FASStR: a framework for ensuring high-quality operational metrics in health care.

OBJECTIVES: Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations. We sought to develop a unifying framework that could be used by administrators, practitioners, and investigators to help define and document operational performance measures that are comparable and reproducible. STUDY DESIGN: Retrospective analysis. METHODS: Health care operations and clinical investigators used an iterative process consisting of (1) literature review, (2) expert assessment and collaborative design, and (3) end-user feedback. We sampled the literature from the medical, health systems research, and health care operations (business and engineering) disciplines to assemble a representative sample of studies in which outpatient health care performance metrics were used to describe the primary or secondary outcome of the research. RESULTS: We identified 2 primary deficiencies in outpatient performance metric definitions: incompletion and inconsistency. From our review of performance metrics, we propose the FASStR framework for the Focus, Activity, Statistic, Scale type, and Reference dimensions of a performance metric. The FASStR framework is a method by which performance metrics can be developed and examined from a multidimensional perspective to evaluate their comprehensiveness and clarity. The framework was tested and revised in an iterative process with both practitioners and investigators. CONCLUSIONS: The FASStR framework can guide the design, development, and implementation of operational metrics in outpatient health care settings. Further, this framework can assist investigators in the evaluation of the metrics that they are using. Overall, the FASStR framework can result in clearer, more consistent use and evaluation of outpatient performance metrics.

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

This study investigates the relation of the incidence of georeferenced tweets related to respiratory illness to the incidence of influenza-like illness (ILI) in the emergency department (ED) and urgent care clinics (UCCs) of a large pediatric hospital. We collected (1) tweets in English originating in our hospital's primary service area between 11/1/2014 and 5/1/2015 and containing one or more specific terms related to respiratory illness and (2) the daily number of patients presenting to our hospital's EDs and UCCs with ILI, as captured by ICD-9 codes. A Support Vector Machine classifier was applied to the set of tweets to remove those unlikely to be related to ILI. Time series of the pooled set of remaining tweets involving any term, of tweets involving individual terms, and of the ICD-9 data were constructed, and temporal cross-correlation between the social media and clinical data was computed. A statistically significant correlation (Spearman ρ = 0.23) between tweets involving the term flu and ED and UCC volume related to ILI 11 days in the future was observed. Tweets involving the terms coughing (Spearman ρ = 0.24) and headache (Spearman ρ = 0.19) individually were also significantly correlated to ILI-related clinical volume four and two days in the future, respectively. In the 2014-2015 cold and flu season, the incidence of local tweets containing the terms flu, coughing, and headache were early indicators of the incidence of ILI-related cases presenting to EDs and UCCs at our children's hospital.

Applying the Chronic Care Model to Improve Care and Outcomes at a Pediatric Medical Center.

BACKGROUND: Cincinnati Children's Hospital Medical Center launched the Condition Outcomes Improvement Initiative in 2012 to help disease-based teams use the principles of improvement science to implement components of the Chronic Care Model and improve outpatient care delivery for populations of children with chronic and complex conditions. The goal was to improve outcomes by 20% from baseline. METHODS: Initiative activities included review of the evidence to choose and measure outcomes, development of condition-specific patient registries and tools for data collection, patient stratification, planning and coordinating care before and after visits, and self-management support. RESULTS: Eighteen condition teams, in sequenced cohorts, fully participated in the three-year initiative. As of October 1, 2015, data from 27,221 active patients with chronic conditions were entered into registries within the electronic health record and being used to inform quality improvement and population management. Overall, 13,601 of these children had an improved outcome. Seven of the teams had implemented their evidence-based interventions with ≥ 90% reliability, 83% of teams were regularly using an electronic template to plan care for a child's condition before an encounter, 89% had stratified their population by severity of medical/psychosocial needs, 56% were using registry care gap data for population management, and 72% were doing self-management assessments. Eleven teams achieved the numeric goal of 20% improvement in their chosen outcome. CONCLUSION: The results suggest that, by implementing quality improvement methods with multidisciplinary support, clinical teams can manage chronic condition populations and improve clinical, functional, and patient-reported outcomes. This work continues to be spread across the institution.

Improving Discharge Efficiency in Medically Complex Pediatric Patients.

BACKGROUND AND OBJECTIVE: Children with medical complexity have unique needs when facilitating transitions from hospital to home. Defining readiness for discharge is challenging, and preparation requires coordination of family, education, equipment, and medications. Our multidisciplinary team aimed to increase the percentage of medically complex hospital medicine patients discharged within 2 hours of meeting medical discharge goals from 50% to 80%. METHODS: We used quality improvement methods to identify key drivers and inform interventions. Medical discharge goals were defined on admission for each patient. Interventions included implementation of a complex care inpatient team with electronic admission order set, weekly care coordination rounds, needs assessment tool, and medication pathway. The primary measure, percentage of patients discharged within 2 hours of meeting medical discharge goals, was followed on a run chart. The secondary measures, pre- and post-intervention length of stay and 30-day readmission rate, were compared by using Wilcoxon rank-sum and χ(2) tests, respectively. RESULTS: The percentage of medically complex patients discharged within 2 hours of meeting medical discharge goals improved from 50% to 88% over 17 months and sustained for 6 months. In preintervention-postintervention comparison, median length of stay did not change (3.1 days [interquartile range, 1.8-7.0] vs 2.9 days [interquartile range, 1.7-6.1]; P = .67) and 30-day readmission rate was not impacted (30.7% vs 26.4%; P = .51). CONCLUSIONS: Efficient discharge for medically complex patients requires support of a multidisciplinary team to proactively address discharge needs, ensuring patients are ready for discharge when medical goals are met.

Using quality improvement to optimise paediatric discharge efficiency.

BACKGROUND: Bed capacity management is a critical issue facing hospital administrators, and inefficient discharges impact patient flow throughout the hospital. National recommendations include a focus on providing care that is timely and efficient, but a lack of standardised discharge criteria at our institution contributed to unpredictable discharge timing and lengthy delays. Our objective was to increase the percentage of Hospital Medicine patients discharged within 2 h of meeting criteria from 42% to 80%. METHODS: A multidisciplinary team collaborated to develop medically appropriate discharge criteria for 11 common inpatient diagnoses. Discharge criteria were embedded into electronic medical record (EMR) order sets at admission and could be modified throughout a patient's stay. Nurses placed an EMR time-stamp to signal when patients met all discharge goals. Strategies to improve discharge timeliness emphasised completion of discharge tasks prior to meeting criteria. Interventions focused on buy-in from key team members, pharmacy process redesign, subspecialty consult timeliness and feedback to frontline staff. A P statistical process control chart assessed the impact of interventions over time. Length of stay (LOS) and readmission rates before and after implementation of process measures were compared using the Wilcoxon rank-sum test. RESULTS: The percentage of patients discharged within 2 h significantly improved from 42% to 80% within 18 months. Patients studied had a decrease in median overall LOS (from 1.56 to 1.44 days; p=0.01), without an increase in readmission rates (4.60% to 4.21%; p=0.24). The 12-month rolling average census for the study units increased from 36.4 to 42.9, representing an 18% increase in occupancy. CONCLUSIONS: Through standardising discharge goals and implementation of high-reliability interventions, we reduced LOS without increasing readmission rates.