On people and complexity in healthcare service supply

Healthcare logistics is treated as a fundamentally emergent complex system primarily because a broad range of stakeholders are included. The patient is the primary “customer” of the service producers in the supply chain, including the doctors, nurses, medicine and insurance providers, and hospital administrators. Problematic issues regarding healthcare quality that need to be solved or improved are discussed, and suggestions for furthering and accelerating progress are offered. Careful application of information technology in designing appropriate information systems is advocated. Three specific illustrative cases of healthcare services that have been analyzed and assessed are summarized. The overall intent is to motivate creative processes for delivering more efficient and effective healthcare utilizing complex system behaviors and engineering principles, and an ethically-founded worldview. Keywords: case studies, collaboration, complex systems engineering, complex systems, ecosystems, healthcare services, information systems, interdependencies, logistics, process emergence, supply chain managementpublishedVersio

The Suitability of Artificial Neural Networks in Service Quality Control and Forecasting for Healthcare Contexts

Over the last decade there has been considerable research into the area of service quality. Service, however, as an intangible, perishable, and heterogenic transaction, is very difficult to quantify and measure, and little success has been reported on a systematic approach in modeling of quality of service transactions (with SERVQUAL and its derivatives as the notable exception). In this paper, we propose an Artificial Neural Network (ANN) to monitor quality of service transaction as a dynamic and real-time monitoring and forecasting system. ANNs are widely used in many engineering fields to model and simulate complex systems. The resulting near-perfect models are particularly suited for applications where real-world complexities make it difficult or even impossible to mathematically model the system. Given the complex nature of healthcare decisions, the following reports on a research in progress study that focuses on applying ANN to a specific healthcare context of the emergency room

Health Parameters Monitoring System

This paper deals with design and implementation of the biomedical system such as Health Parameter Monitoring System. Biomedical engineering is nothing but the application of engineering principles and techniques which will be applied into the medical field. The development of biomedical engineering is responsible for improving healthcare diagnosis also useful in monitoring and therapy of patient’s health. It ensures to provide quality health service to each and everyone. It monitors parameterslike ECG, EMG, temperature and heart beat rate and sending the data to doctor’s end via GSM. Periodic health monitoring or preventative care allows people to discover and treat health problems before they have consequences so that timely checking will result in positive effect. Especially for risk patients and long term applications where doctor need to monitor parameters frequently such a technology offers more freedom, comfort, and opportunities in clinical monitoring and diagnosis. The goal of the health system is to provide service to community. Many a times Chronic diseases have a significant influence on healthcare costs and are frequently caused among people. lack of health and social care personnel force us to study new innovations and really provoked us to improve existing system. Many times senior doctors have to make frequent visits to their nurse to get data regarding parameters measured that consists of Pulserate, Bodytemperature, ECG, EMG etc

London SynEx Demonstrator Site: Impact Assessment Report

The key ingredients of the SynEx-UCL software components are: 1. A comprehensive and federated electronic healthcare record that can be used to reference or to store all of the necessary healthcare information acquired from a diverse range of clinical databases and patient-held devices. 2. A directory service component to provide a core persons demographic database to search for and authenticate staff users of the system and to anchor patient identification and connection to their federated healthcare record. 3. A clinical record schema management tool (Object Dictionary Client) that enables clinicians or engineers to define and export the data sets mapping to individual feeder systems. 4. An expansible set of clinical management algorithms that provide prompts to the patient or clinician to assist in the management of patient care. CHIME has built up over a decade of experience within Europe on the requirements and information models that are needed to underpin comprehensive multiprofessional electronic healthcare records. The resulting architecture models have influenced new European standards in this area, and CHIME has designed and built prototype EHCR components based on these models. The demonstrator systems described here utilise a directory service and object-oriented engineering approach, and support the secure, mobile and distributed access to federated healthcare records via web-based services. The design and implementation of these software components has been founded on a thorough analysis of the clinical, technical and ethico-legal requirements for comprehensive EHCR systems, published through previous project deliverables and in future planned papers. The clinical demonstrator site described in this report has provided the solid basis from which to establish "proof of concept" verification of the design approach, and a valuable opportunity to install, test and evaluate the results of the component engineering undertaken during the EC funded project. Inevitably, a number of practical implementation and deployment obstacles have been overcome through this journey, each of those having contributed to the time taken to deliver the components but also to the richness of the end products. UCL is fortunate that the Whittington Hospital, and the department of cardiovascular medicine in particular, is committed to a long-term vision built around this work. That vision, outlined within this report, is shared by the Camden and Islington Health Authority and by many other purchaser and provider organisations in the area, and by a number of industrial parties. They are collectively determined to support the Demonstrator Site as an ongoing project well beyond the life of the EC SynEx Project. This report, although a final report as far as the EC project is concerned, is really a description of the first phase in establishing a centre of healthcare excellence. New EC Fifth Framework project funding has already been approved to enable new and innovative technology solutions to be added to the work already established in north London

