Hospital Readmissions Following Physician Call System Change: A Comparison of Concentrated and Distributed Schedules

Yarnell, Christopher J.; Shadowitz, Steven; Redelmeier, Donald A.

doi:10.1016/j.amjmed.2016.02.022

Cited by 6 publications

(5 citation statements)

References 65 publications

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“…In the following examples we apply our graphing recommendations to two graphs from ITS studies. These examples were identified from our review of ITS graphs (Section 3), and were selected for their potential to demonstrate many of the graphing recommendations 15,16 . Datasets were extracted from published graphs using WebPlotDigitizer which has been found to be a valid tool for data extraction 17,18 .…”

Section: Application Of Graphing Recommendationsmentioning

confidence: 99%

“…An ITS study examined the impact of a physician call system change on hospital readmissions in Sunnybrook Health Sciences Centre in Toronto, Canada 16 . The intervention involved changing how hospital admissions were distributed to inpatient physician teams.…”

Section: Application Of Graphing Recommendationsmentioning

confidence: 99%

“…These examples were identified from our review of ITS graphs (Section 3), and were selected for their potential to demonstrate many of the graphing recommendations. 15,16 Datasets were extracted from published graphs using WebPlotDigitizer which has been found to be a valid tool for data extraction. 17,18 Note that while our analytical methods may differ to those applied in the original publications, this is inconsequential for our purpose of demonstrating the impact of applying the graphing recommendations.…”

Section: Application Of Graphing Recommendationsmentioning

confidence: 99%

“…An ITS study examined the impact of a physician call system change on hospital readmissions in Sunnybrook Health Sciences Centre in Toronto, Canada. 16 The intervention involved changing how hospital admissions were distributed to inpatient physician teams. Before the system change admissions were concentrated to a single team during a given 24-hour period, after the change, admissions were distributed over all teams each day.…”

Section: Examplementioning

confidence: 99%

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Creating effective interrupted time series graphs: Review and recommendations

Turner

Karahalios

Forbes

et al. 2020

Research Synthesis Methods

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Introduction: Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short-and long-term impact of an interruption. Further, wellconstructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta-analyses. Aim: We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results: Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013-2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion: We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re-use of the data in systematic reviews and metaanalyses.

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Section: Application Of Graphing Recommendationsmentioning

confidence: 99%

Section: Application Of Graphing Recommendationsmentioning

confidence: 99%

Section: Application Of Graphing Recommendationsmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

See 2 more Smart Citations

Creating effective interrupted time series graphs: Review and recommendations

Turner

Karahalios

Forbes

et al. 2020

Research Synthesis Methods

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“…We agree, and this was our rationale for determining diagnosis syndromes for each patient. 1 Overall, the increases in readmissions were observed across all diagnoses and accentuated for patients with stroke or delirium (Figure). This means that hospital admissions may also be influenced by other determinants of health.Arunachalam also wonders about elective readmissions.…”

mentioning

confidence: 94%

The Reply

Yarnell

Shadowitz

Redelmeier

2017

The American Journal of Medicine

Self Cite

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Arunachalam suggests that describing the reasons for readmissions might help in determining how to improve patient care. We agree, and this was our rationale for determining diagnosis syndromes for each patient. 1 Overall, the increases in readmissions were observed across all diagnoses and accentuated for patients with stroke or delirium (Figure). This means that hospital admissions may also be influenced by other determinants of health.Arunachalam also wonders about elective readmissions. This is an important issue, and our reason for analyzing only admissions and readmissions through the emergency department. Virtually all patients arrived through the emergency department in our setting, and the few elective readmissions were unlikely to have changed substantially during the study. Of course, elective admissions are naturally influenced by physician schedules and availability.We agree with Arunachalam's last point that readmission can mean a blend of issues including disease progression, medication side effects, iatrogenic errors, and home supports. However, our study also underscores the beguiling nature of faulty personal beliefs (including our own!). As responsible clinicians, we ourselves had missed the increasing numbers of readmissions in our own patients and only became aware after rigorous statistical analysis.

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