Decision Support Systems In Hospitals Research Articles

A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation

The difficulty that hospital management has been experiencing over the past decade in balancing demand and capacity needs is unprecedented in the United Kingdom. Due to a shortage of capacity, hospitals cannot treat all patients. We developed a whole hospital-level decision support system to assess and respond to the needs of local populations. We integrated a comparative forecasting approach and discrete event simulation modelling using Hospital Episode Statistics and local datasets. It is clear from the literature that this level of whole hospital simulation model has never been developed before (an innovative decision support system). First, the demands of all hospital specialties were forecasted, and the forecasts were embedded into the simulation model as input. Secondly, a simulation model was developed to capture the patient pathway of all specialties. The model integrates every component of a hospital to aid with efficient and effective use of scarce resources (e.g., staff and beds). As a result, the hospital can meet the increasing demand with its current resources. According to the scenario analysis, the hospital bed occupancy rate will reach the national target (i.e., 85%), and the total hospital revenue will increase by approximately 13%, with a 10% increase in A&E and outpatient and a 20% increase in inpatient demand. In conclusion, the hospital-level simulation model can become a crucial instrument for decision-makers to provide an efficient service for hospitals in England and other parts of the world.

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

In recent years, the increasing volume and availability of healthcare and biomedical data are opening up new opportunities for computational methods to enhance healthcare in many hospitals. Medical data classification is regarded as the challenging task to develop intelligent medical decision support systems in hospitals. In this paper, the ensemble approaches based on support vector machines are proposed for classifying medical data. This research’s key contribution is that the ensemble multiple support vector machines use the function kernel in the style of gradient boosting and bagging to produce a more accurate fusion model than the mono-modality models. Extensive experiments have been conducted on forty benchmark medical datasets from the University of California at Irvine machine learning repository. The classification results show that there is a statistically significant difference (p-values < 0.05) between the proposed approaches and the best classification models. In addition, the empirical analysis of forty medical datasets indicated that our models can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.

Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: a systematic review

BackgroundAntibiotic resistance is a major health threat. Inappropriate antibiotic use has been shown to be an important determinant of the emergence of antibiotic resistance. Decision support systems for antimicrobial management can support clinicians to optimize antibiotic prescription.ObjectiveThe aim of this systematic review is to identify factors influencing the implementation of decision support systems for antibiotic prescription in hospitals.MethodsA systematic search of factors impeding or facilitating successful implementation of decision support systems for antibiotic prescription was performed in January 2022 in the databases PubMed, Web of Science and The Cochrane Library. Only studies were included which comprised decision support systems in hospitals for prescribing antibiotic therapy, published in English with a qualitative, quantitative or mixed-methods study design and between 2011 and 2021. Factors influencing the implementation were identified through text analysis by two reviewers.ResultsA total of 14 publications were identified matching the inclusion criteria. The majority of factors relate to technological and organizational aspects of decision support system implementation. Some factors include the integration of the decision support systems into existing systems, system design, consideration of potential end-users as well as training and support for end-users. In addition, user-related factors, like user attitude towards the system, computer literacy and prior experience with the system seem to be important for successful implementation of decision support systems for antibiotic prescription in hospitals.ConclusionThe results indicate a broad spectrum of factors of decision support system implementation for antibiotic prescription and contributes to the literature by identifying important organizational as well as user-related factors. Wider organizational dimensions as well as the interaction between user and technology appear important for supporting implementation.

