The Marginal Utility of Medical Resources in Clinics with Deterministic Patient Arrivals: A Simulation Study

In recent years, dramatic increase in the cost of health care has compelled practitioners to draw a balance between improving efficiency and reducing costs. A discrete-event simulation model has been constructed to assist a typical two-physician family practice healthcare clinic in evaluating potential resource allocations to improve operating efficiencies and patient satisfaction. A performance measure, constructed on a monetary scale (dollars/day), strives to simultaneously satisfy the conflicts of patients, medical staff, and clinic owners by capturing system dynamics. Utility of medical resources is studied from the point of view of a local two-physician nephrology clinic in the light of resource flexibility, resource scheduling, and resource allocation to arrive at an ‘efficient utility frontier’, a reflection of patient satisfaction

When patients get stuck: A systematic literature review on throughput barriers in hospital-wide patient processes

Hospital productivity is of great importance to policymakers, and previous research demonstrates that improved hospital productivity can be achieved by directing more focus towards patient throughput at healthcare organizations. There is also a growing body of literature on patient throughput barriers hampering the flow of patients. These projects rarely, however, encompass complete hospitals. Therefore, this paper provides a systematic literature review on hospital-wide patient process throughput barriers by consolidating the substantial body of studies from single settings into a hospital-wide perspective. Our review yielded a total of 2207 articles, of which 92 were finally selected for analysis. The results reveal long lead times, inefficient capacity coordination and inefficient patient process transfer as the main barriers at hospitals. These are caused by inadequate staffing, lack of standards and routines, insufficient operational planning and a lack in IT functions. As such, this review provides new perspectives on whether the root causes of inefficient hospital patient throughput are related to resource insufficiency or inefficient work methods. Finally, this study develops a new hospital-wide framework to be used by policymakers and healthcare managers when deciding what improvement strategies to follow to increase patient throughput at hospitals

Effect of Appointment Schedules on the Operational Performance of a University Medical Clinic

Healthcare costs in the United States are one of the highest in the world. The healthcare expenditure alone accounted for 17.9% of the Gross Domestic Product of US in 2011. The National Healthcare Expenditure (NHE) is expected to increase at an annual rate of 6.6% from $2.6 Trillion in 2010 to$ 4.5 Trillion in 2019. The per capita expenditure for hospital outpatients and physicians has been the highest among other hospital expenses. This escalation in expenses created a need for productivity improvements in the healthcare industry to control costs. Some of the common problems encountered in outpatient clinics are high patient wait times, physician idle times, physician overtimes and high patient congestion. These problems not only lead to the inefficient operation of a clinic but also cause frustration and dissatisfaction to the physicians and patients. A well designed appointment system is very critical for the effective operation of outpatient clinics by minimizing these problems. The objective of this research was to study the effect of different appointment systems on the operational performance of a university medical clinic. The process at the medical clinic in the LSU Student Health Center (SHC) was modeled using the Rockwell Arena® simulation software. Four scheduling rules: Individual block rule, Bailey rule, 3-Bailey rule, and the Two-at-a-time rule, were studied to understand their effect on the performance parameters of the SHC. The performance parameters considered were the provider measures (provider idle time, startup idle time, provider overtime, provider utilization) and patient measures (patient wait time and patient throughput time). The individual block rule was the most patient friendly with shortest patient measures (patient throughput time - 39.6 min and patient wait time - 15.5 min); however it had the highest provider measures (Idle time – 50.5 min, Startup idle time – 10.4 min, Overtime – 16.2 min). The 3-Bailey rule was the most provider friendly rule with the least provider times (Idle time – 17 min, Startup idle time – 4.6 min, Overtime – 5.6 min) and best provider utilization (95%), but had high patient times (throughput time – 48.1 min and wait time – 24.1 minute). To aid the decision making process of the schedule selection for the SHC, a KT analysis was performed by weighing the performance parameters. The Bailey rule was observed to be the most suitable rule for the SHC as it had a good trade-off between the patient times and provider times compared to the other rules. The Bailey rule had better provider times (Idle time – 31.8 min, Startup idle time – 6.5 min, Overtime – 6.9 min) and better provider utilization rate (92%) when compared to the individual block rule and had marginally higher patient times (throughput time – 41.4 min and wait time – 17.3 min). A test run of the Bailey rule with one provider for ten days also confirmed this behavior of the rule

The hospital-wide patient flow - looking beyond borders for improved productivity

Demand for healthcare is increasing at a faster pace than hospitals’ capacity. In search of newpaths to reverse this development policy makers and healthcare managers look for new methodologies or concepts to improve productivity. One such concept is flow efficiency, focusing on how to better support the throughput of patients, and productivity. Therefore, the aims of this thesis are to examine the phenomenon of hospital-wide patient flows and what is preventing or helping the patient flow to become swift and even across the hospital organization.This thesis builds on a qualitative research design, where process theory and the theory of swift and even flows are used as points of departure when exploring the phenomenon of hospital-wide patient flows. Two papers are presented. The first paper explores barriers to swift and even patient flows and the second paper identifies solutions on how to overcome the identified barriers. This thesis visualizes how important it is to align the hospital around the patient flow for improved productivity. It also explains how hospitals can serve a greater part of their citizens and enable a more sustainable work environment by improving the capacity balance across the hospital to support the patient flow. Lastly, a new framework on how to improve hospital-wide patient flows is developed connecting barriers, root causes, and solutions to swift and even patient flows based on a systematic literature review and on experiences from senior managers at the world’s leading hospitals

Nursing intensity and nurse staffing in perioperative settings

The goal of this study was to design and test a nursing intensity (NI) instrument in perioperative settings to produce information concerning patients’ care needs. This information is intended to be used for knowledge-based management purposes when applying optimal nurse staffing. In Phase I, a Delphi method with two rounds (n=55) was used to define the core elements of perioperative nursing. Then those core elements were tested to evaluate NI during the pre-, intra-, and postoperative phases of the surgical patient’s care process (n=308 patients). In Phase II, the core elements were implemented in an instrument, and further testing was carried out in different perioperative settings (n=876 patients). In Phase III, an integrative review was con-ducted to find out how nurse staffing had been executed in perioperative settings. According to the results, the core elements of perioperative nursing describing patient’s safety or patient’s physiological needs were seen as the most crucial. A principal component analysis revealed that a patient’s care needs vary from the intraoperative to postoperative phases of perioperative nursing. Patients in a high ASA class more frequently had high intraoperative NI points, but patients in a low ASA class did not automatically have fewer intraoperative care needs. The length of stay in the post-anesthesia care unit (PACU) and the type of follow-up unit could be predicted with intraoperative NI. Scant evidence was found concerning nurse staffing in perioperative settings. The need to take into account patients’ care needs showed up in some papers, but these were not expressed in an assessable form. Staffing models in relation to perioperative nursing-sensitive outcomes were not found. This study offers an instrument for evaluating NI in perioperative settings. This information produced can be utilized for nurse staffing and nurse staff allocation purposes. More research is needed that focuses more on the detailed use of information based on NI. Its potential to serve as a knowledge-based management tool also needs clarifying in future studies

Artificial Intelligence for Hospital Health Care:Application Cases and Answers to Challenges in European Hospitals

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects