scholarly journals Trends, determinants and differences in antibiotic use in 68 residential aged care homes in Australia, 2014–2017: a longitudinal analysis of electronic health record data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
M. Z. Raban ◽  
K. E. Lind ◽  
R. O. Day ◽  
L. Gray ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Antibiotic Use ◽  
Aged Care ◽  
Residential Aged Care ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Annual Prevalence ◽  
Record Data ◽  
Electronic Health ◽  
Restricted Use ◽  
Watch List

Abstract Background Internationally, point prevalence surveys are the main source of antibiotic use data in residential aged care (RAC). Our objective was to describe temporal trends in antibiotic use and antibiotics flagged for restricted use, resident characteristics associated with use, and variation in use by RAC home, using electronic health record data. Methods We conducted a retrospective cohort study of 9793 unique residents aged ≥65 years in 68 RAC homes between September 2014 and September 2017, using electronic health records. We modelled the primary outcome of days of antibiotic therapy /1000 resident days (DOT/1000 days), and secondary outcomes of number of courses/1000 days and the annual prevalence of antibiotic use. Antibiotic use was examined for all antibiotics and antibiotics on the World Health Organization’s (WHO) Watch List (i.e. antibiotics flagged for restricted use). Results In 2017, there were 85 DOT/1000 days (99% CI: 79, 92), 8.0 courses/1000 days (99% CI: 7.6, 8.5), and 63.4% (99% CI: 61.9, 65.0) of residents received at least one course of antibiotics. There were 7.7 DOT/1000 days (99% CI: 6.69, 8.77) of antibiotics on the WHO Watch List administered in 2017. Antibiotic use increased annually by 4.09 DOT/1000 days (99% CI: 1.18, 6.99) before adjusting for resident factors, and 3.12 DOT/1000 days (99% CI: − 0.05, 6.29) after adjustment. Annual prevalence of antibiotic use decreased from 68.4% (99% CI: 66.9, 69.9) in 2015 to 63.4% (99% CI: 61.9, 65.0) in 2017, suggesting fewer residents were on antibiotics, but using them for longer. Resident factors associated with higher use were increasing age; chronic respiratory disease; a history of urinary tract infections, and skin and soft tissue infections; but dementia was associated with lower use. RAC home level antibiotic use ranged between 44.0 to 169.2 DOT/1000 days in 2016. Adjusting for resident factors marginally reduced this range (42.6 to 155.5 DOT/1000 days). Conclusions Antibiotic course length and RAC homes with high use should be a focus of antimicrobial stewardship interventions. Practices in RAC homes with low use could inform interventions and warrant further investigation. This study provides a model for using electronic health records as a data source for antibiotic use surveillance in RAC.

scholarly journals Validation of a claims-based algorithm to identify patients with chronic thromboembolic pulmonary hypertension using electronic health record data

2018 ◽  
Vol 9 (1) ◽  
pp. 204589401881477 ◽  
Author(s):  
Simon Teal ◽  
William R. Auger ◽  
Rodney J. Hughes ◽  
Dena Rosen Ramey ◽  
Kelly S. Lewis ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Gold Standard ◽  
Chronic Thromboembolic Pulmonary Hypertension ◽  
Administrative Claims ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Thromboembolic Pulmonary Hypertension ◽  
History Of ◽  
Electronic Health

This study aimed to validate an algorithm developed to identify chronic thromboembolic pulmonary hypertension (CTEPH) among patients with a history of pulmonary embolism. Validation was halted because too few patients had gold-standard evidence of CTEPH in the administrative claims/electronic health records database, suggesting that CTEPH is underdiagnosed.

scholarly journals From screening to ascertainment of the primary outcome using electronic health records: Challenges in the STRIDE trial

2020 ◽  
Vol 17 (4) ◽  
pp. 346-350
Author(s):  
Denise Esserman
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Care Systems ◽  
Electronic Health ◽  
Data Elements