Big Data Capabilities for Hospital: A Systematic Literature Review

The use of Big Data (BD) is considered to have the potential to provide significant benefits for hospitals because it can improve hospital services and can save treatment costs. This study aims to identify the effects of using BD Analytics (BDA) in improving hospital performance, including the key factors to determine the effectiveness of BD in improving the effectiveness of hospital performance, the data sources needed, the utilization of BD in improving patient satisfaction, and the challenges that can hinder the use of BD as the factors to determine the capabilities of BD. Implementing the PRISMA concept to conduct a Systematic Literature Review (SLR) of relevant literature by identifying the selection of relevant data sources, determining inclusion and exclusion indicators, extracting data from selected papers, evaluating the quality of the papers, and analyzing data using the meta-analysis method. Through this process, the authors examined 111 papers from various database sources and found 24 papers for further research. The results of this study indicate that from 24 papers, the data source to conduct data analysis is the main key factor to determine the capabilities of BD for hospitals which affect the further analysis of BDA. It was also found that several challenges hindering the use of BD need to be overcome to maximize the use of BD. However, there were limitations in the data sources used in the study. Therefore, it is necessary to review policies and infrastructure and utilize more varied data sources to maximize the use of BD and decision support systems in hospitals.

Hesitant Fuzzy Linguistic Consensus Model Based on Trust-Recommendation Mechanism for Hospital Expert Consultation

Aiming at validly improving the efficiency and quality of hospital expert consultation, a consensus model under uncertainty is established to operate a hospital decision support system (HDSS). Considering that expert consultation is commonly used in emergency events associated with life rescue where uncertainty and complexity exist, hesitant fuzzy linguistic terms are adopted. Additionally, since pairwise comparison information is efficient to express individual judgment, a novel concept of satisfaction degree toward hesitant fuzzy linguistic preference relation is utilized to measure the group consensus. A trust-recommendation mechanism is built to generate advice for experts. A consensus model is constructed to handle the HDSS in emergency decision-making problems. Some simulation experiments are designed to verify the availability and reasonability of the proposed model. Then, the proposed model is applied to address the selection of an emergency response plan for the influenza H7N9 virus, and finally, the strong robustness and powerful capability of the proposed model in the consensus reaching process is exhibited by making a comparative analysis among several recommendation mechanisms.

What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation

BackgroundAdvanced Computerized Decision Support Systems (CDSSs) assist clinicians in their decision-making process, generating recommendations based on up-to-date scientific evidence. Although this technology has the potential to improve the quality of patient care, its mere provision does not guarantee uptake: even where CDSSs are available, clinicians often fail to adopt their recommendations. This study examines the barriers and facilitators to the uptake of an evidence-based CDSS as perceived by diverse health professionals in hospitals at different stages of CDSS adoption.MethodsQualitative study conducted as part of a series of randomized controlled trials of CDSSs. The sample includes two hospitals using a CDSS and two hospitals that aim to adopt a CDSS in the future. We interviewed physicians, nurses, information technology staff, and members of the boards of directors (n = 30). We used a constant comparative approach to develop a framework for guiding implementation.ResultsWe identified six clusters of experiences of, and attitudes towards CDSSs, which we label as “positions.” The six positions represent a gradient of acquisition of control over CDSSs (from low to high) and are characterized by different types of barriers to CDSS uptake. The most severe barriers (prevalent in the first positions) include clinicians’ perception that the CDSSs may reduce their professional autonomy or may be used against them in the event of medical-legal controversies. Moving towards the last positions, these barriers are substituted by technical and usability problems related to the technology interface. When all barriers are overcome, CDSSs are perceived as a working tool at the service of its users, integrating clinicians’ reasoning and fostering organizational learning.ConclusionsBarriers and facilitators to the use of CDSSs are dynamic and may exist prior to their introduction in clinical contexts; providing a static list of obstacles and facilitators, irrespective of the specific implementation phase and context, may not be sufficient or useful to facilitate uptake. Factors such as clinicians’ attitudes towards scientific evidences and guidelines, the quality of inter-disciplinary relationships, and an organizational ethos of transparency and accountability need to be considered when exploring the readiness of a hospital to adopt CDSSs.

Qualitative analysis of vendor discussions on the procurement of Computerised Physician Order Entry and Clinical Decision Support systems in hospitals.

