Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature

German primary care data collection projects: a scoping review

BACKGROUND

The widespread use of electronic health records (EHRs) has led to a growing number of large routine primary care data collection projects globally, making these records a valuable resource for health services and epidemiological and clinical research. This scoping review aims to comprehensively assess and compare strengths and limitations of all German primary care data collection projects and relevant research publications that extract data directly from practice management systems (PMS).

METHODS

A literature search was conducted in the electronic databases in May 2021 and in June 2022. The search string included terms related to general practice, routine data, and Germany. The retrieved studies were classified as applied studies and methodological studies, and categorised by type of research, subject area, sample of publications, disease category, or main medication analysed.

RESULTS

A total of 962 references were identified, with 241 studies included from six German projects in which databases are populated by EHRs from PMS. The projects exhibited significant heterogeneity in terms of size, data collection methods, and variables collected. The majority of the applied studies (n = 205, 85%) originated from one database with a primary focus on pharmacoepidemiological topics (n = 127, 52%) including prescription patterns (n = 68, 28%) and studies about treatment outcomes, compliance, and treatment effectiveness (n = 34, 14%). Epidemiological studies (n = 77, 32%) mainly focused on incidence and prevalence studies (n = 41, 17%) and risk and comorbidity analysis studies (n = 31, 12%). Only 10% (n = 23) of studies were in the field of health services research, such as hospitalisation.

CONCLUSION

The development and durability of primary care data collection projects in Germany is hindered by insufficient public funding, technical issues of data extraction, and strict data protection regulations. There is a need for further research and collaboration to improve the usability of EHRs for health services and research.

Feasibility and perceptions of a benzodiazepine deprescribing quality improvement initiative for primary care providers in Japan

BACKGROUND

Quality improvement (QI) initiatives in primary care in Japan are rare. One crucial area for QI is the appropriate prescription of benzodiazepines due to the large and growing elderly population in the country.

OBJECTIVE

This study aimed to determine the feasibility and other perceptions of a Benzodiazepine receptor agonist medications (BZRAs) deprescribing QI initiative for primary care providers (PCPs) in Japanese primary care clinics.

DESIGN

A qualitative study within a QI initiative.

PARTICIPANTS

We recruited 11 semi-public clinics and 13 providers in Japan to participate in a BZRAs deprescribing initiative from 2020 to 2021. After stratifying the clinics according to size, we randomly allocated implementation clinics to either an Audit only or an Audit plus Coaching group.

INTERVENTIONS

For the Audit, we presented clinics with two BZRAs-related indicators. We provided monthly web-based meetings for the Coaching to support their QI activities.

APPROACH

After the nine-month initiative, we conducted semi-structured interviews and used content analysis to identify themes. We organized the themes and assessed the key factors of implementation using the Consolidated Framework for Implementation Research (CFIR) framework.

KEY RESULTS

Audit plus Coaching was perceived as more valuable than Audit only intervention. Participants expressed intellectual curiosity about the QI initiative from resources outside their clinic. However, adopting a team-based QI approach in a small clinic was perceived as challenging, and selecting the indicators was important for meaningful QI.

CONCLUSION

The small size of the clinic could be a potential barrier, but enhancing academic curiosity may facilitate QI initiatives in primary care in Japan. Further implementation trials are needed to evaluate the possibility of QI with more various indicators and a more extended period of time.

Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure

Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language models (PLMs), the application prospect and promotion potential of machine learning methods in the relevant field have been further inspired. PLMs have recently achieved tremendous success in diverse text processing tasks, whereas limited by the significant semantic gap between the pre-training corpus and the structured electronic health records (EHRs), PLMs cannot converge to anticipated disease diagnosis and prediction results. Unfortunately, establishing connections between PLMs and EHRs typically requires the extraction of curated predictor variables from structured EHR resources, which is tedious and labor-intensive, and even discards vast implicit information.In this work, we propose an Input Prompting and Discriminative language model with the Mixture-of-experts framework (IPDM) by promoting the model's capabilities to learn knowledge from heterogeneous information and facilitating the feature-aware ability of the model. Furthermore, leveraging the prompt-tuning mechanism, IPDM can inherit the impacts of the pre-training in downstream tasks exclusively through minor modifications. IPDM remarkably outperforms existing models, proved by experiments on one disease diagnosis task and two disease prediction tasks. Finally, experiments with few-feature and few-sample demonstrate that IPDM achieves significant stability and impressive performance in predicting chronic diseases with unclear early-onset characteristics or sudden diseases with insufficient data, which verifies the superiority of IPDM over existing mainstream methods, and reveals the IPDM can powerfully address the aforementioned challenges via establishing a stable and low-resource medical diagnostic system for various clinical scenarios.

