Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Linking observational data from general practice, hospital admissions and diabetes clinic databases: can it be used to predict hospital admission?

BACKGROUND

Linking process of care data from general practice (GP) and hospital data may provide more information about the risk of hospital admission and re-admission for people with type-2 diabetes mellitus (T2DM). This study aimed to extract and link data from a hospital, a diabetes clinic (DC). A second aim was to determine whether the data could be used to predict hospital admission for people with T2DM.

METHODS

Data were extracted using the GRHANITE™ extraction and linkage tool. The data from nine GPs and the DC included data from the two years prior to the hospital admission. The date of the first hospital admission for patients with one or more admissions was the index admission. For those patients without an admission, the census date 31/03/2014 was used in all outputs requiring results prior to an admission. Readmission was any admission following the index admission. The data were summarised to provide a comparison between two groups of patients: 1) Patients with a diagnosis of T2DM who had been treated at a GP and had a hospital admission and 2) Patients with a diagnosis of T2DM who had been treated at a GP and did not have a hospital admission.

RESULTS

Data were extracted for 161,575 patients from the three data sources, 644 patients with T2DM had data linked between the GPs and the hospital. Of these, 170 also had data linked with the DC. Combining the data from the different data sources improved the overall data quality for some attributes particularly those attributes that were recorded consistently in the hospital admission data. The results from the modelling to predict hospital admission were plausible given the issues with data completeness.

CONCLUSION

This project has established the methodology (tools and processes) to extract, link, aggregate and analyse data from general practices, hospital admission data and DC data. This study methodology involved the establishment of a comparator/control group from the same sites to compare and contrast the predictors of admission, addressing a limitation of most published risk stratification and admission prediction studies. Data completeness needs to be improved for this to be useful to predict hospital admissions.

Achieving quality primary care data: a description of the Canadian Primary Care Sentinel Surveillance Network data capture, extraction, and processing in Alberta

Introduction

Electronic medical record (EMR) databases have become increasingly popular for secondary purposes, such as health research. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is the first and only pan-Canadian primary care EMR data repository, with de-identified health information for almost two million Canadians. Comprehensive and freely available documentation describing the data ‘lifecycle’ is important for assessing potential data quality issues and appropriate interpretation of research findings. Here, we describe the flow and transformation of CPCSSN data in the province of Alberta.

Approach

In Alberta, the data originate from 54 publicly-funded primary care settings, including one community pediatric clinic, with 318 providers contributing de-identified EMR data for 410,951 patients (as of December 2018). Data extraction methods have been developed for five different EMR systems, and include both backend and automated frontend extractions. The raw EMR data are transformed according to specific rules, including trimming implausible values, converting values and free text to standard terminologies or classification systems, and structuring the data into a common CPCSSN format. Following local data extraction and processing, the data are transferred to a central repository and made available for research and disease surveillance.

Conclusion

This paper aims to provide important contextual information to future CPCSSN data users.

Estimating Morbidity Rates Based on Routine Electronic Health Records in Primary Care: Observational Study

BACKGROUND

Routinely recorded electronic health records (EHRs) from general practitioners (GPs) are increasingly available and provide valuable data for estimating incidence and prevalence rates of diseases in the population. This paper describes how we developed an algorithm to construct episodes of illness based on EHR data to calculate morbidity rates.

OBJECTIVE

The goal of the research was to develop a simple and uniform algorithm to construct episodes of illness based on electronic health record data and develop a method to calculate morbidity rates based on these episodes of illness.

METHODS

The algorithm was developed in discussion rounds with two expert groups and tested with data from the Netherlands Institute for Health Services Research Primary Care Database, which consisted of a representative sample of 219 general practices covering a total population of 867,140 listed patients in 2012.

RESULTS

All 685 symptoms and diseases in the International Classification of Primary Care version 1 were categorized as acute symptoms and diseases, long-lasting reversible diseases, or chronic diseases. For the nonchronic diseases, a contact-free interval (the period in which it is likely that a patient will visit the GP again if a medical complaint persists) was defined. The constructed episode of illness starts with the date of diagnosis and ends at the time of the last encounter plus half of the duration of the contact-free interval. Chronic diseases were considered irreversible and for these diseases no contact-free interval was needed.

CONCLUSIONS

An algorithm was developed to construct episodes of illness based on routinely recorded EHR data to estimate morbidity rates. The algorithm constitutes a simple and uniform way of using EHR data and can easily be applied in other registries.

Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

OBJECTIVES

There is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals' experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.

SETTING

Four tertiary hospitals in Beijing, China.

PARTICIPANTS

Nineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.

METHODS

A qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.

RESULTS

The main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.

CONCLUSIONS

These results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.

Patients with overlapping diagnoses of asthma and COPD: is livestock exposure a risk factor for comorbidity and coexisting symptoms and infections?

BACKGROUND

Epidemiological research on health effects of livestock exposure in population subgroups with compromised respiratory health is still limited. The present study explored the association between livestock exposure and comorbid/concurrent conditions in patients with overlapping diagnoses of asthma and COPD.

