In order to provide a reliable measure of the similarity of uppercase English letters, a confusion matrix based on 1,200 presentations of each letter was established. To facilitate an analysis of the perceived structural characteristics, the confusion matrix was decomposed according to Luce's choice model into a symmetrical similarity matrix and a response bias vector. The underlying structure of the similarity matrix was assessed with both a hierarchical clustering and a multidimensional scaling procedure. This data is offered to investigators of visual information processing as a valuable tool for controlling not only the overall similarity of the letters in a study, but also their similarity on individual feature dimensions.

Hospitals have become increasingly interested in maximizing patient throughput and bed utilization in all units to improve efficiency. To study tradeoffs in blocking and system efficiency, a simulation model using a path-based approach is developed for an obstetric unit. The model focuses on patient flow, considering patient classification, blocking effects, time dependent arrival and departure patterns, and statistically supported distributions for length of stay (LOS). The model is applied to DeKalb Medical's Women's Center, a large obstetrics hospital in Atlanta, GA, to analyze the hospital's readiness for potential changes to patient mix and patient volume. A comparison of results predicted by the simulation model and actual performance after implementation of "swing" rooms is presented, suggesting the value of implementing "swing" rooms to balance bed allocation.

Objectives We describe an approach for analyzing failures in diagnostic processes in a small, enriched cohort of general medicine patients who expired during hospitalization and experienced medical error. Our objective was to delineate a systematic strategy for identifying frequent and significant failures in the diagnostic process to inform strategies for preventing adverse events due to diagnostic error. Methods Two clinicians independently reviewed detailed records of purposively sampled cases identified from established institutional case review forums and assessed the likelihood of diagnostic error using the Safer Dx instrument. Each reviewer used the modified Diagnostic Error Evaluation and Research (DEER) taxonomy, revised for acute care (41 possible failure points across six process dimensions), to characterize the frequency of failure points (FPs) and significant FPs in the diagnostic process. Results Of 166 cases with medical error, 16 were sampled: 13 (81.3%) had one or more diagnostic error(s), and a total of 113 FPs and 30 significant FPs were identified. A majority of significant FPs (63.3%) occurred in “Diagnostic Information and Patient Follow-up” and “Patient and Provider Encounter and Initial Assessment” process dimensions. Fourteen (87.5%) cases had a significant FP in at least one of these dimensions. Conclusions Failures in the diagnostic process occurred across multiple dimensions in our purposively sampled cohort. A systematic analytic approach incorporating the modified DEER taxonomy, revised for acute care, offered critical insights into key failures in the diagnostic process that could serve as potential targets for preventative interventions.

The drug shortage crisis in the last decade not only increased health care costs but also jeopardized patients’ health across the United States. Ensuring that any drug is available to patients at health care centers is a problem that official health care administrators and other stakeholders of supply chains continue to face. Furthermore, managing pharmaceutical supply chains is very complex, as inevitable disruptions occur in these supply chains (exogenous factors), which are then followed by decisions members make after such disruptions (internal factors). Disruptions may occur due to increased demand, a product recall, or a manufacturer disruption, among which product recalls—which happens frequently in pharmaceutical supply chains—are least studied. We employ a mathematical simulation model to examine the effects of product recalls considering different disruption profiles, e.g., the propagation in time and space, and the interactions of decision makers on drug shortages to ascertain how these shortages can be mitigated by changing inventory policy decisions. We also measure the effects of different policy approaches on supply chain disruptions, using two performance measures: inventory levels and shortages of products at health care centers. We then analyze the results using an approach similar to data envelopment analysis to characterize the efficient frontier (best inventory policies) for varying cost ratios of the two performance measures as they correspond to the different disruption patterns. This analysis provides insights into the consequences of choosing an inappropriate inventory policy when disruptions take place.

Objectives To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care. Methods We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution’s mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort. Results Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen’s kappa 0.41) and substantial with the expert panel (Cohen’s kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively. Conclusions We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

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With greater demand for outpatient services, the importance of patient-centric clinic layout design that improves timeliness of patient care has become more elucidated. In this paper, a novel simulation-optimisation (SO) framework is proposed focusing on the physical and process flows of patients in the design of a paediatric orthopaedic outpatient clinic. A discrete-event simulation model is used to estimate the frequency of movements between clinic units. The resulting information is utilised as input to a mixed integer programming (MIP) model, optimising the clinic layout design. In order to solve the MIP model, Particle Swarm Optimisation (PSO), a metaheuristic approach enhanced with several heuristics is utilised. Finally, the optimisation model outputs are evaluated with the simulation model. The results demonstrate that improvements to the quality of the patient experience can be achieved through incorporating SO methods into the clinic layout design process.

To address prolonged lengths of stay (LOS) in ambulatory care clinics, we analyze the impact of implementing flexible and dynamic policies for assigning exam rooms to providers. In contrast to the traditional approaches of assigning specific rooms to each provider or pooling rooms among all practitioners, we characterize the impact of alternate compromise policies that have not been explored in previous studies. Since ambulatory care patients may encounter multiple different providers in a single visit, room allocation can be determined separately for each encounter accordingly. For the first phase of the visit, conducted by the medical assistant, we define a dynamic room allocation policy that adjusts room assignments based on the current state of the clinic. For the second phase of the visit, conducted by physicians, we define a series of room sharing policies which vary based on two dimensions, the number of shared rooms and the number of physicians sharing each room. Using a discrete event simulation model of an outpatient cardiovascular clinic, we analyze the benefits and costs associated with the proposed room allocation policies. Our findings show that it is not necessary to fully share rooms among providers in order to reduce patient LOS and physician idle time. Instead, most of the benefit of pooling can be achieved by implementation of a compromise room allocation approach, limiting the need for significant organizational changes within the clinic. Also, in order to achieve most of the benefits of room allocation policies, it is necessary to increase flexibility in the two dimensions simultaneously. These findings are shown to be consistent in settings with alternate patient scheduling and distinctions between physicians.