On the Estimation of Hospital Beds Occupancy After Hip Surgery

For hospitals, the variability on demand (number of daily urgent admissions) and the variability in the Length of Stay (LoS) (bed occupancy) may affect the quality of service provided to patients and the effectiveness of the overall service. This paper studies the LoS of 238 patients who performed hip surgery in the orthopedic service of a Portuguese hospital in 2014. It uses variables available in electronic databases, such as Age, Gender, ASA classification; Surgical Apgar Score, Type of hip surgery; Weekday of the surgery; Starting hour of the surgery and Duration of surgery to predict LoS and provides a model that correctly indicate if a patient stays more than 7 days in 72.1% of the cases.- This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Man vs. machine : predicting hospital bed demand

Background: The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems. Objective: Compare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED. Methods: This was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC). Results: All graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77–0.87], 0.80 (95% CI: 0.75–0.85), 0.76 (95% CI: 0.71–0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR. Conclusions: Our data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task

Scheduling the hospital-wide flow of elective patients

In this paper, we address the problem of planning the patient flow in hospitals subject to scarce medical resources with the objective of maximizing the contribution margin. We assume that we can classify a large enough percentage of elective patients according to their diagnosis-related group (DRG) and clinical pathway. The clinical pathway defines the procedures (such as different types of diagnostic activities and surgery) as well as the sequence in which they have to be applied to the patient. The decision is then on which day each procedure of each patient’s clinical pathway should be done, taking into account the sequence of procedures as well as scarce clinical resources, such that the contribution margin of all patients is maximized. We develop two mixed-integer programs (MIP) for this problem which are embedded in a static and a rolling horizon planning approach. Computational results on real-world data show that employing the MIPs leads to a significant improvement of the contribution margin compared to the contribution margin obtained by employing the planning approach currently practiced. Furthermore, we show that the time between admission and surgery is significantly reduced by applying our models

Dynamic Resource Allocation For Coordination Of Inpatient Operations In Hospitals

Healthcare systems face difficult challenges such as increasing complexity of processes, inefficient utilization of resources, high pressure to enhance the quality of care and services, and the need to balance and coordinate the staff workload. Therefore, the need for effective and efficient processes of delivering healthcare services increases. Data-driven approaches, including operations research and predictive modeling, can help overcome these challenges and improve the performance of health systems in terms of quality, cost, patient health outcomes and satisfaction. Hospitals are a key component of healthcare systems with many scarce resources such as caregivers (nurses, physicians) and expensive facilities/equipment. Most hospital systems in the developed world have employed some form of an Electronic Health Record (EHR) system in recent years to improve information flow, health outcomes, and reduce costs. While EHR systems form a critical data backbone, there is a need for platforms that can allow coordinated orchestration of the relatively complex healthcare operations. Information available in EHR systems can play a significant role in providing better operational coordination between different departments/services in the hospital through optimized task/resource allocation. In this research, we propose a dynamic real-time coordination framework for resource and task assignment to improve patient flow and resource utilization across the emergency department (ED) and inpatient unit (IU) network within hospitals. The scope of patient flow coordination includes ED, IUs, environmental services responsible for room/bed cleaning/turnaround, and patient transport services. EDs across the U.S. routinely suffer from extended patient waiting times during admission from the ED to the hospital\u27s inpatient units, also known as ED patient `boarding\u27. This ED patient boarding not only compromises patient health outcomes but also blocks access to ED care for new patients from increased bed occupancy. There are also significant cost implications as well as increased stress and hazards to staff. We carry out this research with the goal of enabling two different modes of coordination implementation across the ED-to-IU network to reduce ED patient boarding: Reactive and Proactive. The proposed `reactive\u27 coordination approach is relatively easy to implement in the presence of modern EHR and hospital IT management systems for it relies only on real-time information readily available in most hospitals. This approach focuses on managing the flow of patients at the end of their ED care and being admitted to specific inpatient units. We developed a deterministic dynamic real-time coordination model for resource and task assignment across the ED-to-IU network using mixed-integer programming. The proposed \u27proactive\u27 coordination approach relies on the power of predictive analytics that anticipate ED patient admissions into the hospital as they are still undergoing ED care. The proactive approach potentially allows additional lead-time for coordinating downstream resources, however, it requires the ability to accurately predict ED patient admissions, target IU for admission, as well as the remaining length-of-stay (care) within the ED. Numerous other studies have demonstrated that modern EHR systems combined with advances in data mining and machine learning methods can indeed facilitate such predictions, with reasonable accuracy. The proposed proactive coordination optimization model extends the reactive deterministic MIP model to account for uncertainties associated with ED patient admission predictions, leading to an effective and efficient proactive stochastic MIP model. Both the reactive and proactive coordination methods have been developed to account for numerous real-world operational requirements (e.g., rolling planning horizon, event-based optimization and task assignments, schedule stability management, patient overflow management, gender matching requirements for IU rooms with double occupancy, patient isolation requirements, equity in staff utilization and equity in reducing ED patient waiting times) and computational efficiency (e.g., through model decomposition and efficient construction of scenarios for proactive coordination). We demonstrate the effectiveness of the proposed models using data from a leading healthcare facility in SE-Michigan, U.S. Results suggest that even the highly practical optimization enabled reactive coordination can lead to dramatic reduction in ED patient boarding times. Results also suggest that signification additional reductions in patient boarding are possible through the proposed proactive approach in the presence of reliable analytics models for prediction ED patient admissions and remaining ED length-of-stay. Future research can focus on further extending the scope of coordination to include admissions management (including any necessary approvals from insurance), coordination needs for admissions that stem from outside the ED (e.g., elective surgeries), as well as ambulance diversions to manage patient flows across the region and hospital networks