METHODS

Electronic health record data from 23 general practices in the Netherlands were collected from 425 patients diagnosed with both asthma and COPD, living in rural areas with high livestock density ("study area"). Data of 341 patients with the same overlapping diagnoses, living in rural areas with lower livestock density ("control areas") were obtained from 19 general practices. First, the prevalence of comorbid disorders and symptoms/infections were compared between the study and control area. Second, the examined health outcomes were analyzed in relation to measures of individual livestock exposure.

RESULTS

Pneumonia was twice as common among patients living in areas with a high livestock density (OR 2.29, 99% CI 0.96-5.47); however, there were generally no statistically significant differences in the investigated outcomes between the study and control area. Significant associations were observed between presence of goats within 1000 m and allergic rhinitis (OR 5.71, 99% CI 1.11-29.3, p < 0.01), number of co-occurring symptoms (IRR 1.69, 99% CI 1.03-2.77, p < 0.01) and anxiety (OR 8.18, 99% 1.5-44.7, p < 0.01). Presence of cattle within 500 m was associated with pneumonia prevalence (OR 2.48, 99% CI 1.05-5.84, p < 0.01).

CONCLUSION

Livestock exposure is not associated with comorbid chronic conditions but appears to be a risk factor for symptomatic effects in patients with overlapping diagnoses of asthma and COPD.

Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR

Background

Clinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important.

Objective

This study aimed to develop a retrieval method to identify CIMs online to represent EHR data.

Methods

We proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics.

Results

We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering “diagnostic reports,” “problem list,” “patients background,” “clinical decision,” etc, as well as models that other retrieval methods could not find, such as “problems and diagnoses.”

Conclusions

The graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable.

European Hospitals' Transition Toward Fully Electronic-Based Systems: Do Information Technology Security and Privacy Practices Follow?

BACKGROUND

Traditionally, health information has been mainly kept in paper-based records. This has deeply changed throughout approximately the last three decades with the widespread use of multiple health information technologies. The digitization of health care systems contributes to improving health care delivery. However, it also exposes health records to security and privacy breaches inherently related to information technology (IT). Thus, health care organizations willing to leverage IT for improved health care delivery need to put in place IT security and privacy measures consistent with their use of IT resources.

OBJECTIVE

In this study, 2 main objectives are pursued: (1) to assess the state of the implementation of IT security and privacy practices in European hospitals and (2) to assess to what extent these hospitals enhance their IT security and privacy practices as they move from paper-based systems toward fully electronic-based systems.

METHODS

Drawing on data from the European Commission electronic health survey, we performed a cluster analysis based on IT security and privacy practices implemented in 1723 European hospitals. We also developed an IT security index, a compounded measure of implemented IT security and privacy practices, and compared it with the hospitals' level in their transition from a paper-based system toward a fully electronic-based system.

RESULTS

A total of 3 clearly distinct patterns of health IT-related security and privacy practices were unveiled. These patterns, as well as the IT security index, indicate that most of the sampled hospitals (70.2%) failed to implement basic security and privacy measures consistent with their digitization level.

CONCLUSIONS

Even though, on average, the most electronically advanced hospitals display a higher IT security index than hospitals where the paper system still dominates, surprisingly, it appears that the enhancement of IT security and privacy practices as the health information digitization advances in European hospitals is neither systematic nor strong enough regarding the IT-security requirements. This study will contribute to raising awareness among hospitals' managers as to the importance of enhancing their IT security and privacy measures so that they can keep up with the security threats inherently related to the digitization of health care organizations.

Completeness of Electronic Dental Records in a Student Clinic: Retrospective Analysis

BACKGROUND

A well-designed, adequately documented, and properly maintained patient record is an important tool for quality assurance and care continuity. Good clinical documentation skills are supposed to be a fundamental part of dental student training.

OBJECTIVE

The goal of this study was to assess the completeness of electronic patient records in a student clinic.

METHODS

Completeness of patient records was assessed using comparative review of validated cases of alveolar osteitis treated between August 2011 and May 2017 in a student clinic at Columbia University College of Dental Medicine, New York, USA. Based on a literature review, population-based prevalence of nine most frequently mentioned symptoms, signs, and treatment procedures of alveolar osteitis was identified. Completeness of alveolar osteitis records was assessed by comparison of population-based prevalence and frequency of corresponding items in the student documentation. To obtain all alveolar osteitis cases, we ran a query on the electronic dental record, which included all cases with diagnostic code Z1820 or any variation of the phrases "dry socket" and "alveolar osteitis" in the notes. The resulting records were manually reviewed to definitively confirm alveolar osteitis and to extract all index items.

RESULTS

Overall, 296 definitive cases of alveolar osteitis were identified. Only 22% (64/296) of cases contained a diagnostic code. Comparison of the frequency of the nine index categories in the validated alveolar osteitis cases between the student clinic and the population showed the following results: severe pain: 94% (279/296) vs 100% (430/430); bare bone/missing blood clot: 27% (80/296) vs 74% (35/47) to 100% (329/329); malodor: 7% (22/296) vs 33%-50% (18/54); radiating pain to the ear: 8% (24/296) vs 56% (30/54); lymphadenopathy: 1% (3/296) vs 9% (5/54); inflammation: 14% (42/296) vs 50% (27/54); debris: 12% (36/296) vs 87% (47/54); alveolar osteitis site noted: 96% (283/296) vs 100% (430/430; accepted documentation requirement); and anesthesia during debridement: 77% (20/24) vs 100% (430/430; standard of anesthetization prior to debridement).