ObjectivesWe studied vendor perspectives about potentially transferable lessons for implementing organisations and national strategies surrounding the procurement of Computerised Physician Order Entry (CPOE)/Clinical Decision Support (CDS) systems in English hospitals.SettingData were collected from digitally audio-recorded discussions from a series of CPOE/CDS vendor round-table discussions held in September 2014 in the UK.ParticipantsNine participants, representing 6 key vendors operating in the UK, attended. The discussions were transcribed verbatim and thematically analysed.ResultsVendors reported a range of challenges surrounding the procurement and contracting processes of CPOE/CDS systems, including hospitals’ inability to adequately assess their own needs and then select a suitable product, rushed procurement and implementation processes that resulted in difficulties in meaningfully engaging with vendors, as well as challenges relating to contracting leading to ambiguities in implementation roles. Consequently, relationships between system vendors and hospitals were often strained, the vendors attributing this to a lack of hospital management's appreciation of the complexities associated with implementation efforts. Future anticipated challenges included issues surrounding the standardisation of data to enable their aggregation across systems for effective secondary uses, and implementation of data exchange with providers outside the hospital.ConclusionsOur results indicate that there are significant issues surrounding capacity to procure and optimise CPOE/CDS systems among UK hospitals. There is an urgent need to encourage more synergistic and collaborative working between providers and vendors and for a more centralised support for National Health Service hospitals, which draws on a wider body of experience, including a formalised procurement framework with value-based product specifications.

Minimally Invasive Surgery Is Associated with Decreased Overall Hospital Costs in Elective Colorectal Procedures

METHODS: Elective colorectal resections from January 2010 to June 2013 were included in a retrospective, single institution study. Chart abstraction was used to assess colectomy type, operative approach, and patient candidacy for MIS among the open procedures (prior surgery, extent of disease, etc.). Matching was performed based on colectomy type, indication (neoplasm, diverticulitis, etc.), and BMI. Outcomes were collected from NSQIP data. Operative and overall hospital costs were obtained from the hospital decision support system.

Pediatric Emergency Department Recidivism: Demographic Characteristics and Diagnostic Predictors

Overcrowding in emergency departments remains a national problem. Increases in patient volume and illness severity are among the factors contributing to this crisis. Of particular interest is a small group of patients who account for a disproportionate number of ED visits and are known as recidivists. Demographic and diagnostic characteristics were examined in an attempt to describe this national issue with more clarity. The hospital decision support system was used to identify 2 readmission indicators (3-month return visits and 48-hour returns). Descriptive statistics and multiple regression techniques were used to analyze the characteristics of recidivists. Chart reviews and telephone interviews were conducted to further explore ED utilization. Twenty-five percent of the sample (N = 932) had at least one visit during the previous 3 months (N = 237). Four percent of the sample had a 48-hour return visit (N = 38). The findings revealed that the traditional determinants of ED utilization such as insurance and chronicity of symptoms are not reliable predictors of return visits. The risk factors of age, race, and diagnosis were significantly associated with return visit at a significance level less than .05. The findings confirm that some demographic and diagnostic predictability of return visits is indeed evident for pediatric patients. Although the sample is not representative, the data are helpful in identifying some of the issues of pediatric recidivism. Findings provide some understanding of parents' utilization of the emergency department and identified high-risk pediatric recidivists. Data also revealed specific disease entities warranting focused attention, such as nervous system diseases, sense organ diseases, digestive system diseases, infectious diseases, and parasitic diseases for children younger than 1 year. Patients with these problems may be at greater risk for return visits. ED nurses might consider nurse-initiated treatment protocols.

Allocation of surgical procedures to operating rooms.

Reduction of health care costs is of paramount importance in our time. This paper is a part of the research which proposes an expert hospital decision support system for resource scheduling. The proposed system combines mathematical programming, knowledge base, and database technologies, and what is more, its friendly interface is suitable for any novice user. Operating rooms in hospitals represent big investments and must be utilized efficiently. In this paper, first a mathematical model similar to job shop scheduling models is developed. The model loads surgical cases to operating rooms by maximizing room utilization and minimizing overtime in a multiple operating room setting. Then a prototype expert system which replaces the expertise of the operations research analyst for the model, drives the modelbase, database, and manages the user dialog is developed. Finally, an overview of the sequencing procedures for operations within an operating room is also presented.

Decision support systems in hospitals.

Decision support systems in hospitals.