Costing hospital resources for stroke patients using phase-type models

Optimising resources in healthcare facilities is essential for departments to cope with the growing population’s requirements. An aspect of such performance modelling involves investigating length of stay, which is a key performance indicator. Stroke disease costs the United Kingdom economy seven billion pounds a year and stroke patients are known to occupy long periods of time in acute and long term beds in hospital as well as requiring support from social services. This may be viewed as an inefficient use of resources. Thrombolysis is a therapy which uses a clot-dispersing drug which is known to decrease the institutionalisation of eligible stroke patients if administered 3 h after incident but it is costly to administer to patients. In this paper we model the cost of treating stroke patients within a healthcare facility using a mixture of Coxian phase type model with multiple absorbing states. We also discuss the potential benefits of increasing the usage of thrombolysis and if these benefits balance the expense of administering the drug.peer-reviewe

Developing service supply chains by using agent based simulation

The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managers’ preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Master of Science

thesisAnnually, 46 million patients, or 37% of patients seen in the emergency department (ED), receive laboratory testing in the U.S.; thus, making efficient lab order and result management critical to improving ED throughput, clinical efficacy, and safety. In order to manage labs and other processes, electronic emergency department tracking systems (EDTS) or electronic whiteboards have evolved features that support clinical, operational, and administrative needs. EDTSs have often augmented manual data entry with interfaces and/or integration with other systems such as registration, laboratory, radiology, and clinical information systems (CIS). One such integration evaluated in this study, EDTS/CIS context sharing, was added to automatically pass all necessary user, patient, and application parameters between the two systems in order to open the CIS lab module for a selected patient when the user is notified in the EDTS that laboratory test results for that patient are available for review. Therefore, context sharing eliminated multiple user steps needed to log-on, search, select, and navigate to the lab viewing module in order to view a patient's lab results. This study evaluates the effects of adding EDTS/CIS context sharing to an EDTS with lab notifications on ED process times. These effects were measured utilizing a pre- and post-intervention design for all ED encounters where specific common labs were resulted. A method of analyzing CIS audit logs in combination with EDTS and laboratory information system timestamps was implemented to measure patient management processes for quality improvement. After adding context sharing to lab notification features, the median interval between the availability of lab results and review of those results by the ordering provider decreased from 22.7 min., by 25% or 5.7 min. (p-value < 0.001), to 17.0 min. However, median time from resulting of labs to patient discharge were essentially unchanged, decreasing from 106.6 min. to 105.0 min. (p-value = 0.080). The proportion of lab results reviewed by physicians in the CIS integrated with the EDTS increased from 66% to 86% after the intervention (p-value < 0.001). EDTS/CIS context sharing and passive lab notification features improved the timeliness and completion of lab result review in the CIS and increased system adoption in this setting. However, reductions in the time intervals to review of lab results in the CIS did not result in an operationally or statistically significant improvement in time to discharge after the availability of results

A decision support simulation model for bed management in healthcare

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources such as beds. Bed management is key to the effective delivery of high-quality and low-cost healthcare. An efficient utilization of beds requires a detailed understanding of the hospital\u27s operational behavior. It is necessary to understand the behavior of a hospital in order to make necessary adjustments to its resources, and policies, which can improve patient\u27s access to care. The aim of this research was to develop a discrete event simulation to assist in planning and staff scheduling decisions. Each department\u27s performance measures were taken into consideration separately to understand and quantify the behavior of individual departments, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time for patients, and length of stay of patients. From the results, the departments that have long queues of patients, waiting times, and lengths of stay are detailed to predict how the hospital reacts to patient flow --Abstract, page iv

Endpoints In Intensive Care Unit Based Randomized Clinical Trials

With few exceptions, intensive care unit (ICU)-based randomized clinical trials (RCTs) have failed to demonstrate hypothesized treatment effects. Undoubtedly, some of these failures are attributable to interventions that truly do not provide hoped-for benefits. However, this dissertation pursues the thesis that many null findings represent “false negatives” that are due not to ineffective therapies but to flawed study designs or analytic approaches. We examine the design and statistical methods traditionally employed in ICU-based RCTs, and their potential impacts on the efficient measurement and interpretation of treatment effects. Paper one presents a systematic review of 146 contemporary ICU-based RCTs in which we find that most trials were underpowered to detect small but potentially important mortality differences between treatment arms. We also find that the majority of RCTs (73%) specified primary outcomes other than mortality, that trials employing nonmortal primary outcomes more frequently identified significant treatment effects, and that both mortal and nonmortal endpoints were heterogeneously defined, measured and analyzed across RCTs. Thus, papers two and three focus on nonmortal endpoints, using ICU length of stay (LOS) as a case study to evaluate how best to measure and analyze duration-based nonmortal endpoints. In paper two, we conduct a statistical simulation study, demonstrating that nonmortal endpoints are interlinked with and confounded by mortality, and that the manner in which investigators choose to account for deaths in LOS analyses may influence their conclusions. In paper three, we examine another potential source of error in LOS analyses, namely the measurement error attributable to the additional ICU time that patients commonly accrue after they are clinically ready for ICU discharge. Using simulated data informed by our own ICU-based RCT, we demonstrate that this “immutable time” (which cannot plausibly be altered by the interventions under study) combines with clinically necessary ICU time to produce overall LOS distributions that may either mask true treatment effects or suggest false treatment effects. Our work provides evidence of the potential benefits and pitfalls when employing nonmortal outcomes in ICU-based RCTs, and also identifies a clear need for standardized methods for defining and analyzing such outcomes