Electronic Health Record Data Quality Research Articles

Suitability of Electronic Health Record Data for Computational Phenotyping of Diabetes Mellitus at Nairobi Hospital, Nairobi City County, Kenya

This research aims to determine the applicability of routine healthcare in clinical informatics research. One of the key areas of research in precision medicine is computational phenotyping from longitudinal Electronic Health Record (EHR) data. The objective of this research was to determine how the interplay of EHR software design, the use of a data dictionary, the process of data collection, and the training and motivation of the human resource involved in the collection and entry of data into the EHR affect the quality of EHR data thus the suitability of such data for utility in computational phenotyping of diabetes mellitus. This research employed a prospective/retrospective study design at the diabetes clinic in Nairobi Hospital. The first source of data was from interviews with 32 staff; nurses, doctors, and health record officers using a referenced peer-reviewed usability questionnaire. Thereafter, a sample of EHR data collected during routine care between January 2012 and December 2016 was also analyzed by looking into the quality of clusters identified in the data using a density-based clustering algorithm and Statistical Package for Social Sciences (SPSS) version 21. Regression analysis shows that software design and the utility of a data dictionary explained 50.7% and 32.3% respectively in the improvement of the suitability of EHR data for computational phenotyping of diabetes mellitus. Also, EHR software was rated useful (82%) in accomplishing users’ daily tasks. However, EHR data were found to be unsuitable for utility in computational phenotyping of diabetes. Despite the fact that 88% of EHR data were clustered as noise, the clustering algorithm identified a total of 23 clusters from the diabetes dataset. However, with improved quality of EHR data, sub-phenotyping tasks would be achievable. This research concludes that the poor quality of EHR data is a result of employees’ unmet intrinsic factors of motivation.

Cohort selection for construction of a clinical natural language processing corpus

In Electronic Health Record (EHR) systems, key patient information is often captured in the form of unstructured clinical notes. The information from these notes can be extracted using Clinical Natural Language Processing (NLP). Training corpus is a key factor in development of efficient clinical NLP models. Clinical NLP corpus construction is complex and multifaceted. There are several challenges in corpus construction, but one challenge often not researched well is cohort selection aspect. In this study, we present methods employed and challenges encountered in cohort selection for construction of a clinical NLP corpus. In specific we present our methods in selection of cancer pathology reports to construct a corpus for automatic deidentification. 2100 pathology reports were extracted from 1833 (518 male and 1313 female) cancer patients using Health Level-7 (HL7) message standard. In terms of the age group distribution, the age group 60–70 years was highest with 872 patients. Our findings suggest deciphering the segment information from HL7 messages that are collected from different hospitals is a challenging task. The quality of HL7 messages also varied significantly with inconsistent tags making it difficult to identify reports that meet criteria set a priori. One of key lessons learned is linking the HL7 reports data with additional EMR data such as admissions, would help in identifying high quality reports and resolve duplicates. Also, our findings suggest that, in general the EHR data quality is poor with varying clinical coding and metadata standards between different hospitals. It is vital to identify and address these challenges for development of a high-quality corpus.

Electronic health records in IS research: Quality issues, essential thresholds and remedial actions

The increase in adoption of Electronic Health Record (EHR) systems by healthcare organizations has led to the proliferation of the use of EHR as a secondary data source in both IS and supporting fields. It is imperative that EHR data is exploited appropriately, which would lead to high quality results and enhanced reproducibility. However, the quality of the EHR data being used can vary significantly and can have repercussions for research outcomes. In this paper, we first discuss four major data quality issues present in EHR data. These issues are: (a) non-standard coding schemes, (b) missing data, (c) inconsistencies and (d) aggregation and augmentation of EHR data. Then, we discuss quality thresholds that need to be met in order to avoid the negative impacts of quality issues. Lastly, we discuss some remedial actions that researchers can take to enhance the quality of EHR data to meet the quality thresholds. The discussed issues, thresholds and remedial actions can also apply to a much wider set of data sources when used as secondary data in research.

