Addressing Missing Data in Patient‐Reported Outcome Measures (PROMS): Implications for the Use of PROMS for Comparing Provider Performance

Abstract

Patient‐reported outcome measures (PROMs) are now routinely collected in the English National Health Service and used to compare and reward hospital performance within a high‐powered pay‐for‐performance scheme. However, PROMs are prone to missing data. For example, hospitals often fail to administer the pre‐operative questionnaire at hospital admission, or patients may refuse to participate or fail to return their post‐operative questionnaire. A key concern with missing PROMs is that the individuals with complete information tend to be an unrepresentative sample of patients within each provider and inferences based on the complete cases will be misleading. This study proposes a strategy for addressing missing data in the English PROM survey using multiple imputation techniques and investigates its impact on assessing provider performance. We find that inferences about relative provider performance are sensitive to the assumptions made about the reasons for the missing data. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.

Choice of hospital: Which type of quality matters?

“…The HSCIC case-mix adjustment methodology takes into account a range of patient characteristics including age, sex, pre-operative PROM score, socio-economic status, comorbidity burden, whether the patient lives alone as well as other indicators of disability (Department of Health, 2012). The hospital-specific mean scores have been found to be robust to missing data (Gomes et al., 2015). They are also unlikely to be affected by survivorship bias since less than 0.1% of patients die during the six-month follow-up.…”

Comparing the performance of English mental health providers in achieving patient outcomes

“…There is some evidence to suggest that routine use of outcome measures in mental health care is associated with aspects of provider performance (Slade et al, 2006). In acute care, inferences about relative provider performance are sensitive to the assumptions made about the reasons for missing data on PROMs (Gomes, Gutacker, Bojke, & Street, 2015). While assumptions regarding the missing data mechanism can be ascertained from the data under study, these assumptions cannot be definitively verified from the observed data (Bartlett & Carpenter, 2013).…”

Elective hospital admissions: secondary data analysis and modelling with an emphasis on policies to moderate growth

“…Gomes et al 149 explore the consequences of alternative assumptions about these data and propose a strategy for addressing them. Although we acknowledge the importance of missing data, we use complete-case analysis here.…”