Hospital resource planning : a case-based application for surgical services of a Colombian hospital

Para la toma de decisiones estratégicas se hace uso de la Planificación de recursos hospitalarios (HRP, por sus siglas en inglés) la cual es importante para permitir una gestión eficiente de los recursos, identificar aquellos que causan cuellos de botella, mejorar el flujo de pacientes, brindar tratamiento oportuno y reducir los costos. Este proyecto propone y desarrolla un aplicativo que implementa un modelo cuantitativo de HRP para el servicio quirúrgico de cirugías electivas en un hospital colombiano mediante el uso de un algoritmo genético con escenarios de demanda estocástica como método de solución. El aplicativo propuesto toma en cuenta el impacto en la programación táctica y operativa de los recursos quirúrgicos para cirugías electivas, este impacto se refleja en la interacción con una herramienta de programación.For strategic decision making, the implementation of Hospital Resource Planning (HRP) is important to allow efficient resource management, identify those resources which are causing bottlenecks, improve patient flow, and provide timely treatment and reduce costs. This project proposes and develops an application that implements a quantitative HRP model for the elective surgical service for a Colombian Hospital by using a Genetic Algorithm with stochastic demand scenarios as solution method. The application takes into account its impact on tactical and operative scheduling of surgical resources for elective surgeries, this impact is reflected in the interaction with a scheduling tool.Ingeniero (a) IndustrialPregrad

Defining and Measuring "Knowledge Capital" in Health Service

"Knowledge capital" comes in many forms based on the context of its creation, some in terms of pure knowledge, some of which is public good and some rival. Furthermore, some of its forms are less easy to describe since it includes custom, practice and understanding of how best to organise things. Practitioners and academics, across disciplines of organisational management, economics, and accounting define the concept of "knowledge capital" (KC) or "intellectual capital" (IC) as human skills enhanced by organisational structures, resources and relationships to form a composite knowledge based resource, which creates competencies, capability and capacity that generate revenue for the organisation. Health service provision is based on the transfer of tacit, explicit, established and emerging knowledge. The capture of learning gained during service delivery is therefore critical for the safety, effectiveness and quality of service provision co-creating knowledge based resources including enhanced understanding, skills, processes and routines. The need arises, therefore, to understand if the way resources are managed should change to take account of the generation of more or less of something that is of value to health organisations and systems. The joint production and "public goods" features of inexhaustibility and non-exclusivity, in certain circumstances, make the measurement of "knowledge capital" in health challenging. The management and maintenance of this key resource in health service requires it to be recognised and measured, although there are problems in defining "knowledge capital". There are challenges in measuring it and even bigger ones in valuing it. There is a need, therefore, to start with a clearer understanding on what it is and then attempt to measure it. This research through an empirical case study highlights the co-creation of, explores its nature and attempts to measures the scale of "knowledge capital" in health service, as a resource. The models "knowledge creation cycle in health" and "dimensions of knowledge capital in health" developed from the literature review are investigated in the study of the specialised pulmonary hypertension (PH) services at Papworth hospital, a NHS specialist centre. The additional dimensions of "public goods in health" and "capacity in health" are surfaced in this study. Management accountancy method of costing, informed by the economic concept of opportunity cost of capital, provides a helpful mechanism for the measurement of this difficult to measure resource in this study. This method is based on the estimates of the inputs of joint production of "knowledge capital" using the "bottom up" approach being recommended by NHS guidance. This case study at Papworth hospital reveals that the scale of the value of stock of "knowledge capital" can be more than twice the value of its tangible assets. This highlights the necessity for management strategies of health organisations and health systems to recognise fully its co-creation and measure the scale of “knowledge capital” in health service. A systematic stock take of "knowledge capital" assets in health organisations and systems is therefore recommended to enable informed decision making for effective and efficient management of health services