Literature DB >> 25830615

Pocket change: a simple educational intervention increases hospitalist documentation of comorbidities and improves hospital quality performance measures.

Rachel Sparks1, Alex H Salskov, Anita S Chang, Kelly L Wentworth, Pritha P Gupta, Thomas O Staiger, Bradley D Anawalt.   

Abstract

BACKGROUND: Complete documentation of patient comorbidities in the medical record is important for clinical care, hospital reimbursement, and quality performance measures. We designed a pocket card reminder and brief educational intervention aimed at hospitalists with the goal of improving documentation of 6 common comorbidities present on admission: coagulation abnormalities, metastatic cancer, anemia, fluid and electrolyte abnormalities, malnutrition, and obesity.
METHODS: Two internal medicine inpatient teams led by 10 hospitalist physicians at an academic medical center received the educational intervention and pocket card reminder (n = 520 admissions). Two internal medicine teams led by nonhospitalist physicians served as a control group (n = 590 admissions). Levels of documentation of 6 common comorbidities, expected length of stay, and expected mortality were measured at baseline and during the 9-month study period.
RESULTS: The intervention was associated with increased documentation of anemia, fluid and electrolyte abnormalities, malnutrition, and obesity in the intervention group, both compared to baseline and compared to the control group during the study period. The expected length of stay increased in the intervention group during the study period.
CONCLUSIONS: A simple educational intervention and pocket card reminder were associated with improved documentation and hospital quality measures at an academic medical center.

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Year:  2015        PMID: 25830615     DOI: 10.1097/QMH.0000000000000052

Source DB:  PubMed          Journal:  Qual Manag Health Care        ISSN: 1063-8628            Impact factor:   0.926


  1 in total

1.  Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.

Authors:  Ayis Pyrros; Adam Eugene Flanders; Jorge Mario Rodríguez-Fernández; Andrew Chen; Patrick Cole; Daniel Wenzke; Eric Hart; Samuel Harford; Jeanne Horowitz; Paul Nikolaidis; Nadir Muzaffar; Viveka Boddipalli; Jai Nebhrajani; Nasir Siddiqui; Melinda Willis; Houshang Darabi; Oluwasanmi Koyejo; William Galanter
Journal:  Acad Radiol       Date:  2021-05-21       Impact factor: 3.173

  1 in total

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