Electronic health record data are a rich resource and can be utilized to answer a wealth of research questions. It is important when using electronic health record data in clinical trials that systems be put in place and vetted prior to enrollment to ensure data elements can be collected consistently across all health care systems. It is often overlooked how something conceptualized on paper (e.g. use of the electronic health record in a study) can be difficult to implement in practice. This article discusses some of the challenges in using electronic health records in the conduct of the STRIDE (Strategies to Reduce Injuries and Develop Confidence in Elders) trial, how we handled those challenges, and the lessons we learned for the conduct of future trials looking to employ the electronic health record.

scholarly journals Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care

2017 ◽  
Vol 25 (3) ◽  
pp. 951-959 ◽  
Author(s):  
Gregor Stiglic ◽  
Primoz Kocbek ◽  
Nino Fijacko ◽  
Aziz Sheikh ◽  
Majda Pajnkihar
Keyword(s):  
Missing Data ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health

The increasing availability of data stored in electronic health records brings substantial opportunities for advancing patient care and population health. This is, however, fundamentally dependant on the completeness and quality of data in these electronic health records. We sought to use electronic health record data to populate a risk prediction model for identifying patients with undiagnosed type 2 diabetes mellitus. We, however, found substantial (up to 90%) amounts of missing data in some healthcare centres. Attempts at imputing for these missing data or using reduced dataset by removing incomplete records resulted in a major deterioration in the performance of the prediction model. This case study illustrates the substantial wasted opportunities resulting from incomplete records by simulation of missing and incomplete records in predictive modelling process. Government and professional bodies need to prioritise efforts to address these data shortcomings in order to ensure that electronic health record data are maximally exploited for patient and population benefit.

scholarly journals Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality

2022 ◽  
Author(s):  
Anna E. Schorer ◽  
Richard Moldwin ◽  
Jacob Koskimaki ◽  
Elmer V. Bernstam ◽  
Neeta K. Venepalli ◽  
...  
Keyword(s):  
Quality Measures ◽  
Health Technology ◽  
Structured Data ◽  
Malignant Neoplasms ◽  
Incentive Payment ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Record Data ◽  
Electronic Health ◽  
Data Elements

PURPOSE The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.

scholarly journals External Validation of Difficult-to-Treat Resistance Prevalence and Mortality Risk in Gram-Negative Bloodstream Infection Using Electronic Health Record Data From 140 US Hospitals

2019 ◽  
Vol 6 (4) ◽  
Author(s):  
Sameer S Kadri ◽  
Yi Ling (Elaine) Lai ◽  
Emily E Ricotta ◽  
Jeffrey R Strich ◽  
Ahmed Babiker ◽  
...  
Keyword(s):  
Bloodstream Infection ◽  
Mortality Risk ◽  
External Validation ◽  
Acute Illness ◽  
First Line ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Gram Negative ◽  
Record Data ◽  
Electronic Health

Abstract Difficult-to-treat resistance (DTR; ie, co-resistance to all first-line antibiotics) in gram-negative bloodstream infection (GNBSI) is associated with decreased survival in administrative data models. We externally validated DTR prevalence and associated mortality risk in GNBSI using detailed clinical data from electronic health records to adjust for baseline differences in acute illness severity.

scholarly journals The STELAR ICU: Leveraging Electronic Health Record Data to Foster Research and Optimize Patient Care

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 37
Author(s):  
Christopher Horvat ◽  
Srinivasan Suresh ◽  
Robert Clark
Keyword(s):  
Best Practices ◽  
Patient Outcomes ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Data Infrastructure ◽  
Record Data ◽  
Electronic Health ◽  
And Performance

Electronic health records (EHR) combined with robust data collection systems can be used to simultaneously drive research and performance improvement initiatives. Our Smart, Transformative, EHR-based Approaches to Revolutionizing the Intensive Care Unit (STELAR ICU) consists of a framework of five best practices that make optimal use of objective data to guide clinicians caring for the sickest patients in our quaternary center. Our strategy has relied on an accessible data infrastructure, standardizing without protocolizing care, using technology to increase patient contact and time spent at the bedside, continuously re-evaluating performance in real-time, and acknowledging uncertainty by using electronic data to provide probabilistic weight to clinical decision-making. These strategies blur the lines between research and quality improvement, with the aim of achieving truly stellar patient outcomes.

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