Commercializing Personal Health Information: A Critical Qualitative Content Analysis of Documents Describing Proprietary Primary Care Databases in Canada

BACKGROUND

Commercial data brokers have amassed large collections of primary care patient data in proprietary databases. Our study objective was to critically analyze how entities involved in the collection and use of these records construct the value of these proprietary databases. We also discuss the implications of the collection and use of these databases.

METHODS

We conducted a critical qualitative content analysis using publicly available documents describing the creation and use of proprietary databases containing Canadian primary care patient data. We identified relevant commercial data brokers, as well as entities involved in collecting data or in using data from these databases. We sampled documents associated with these entities that described any aspect of the collection, processing, and use of the proprietary databases. We extracted data from each document using a structured data tool. We conducted an interpretive thematic content analysis by inductively coding documents and the extracted data.

RESULTS

We analyzed 25 documents produced between 2013 and 2021. These documents were largely directed at the pharmaceutical industry, as well as shareholders, academics, and governments. The documents constructed the value of the proprietary databases by describing extensive, intimate, detailed patient-level data holdings. They provided examples of how the databases could be used by pharmaceutical companies for regulatory approval, marketing and understanding physician behaviour. The documents constructed the value of these data more broadly by claiming to improve health for patients, while also addressing risks to privacy. Some documents referred to the trade-offs between patient privacy and data utility, which suggests these considerations may be in tension.

CONCLUSION

Documents in our analysis positioned the proprietary databases as socially legitimate and valuable, particularly to pharmaceutical companies. The databases, however, may pose risks to patient privacy and contribute to problematic drug promotion. Solutions include expanding public data repositories with appropriate governance and external regulatory oversight.

[BeoNet-Halle-development of a multifunctional database for the automated extraction of healthcare data from general practitioner and specialist practices]

The Beobachtungspraxennetzwerk Halle (BeoNet-Halle) is an innovative database of outpatient care that has been collecting patient data from participating primary care and specialty practices throughout Germany since 2020 and making it available for research and care. The database is set up and maintained by the Institute of Medical Epidemiology, Biometrics and Informatics and the Institute of General Practice and Family Medicine of the Martin Luther University Halle-Wittenberg. Furthermore, the Data Integration Center of the University Medical Center Halle is involved in the project. In principle, anonymized and pseudonymized patient data from all commercially available practice management systems should flow into the databases.In this article, we describe the structure and methods of the multi-purpose database BeoNet and quantify the current data stock. The workflow of collection, transfer, and storage of broad consents is described and advantages and limitations of the database are discussed.BeoNet-Halle currently contains anonymized data of approximately 73,043 patients from five physician practices. Furthermore, it includes data from more than 2,653,437 ICD-10 diagnoses, 1,403,726 prescriptions, and 1,894,074 laboratory results. Pseudonymized data were successfully exported from 481 patients.BeoNet-Halle enables an almost seamless representation of the care provided in the participating practices. In the future, the database will map patient treatment pathways across practices and provide high-quality care data to contribute to health policy decision-making and optimization of care processes.

Agreement of acute serious events recorded across datasets using linked Australian general practice, hospital, emergency department and death data: implications for research and surveillance

Introduction

Understanding the level of recording of acute serious events in general practice electronic health records (EHRs) is critical for making decisions about the suitability of general practice datasets to address research questions and requirements for linking general practice EHRs with other datasets.

