Potential impact of changes in administrative database coding methodology on research and policy decisions: an example f
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CORRESPONDENCE
Potential impact of changes in administrative database coding methodology on research and policy decisions: an example from the Ontario Health Insurance Plan Ana P. Johnson, PhD . Brian Milne, MD . Marlo Whitehead, MSc . Jianfeng Xu, PhD . Joel L. Parlow, MD
Received: 1 October 2019 / Revised: 2 October 2019 / Accepted: 2 October 2019 Ó Canadian Anesthesiologists’ Society 2019
To the Editor, Reliable and complete data are needed for monitoring and evaluating the provision of healthcare services, physician and hospital remuneration, and service planning.1 In the Canadian province of Ontario, the Institute for Clinical Evaluative Sciences (ICES) houses healthcare administrative databases obtained from physician and hospital billing records from a single-payer public health insurance physician reimbursement system, the Ontario Health Insurance Plan (OHIP). These databases may be linked, using unique patient identifiers, to a number of other databases. Such databases, containing data points such as procedures, diagnostic codes, as well as costing and resource utilization, are often used for research purposes that may subsequently inform clinical care and policy decisions. Within the medical specialties, a vast array of data related to the field of anesthesiology and perioperative medicine are accessible.2,3 Nevertheless, researchers need
A. P. Johnson, PhD Institute for Clinical Evaluative Sciences (ICES) Queen’s, Queen’s University, Kingston, ON, Canada Department of Public Health Sciences, Queen’s University, Kingston, ON, Canada B. Milne, MD J. L. Parlow, MD (&) Department of Anesthesiology and Perioperative Medicine, Queen’s University, Kingston, ON, Canada e-mail: [email protected] M. Whitehead, MSc J. Xu, PhD Institute for Clinical Evaluative Sciences (ICES) Queen’s, Queen’s University, Kingston, ON, Canada
to be wary of the impact that administrative changes in the way data are coded and retrieved may have on the interpretation of results. For example, following the conversion of the International Classification of Diseases (ICD) diagnostic codes ICD-9 to the ICD-10, the sensitivity of comorbidity detection was variable for a period of six months to three years, as coders adapted to the new coding system in Canada and other countries.4 Another example relates to the field of pediatric anesthesia, where OHIP provides incentives to physicians in the form of age premiums, recognizing the unique skills needed in managing the pediatric population. Here, specific codes are added to physician remuneration related to age categories. Up until June 2008, these codes needed to be specified by the anesthesia provider in addition to the pertinent procedure and time codes. In June 2008, OHIP instituted a change in the billing system, whereby pediatric premium codes (aside from premature newborns) were automated rather than billed separately.5 Unfortunately, the methodology of this automation prevented the codes from being identified in ICES administrative data. Our group sought to identify the volume
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