30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis

Abstract

BackgroundThe Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator.Methods and FindingsThree diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013.ConclusionsSince 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved.

Associations between work satisfaction, engagement and 7-day patient mortality: a cross-sectional survey

“…In the event where a patient admission involved more than one hospital, the patient mortality probability was split between the two hospitals according to the time the patient spent at each hospital in order to reduce potential bias. The management and analysis methods of the Norwegian survival data are described in great detail by Hassani et al 31 32. This study used risk-adjusted patient data from the FHI for patients admitted to hospitals with AMI, stroke and hip fracture.…”

The Rates of LSS Surgery in Norwegian Public Hospitals

“…Hospital patient administrative data were retrieved from a national database at the Norwegian Knowledge Centre for the Health Services. A detailed description of the methods employed for data collection is published elsewhere 21…”

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture

“…Estimated hospital effects were compared to a reference value, defined as the 10% trimmed mean of the hospital effects (on the logistic scale) [ 7 ]. A detailed description of algorithms and methods can be found in [ 5 ]. Eventually, the hospitals were stratified according to 30-day mortality status (hereafter 30D status): low (L30D) if the hospital effect was significantly different from and at least log (1.2) ≈ 0.18 below the reference, high (H30D) mortality if significantly different from and at least log (1.2) above, and medium (M30D) for the remaining hospitals.…”

“…The Norwegian Institute for Public Health reports one overall indicator (based on diagnosis groups leading to 80% of all deaths within 30 days) and three condition-specific indicators; i.e. first time acute myocardial infarction (AMI), stroke and hip fracture [ 5 – 8 ] [ 9 ]. Hospitals with case-mix adjusted mortality, defined as the probability of (all-cause) death within 30 days of hospitalization, that is significantly higher or lower compared to the (trimmed) hospital mean are identified for each indicator, and the outlier hospitals are reported.…”