Electronic health record data use in the assessment of quality indicators for glucocorticoid osteoporosis screening in systemic lupus erythematosus.


Journal

Lupus
ISSN: 1477-0962
Titre abrégé: Lupus
Pays: England
ID NLM: 9204265

Informations de publication

Date de publication:
Oct 2022
Historique:
pubmed: 22 7 2022
medline: 20 10 2022
entrez: 21 7 2022
Statut: ppublish

Résumé

To determine whether electronic health record (EHR) data components could be identified and used to assess bone health quality indicators in patients with systemic lupus erythematosus as a foundation for population health management. We identified patients in our EHR system who had diagnosis codes for lupus from 2012 to 2017 and characterized them based on the frequency and dosage of prescribed glucocorticoid medications. The medical records of patients who received repeated high-dose glucocorticoid orders were further reviewed for osteoporosis, osteoporotic fractures, receipt of appropriate preventive screening, and orders for protective medications based on established quality indicators. Descriptive statistics were calculated to summarize results. We identified 617 patients with a lupus diagnosis; 414 received glucocorticoid prescriptions, 189 received chronic, high-dose; and 83 received chronic, low-dose prescription orders. Of those with chronic high-dose glucocorticoid prescriptions, 14% had an osteoporosis diagnosis, 3% had an osteoporotic fracture, 51% received a prescription for calcium/vitamin D, 43% had bone mineral density screening orders, 20% received a spine radiograph order, 29% had a documented T-score, 12% received a prescription for osteoporosis medication, and 6% had a documented osteoporosis screening. We were able to identify data elements in the EHR for all nine components of the osteoporosis management quality indicator. It is possible to identify data in the EHR for all attributes of the quality indicator for osteoporosis in lupus patients who receive chronic high-dose glucocorticoids. However, missing data and need to extract data from text-based notes may make development of population management tools challenging.

Identifiants

pubmed: 35861184
doi: 10.1177/09612033221116726
pmc: PMC9588551
mid: NIHMS1823927
doi:

Substances chimiques

Glucocorticoids 0
Vitamin D 1406-16-2
Calcium SY7Q814VUP

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1516-1522

Subventions

Organisme : NIAMS NIH HHS
ID : P30 AR072579
Pays : United States
Organisme : NIAMS NIH HHS
ID : R21 AR072263
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001422
Pays : United States

Références

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Auteurs

Danette Ko (D)

Division of Rheumatology, Department of Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Noah Forrest (N)

Center for Health Information Partnerships, Institute for Public Health and Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Quan Mai (Q)

Division of Rheumatology, Department of Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Anna Pawlowski (A)

Division of Rheumatology, Department of Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Kate Balsley (K)

Center for Health Information Partnerships, Institute for Public Health and Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Anh Chung (A)

Division of Rheumatology, Department of Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Rosalind Ramsey-Goldman (R)

Division of Rheumatology, Department of Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Theresa L Walunas (TL)

Center for Health Information Partnerships, Institute for Public Health and Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Division of General Internal Medicine, Department of Medicine, 12244Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

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Classifications MeSH