CONCLUSIONS

There was a significant discrepancy between the index category frequency in alveolar osteitis cases documented by dental students and in the population (reported in peer-reviewed literature). More attention to clinical documentation skills is warranted in dental student training.

Quality criteria, instruments, and requirements for nursing documentation: A systematic review of systematic reviews

AIM

To obtain an overview of existing evidence on quality criteria, instruments, and requirements for nursing documentation.

DESIGN

Systematic review of systematic reviews.

DATA SOURCES

We systematically searched the databases PubMed and CINAHL for the period 2007-April 2017. We also performed additional searches.

REVIEW METHODS

Two reviewers independently selected the reviews using a stepwise procedure, assessed the methodological quality of the selected reviews, and extracted the data using a predefined extraction format. We performed descriptive synthesis.

RESULTS

Eleven systematic reviews were included. Several quality criteria were described referring to the importance of following the nursing process and using standardized nursing terminologies. In addition, some evidence-based instruments were described for assessing the quality of nursing documentation, such as the D-Catch. Furthermore, several requirements for formats and systems of electronic nursing documentation were found that refer to the importance of user-friendliness and development in consultation with nursing staff.

CONCLUSION

Aligning documentation with the nursing process, using standard terminologies, and using user-friendly formats and systems appear to be important for high-quality nursing documentation. The lack of evidence-based quality indicators presents a challenge in the pursuit of high-quality nursing documentation.

IMPACT

There is uncertainty in nursing practice about which criteria have to be met to achieve high-quality documentation. Aligning documentation with the nursing process, using standard terminologies, and using user-friendly formats and systems appear to be important. These findings can help nursing staff and care organizations enhance the quality of nursing documentation.

Health Data for Research Through a Nationwide Privacy-Proof System in Belgium: Design and Implementation

Background

Health data collected during routine care have important potential for reuse for other purposes, especially as part of a learning health system to advance the quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures and, thus, ensure privacy.

Objective

This study aims to address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring the accuracy, validity, safety, and privacy. In addition, the study demonstrates how these processes can be used in various research designs relevant for learning health systems.

Methods

The Healthdata.be platform urges uniformity of the data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer are organized through end-to-end encrypted electronic health channels, and data are encoded using token keys. In addition, patient identifiers are pseudonymized so that health data from the same patient collected across various sources can still be linked without compromising the deidentification.

Results

The Healthdata.be platform currently collects data for >150 clinical registries in Belgium. We demonstrated how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial.

Conclusions

Collecting health data in various sources and linking these data to a single patient is a promising feature that can potentially address important concerns on the validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to a research facility. More research is required to demonstrate that these methods improve the quality of data collection, allowing researchers to rely on electronic health records as a valid source for scientific data.

The influence of gender concordance between general practitioner and patient on antibiotic prescribing for sore throat symptoms: a retrospective study

Background

Patient gender as well as doctor gender are known to affect doctor-patient interaction during a medical consultation. It is however not known whether an interaction of gender influences antibiotic prescribing. This study examined GP’s prescribing behavior of antibiotics at the first presentation of patients with sore throat symptoms in primary care. We investigated whether GP gender, patient gender and gender concordance have an effect on the GP’s prescribing behavior of antibiotics in protocolled and non-protocolled diagnoses.

Methods

We analyzed electronic health record data of 11,285 GP practice consultations in the Netherlands in 2013 extracted from the Nivel Primary Care Database. Our primary outcome was the prescription of antibiotics for throat symptoms. Sore throat symptoms were split up in ‘protocolled diagnoses’ and ‘non-protocolled diagnoses’. The association between gender concordance and antibiotic prescription was estimated with multilevel regression models that controlled for patient age and comorbidity.

Results

Antibiotic prescription was found to be lower among female GPs (OR 0.88, CI 95% 0.67–1.09; p = .265) and female patients (OR 0.93, 95% 0.84–1.02; p = .142), but observed differences were not statistically significant. The difference in prescription rates by gender concordance were small and not statistically significant in non-protocolled consultations (OR 0.92, OR 95% CI: 0.83–1.01; p = .099), protocolled consultations (OR 1.00, OR 95% CI: 0.68–1.32; p = .996) and all GP practice consultations together (OR 0.92, OR 95% CI: 0.82–1.02; p = .118). Within the female GP group, however, gender concordance was associated with reduced prescribing of antibiotics (OR 0.85, OR 95% CI: 0.72–0.99; p = 0.034).

Conclusions

In this study, female GPs prescribed antibiotics less often than male GPs, especially in consultation with female patients. This study shows that, in spite of clinical guidelines, gender interaction may influence the prescription of antibiotics with sore throat symptoms.