence was a negative response to disease presence on admission despite both early clinical evidence and documentation. Survey of study clinicians found that 75% endorsed some intentional inaccuracy; the commonest reason given was that questions were sometimes irrelevant to the clinical situation at the point asked. CONCLUSION: Through faults in order-set design, limitations of software, and/or because of an inherent tendency to resist directed behavior, clinicians may often ignore questions embedded in order-sets.

The ratio of observed-to-expected deaths is considered a measure of hospital quality and for this reason will soon become a basis for payment. However, there are drivers of that metric more potent than quality: most important are medical documentation and patient acuity. If hositals underdocument and therefore do not capture the full "expected mortality" they may be tempted to lower their observed/expected ratio by reducing "observed mortality" through limiting access to the very ill. Underdocumentation occurs because hospitals do not recognize, and therefore cannot seek to confirm, specific comorbidities conferring high mortality risk. To help hospitals identify these comorbidities, this paper describes an easily implemented spread-sheet for evaluating comorbid conditions associated, in any particular hospital, with each discharge. This method identifies comorbidities that increase in frequency as mortality risk increases within each diagnostic grouping. The method is inductive and therefore independent of any particular risk-adjustment technique.