Objectives

To examine data source agreement of five serious acute events (myocardial infarction, stroke, venous thromboembolism (VTE), pancreatitis and suicide) recorded in general practice EHRs compared with hospital, emergency department (ED) and mortality data.

Methods

Data from 61 general practices routinely contributing data to the MedicineInsight database was linked with New South Wales administrative hospital, ED and mortality data. The study population comprised patients with at least three clinical encounters at participating general practices between 2019 and 2020 and at least one record in hospital, ED or mortality data between 2010 and 2020. Agreement was assessed between MedicineInsight diagnostic algorithms for the five events of interest and coded diagnoses in the administrative data. Dates of concordant events were compared.

Results

The study included 274,420 general practice patients with at least one record in the administrative data between 2010 and 2020. Across the five acute events, specificity and NPV were excellent (>98%) but sensitivity (13%-51%) and PPV (30%-75%) were low. Sensitivity and PPV were highest for VTE (50.9%) and acute pancreatitis (75.2%), respectively. The majority (roughly 70-80%) of true positive cases were recorded in the EHR within 30 days of administrative records.

Conclusion

Large proportions of events identified from administrative data were not detected by diagnostic algorithms applied to general practice EHRs within the specific time period. EHR data extraction and study design only partly explain the low sensitivities/PPVs. Our findings support the use of Australian general practice EHRs linked to hospital, ED and mortality data for robust research on the selected serious acute conditions.

Developing a national primary care research network: a qualitative study of stakeholder views

Introduction Primary care research is critical to address Aotearoa New Zealand's (NZ) health sector challenges. These include health inequities, workforce issues and the need for evaluation of health system changes. Internationally, primary care data are routinely collected and used to understand these issues by primary care research and surveillance networks (PCRN). NZ currently has no such infrastructure. Aim To explore health sector stakeholders' views on the utility of, and critical elements needed for, a national PCRN in NZ. Methods Twenty semi-structured interviews and a focus group were conducted with key stakeholders, representing different perspectives within the health sector, including Hauora Māori providers. Data were analysed thematically. Results Six themes were identified that included both challenges within current primary care research and ideas for a future network. The themes were: disconnection between research, practice and policy; desire for better infrastructure; improving health equity for Māori and other groups who experience inequity; responding to the research needs of communities; reciprocity between research and practice; and the need for data to allow evidence-informed decision-making. Improving health equity for Māori was identified as a critical function for a national PCRN. Discussion Stakeholders identified challenges in conducting primary care research and translating research into practice and policy in NZ. Stakeholders from across the health sector supported a national PCRN and identified what its function should be and how it could operate. These views were used to develop a set of recommendations to guide the development of a national PCRN.

Electronic Health Records and Antimicrobial Stewardship Research: a Narrative Review

Purpose of Review

This review summarises epidemiological research using electronic health records (EHR) for antimicrobial stewardship.

Recent Findings

EHRs enable surveillance of antibiotic utilisation and infection consultations. Prescribing for respiratory tract infections has declined in the UK following reduced consultation rates. Reductions in prescribing for skin and urinary tract infections have been less marked. Drug selection has improved and use of broad-spectrum antimicrobics reduced. Diagnoses of pneumonia, sepsis and bacterial endocarditis have increased in primary care. Analytical studies have quantified risks of serious bacterial infections following reduced antibiotic prescribing. EHRs are increasingly used in interventional studies including point-of-care trials and cluster randomised trials of quality improvement. Analytical and interventional studies indicate patient groups for whom antibiotic utilisation may be more safely reduced.

Summary

EHRs offer opportunities for surveillance and interventions that engage practitioners in the effects of improved prescribing practices, with the potential for better outcomes with targeted study designs.

Evaluating the accuracy of data extracted from electronic health records into MedicineInsight, a national Australian general practice database

Introduction

MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. Previous research validated algorithms used to derive medical condition flags in MedicineInsight, but the accuracy of data fields following EHR extractions from clinical practices and data warehouse transformation processes have not been formally validated.

Objectives

To examine the accuracy of the extraction and transformation of EHR fields for selected demographics, observations, diagnoses, prescriptions, and tests into MedicineInsight.

