Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

Giori, Nicholas J.; Radin, John W.; Callahan, Alison; Fries, Jason; Halilaj, Eni; Ré, Christopher; Delp, Scott L.; Shah, Nigam H.; Harris, Alex H. S.

doi:10.1001/jamanetworkopen.2021.1728

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…The accuracy of EHR data extraction has previously been shown for example, in joint implant registries. 43 The limitation of EHR extraction method is that physicians may make EHR entries in different ways, or some information may not be found in the EHR at all. This source of bias was minimized by extracting data of a random sample of patients over a long period of time, from different physicians, and from a long follow-up period.…”

Section: Discussionmentioning

confidence: 99%

“…The data showed that extracting EHR data from the selected variables worked well in this type of study. The accuracy of EHR data extraction has previously been shown for example, in joint implant registries 43 . The limitation of EHR extraction method is that physicians may make EHR entries in different ways, or some information may not be found in the EHR at all.…”

Section: Discussionmentioning

confidence: 99%

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The relative proportion of comorbidities among rhinitis and rhinosinusitis patients and their impact on visit burden

Nuutinen

Lyly

Virkkula

et al. 2022

Clinical & Translational All

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Background The aim was to evaluate the relative proportion of Non‐steroidal anti‐inflammatory drug exacerbated respiratory disease (NERD) and other comorbidities, and their impact on the burden of outpatient visits due to allergic rhinitis (AR), non‐allergic rhinitis (NAR), acute rhinosinusitis (ARS), and chronic rhinosinusitis with nasal polyps (CRSwNP) and without (CRSsNP). Methods We used hospital registry data of a random sample of 5080 rhinitis/rhinosinusitis patients diagnosed during 2005–2019. International Statistical Classification of Diseases and Related Health Problems (ICD10) diagnoses, visits, and other factors were collected from electronic health records by using information extraction and data processing methods. Cox's proportional hazards model was used for modeling the time to the next outpatient visit. Results The mean (±standard deviation) age of the population was 33.6 (±20.7) years and 56.1% were female. The relative proportion of AR, NAR, ARS, CRSsNP and CRSwNP, were 33.5%, 27.5%, 27.2%, 20.7%, and 10.9%, respectively. The most common other comorbidities were asthma (44.4%), other chronic respiratory diseases (38.5%), musculoskeletal diseases (38.4%), and cardiovascular diseases (35.7%). Non‐steroidal anti‐inflammatory drug exacerbated respiratory disease existed in 3.9% of all patients, and 17.7% of the CRSwNP group. The relative proportion of subjects having 1, 2, 3 and ≥ 4 other diseases were 18.0%, 17.6%, 17.0%, 37.0%, respectively. All diseases except AR, ARS, and mouth breathing, were associated with a high frequency of outpatient visits. Conclusions Our results revealed a high relative proportion of NERD and other comorbidities, which affect the burden of outpatient visits and hence confirm the socioeconomic impact of upper airway diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The relative proportion of comorbidities among rhinitis and rhinosinusitis patients and their impact on visit burden

Nuutinen

Lyly

Virkkula

et al. 2022

Clinical & Translational All

View full text Add to dashboard Cite

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“…A descriptive analysis of the main characteristics was performed: age, sex, length of stay, surgery act (replacement, repositioning, change, or reconstruction), surgery motif (arthrosis, fracture, cancer, infection, osteonecrosis or other main diagnosis) and device provided (head, stem, shell, or other) following Giori et al [6]. This descriptive analysis specifically specified the source of each of these data and missing data.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, the classic manual constitution of a cohort is a long expensive process requiring a high workload for the teams. Moreover, a dynamic link between patients and outcomes could allow real-time updated surveillance [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Pilot Study of an e-Cohort to Monitor Adverse Event for Patient with Hip Prostheses from Clinical Data Warehouse

