Patient-Centered Insights: Using Health Care Complaints to Reveal Hot Spots and Blind Spots in Quality and Safety

Abstract

Policy Points: Health care complaints contain valuable data on quality and safety; however, there is no reliable method of analysis to unlock their potential. We demonstrate a method to analyze health care complaints that provides reliable insights on hot spots (where harm and near misses occur) and blind spots (before admissions, after discharge, systemic and low-level problems, and errors of omission). Systematic analysis of health care complaints can improve quality and safety by providing patient-centered insights that localize issues and shed light on difficult-to-monitor problems.

Context: The use of health care complaints to improve quality and safety has been limited by a lack of reliable analysis tools and uncertainty about the insights that can be obtained. The Healthcare Complaints Analysis Tool, which we developed, was used to analyze a benchmark national data set, conceptualize a systematic analysis, and identify the added value of complaint data.

Methods: We analyzed 1,110 health care complaints from across England. "Hot spots" were identified by mapping reported harm and near misses onto stages of care and underlying problems. "Blind spots" concerning difficult-to-monitor aspects of care were analyzed by examining access and discharge problems, systemic problems, and errors of omission.

Findings: The tool showed moderate to excellent reliability. There were 1.87 problems per complaint (32% clinical, 32% relationships, and 34% management). Twenty-three percent of problems entailed major or catastrophic harm, with significant regional variation (17%-31%). Hot spots of serious harm were safety problems during examination, quality problems on the ward, and institutional problems during admission and discharge. Near misses occurred at all stages of care, with patients and family members often being involved in error detection and recovery. Complaints shed light on 3 blind spots: (1) problems arising when entering and exiting the health care system; (2) systemic failures pertaining to multiple distributed and often low-level problems; and (3) errors of omission, especially failure to acknowledge and listen to patients raising concerns.

Conclusions: The analysis of health care complaints reveals valuable and uniquely patient-centered insights on quality and safety. Hot spots of harm and near misses provide an alternative data source on adverse events and critical incidents. Analysis of entry-exit, systemic, and omission problems provides insight on blind spots that may otherwise be difficult to monitor. Benchmark data and analysis scripts are downloadable as supplementary files.

Keywords: health care complaints; patient participation; patient safety; patient-centered care; risk management.

Figure 1

Hierarchical Coding Framework of Domains, Problems, and Severity Indicators

Figure 2

Association Between Harm and Stages of Care^{a a}The area of tiles is proportional to the number of problems within the cross categorization. Tile text reports the number of problems as a percent of all problems. Text did not fit in some small tiles. Tile border indicates more (solid) and fewer (dotted) observations than expected. Tile shading indicates chi‐square test residuals less than 2 (white, approximating p > .05), between 2 and 4 (light shading, approximating p < .05), and greater than 4 (dark shading, approximating p < .0001). [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

Association Between Problem Type and Stages of Care for Problems With Major and Catastrophic Harm^{a a}The area of tiles is proportional to the number of problems within the cross categorization. Tile text reports the number of problems as a percent of all problems. Text did not fit in some small tiles. Tile border indicates more (solid) and fewer (dotted) observations than expected. Tile shading indicates chi‐square test residuals less than 2 (white, approximating p > .05), between 2 and 4 (light shading, approximating p < .05), and greater than 4 (dark shading, approximating p < .0001). [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 4

Association Between Problem Type and Stages of Care for Near Misses^{a a}The area of tiles is proportional to the number of problems within the cross categorization. Tile text reports the number of problems as a percent of all problems. Text did not fit in some small tiles. Tile border indicates more (solid) and fewer (dotted) observations than expected. Tile shading indicates chi‐square test residuals larger than 2, approximating .05 significance. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 5

Association Between Harm and Number of Reported Problems^{a a}The area of tiles is proportional to the number of problems within the cross categorization. Tile text reports the number of problems as a percent of all problems. Text did not fit in some small tiles. Tile border indicates more (solid) and fewer (dotted) observations than expected. Tile shading indicates chi‐square test residuals larger than 2, approximating .05 significance. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 6

Association Between Problem Type and the Order That These Problems Are Reported Within Complaints^{a a}The area of tiles is proportional to the number of problems within the cross categorization. Tile text reports the number of problems as a percent of all problems. Text did not fit in some small tiles. Tile border indicates more (solid) and fewer (dotted) observations than expected. Tile shading indicates chi‐square test residuals larger than 2, approximating .05 significance. [Color figure can be viewed at http://wileyonlinelibrary.com]