Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects

Hagiwara, Magnus Andersson; Gäre, Boel Andersson; Elg, Mattias

doi:10.1097/ncq.0000000000000130

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…A significant percentage (25%, N=6) of the methods papers provided a comparative analysis of existing ITS methods where empirical analysis and/or simulated data were used to compare performance of the different methods. 21,[34][35][36][37][38]…”

Section: Review Of Statistical Methods For Its Analysismentioning

confidence: 99%

“…Six of the studies focused on comparative evaluation of statistical methods used in the analysis of ITS studies. 21,[34][35][36][37][38] Shardell et al (2007) performed empirical evaluation to compare the 2 group tests, SLR analysis and time series (ARIMA and ARMA) analysis in terms of the characteristics, assumptions, strengths and limitations of the methods. The authors used data from a study conducted with the objective of evaluating the impact of a hospital-based intervention to reduce antimicrobial infection rates and overall length of stay.…”

Section: Papers On Methodological Comparisonsmentioning

confidence: 99%

“…Based on their analysis of the empirical data, the authors concluded that SLR analysis was more statistically robust than SPC analysis in comparing the effectiveness of different interventions, while the SPC was more appropriate for controlling a process since it is relatively easy to conduct and interpret. 34 Kontopantelis et al (2015) compared different regression modeling approaches for ITS analysis, based on the level of complexity in their implementation, which they labeled as basic, advanced and expert levels. The authors provided a description of the method processes, the assumptions made, and the technical details of each modeling technique using secondary data obtained from a quality and outcomes framework performance research.…”

Section: Papers On Methodological Comparisonsmentioning

confidence: 99%

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<p>Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review</p>

Ewusie¹,

Soobiah²,

Blondal³

et al. 2020

JMDH

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Objective: Interrupted time series (ITS) designs are robust quasi-experimental designs commonly used to evaluate the impact of interventions and programs implemented in healthcare settings. This scoping review aims to 1) identify and summarize existing methods used in the analysis of ITS studies conducted in health research, 2) elucidate their strengths and limitations, 3) describe their applications in health research and 4) identify any methodological gaps and challenges. Design: Scoping review. Data Sources: Searches were conducted in MEDLINE, JSTOR, PUBMED, EMBASE, CINAHL, Web of Science and the Cochrane Library from inception until September 2017. Study Selection: Studies in health research involving ITS methods or reporting on the application of ITS designs. Data Extraction: Screening of studies was completed independently and in duplicate by two reviewers. One reviewer extracted the data from relevant studies in consultations with a second reviewer. Results of the review were presented with respect to methodological and application areas, and data were summarized using descriptive statistics. Results: A total of 1389 articles were included, of which 98.27% (N=1365) were application papers. Segmented linear regression was the most commonly used method (26%, N=360). A small percentage (1.73%, N=24) were methods papers, of which 11 described either the development of novel methods or improvement of existing methods, 7 adapted methods from other areas of statistics, while 6 provided comparative assessment of conventional ITS methods. Conclusion: A significantly increasing trend in ITS use over time is observed, where its application in health research almost tripled within the last decade. Several statistical methods are available for analyzing ITS data. Researchers should consider the types of data and validate the required assumptions for the various methods. There is a significant methodological gap in ITS analysis involving aggregated data, where analyses involving such data did not account for heterogeneity across patients and hospital settings.

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Section: Review Of Statistical Methods For Its Analysismentioning

confidence: 99%

Section: Papers On Methodological Comparisonsmentioning

confidence: 99%

Section: Papers On Methodological Comparisonsmentioning

confidence: 99%

See 1 more Smart Citation

<p>Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review</p>

Ewusie¹,

Soobiah²,

Blondal³

et al. 2020

JMDH

View full text Add to dashboard Cite

show abstract

“…18 We chose this method because we sought generalizable knowledge from our quasi-experimental design. 19 In addition, this approach provided statistical details on secular trends in the pre-and post-intervention periods. We tested for serial autocorrelation using the Breusch-Godfrey test.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Let Them Speak for Themselves: Improving Adolescent Self-Report Rate on Pre-Visit Screening

Grout

Aalsma

Downs

2019

Academic Pediatrics

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BackgroundAdolescent pre-visit screening on patient-generated health data is a common and efficient practice to guide clinical decision making. However, proxy informants (e.g., parents or caregivers) often complete these forms, which may lead to incorrect information or lack of confidentiality. Our objective was to improve the adolescent self-report rate on pre-visit screening. MethodsWe conducted an interventional study using an interrupted time-series design to compare adolescent self-report rates (percent of adolescents ages 12-18 years completing their own previsit screening) over 16 months in general pediatric ambulatory clinics. We collected data using a computerized clinical decision support system with waiting room electronic tablet screening. Pre-intervention rates were low, and we created and implemented two electronic workflow alerts, one each to the patient/caregiver and clinical staff, reminding them that the adolescent should

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“…But the sample size used, and the quality of data joined to the statistical tools that we have used, we think that they are enough to consider adequate our conclusions. The best statistical methodology to analyse the temporal trend changes and the association between variables in historic cohorts is the ITS 26 , 27 in a first approach and the multivariable analyses to avoid confusion on variables in the prognosis analyses. 28 , 29 …”

Section: Discussionmentioning

confidence: 99%

Impact of a clinician-to-clinician electronic consultation in heart failure patients with previous hospital admissions

Garcia-Vega,

Mazón-Ramos,

Portela-Romero

et al. 2023

European Heart Journal - Digital Health

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Aims To evaluate the impact of an outpatient care management programme that includes a clinician-to-clinician e-consultation on delay time in care, hospital admissions, and mortality in a high-risk group of patients with heart failure (HF) and previous episodes of HF hospitalization (HFH). Methods and results We selected 6444 HF patients who visited the cardiology service at least once between 2010 and 2021. Of these, 4851 were attended in e-consult, and 2230 had previous HFH. Using an interrupted time series regression model, we analysed the impact of incorporating e-consult into the healthcare model in the group of patients with HFH and evaluated the elapsed time to cardiology care, HF, cardiovascular (CV), and all-cause hospital admissions and mortality, calculating the incidence relative risk (iRR). In the group of patients with HFH, the introduction of e-consult substantially decreased waiting times to cardiology care (8.6 [8.7] vs. 55.4 [79.9] days, P < 0.001). In that group of patients, after e-consult implantation, hospital admissions for HF were reduced (iRR [95%CI]: 0.837 [0.840–0.833]), 0.900 [0.862–0.949] for CV and 0.699 [0.678–0.726] for all-cause hospitalizations. There was also lower mortality (iRR [95%CI]: 0.715 [0.657–0.798] due to HF, 0.737 [0.764–0.706] for CV and 0.687 [0.652–0.718] for all-cause). The improved outcomes after e-consultation implementation were significantly higher in the group of patients with previous HFH. Conclusion In patients with HFH, an outpatient care programme that includes an e-consult significantly reduced waiting times to cardiology care and was safe, with a lower rate of hospital admissions and mortality in the first year.

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Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects

Cited by 10 publications

References 14 publications

<p>Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review</p>

<p>Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review</p>

Let Them Speak for Themselves: Improving Adolescent Self-Report Rate on Pre-Visit Screening

Impact of a clinician-to-clinician electronic consultation in heart failure patients with previous hospital admissions

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