Service organizations face a trade-off between high utilization and responsiveness. High utilization can improve financial performance, but causes congestion, which increases throughput time. Employees may manage this trade-off by reducing processing times during periods of high workload, resulting in an inverted U-shaped relationship between utilization and throughput time. Using two years of inpatient data from 203 California hospitals, we find evidence that patient length of stay (LOS) increases as occupancy increases, until a tipping point, after which patients are discharged early to alleviate congestion. More interestingly, we find a second tipping point—at 93% occupancy—beyond which additional occupancy leads to a longer LOS. These results are indicative of a workload-related “saturation effect” where employees can no longer overcome high workload by speeding up. Our data suggest that the saturation effect is due to an increase in the workload requirements of the remaining patients. Collectively, we find that the underlying relationship between occupancy and LOS is N-shaped. Consequently, managers who seek cost efficiencies via a strategy of high utilization in tandem with speeding up may find that their strategy backfires because there is a point at which employees are no longer able to compensate for a high workload by working harder, and throughput time counterproductively increases. We perform a counterfactual analysis and find that an alternate strategy of employing flexible labor when faced with high occupancy levels might be a more productive approach, and could save the hospitals in our sample up to $138 million over 23 months. This paper was accepted by Serguei Netessine, operations management.

We study the role of process friction in increasing efficiency of service provisions. We examine one potential lever for reducing the provision of discretionary services: “justification”—an otherwise non‐value‐added process step that introduces process friction by forcing workers to explain the rationale for requesting an optional service. We exploit the presence—and absence—of a justification step in the ultrasound (US) ordering process at two emergency departments (EDs). We find that patients with abdominal pain are less than half as likely to receive an US when there is a justification step compared to when there is not. Additionally, we find a spillover effect: other diagnostic tests are also ordered less frequently. The decrease in testing reduces the average length of stay of the patients, and reduces testing costs by more than $200,000, with no decrease in quality. We show that two mechanisms underlie these results: (a) justification reduces clinicians' available time, and (b) justification forces clinicians to reflect on a patient's need for service. Our paper contributes to recent theory on friction and reflection as drivers of efficiency in services. We show that justification can serve as an effective lever for reducing medical tests—and costs—without negatively impacting quality.

Background: Multidisciplinary transitional care teams represent a model for reducing heart failure readmissions. Within this context, early identification of patients hospitalized with acute decompensated heart failure (ADHF) permits meaningful transitional care plan development. Improving the efficiency of early identification of the higher risk ADHF patient represents an area not well studied in hospitalized heart failure (HF). Objective: To validate the sensitivity and specificity of an enterprise data warehouse (EDW)-based strategy for early identification of patients with ADHF. Methods: An EDW query was constructed to identify patients with ADHF based on clinical and diagnosis-related parameters, including BNP level and administration of intravenous diuretics. The EDW query was run daily; expert clinicians verified the diagnosis of ADHF based on comprehensive chart review. This classification was used to determine specificity of the query for ADHF. We computed the sensitivity of the EDW-based approach by matching query results to heart failure diagnosis related group (DRG) data and primary discharge diagnosis data from separate hospital systems. Results: During the study period of 70 days, a total of 2354 charts were screened (33.6 charts per day). A total of 410 patients were identified by chart review as having heart failure requiring active management, for a specificity of 17.4%. Sensitivity was computed using both heart failure DRG data and primary discharge diagnosis data. Of the 114 patients discharged with a heart failure DRG (291, 292, or 293), all 114 were detected a priori by the admission EDW screen, for a sensitivity of 100%. A similar analysis conducted using HF principal diagnoses, which includes cardiac surgery-related admissions, yielded a sensitivity of 97.2%. Conclusions: EDW-based screening of patients based on simple clinical parameters early in the hospitalization is highly sensitive for detection of ADHF hospitalizations, but specificity is low. Brief chart review by expert clinicians is rapid, and identifies a specific cohort of patients that can be targeted for multidisciplinary HF transitional care. A better delineation of risk has broad outpatient workflow implications. Ongoing process improvements will demonstrate if early identification of at-risk patients yields significant reduction in HF readmissions.

Background: Heart failure (HF) readmissions remain a major driver of cost and health care utilization. Timely follow-up of patients post-discharge represents an evidence-based intervention proven to reduce readmission rates. A previously unexplored characteristic of hospital discharges is variability in discharge caseload. This variability thwarts the timeliness of follow-up, negates the benefit of transition care planning and may lead to a higher risk of HF readmissions. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to determine if caseload can be determined more precisely in a manner that sufficiently accommodates HF discharge variability. Objective: To analyze the impact of hospital discharge rate variability on outpatient clinic capacity needs using HF hospitalization discharge data and operations management approaches. Methods: Higher risk hospitalizations requiring active transitional care heart failure management were detected using an enterprise data warehouse-supported process over the study period. Queuing theory approaches were used to model the impact of HF discharge clinic capacity on wait time to an appointment. Discharge clinic was modeled as a single 7-day follow-up appointment, with an acceptable scheduling window of 5 to 9 days post-discharge. Results: During the study period of 100 days, 566 HF discharges were made, for a median of 5.66 discharges daily, or 39.6 discharges weekly. The distribution of daily discharges was skewed rightward (mode = 3, range = 0 to 18, standard deviation = 3.3, coefficient of variation = 0.58). Current clinic design: Providing one discharge slot for every hospital discharge (100% utilization) leads to an average wait of 18.3 days prior to an appointment, with only 31.9% of appointments scheduled within 7 days, and 38.9% of appointments scheduled within 9 days. Clinic re-design (queuing theory): Providing five extra discharge appointment slots per week (88% utilization or 13.6% excess capacity) reduces the expected waiting period to 1.1 days, with 99.8% of patients seen within 7 days, and virtually all patients seen within 9 days of discharge. Conclusions: Deployment of queuing theory allows for a more precise quantification of needed clinical capacity to accomplish appropriate HF follow-up with a reasonable degree of certainty. Our simplified model demonstrates that variability in hospital discharge rates leads to excessive clinic wait times in the absence of a modest capacity buffer and consequently exposes patients to a higher risk of HF readmission. We show using single center HF discharge data that a 10-15% increase in capacity is needed to ensure an adequate follow-up service level. Ongoing process of care work will demonstrate if optimization of clinic load yields a significant reduction in HF readmissions.