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A basic model for assessing primary health care electronic medical record data quality.

ABSTRACT: BACKGROUND:The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. METHODS:Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. RESULTS:A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). CONCLUSIONS:This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

SUBMITTER: Terry AL

PROVIDER: S-EPMC6373085 | biostudies-other | 2019 Feb

REPOSITORIES: biostudies-other

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Project description:ObjectiveEfficient, effective health care requires rapid availability of patient information. We designed, implemented, and assessed the impact of a primary care electronic medical record (EMR) in three rural Kenyan health centers.MethodLocal clinicians identified data required for primary care and public health reporting. We designed paper encounter forms to capture these data in adult medicine, pediatric, and antenatal clinics. Encounter form data were hand-entered into a new primary care module in an existing EMR serving onsite clinics serving patients infected with the human immunodeficiency virus (HIV). Before subsequent visits, Summary Reports were printed containing selected patient data with reminders for needed HIV care. We assessed effects on patient flow and provider work with time-motion studies before implementation and two years later, and we surveyed providers' satisfaction with the EMR.ResultsBetween September 2008 and December 2011, 72 635 primary care patients were registered and 114 480 encounter forms were completed. During 2011, 32 193 unique patients visited primary care clinics, and encounter forms were completed for all visits. Of 1031 (3.2%) who were HIV-infected, 85% received HIV care. Patient clinic time increased from 37 to 81 min/visit after EMR implementation in one health center and 56 to 106 min/visit in the other. However, outpatient visits to both health centers increased by 85%. Three-quarters of increased time was spent waiting. Despite nearly doubling visits, there was no change in clinical officers' work patterns, but the nurses' and the clerks' patient care time decreased after EMR implementation. Providers were generally satisfied with the EMR but desired additional training.ConclusionsWe successfully implemented a primary care EMR in three rural Kenyan health centers. Patient waiting time was dramatically lengthened while the nurses' and the clerks' patient care time decreased. Long-term use of EMRs in such settings will require changes in culture and workflow.

Project description:ImportanceCardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure).ObjectiveTo compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis.Design, setting, and participantsThis cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019.Main outcomes and measuresThe main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models.ResultsA total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score.Conclusions and relevanceEnsuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

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A basic model for assessing primary health care electronic medical record data quality.

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