Reducing Unnecessary Respiratory Viral Testing to Promote High-Value Care

BACKGROUND AND OBJECTIVES: Viral respiratory infections are common in children, and practice guidelines do not recommend routine testing for typical viral illnesses. Despite results often not impacting care, nasopharyngeal swabs for viral testing are frequently performed and are an uncomfortable procedure. The aim of this initiative was to decrease unnecessary respiratory viral testing (RVT) in the emergency department (ED) and the pediatric medicine wards (PMWs) by 50% and 25%, respectively, over 36 months. METHODS: An expert panel reviewed published guidelines and appropriate evidence to formulate an RVT pathway using plan-do-study-act cycles. A multifaceted improvement strategy was developed that included implementing 2 newer, more effective tests when testing was deemed necessary; electronic order modifications with force functions; audit and feedback; and education. By using statistical process control charts, the outcomes analyzed were the percentage of RVT ordered in the ED and the rate of RVT ordered on the PMWs. Balancing measures included return visits leading to admission and inpatient viral nosocomial outbreaks. RESULTS: The RVT rate decreased from a mean of 3.0% to 0.5% of ED visits and from 44.3 to 30.1 per 1000 patient days on the PMWs and was sustained throughout the study. Even when accounting for the new rapid influenza test available in the ED, a 50% decrease in overall ED RVT was still achieved without any significant impact on return visits leading to admission or inpatient nosocomial infections. CONCLUSIONS: Through implementation of a standardized, electronically integrated RVT pathway, a decrease in unnecessary RVT was successfully achieved. Audit and feedback, reminders, and biannual education all supported long-term sustainability of this initiative.

Quality improvement methodology can reduce hospitalisation for abscess management

BackgroundAbscesses are a common reason for ED visits. While many are drained in the ED, some require drainage in the operating room (OR). We observed that a higher percentage of patients at our institution in Columbus, Ohio, were admitted to the hospital with abscesses for incision and drainage (I&D) in the OR than other institutions, including paediatric institutions. Our aim was to decrease hospitalisations for abscess management.MethodsA multidisciplinary team convened to decrease hospitalisation for patients with abscesses and completed multiple ‘Plan-Do-Study-Act’ cycles, including increasing I&Ds performed in the ED. Other interventions included implementation of a clinical pathway, training of procedure technicians (PT), updating the electronic medical record (EMR), credentialing advanced practice nurses in sedation and individual follow-up with providers for admitted patients. Data were analysed using statistical process control charts. Gross average charges were assessed.ResultsAdmissions for I&D decreased from 26.3% to 13.7%. Abscess drainage in the ED improved from 79.3% to 96.5%. Mean length of stay decreased from 19.5 to 11.5 hours for all patients. Patients sedated increased from 3.3% to 18.2%. The number of repeat I&Ds within 30 days decreased from 4.3% to 1.7%.ConclusionWe decreased hospitalisations for abscess I&D by using quality improvement methodology. The most influential intervention was an initiative to increase I&Ds performed in the ED. Additional interventions included expanded training of PTs, implementation of a clinical pathway, updating the EMR, improving interdepartmental communication and increasing sedation providers.

Statistical Tests of Type I Error

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of sampling error or chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis, that there is no difference between compared phases, and accepting the alternative one, that true differences exist. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed, and examples are provided to illustrate each. The method for transforming autocorrelated data and merging data sets is discussed further in the context of utilizing transformed data sets to test of Type 1 error.

Analyzing Group Data

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis and accepting the alternative one. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed. The method for transforming autocorrelated data and merging data sets is discussed. Once new data sets are created using the Append() function, they can be tested for Type I error using the techniques discussed in the chapter.

Overview of SSDforR Functions

In this chapter, readers are given step-by-step instructions on how to access the software necessary to use SSD for R. They are also presented with a brief overview of the capabilities of the SSD for R package. These include basic graphing functions, descriptive statistics, many effect size functions, autocorrelation, regression, statistical process control charts, hypothesis testing, and functions associated with analyzing group data. In combination, R, RStudio, and SSD for R, all of which are freely available, provide a robust way to analyze single-system research data. This chapter demonstrates how to download the necessary software and provides an overview of the visual and statistical capability available with SSD for R.