DEVELOPING AND IMPLEMENTING A PRACTICAL MODEL OF REAL-TIME REDESIGN AND PROBLEM SOLVING FOR FRONTLINE HEALTHCARE PROFESSIONALS

This research develops and implements a practical model of real-time redesign and problem solving for front line healthcare professionals using systems thinking methodologies. Healthcare quality, safety and service issues have been well-documented and lamented, calling into question the current approaches for addressing these issues. The work environment for healthcare professionals has become overburdened with time pressure, workarounds, waste, and failure to learn from the small events which occur on a frequent basis at the front-line. Desensitization may occur until sentinel events stimulate an organizational reaction. Other industries have developed system engineering methodologies, including the Toyota production system, theory of constraints, six sigma and others, to address manufacturing quality, service and safety issues. Many of these concepts were developed within the context of a linear manufacturing environment, with solutions often derived "off-line" by external experts. Healthcare reality is considered more complex and requires adaptive approaches, suggesting that modifications based on complex adaptive systems theory may be necessary. The development of the model evolved based on key systems thinking principles adapted to meet the needs of the healthcare experience and introduced to front-line healthcare workers using on-line problem solving. This research includes real-time understanding of what is working or not working in the current condition as it occurs, the ideas of the staff to improve the patient experience, including asset-based problem-solving and introduction of system thinking and design principles using ideas from various systems engineering methodologies in a healthcare worker friendly way. The research focuses on the deep systems of the organization (or clinical microsystem) and ability of front line teams to redesign processes in real-time using rapid cycle mini-experiments and the results of the redesign. Using case study and action research design, the research analyzes the experiences of an intact work group of a clinical microsystem to test the implementation of a model, labeled an Excellence Makeover. The researcher acts as a participant-observer of the emergent experience and solutions from the staff. The model will then be analyzed and additional refinements will be suggested for additional research

A systems approach to improving patient safety through medical device purchasing

The purchase of medical devices involves engaging various stakeholders as well as balancing clinical, technical and financial requirements. Failure to consider these requirements can lead to wider consequences in the delivery of care. This study first builds a general knowledge base of current purchasing practice in a sample of NHS Trusts, which confirms the direction and guidance given by policy documents and literature as to the extent of the challenges faced by purchasing stakeholders. This then leads to an analysis to identify inefficiencies in the purchasing process, and how such practice can lead to risks in the delivery of care. These risks range from injury to individuals, impacts to the healthcare delivery service, and financial and litigation risks. Finally, a framework that highlights these potential risks in the life-cycle of medical devices in hospitals is presented. Key policy guidance has encouraged both researchers and implementers of healthcare services to approach patient safety from a systems perspective, acknowledging that medical device errors are not only directly related to device design, but to the design of the healthcare delivery service system in which the device operates. Little evidence exists of successfully applying systems approaches specifically to medical device purchasing practice. Medical device purchasing, because of its implications to patient safety on the one hand, and the uniqueness of the healthcare context, requires a unique approach. By demonstrating the influence of purchasing practice to service delivery and patient care, the thesis made is that taking a holistic systems approach is one method to improve device purchasing practice, and hence influence better care.This work was supported by the UK Engineering and Physical Sciences Research Council [EP/E001777/1

