An Exploratory Case Study to Understand Primary Care Users and Their Data Quality Tradeoffs

Abstract

Primary care data is an important part of the evolving healthcare ecosystem. Generally, users in primary care are expected to provide excellent patient care and record high-quality data. In practice, users must balance sets of priorities regarding care and data. The goal of this study was to understand data quality tradeoffs between timeliness, validity, completeness, and use among primary care users. As a case study, data quality measures and metrics are developed through a focus group session with managers. After calculating and extracting measurements of data quality from six years of historic data, each measure was modeled with logit binomial regression to show correlations, characterize tradeoffs, and investigate data quality interactions. Measures and correlations for completeness, use, and timeliness were calculated for 196,967 patient encounters. Based on the analysis, there was a positive relationship between validity and completeness, and a negative relationship between timeliness and use. Use of data and reductions in entry delay were positively associated with completeness and validity. Our results suggest that if users are not provided with sufficient time to record data as part of their regular workflow, they will prioritize spending available time with patients. As a measurement of a primary care system's effectiveness, the negative correlation between use and timeliness points to a self-reinforcing relationship that provides users with little external value. In the future, additional data can be generated from comparable organizations to test several new hypotheses about primary care users.

Applying Persuasive Design Techniques to Change Data Entry Behaviour in Primary Care (Preprint)

“…Facilitating these processes would be a good use of PD. In a previous study, there was a positive relationship between use and completeness for the reporting tool [30], suggesting that improving use would have positives impacts on data within the reporting tool.…”

“…These measures were developed collaboratively with the FHT during a previous study of the same system [19]. Each measure is defined in Table 2.…”

“…In a previous study of the same reporting system [19], several data quality benchmarks were developed based on the historical analysis of records over several years. Based on data from 2012 to 2015, the study found that 97.4% of the records were valid (e.g., logically consistent) and that 21.7% of the records were considered complete (e.g., users had entered all the necessary information).…”

“…During previous work, benchmarks were developed as a baseline of data quality, and the improvement of these measures represented measurable enhancements to data quality. Each measure was developed collaboration with stakeholders and managers at the FHT [19].…”

Applying Persuasive Design Techniques to Influence Data-Entry Behaviors in Primary Care: Repeated Measures Evaluation Using Statistical Process Control

“…In a previous study [ 18 ], several data quality benchmarks were developed based on the historical analysis of entries in a system designed to measure the effectiveness and costs of services. The study found that while 97.4% of the entries were valid (ie, logically consistent), only 21.7% of the entries were considered complete (ie, users had entered all the necessary information).…”