Improving the Accuracy of Length of stay Risk Adjustment Models using Linked Data IJPDS (2017) Issue 1, Vol 1:360 Proceedings of the IPDLN Conference (August 2016)

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Tolulope Sajobi
Mingshan Lu
Jason Jiang
Hude Quan
Published online: Apr 19, 2017


Hospital length of stay (LOS) is a widely used measure for assessing cross-jurisdiction health system performance and informs resource allocation decisions. However, the accuracy of existing LOS risk adjustment models are limited, because they are mostly derived from administrative data, which mostly contain clinical/diagnostic information but lack detailed information on relevant demographic, socio-economic (SES), and self-reported health-related quality of life (HRQOL) risk factors, which have been shown to improve the accuracy of LOS risk adjustment models. The study investigates the relative contribution of demographic, socio-economic, and health status risk factors derived through data linkage in improving the accuracy of LOS risk adjustment models.

Population-based data on 8000 individuals hospitalized for coronary heart disease were obtained from Alberta Provincial Project on Outcomes Assessment in Coronary Heart Disease (APPROACH) registry and linked to Alberta Discharge Abstract Database (DAD). SES was measured using multi-domain measure of SES derived from area-level census information, while the health-related quality of life outcome was measured using the Seattle Angina Questionnaire. LOS risk adjustment model based on hierarchical logistic regression models was developed to assess relative impact of each SES measure and HRQOL measure improving the predictive accuracy of LOS adjustment models. The relative impact of each predictor was assessed by its adjusted odds ratio (OR) and improvement over the predictive accuracy of a reference model that included patients’ clinical risk factors only. 

More than 80% of the hospitalized individuals had prolonged LOS more than 10 days. The HRQOL and single-domain measures of SES had significant impact in accurately predicting LOS. But the inclusion of the multi-domain measure SES did not significantly improve the accuracy of LOS risk adjustment models

Using large population-based Canadian data, our study suggests that the inclusion of patients’ SES and health status information through data linkage can improve the accuracy of LOS risk adjustment models. The development of more accurate risk adjustment models can aid the identification of individuals at risk of prolonged LOS and comparison of health system performance across several cross-jurisdictions.


Changes in physician reimbursement policies may hinder the collection of billing claims in administrative databases. Various provincial academic alternative payment programs (APPs) use incentive- or punitive-based tools to motivate physicians to submit billing claims called shadow billings; however, these incentives are not well documented in the literature. We conducted a nation-wide survey and semi-structured face-to-face interviews in Alberta, Canada to determine existing policies and guidelines for incentivizing and promoting physician billing practices.


Mail and online surveys were sent out to academic department head physicians in the following provinces: British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, New Brunswick, Prince Edward Island and Newfoundland and Labrador. Face-to-face interviews were conducted in the province of Alberta with managers, government stakeholders, and physicians/administrators from academic APPs and Fee-for-Service plans. Face-to-face interviews and responses by mail and email submission were summarized using content analysis grouped by question type.


In total, there were 46 respondents (15 interviews, 26 mail/online). Content analysis revealed three primary perspectives, grouped at the level of individual physician, academic, and government. Across all of these unique perspectives, three primary themes emerged: 1) governance; 2) accountability; and 3) funding. Within these themes, findings were categorized as either (a) instruments or tools to promote physician billing in AAPPs; (b) enabling factors to support physician billing in AAPPs; and, (c) constraining factors impeding physician billing in AAPPs.


According to the majority of our respondents, financial disincentives (i.e. income at risk, financial clawbacks) appear to be most effective as a mechanism to motivate physicians within an academic APP to submit their billings. However, key barriers to successful implementation and delivery of academic APPs include a lack of alignment between government stakeholders, academic leadership and APP physician members and differences in the organizational and accountability structures of APP plans between academic facilities. It is necessary in moving forward to achieve commonly defined standards and frameworks between the various APP models across provinces and academic institutions.

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