Using Visual Analytics to Improve Hospital Scheduling and Patient Flow

Fitzgerald, Anneke

doi:10.4067/s0718-18762009000200003

Cited by 18 publications

(17 citation statements)

References 37 publications

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“…Most recently, computer simulation, including artificial intelligence, has been used to understand the source of the problem—optimal resource allocation in hospitals 16. Optimisation models have been implemented in specialty services where scheduling problems often result in procedural delays.…”

Section: Discussionmentioning

confidence: 99%

Waiting it out: consultation delays prolong in-patient length of stay

Rahman

Shi

Meza

et al. 2019

Postgraduate Medical Journal

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BackgroundDecreasing delays for hospitalised patients results in improved hospital efficiency, increased quality of care and decreased healthcare expenditures. Delays in subspecialty consultations and procedures can cause increased length of stay due to reasons outside of necessary medical care.ObjectiveTo quantify, describe and record reasons for delays in consultations and procedures for patients on the general medicine wards.MethodologyWe conducted weekly audits of all admitted patients on five Internal Medicine teams over 8 weeks. A survey was reviewed with attending physicians and residents on five internal medicine teams to identify patients with a delay due to consultation or procedure, quantify length of delay and record reason for delay.ResultsDuring the study period, 316 patients were reviewed and 48 were identified as experiencing a total of 53 delays due to consultations or procedures. The average delay was 1.8 days for a combined total of 83 days. Top reasons for delays included scheduling, late response to page and a busy service. The frequency in length of consult delays vary among different specialties. The highest frequency of delays was clustered in procedure-heavy specialties.ConclusionThis report highlights the importance of reviewing system barriers that lead to delayed service in hospitals. Addressing these delays could lead to reductions in length of stay for inpatients.

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Section: Discussionmentioning

confidence: 99%

Waiting it out: consultation delays prolong in-patient length of stay

Rahman

Shi

Meza

et al. 2019

Postgraduate Medical Journal

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“…Visual analytics therefore encompass aspects of visualizations that we previously mentioned, including identification, integration, immediacy, and interactivity. Visual analytics not only contribute to the scientific process, but serve to illuminate organizational decision making (Fitzgerald & Dadich, 2009; Kohlhammer, May, & Hoffmann, 2009) and are most connected to big data methodologies.…”

Section: Visualization: a Primer In Terminologiesmentioning

confidence: 99%

Big Data Visualizations in Organizational Science

Tay

Malik³

et al. 2017

Organizational Research Methods

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Visualizations in organizational research have primarily been used in the context of traditional survey data, where individual data points (e.g., responses) can typically be plotted, and qualitative (e.g., language data) and quantitative (e.g., frequency data) information are not typically combined. Moreover, visualizations are typically used in a hypothetico-deductive fashion to showcase significant hypothesized results. With the advent of big data, which has been characterized as being particularly high in volume, variety, and velocity of collection, visualizations need to more explicitly and formally consider the issues of (a) identification (isolating or highlighting relevant data pertaining to the phenomena of interest), (b) integration (combining different modes of data to reveal insights about a phenomenon of interest), (c) immediacy (examining real-time data in a time-sensitive manner), and (d) interactivity (inductively uncovering and identifying new patterns). We discuss basic ideas for addressing these issues and provide illustrative examples of visualizations that incorporate and highlight ways of addressing these issues. Examples in our article include visualizing multiple performance criteria for police officers, publication network of organizational researchers, and social media language of Fortune 500 companies.

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“…High demand for emergency hospital services is noticeable to circumvent the regular admission process. In general, patients use AEHs for an immediate response for regular and urgent needs and efficient and rapid assessment of the medical treatment (Thomas et al , 2007; Fitzgerald and Dadich, 2009). Most patients enter an AEH for easy access for health care (Mital, 2010; Gomes, et al , 2010).…”

Section: Background and Introductionmentioning

confidence: 99%

From process flows to benchmark in an accident and emergency hospital with PERT approach

Ahmed

Mohammed

2019

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Purpose Accident emergency hospital (AEH) services require cohesive, collective, uninterrupted streamlined medical diagnostic and satisfactory patient care. Medical service efficiency in AEHs is difficult to quantify due to the clinical complexity involved in treatment involving various units, patient conditions, changes in contemporary medical practices and technological developments. This paper aims to show how to measure efficiency by eliminating waste in AEH system, identify service failure points, identify benchmark medical services, identify patient throughput time and measure treatment time when AEH services are nonstandard. The applications shown in this paper are distinct in particular; we the authors use nontraditional and systems engineering approach to collect data as the traditional data collection is difficult in real-time AEHs. Design/methodology/approach The authors show in this study how to measure overall patient treatment time from admission to discharge. Project evaluation and review technique (PERT) captures the inconsistencies involved in measuring treatment time, including measures of variability. The irregular treatment time and complexity involved in the emergency health-care services are usual. The research methodology illustrates how the time function map and service blueprint can improve value-added time in AEHs and benchmark services between similar AEHs. Findings The inconsistency in treatment time between AEH in public and private hospital is found to be in ratio of 1:20. The private hospital suggests variety of treatments and long stays for recovery. The PERT computations show that the average time a patient remains in a government AEH is about 10 days. The standard deviation of the AEH treatment time is about 0.043 per cent of the expected patient care time. The inconsistency is not significant as compared to the expected value. In 89.64 per cent of the cases, a patient may be discharged in less than 10 days’ time. The patient on average is discharged in 13 days in a private hospital. Originality/value The patient treatment time of an AEH is evaluated with PERT project management approach to account for inconsistencies in treatment time. This research makes new contributions in benchmarking AEH throughput time, identify medical service failure points with service blueprint, measure the efficiency with time function map and collect patient data with nontraditional methods. The inherent inconsistencies in a clinical process are identified by PERT analysis with the variance as a characteristic of the treatment time. Improvement of variability implies cost reduction in AEH system.

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Using Visual Analytics to Improve Hospital Scheduling and Patient Flow

Cited by 18 publications

References 37 publications

Waiting it out: consultation delays prolong in-patient length of stay

Waiting it out: consultation delays prolong in-patient length of stay

Big Data Visualizations in Organizational Science

From process flows to benchmark in an accident and emergency hospital with PERT approach

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