Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects

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

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.

Collaborative to Increase Lethal Means Counseling for Caregivers of Youth With Suicidality

BACKGROUND

The number of youth presenting to hospitals with suicidality and/or self-harm has increased substantially in recent years. We implemented a multihospital quality improvement (QI) collaborative from February 1, 2018 to January 31, 2019, aiming for an absolute increase in hospitals' mean rate of caregiver lethal means counseling (LMC) of 10 percentage points (from a baseline mean performance of 68% to 78%) by the end of the collaborative, and to evaluate the effectiveness of the collaborative on LMC, adjusting for secular trends.

METHODS

This 8 hospital collaborative used a structured process of alternating learning sessions and action periods to improve LMC across hospitals. Electronic medical record documentation of caregiver LMC was evaluated during 3 phases: precollaborative, active QI collaborative, and postcollaborative. We used statistical process control to evaluate changes in LMC monthly. Following collaborative completion, interrupted time series analyses were used to evaluate changes in the level and trend and slope of LMC, adjusting for covariates.

RESULTS

In the study, 4208 children and adolescents were included-1314 (31.2%) precollaborative, 1335 (31.7%) during the active QI collaborative, and 1559 (37.0%) postcollaborative. Statistical process control analyses demonstrated that LMC increased from a hospital-level mean of 68% precollaborative to 75% (February 2018) and then 86% (October 2018) during the collaborative. In interrupted time series analyses, there were no significant differences in LMC during and following the collaborative beyond those expected based on pre-collaborative trends.

CONCLUSIONS

LMC increased during the collaborative, but the increase did not exceed expected trends. Interventions developed by participating hospitals may be beneficial to others aiming to improve LMC for caregivers of hospitalized youth with suicidality.

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

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.

The Reducing Opioid Use in Children with Clefts Protocol: A Multidisciplinary Quality Improvement Effort to Reduce Perioperative Opioid Use in Patients Undergoing Cleft Surgery

BACKGROUND

Cleft repair requires multiple operations from infancy through adolescence, with repeated exposure to opioids and their associated risks. The authors implemented a quality improvement project to reduce perioperative opioid exposure in their cleft lip/palate population.

METHODS

After identifying key drivers of perioperative opioid administration, quality improvement interventions were developed to address these key drivers and reduce postoperative opioid administration from 0.30 mg/kg of morphine equivalents to 0.20 mg/kg of morphine equivalents. Data were retrospectively collected from January 1, 2015, until initiation of the quality improvement project (May 1, 2017), tracked over the 6-month quality improvement study period, and the subsequent 14 months. Metrics included morphine equivalents of opioids received during admission, administration of intraoperative nerve blocks, adherence to revised electronic medical record order sets, length of stay, and pain scores.

RESULTS

The final sample included 624 patients. Before implementation (n =354), children received an average of 0.30 mg/kg of morphine equivalents postoperatively. After implementation (n = 270), children received an average of 0.14 mg/kg of morphine equivalents postoperatively (p < 0.001) without increased length of stay (28.3 versus 28.7 hours; p = 0.719) or pain at less than 6 hours (1.78 versus 1.74; p = 0.626) or more than 6 hours postoperatively (1.50 versus 1.49; p = 0.924).

CONCLUSIONS

Perioperative opioid administration after cleft repair can be reduced in a relatively short period by identifying key drivers and addressing perioperative education, standardization of intraoperative pain control, and postoperative prioritization of nonopioid medications and nonpharmacologic pain control. The authors' quality improvement framework has promise for adaptation in future efforts to reduce opioid use in other surgical patient populations.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, III.

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

BACKGROUND

Adolescent pre-visit screening on patient-generated health data is a common and efficient practice to guide clinical decision making. However, proxy informants (eg, 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.

METHODS

We 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 pre-visit 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. Preintervention rates were low, and we created and implemented 2 electronic workflow alerts, one each to the patient/caregiver and clinical staff, reminding them that the adolescent should answer the questions independently. We included the first encounter from each adolescent and evaluated changes in adolescent self-reporting between pre- and postintervention periods using interrupted time series analysis.

RESULTS

Patients or caregivers completed 2670 qualifying pre-visit screenings across 19 preintervention, 7 intervention, and 44 postintervention weeks. Self-reporting by younger adolescents nearly doubled, with a significant increase of 19.3 percentage points (confidence interval [CI], 9.1-29.5) from the baseline 20.5%. Among older adolescents, the stable baseline rate of 53.6% increased by 9.2 absolute percentage points (CI, -7.0 to 25.3). There were no significant pre- or postintervention secular trends.

CONCLUSIONS

Two automated alerts directing clinic personnel and families to have adolescents self-report significantly and sustainably improved younger adolescent self-reporting on electronic patient-generated health data instruments.

Magic Mirror, on the Wall-Which Is the Right Study Design of Them All?-Part I

The assessment of a new or existing treatment or intervention typically answers 1 of 3 research-related questions: (1) "Can it work?" (efficacy); (2) "Does it work?" (effectiveness); and (3) "Is it worth it?" (efficiency or cost-effectiveness). There are a number of study designs that on a situational basis are appropriate to apply in conducting research. These study designs are classified as experimental, quasi-experimental, or observational, with observational studies being further divided into descriptive and analytic categories. This first of a 2-part statistical tutorial reviews these 3 salient research questions and describes a subset of the most common types of experimental and quasi-experimental study design. Attention is focused on the strengths and weaknesses of each study design to assist in choosing which is appropriate for a given study objective and hypothesis as well as the particular study setting and available resources and data. Specific studies and papers are highlighted as examples of a well-chosen, clearly stated, and properly executed study design type.