Improving Hospital Infant Safe Sleep Compliance by Using Safety Prevention Bundle Methodology

BACKGROUND AND OBJECTIVES Sudden unexpected infant death often results from unsafe sleep environments and is the leading cause of postneonatal mortality in the United States. Standardization of infant sleep environment education has been revealed to impact such deaths. This standardized approach is similar to safety prevention bundles typically used to monitor and improve health outcomes, such as those related to hospital-acquired conditions (HACs). We sought to use the HAC model to measure and improve adherence to safe sleep guidelines in an entire children’s hospital. METHODS A hospital-wide safe sleep bundle was implemented on September 15, 2017. A safe sleep performance improvement team met monthly to review data and discuss ideas for improvement through the use of iterative plan-do-study-act cycles. Audits were performed monthly from March 2017 to October 2019 and monitored safe sleep parameters. Adherence was measured and reviewed through the use of statistical process control charts (p-charts). RESULTS Overall compliance improved from 9% to 72%. Head of bed flat increased from 62% to 93%, sleep space free of extra items increased from 52% to 81%, and caregiver education completed increased from 10% to 84%. The centerline for infant in supine position remained stable at 81%. CONCLUSIONS Using an HAC bundle safety prevention model to improve adherence to infant safe sleep guidelines is a feasible and effective method to improve the sleep environment for infants in all areas of a children’s hospital.

Fidelity and the impact of patient safety huddles on teamwork and safety culture: an evaluation of the Huddle Up for Safer Healthcare (HUSH) project

Background The Patient Safety Huddle (PSH) is a brief multidisciplinary daily meeting held to discuss threats to patient safety and actions to mitigate risk. Despite growing interest and application of huddles as a mechanism for improving safety, evidence of their impact remains limited. There is also variation in how huddles are conceived and implemented with insufficient focus on their fidelity (the extent to which delivered as planned) and potential ways in which they might influence outcomes. The Huddle Up for Safer Healthcare (HUSH) project attempted to scale up the implementation of patient safety huddles (PSHs) in five hospitals – 92 wards - across three UK NHS Trusts. This paper aims to assess their fidelity, time to embed, and impact on teamwork and safety culture. Methods A multi-method Developmental Evaluation was conducted. The Stages of Implementation Checklist (SIC) was used to determine time taken to embed PSHs. Observations were used to check embedded status and fidelity of PSH. A Teamwork and Safety Climate survey (TSC) was administered at two time-points: pre- and post-embedding. Changes in TSC scores were calculated for Trusts, job role and clinical speciality. Results Observations confirmed PSHs were embedded in 64 wards. Mean fidelity score was 4.9/9. PSHs frequently demonstrated a ‘fear free’ space while Statistical Process Control charts and historical harms were routinely omitted. Analysis showed a positive change for the majority (26/27) of TSC questions and the overall safety grade of the ward. Conclusions PSHs are feasible and effective for improving teamwork and safety culture, especially for nurses. PSH fidelity criteria may need adjusting to include factors deemed most useful by frontline staff. Future work should examine inter-disciplinary and role-based differences in TSC outcomes.

Improving Healthcare Value: Effectiveness of a Program to Reduce Laboratory Testing for Non-Critically-Ill Patients With COVID-19

Avoiding routine, repetitive inpatient laboratory testing is a Choosing Wisely® recommendation, with benefits that may be even more pronounced in the setting of the COVID-19 pandemic, considering the need to limit unnecessary exposure, use of personal protective equipment, and laboratory resources. However, the COVID-19 pandemic presented a unique challenge: how to efficiently develop and standardize care for a disease process that had yet to be fully characterized. This article describes the development of a local committee to critically review evidence-based practices, reach consensus, and guide practice patterns, with the aim of delivering high-value care. Following the local introduction of recommendations and electronic health record order sets, non-critically-ill COVID-19 patients at our hospital had more inpatient days where they did not receive laboratory tests, achieving sustained special cause variation on statistical process control charts. The principles of Choosing Wisely® can be applied even within novel and rapidly evolving situations.