Hospital factors associated with clinical data quality


Related Publications

Smolina K, Weymann D, Morgan S, Ross C, Carleton B. Association between regulatory advisories and codeine prescribing to postpartum women. Journal of the American Medical Association. 2015;313(18):1861-2.
Suter E, Misfeldt R, Mallinson S, Wilhelm A, Boakye O, Marchildon G, et al. Comparative Review of the Policy Landscape of Team-based Primary Health Care Service Delivery in Western Canada. Alberta Health Services; 2014.
Laberge M, Pang J, Walker K, Wong ST, Hogg W, Wodchis WP. QUALICOPC (Quality and Costs of Primary Care) Canada: A focus on the aspects of primary care most highly rated by current patients of primary care practices. Ottawa, ON: Canadian Foundation for Healthcare Improvement; 2014.
McGregor MJ, Abu-Laban RB, Ronald L, McGrail KM, Andrusiek D, Baumbusch J, et al. Nursing Home Characteristics Associated with Resident Transfers to Emergency Department. Canadian Journal on Aging. 2012;33(1):38-48.

Publication Topics

Hospital factors associated with clinical data quality

Publication TypeJournal Article
Year of Publication2009
AuthorsSutherland JM, Steinum O
JournalHealth policy (Amsterdam, Netherlands)Health Policy
Pages321 - 326
Date Published2009
KeywordsAdult, Aged, Aged, 80 and over, Chronic Disease, Cohort Studies, Data Collection/standards, Diagnosis-Related Groups/standards, Female, Hospitals, Humans, Male, Medical Record Linkage, Middle Aged, Ontario, Retrospective Studies
AbstractOBJECTIVES: As chronic conditions affect the evaluation, treatment, and possible clinical outcomes of patients, accurate reporting of chronic diseases into the patient record is expected. In some countries, the reported magnitude of comorbidity inaccuracy and incompleteness is compelling. Beyond incentives provided in payment systems, the role and significance of other factors that contribute to inaccurate and incomplete reporting of chronic conditions is not well understood. A complementary approach that identifies factors associated with inaccurate and incomplete data is proposed. METHODS: In a two-step process, the method links hospitalizations of patients who are repeatedly hospitalized over a determined period and identifies characteristics associated with accurate and complete reporting of chronic conditions. These methods leverage the high prevalence of chronic conditions amongst patients with multiple hospitalizations. The study is based on retrospective analysis of longitudinal hospital discharge data from a cohort of Ontario (Canada) patients. RESULTS: There are a multitude of factors associated with incomplete clinical data reporting. Patients discharged from community or small hospitals, discharged alive, or transferred to another acute inpatient hospital tend to have less complete comorbidity reporting. For some chronic diseases, very old age affects chronic disease reporting. CONCLUSIONS: Longitudinally analyzing chronically ill patients is a novel approach to identifying incompletely reported clinical data. Using these results, coding quality initiatives can be focused in a directed manner.
Citation Key490