Methodology for clinical integration of e-Health sensor-based smart device technology with cloud architecture

The international healthcare systems interoperability is an unresolved technological area at the moment. This paper demonstrates the results of the software engineering research for simplex syntactic and semantic technical interoperability of hospital information systems, eHealth smart device technology and clinical telemedicine instruments through the recently developed Telemedicine Interoperability Hub. Several similar experiments exist. This research is unique in building a prototype interconnecting not only healthcare information systems with each other, but aiming to establish a general healthcare interoperability scheme including also the eHealth smart devices and telemedicine instruments. The aim of this research is to establish cloud-based data interchange capability with the newly developed information technology system interconnected with the emerging eHealth Internet of Things solutions and the classical hospital information system architecture. Notwithstanding the international information technology medical data exchange standards, like Health Level Seven, the adoption of an industry-wide open telemedicine syntactic and semantic interoperability standard is necessary. The research studied varying simplex, duplex, full-duplex, data package- and file-based information technology modalities establishing stable system interconnection among clinical instruments, healthcare systems and eHealth smart devices. This research is the manifestation of the trilateral cooperation of the University of Debrecen Department of Information Technology, Semmelweis University Second Paediatric Clinic and T-Systems Healthcare Competence Center Central and Eastern Europe. The developed experimental software engineering solution was embedded in hybrid cloud architecture after testing private cloud Infrastructure-as-a-Service and Public Cloud Software-as-a-Service technical solutions

Critical Knowledge Monitor System Model: healthcare context

In order to provide a better service, sharing knowledge with partners and communities is becoming part of the healthcare organizations culture. Data, information and clinic knowledge require specific cautious, because it involves ethical and legal issues. The constant evolution of Information and Communication Technologies brings new opportunities with multiple forms of communication (web 2.0), therefore, new ways of sharing knowledge. Further, there is a wide knowledge sources: patient’s feedback; knowledge from Internet sources; knowledge from decision support systems; and inference knowledge (e.g. Knowledge from Data Mining techniques) justifying the use of knowledge management systems to get its benefits. The Critical Knowledge Monitor System Model, proposed here, allows knowledge sharing in a controlled ambient and could be a part of the answer to this paradigm that healthcare organizations face. To implement the Critical Knowledge Monitor System model we’ll need to apply knowledge engineering techniques such as ontology construction, text mining, techniques, Information retrieval, among others. Since not all knowledge manage by healthcare organizations could be considered critical (or much critical), it’s necessary to define constructs to classify clinic knowledge. To achieve this, we’ll implement a focus group approach with the use of risk management techniques to classify knowledge as critical and its critical level to driven ontology with the class and terms used by the healthcare organization under study. Essentially, these are the motives of this research.This work is financed by FEDER funds through the Competitive Factors Operational Program – COMPETE and Portuguese national funds through FCT – Fundação para a ciência e tecnologia in project FCOMP-01-0124-FEDER-022674

Experiences of advanced psychiatric nursing graduates involved in a service-learning project at a higher educational institution in the Western Cape

Magister Curationis - MCurThe re-engineering of the healthcare landscape requires Higher Educational Institutions (HEI) to employ teaching and learning approaches that would produce graduates, who could respond to the transformative initiatives within the healthcare system. Graduates are required to become involved in a service-learning project, as part of their learning experience, within the Masters of Nursing in Advanced Psychiatric Nursing programme. Their learning and teaching activity is intended to prepare them to become competent advanced psychiatric nurse specialists, who are able to address social transformation

A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis

<p>Abstract</p> <p>Objectives</p> <p>A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility.</p> <p>Method</p> <p>Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery.</p> <p>Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.</p> <p>As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission.</p> <p>Results</p> <p>We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times.</p> <p>Conclusions</p> <p>One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.</p