Electronic Health Record Data Quality Issues Are Not Remedied by Increasing Granularity of Diagnosis Codes

Electronic Health Record Data Quality Issues Are Not Remedied by Increasing Granularity of Diagnosis Codes

Evaluation of interventions to improve inpatient hospital documentation within electronic health records: A Systematic Review

IntroductionDespite increased use of electronic health records (EHRs), EHR documentation quality remains poor. Consequently, EHR data quality is also negatively affected. Many services, including disease surveillance and health services research, utilize EHR data. Accordingly, several studies have attempted to improve EHR documentation quality in the inpatient setting using various interventions.
 Objectives and ApproachThe purpose of this systematic review was to synthesize the literature, and assess the effectiveness of interventions seeking to improve inpatient EHR documentation quality. To identify relevant experimental, quasi-experimental and observational studies, a search strategy was developed based on elaborate inclusion/exclusion criteria using four main themes: EHR, documentation, interventions, and type of study. Four databases, Cochrane, Medline, EMBASE, and CINAHL, were searched. Study quality assessment and data extraction from selected studies were performed using a Downs and Black and Newcastle-Ottawa Scale hybrid tool, and a REDCap form, respectively. Data was then analyzed and synthesized in a narrative semi-quantitative manner.
 ResultsAn in-depth search of the identified databases, grey literature and reference lists, revealed a final 20 studies for inclusion in this systematic review. Due to high heterogeneity in study design, population, interventions, comparators, document types and outcomes, data could not be standardized for a quantitative comparison. However, statistically significant results in interventions and affected outcomes were further presented and discussed. A higher number of studies reported significantly improved EHR documentation when using the interventions: ‘Education’ and ‘Implementing a new EHR Reporting System’. When implementing two or more interventions, more outcome measures were affected. There was no association between study quality or study design and number of interventions used. Only one of the 20 studies found EHR documentation worsened with the interventions used.
 Conclusion/ImplicationsInterventions implemented to enhance EHR documentation are highly variable and require standardization. Emphasis should be placed on this novel area of research to improve communication between healthcare providers, enhance continuity of care, reduce the burden in health information management, and to facilitate data sharing between centers, provinces, and countries.

PRM44 - Evaluation of Electronic Health Record Data Quality to Assess the Clinical Indices of Anemia in Dialysis Patients

To assess the quality and completeness of data in eHRs from an LDO in the US, focusing on demographic and clinical variables. We used Kahn’s framework (Analytic Methods, 2012) to objectively assess data quality from all de-identified patients across 2,900 US dialysis centers in 2015 (~229,000). Specific focus was made on the following quality domains as they related to anemia associated with CKD and its treatment: 1) missing values and anomalies in data, 2) accurate relationships between data tables, 3) temporal relationships, and 4) logical events. Demographic, clinical, and treatment variables included age, sex, hemoglobin (Hb), iron parameters (transferrin saturation [TSAT], ferritin), and erythropoietin stimulating agent (ESA) use were descriptively assessed using means, ranges, counts, and proportions. Results were analyzed with and without lab results that were missing or entered as zero. Low missingness was seen for age (0.08%), sex (0.04%), and dialysis treatment (0%) data. TSAT was recorded for 93% of patients (range 0-100%; mean 30.6%) and ferritin recorded for 93% of patients (range 0-15,743 ng/mL; mean 733 ng/mL). Hb labs were recorded for 96% of the LDO cohort (range 0-46.8 g/dL; mean 10.9 g/dL, same result obtained without zero values). Among patients with available Hb concentrations, 78% had non-zero Hb values <10.0 g/dL (the KDIGO suggested threshold for treatment). Of these patients, 11% were below this threshold for a span of at least 90 days based on subsequent lab draw dates. Of patients with Hb <10 g/dL, 98% were receiving an ESA. Using Kahn’s framework, certain demographic features, anemia treatment, and iron indices were consistently recorded in eHR LDO data. This provides support for the use of these LDO eHR anemia-related variables for future research. Most patients with Hb labs were anemic (78%) but received ESA treatment as suggested by KDIGO guidelines.

Can we trust electronic health records? The smoking test for commission errors

BackgroundConsiderable interest exists on using general practice electronic health records (EHRs) for research and other uses. There is also concern on their quality.AimWe suggest a simple test to assess errors...

Extracting autism spectrum disorder data from the electronic health record.

