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Rationale: Cardiac monitoring has often been identified as an area of overutilization and remains a limited resource in many hospitals. With the aim of reducing telemetry overuse, we added clinical decision support to our health system's telemetry order with guidance on appropriate indications for monitoring. The new order requires selection of an appropriate clinical indication. Aims and Objectives: In this study, we aimed to understand provider engagement with this tool by assessing concordance between selected indications within the order and the clinical presence of those conditions as documented within the patient chart. Methods: We randomly selected 100 telemetry orders from July to October 2022 across four different hospitals at NYU Langone Health. Two independent, blinded reviewers used a structured protocol to identify documentation of actual indications for telemetry in each selected chart. We calculated the rate of concordance between indications selected in the order and indications that were determined to be clinically present on chart review. Results: There were 30,839 telemetry orders placed during the study timeframe. Overall concordance between the selection within the order and the actual indication was 48% (95% confidence interval [CI], 38.21%−57.79%). We observed especially low concordance rates for vague indications, such as 'Other', and for 'Confirmed Stroke', which was the only indication allowing for indefinite telemetry. Conclusion: The overall low concordance suggests a disconnect between the support tool and clinical practice. Providers are more likely to select an indication that reduces downstream work regardless of a patient's true clinical indication. [ABSTRACT FROM AUTHOR]

Objectives: To identify ICD-10-CM diagnostic codes associated with the social determinants of health (SDOH), determine frequency of use of the code for homelessness across time, and examine the frequency of interrupted periods of Medicaid eligibility (ie, Medicaid churn) for beneficiaries with and without this code.Design: Retrospective data analyses of New York State (NYS) Medicaid claims data for years 2006-2017 to determine reliable indicators of SDOH hypothesized to affect Medicaid churn, and for years 2016-2017 to examine frequency of Medicaid churn among patients with and without an indicator for homelessness.Main Outcome Measures: Any interruption in the eligibility for Medicaid insurance (Medicaid churn), assessed via client identification numbers (CIN) for continuity.Methods: Analyses were conducted to assess the frequency of use and pattern of New York State Medicaid claims submission for SDOH codes. Analyses were conducted for Medicaid claims submitted for years 2016-2017 for Medicaid patients with and without a homeless code (ie, ICD-10-CM Z59.0) in 2017.Results: ICD-9-CM / ICD-10-CM codes for lack of housing / homelessness demonstrated linear reliability over time (ie, for years 2006-2017) with increased usage. In 2016-2017, 22.9% of New York Medicaid patients with a homelessness code in 2017 experienced at least one interruption of Medicaid eligibility, while 18.8% of Medicaid patients without a homelessness code experienced Medicaid churn.Conclusions: Medicaid policies would do well to take into consideration the barriers to continued enrollment for the Medicaid population. Measures ought to be enacted to reduce Medicaid churn, especially for individuals experiencing homelessness. [ABSTRACT FROM AUTHOR]

Background: Hospital care on weekends has been associated with delays in care, reduced quality, and poor clinical outcomes.Objective: The purpose of this study was to evaluate the impact of a weekend hospital intervention on processes of care and clinical outcomes. The multifaceted intervention included expanded weekend diagnostic services, improved weekend discharge processes, and increased physician and care management services on weekends.Design and Patients: This was an interrupted time series observational study of adult non-obstetric patients hospitalized at a single academic medical center between January 2011 and January 2014. The study included 18 months prior to and 19 months following the implementation of the intervention. Data were analyzed using segmented regression analysis with adjustment for confounders.Main Measures: The primary outcome was average length of stay. Secondary outcomes included percent of patients discharged on weekends, 30-day readmission rate, and in-hospital mortality rate.Key Results: The study included 57,163 hospitalizations. Following implementation of the intervention, average length of stay decreased by 13 % (95 % CI 10-15 %) and continued to decrease by 1 % (95 % CI 1-2 %) per month as compared to the underlying time trend. The proportion of weekend discharges increased by 12 % (95 % CI 2-22 %) at the time of the intervention and continued to increase by 2 % (95 % CI 1-3 %) per month thereafter. The intervention had no impact on readmissions or mortality. During the post-implementation period, the hospital was evacuated and closed for 2 months due to damage from Hurricane Sandy, and a new hospital-wide electronic health record was introduced. The contributions of these events to our findings are not known. We observed a lower inpatient census and found differences in patient characteristics, including higher rates of Medicaid insurance and comorbidities, in the post-Hurricane Sandy period as compared to the pre-Sandy period.Conclusions: The intervention was associated with a reduction in length of stay and an increase in weekend discharges. Our longitudinal study also illuminated the challenges of evaluating the effectiveness of a large-scale intervention in a real-world hospital setting. [ABSTRACT FROM AUTHOR]