Overall Sigma Level of an Imaging Department through Process Innovation

Garcia-Porres, J.; Ortiz-Posadas, M. R.

doi:10.1007/978-3-642-03904-1_105

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Full text for 3 articles could not be obtained from the British Library and were therefore excluded. Seventy-eight articles were excluded with reasons: did not report the outcome measures of interest ; did not report objective measure of patients' waiting times, is an opinion piece, an editorial or theoretical paper [69][70][71][72][73][74][75][76][77][78][79][80][81][82][83]; did not report any intervention [84][85][86][87][88][89] and was withdrawn from publication [90]. Sixty-three articles (57 studies) met the inclusion criteria.…”

Section: Resultsmentioning

confidence: 99%

The Effectiveness of Service Delivery Initiatives at Improving Patients’ Waiting Times in Clinical Radiology Departments: A Systematic Review

et al. 2014

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We reviewed the literature for the impact of service delivery initiatives (SDIs) on patients' waiting times within radiology departments. We searched MEDLINE, EMBASE, CINAHL, INSPEC and The Cochrane Library for relevant articles published between 1995 and February, 2013. The Cochrane EPOC risk of bias tool was used to assess the risk of bias on studies that met specified design criteria. Fifty-seven studies met the inclusion criteria. The types of SDI implemented included extended scope practice (ESP, three studies), quality management (12 studies), productivity-enhancing technologies (PETs, 29 studies), multiple interventions (11 studies), outsourcing and pay-for-performance (one study each). The uncontrolled pre-and post-intervention and the postintervention designs were used in 54 (95 %) of the studies. The reporting quality was poor: many of the studies did not test and/or report the statistical significance of their results. The studies were highly heterogeneous, therefore metaanalysis was inappropriate. The following type of SDIs showed promising results: extended scope practice; quality management methodologies including Six Sigma, Lean methodology, and continuous quality improvement; productivityenhancing technologies including speech recognition reporting, teleradiology and computerised physician order entry systems. We have suggested improved study design and the mapping of the definitions of patient waiting times in radiology to generic timelines as a starting point for moving towards a situation where it becomes less restrictive to compare and/or pool the results of future studies in a meta-analysis.

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Section: Resultsmentioning

confidence: 99%

The Effectiveness of Service Delivery Initiatives at Improving Patients’ Waiting Times in Clinical Radiology Departments: A Systematic Review

et al. 2014

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“…The sigma level is a representation of the number of defects per million (DPM) in a manufacturing process or service delivery [7]. In the service delivery area, a sigma level between 1 and 2 is considered [8], which represents between 691,500 and 308,508 DPM.…”

Section: Sigma Level For the Processmentioning

confidence: 99%

“…Calculate the time in which it is prognosticated that the process will reach 80 % of the maximum learning, for information flow and wait times using Eqs. (7) and (8), respectively. ' 121 days Wait time: PðtÞ TE ¼ 1 À e À 0:014tþ0:66 ð Þ ¼ 0:80 Finding t value: t ' 60 days It is prognosticated that in 121 days, an 80 % of learning level will be reached in the global ID process; for wait times, there are 60 days with a growing positive tendency in both cases.…”

Section: Impact Of the Innovated Processesmentioning

confidence: 99%

Sigma Level Performance of the Innovated Process in the Imaging Department at a Mexican Health Institute

Garcia-Porres

Ortiz-Posadas

2012

J Digit Imaging

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The National Institute of Respiratory Diseases is a third level public hospital in Mexico City, which in 2007 acquired an RIS-PACS to be implemented at its Imaging Department (ID), with the objective to enhance its service. This department attends an average of 3,500 patients per month developing different image modalities. The objective of this work was to determine the overall sigma level performance of four processes of the ID: reception, X-ray, computed tomography, and radiologist diagnosis, considering process analysis and innovation through Six Sigma methodology, measuring the innovation effectiveness by means of indicators and learning curves. Initially, a first measurement (M 1) of the original processes was determined; once 13 innovations were implemented in a pilot program, two more measurements were done, 15 days after (M 2) and 30 days after (M 3), in order to know the impact of the innovations in the ID processes. The initial sigma level of the ID before innovations was σ 1 = 2.0, which means that there were 36 patients per day with a process defect during their stay at the ID. In the two following measurements, σ 2 = 2.2 which means that there were 28 patients per day with a process defect, and σ 3 = 2.3 with 24 patients per day with a process defect. These results demonstrate that the percentage of performance enhancement between the original process and 15 days later was 23 % and 30 days later an enhancement of 15 %. In total, an overall enhancement of 38 % was obtained at the ID of the institute.

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Patient-journey modelling and simulation in computed tomography

Samaranayake

Hayes

Sloan

2015

Business Process Management Journal

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Purpose – The purpose of this paper is to present a business process reengineering (BPR) framework of process and data integration with patient journey as the basis for process evaluation and the improvement of patient-flow. Design/methodology/approach – A BPR framework is developed using a mixed-method research design, which incorporated a case study to demonstrate a healthcare scenario with associated processes and data elements, using process models based on event-driven process chain methodology as well as patient and data models, based on unitary structuring technique. The framework includes key processes including patient booking and rebooking, and associated inputs, outputs, and control parameters. In this case, the framework is demonstrated through application to computed tomography (CT) services in a hospital to improve patient-flow, with numerical simulation of CT data collected over time. Findings – The framework supports flexible patient scheduling and the associated planning of healthcare operations and logistics – this in turn helps to improve patient-flow. Furthermore, mathematical modelling and simulation precisely reveal the impact of booking and rebooking on the performance of the CT department. Research limitations/implications – This innovative framework has potential value for other services, within and beyond the hospital setting. Originality/value – The proposed framework of process modelling, data, and patient journey addresses the lack of a holistic approach to monitoring and evaluating service performance in hospital settings. Patient journey modelling is an integral part of process and data models that can be implemented in an integrated system environment such as an enterprise resource planning system for real-time monitoring of patient-flow under dynamic conditions.

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Overall Sigma Level of an Imaging Department through Process Innovation

Cited by 6 publications

References 4 publications

The Effectiveness of Service Delivery Initiatives at Improving Patients’ Waiting Times in Clinical Radiology Departments: A Systematic Review

The Effectiveness of Service Delivery Initiatives at Improving Patients’ Waiting Times in Clinical Radiology Departments: A Systematic Review

Sigma Level Performance of the Innovated Process in the Imaging Department at a Mexican Health Institute

Patient-journey modelling and simulation in computed tomography

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