Little is known about the health care utilization patterns of individuals with pediatric autism spectrum disorder (ASD). Electronic health record (EHR) data provide an opportunity to study medical utilization and track outcomes among children with ASD. Methods: Using a pediatric, tertiary, academic hospital's Epic EHR, search queries were built to identify individuals aged 2-18 with International Classification of Diseases, Ninth Revision (ICD-9) codes, 299.00, 299.10, and 299.80 in their records. Codes were entered in the EHR using four different workflows: (1) during an ambulatory visit, (2) abstracted by Health Information Management (HIM) for an encounter, (3) recorded on the patient problem list, or (4) added as a chief complaint during an Emergency Department visit. Once individuals were identified, demographics, scheduling, procedures, and prescribed medications were extracted for all patient-related encounters for the period October 2010 through September 2012. There were 100,000 encounters for more than 4,800 unique individuals.Individuals were most frequently identified with an HIM abstracted code (82.6%) and least likely to be identified by a chief complaint (45.8%).Categorical frequency for reported race (2 = 816.5, p < 0.001); payor type (2 = 354.1, p < 0.001); encounter type (2 = 1497.0, p < 0.001); and department (2 = 3722.8, p < 0.001) differed by search query. Challenges encountered included, locating available discrete data elements and missing data. This study identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.

What evidence is there for a delay in diagnostic coding of RA in UK general practice records? An observational study of free text

ObjectivesMuch research with electronic health records (EHRs) uses coded or structured data only; important information captured in the free text remains unused. One dimension of EHR data quality assessment is...

Assessing and Improving EHRs Data Quality through a Socio-technical Approach

The implementation of Electronic Health Records (EHRs) in developing countries is considered a means for improving data quality and high quality care. However, existing research, indicate that EHRs have recorded greater quantity of bad data instead of improving the quality of data. The reasons for this is not exactly clear. Accordingly, this paper takes a broader socio-technical approach to explore the issues in more detail. The paper explores the relationship between EHRs and quality at the Pentecost Hospital Madina-Ghana by using an interpretative research approach. The result of our study indicates that the EHRs introduced at the hospital have so far had limited effect on data quality and that context-related challenges are the major pitfalls identified in the study. Based on a socio- technical approach the paper discusses the need to; consider users and technology as intertwined, how technology changes practice and how it formalizes roles and responsibilities in the process, why it requires collaborative work and why realistic goals as opposed to exaggerated expectation.

Reported Use of Electronic Health Records to Implement Evidence Based Approaches to Colorectal Cancer Screening in Community Health Centers

Community health centers (CHCs) are critical sources of primary care for medically underserved populations. Electronic health records (EHRs) are important for implementation of evidence-based approaches for cancer control. Cross-sectional study of CHCs from the Cancer Prevention Control Research Network's community health center Clinic Characteristics Survey. Proportions of CHCs using EHR data to: 1) measure colorectal cancer screening, 2) deliver reports, and 3) provide patient reminders for colorectal cancer screening. Only 27% of CHCs perceive EHR system's colorectal cancer screening data as very accurate. Over half (57%) of respondent CHCs with EHRs reported it is easy or very easy to do colorectal cancer screening activities using EHR systems. Poor EHR data quality and cumbersome EHR systems may be significant barriers to implementation of evidence-based approaches to colorectal cancer screening in CHCs.

Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.

ObjectiveTo review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research.Materials and methodsA review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used.ResultsFive dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence.DiscussionExamination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment.ConclusionThere is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.

Review: Electronic Health Records and the Reliability and Validity of Quality Measures: A Review of the Literature

Previous reviews of research on electronic health record (EHR) data quality have not focused on the needs of quality measurement. The authors reviewed empirical studies of EHR data quality, published from January 2004, with an emphasis on data attributes relevant to quality measurement. Many of the 35 studies reviewed examined multiple aspects of data quality. Sixty-six percent evaluated data accuracy, 57% data completeness, and 23% data comparability. The diversity in data element, study setting, population, health condition, and EHR system studied within this body of literature made drawing specific conclusions regarding EHR data quality challenging. Future research should focus on the quality of data from specific EHR components and important data attributes for quality measurement such as granularity, timeliness, and comparability. Finally, factors associated with poor or variability in data quality need to be better understood and effective interventions developed.