Dhalluin

Ansoborlo

Rosset

et al. 2021

Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era

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Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Automating monitoring from clinical data warehouses is an opportunity to dynamically monitor devices and patient outcomes allowing improve clinical practices. Our objective was to assess quantitative and qualitative concordance between claim data and device supply data in order to create an e-cohort of patients undergoing a hip replacement. We performed a single-centre cohort pilot study, from one clinical data warehouse of a French University Hospital, from January 1, 2010 to December 31, 2019. We included all adult patients undergoing a hip arthroplasty, and with at least one hip medical device provided. Patients younger than 18 years or opposed to the reuse of their data were excluded from the analysis. Our primary outcome was the percentage of hospital stays with both hip arthroplasty and hip device provided. The patient and stay characteristics assessed in this study were: age, sex, length of stay, surgery procedure (replacement, repositioning, change, or reconstruction), medical motif for surgery (osteoarthritis, fracture, cancer, infection, or other) and device provided (head, stem, shell, or other). We found 3,380 stays and 2,934 patients, 96.4% of them had both a hip surgery procedure and a hip device provided. These data from different sources are close enough to be integrated in a common clinical data warehouse.

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“…We completed a VHA pilot project to determine whether data in the VHA CDW are complete, accurate, and extractable enough to build a meaningful Level-I joint replacement registry 14 . By evaluating CDW data from 2000 to 2017, we found that the number of primary and revision total hip arthroplasties (THAs) performed in a VHA facility and recorded in the CDW increased steadily from 2000 to 2014, and then stabilized at about 4,000 to 4,500 per year.…”

Section: Use Of the Cdw To Develop A Vha Total Hip Arthroplasty Registrymentioning

confidence: 99%

Large Surgical Databases with Direct Data Abstraction: VASQIP and ACS-NSQIP

Habermann

Harris

Giori

2022

Journal of Bone and Joint Surgery

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Direct data abstraction from a patient's chart by experienced medical professional data abstractors has been the historical gold standard for quality and accuracy in clinical medical research. The limiting challenge to population-wide studies for quality and public health purposes is the high personnel costs associated with very large-scale efforts of this type. Two historically related programs that are at least partially able to successfully circumvent this problem and provide high-quality data relating to surgical procedures and the early postoperative period are reviewed in this article. Both utilize similar data abstraction efforts by specially trained and qualified medical abstractors of a sample subset of the total procedures performed at participating hospitals.The Veterans Affairs Surgical Quality Improvement Program (VASQIP), detailed by Nicholas J. Giori, MD, PhD, in the first section of this article, makes use of trained abstractors and has undergone recent additions and updates, including the development of an associated total hip registry for the VA system. The data elements and data integrity provided by both of these programs establish important benchmarks for other "big data" efforts, which often attempt to use alternative less-expensive methods of data collection in order to achieve more widespread or even nationwide data collection.In the second section, Elizabeth B. Habermann, PhD, MPH, provides a detailed review of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), the data elements collected, and examples of the range of quality improvement and outcomes studies in orthopaedic surgery that it has made possible, along with information on data that have not been collected and the resulting limitations. The ACS NSQIP was actually modeled after the very similar earlier effort started by the United States Department of Veterans Affairs (VA). VA DatabaseT he Veterans Health Administration (VHA) is a health-care system, and increasingly a health-care insurer, that is run by the United States Department of Veterans Affairs. There are 17.4 million veterans in the United States. Of these, >9 million are served each year by the VHA. There are 170 VHA medical centers and 1,074 VHA outpatient facilities. Among the 170 VHA medical centers, 138 provide surgical services. These are located in 48 states, the District of Columbia, and Puerto Rico. Ninety-eight of the surgical facilities perform total joint arthroplasty 1 . Corporate Data WarehouseThe VHA was a pioneer in the use of electronic health records (EHRs) 2 . The shift to EHRs began in the 1980s. There was a steady shift of data and functionality to the electronic format that continued over subsequent decades. A graphical user interface that was introduced in 1997 increased adoption and ease of use. The VHA health information is now stored in the Corporate Data Warehouse (CDW) 3 , the central repository of all VHA health data, including a relational database of patient, encounter, procedure, pharmacy, and other...

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Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

Cited by 10 publications

References 15 publications

The relative proportion of comorbidities among rhinitis and rhinosinusitis patients and their impact on visit burden

The relative proportion of comorbidities among rhinitis and rhinosinusitis patients and their impact on visit burden

Pilot Study of an e-Cohort to Monitor Adverse Event for Patient with Hip Prostheses from Clinical Data Warehouse

Large Surgical Databases with Direct Data Abstraction: VASQIP and ACS-NSQIP

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