An analysis of missing items in real-world electronic patient reported outcomes data: implications for clinical care

Support Care Cancer. 2020 Nov;28(11):5099-5107. doi: 10.1007/s00520-020-05338-8. Epub 2020 Feb 10.

Abstract

Purpose: Utilization of electronic patient-reported outcomes (ePROs) in the clinic can improve quality of life and prolong survival in cancer care. However, there remain unanswered questions regarding trends in missing data and the potential effect on real-time patient care.

Methods: This study utilized a prospectively collected dataset of ePROs from oncology clinics that administered the Patient Care Monitor 2.0 (PCM), a validated symptoms survey assessing 78 items for men, and 86 for women. We tabulated the frequency of missing items, by item and domain (emotional, functional and physical symptom-related), and examined these by age, gender, education, race and marital status.

Results: Within 20,986 encounters, there were responses to at least 1 PCM item from 6933 unique patients. The highest frequency of missing answers occurred for: "attend a paid job" (10.7%), "reduced sexual enjoyment" (3.8%), and "run" (3.7%). By domain, 12.3% of functional, 8.4% of physical symptom-related, and 1.6% of emotional constructs contained at least one missing item. For functional and physical symptom-related items, missingness was most common in patients >60 years old.

Conclusion: The frequency of missingness was highest for functional items, like attending a paid job, suggesting that some respondents (e.g., retirees without a paid job) skipped questions that were less applicable to them. More universal issues for cancer patients, such as emotional well-being, had much lower frequencies of missingness. This suggests differential item completion that warrants further study to understand the inherent drivers. Identifying causes of missingness could improve the clinical utility of ePROs and highlight opportunities to personalize care.

Keywords: Oncology; Patient-reported outcomes; Quality of life; Supportive care.

MeSH terms

  • Adult
  • Data Collection / methods
  • Data Interpretation, Statistical*
  • Databases, Factual
  • Electronic Health Records*
  • Female
  • Humans
  • Male
  • Mental Health
  • Middle Aged
  • Neoplasms / diagnosis*
  • Neoplasms / psychology
  • Neoplasms / therapy*
  • Patient Reported Outcome Measures*
  • Quality of Life
  • Surveys and Questionnaires