Methods

We benchmarked MedicineInsight values against those recorded in original EHRs. Forty-six general practices contributing data to MedicineInsight met our eligibility criteria, eight were randomly selected, and four agreed to participate. We randomly selected 200 patients >18 years of age within each participating practice from MedicineInsight. Trained staff reviewed the original EHRs for the selected patients and recorded data from the relevant fields. We calculated the percentage of agreement (POA) between MedicineInsight and EHR data for all fields; Cohen's Kappa for categorical and intra-class correlation (ICC) for continuous measures; and sensitivity, specificity, and positive and negative predictive values (PPV/NPV) for diagnoses.

Results

A total of 796 patients were included in our analysis. All demographic characteristics, observations, diagnoses, prescriptions and random pathology test results had excellent (>90%) POA, Kappa, and ICC. POA for most recent pathology/imaging test was moderate (81%, [95% CI: 78% to 84%]). Sensitivity, specificity, PPV, and NPV were excellent (>90%) for all but one of the examined diagnoses which had a poor PPV.

Conclusions

Overall, our study shows good agreement between the majority of MedicineInsight data and those from original EHRs, suggesting MedicineInsight data extraction and warehousing procedures accurately conserve the data in these key fields. Discrepancies between test data may have arisen due to how data from pathology, radiology and other imaging providers are stored in EHRs and MedicineInsight and this requires further investigation.

Identifying cases of spinal cord injury or disease in a primary care electronic medical record database

OBJECTIVE

To identify cases of spinal cord injury or disease (SCI/D) in an Ontario database of primary care electronic medical records (EMR).

DESIGN

A reference standard of cases of chronic SCI/D was established via manual review of EMRs; this reference standard was used to evaluate potential case identification algorithms for use in the same database.

SETTING

Electronic Medical Records Primary Care (EMRPC) Database, Ontario, Canada.

PARTICIPANTS

A sample of 48,000 adult patients was randomly selected from 213,887 eligible patients in the EMRPC database.

INTERVENTIONS

N/A.

MAIN OUTCOME MEASURE(S)

Candidate algorithms were evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-score.

RESULTS

126 cases of chronic SCI/D were identified, forming the reference standard. Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9-78.4), 98.5% specificity (97.3-99.3), 89.9% PPV (82.2-95.0), 94.7% NPV (92.8-96.3), and an F-score of 79.1.

CONCLUSIONS

Identifying cases of chronic SCI/D from a database of primary care EMRs using free-text entries is feasible, relying on a comprehensive case definition. Identifying a cohort of patients with SCI/D will allow for future study of the epidemiology and health service utilization of these patients.

A systematic review of risk stratification tools internationally used in primary care settings

BACKGROUND AND AIMS

In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.

METHODS

We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes.

RESULTS

The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).

CONCLUSION

Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.

How ready is the system to deliver primary healthcare? Results of a primary health facility assessment in Enugu State, Nigeria

Primary health centres are an effective means of achieving access to primary healthcare (PHC) in low- and middle-income countries. We assessed service availability, service readiness and factors influencing service delivery at public PHC centres in Enugu State, Nigeria. We conducted a cross-sectional study of 60 randomly selected public health centres in Enugu using the World Health Organization’s Service Availability and Readiness Assessment (SARA) survey. The most senior health worker available was interviewed using the SARA questionnaire, and an observational checklist was used for the facility assessment. None of the PHC centres surveyed had all the recommended service domains, but 52 (87%) offered at least half of the recommended service domains. Newborn care and immunization (98.3%) were the most available services across facilities, while mental health was the least available service (36.7%). None of the surveyed facilities had a functional ambulance or access to a computer on the day of the assessment. The specific-service readiness score was lowest in the non-communicable disease (NCD) area (33% in the rural health centres and 29% in the urban health centres) and NCD medicines and supplies. Availability of medicine and supplies was also low in rural PHC centres for the communicable disease area (36%) and maternal health services (38%). Basic equipment was significantly more available in urban health centres (P = 0.02). Urban location of facilities and the presence of a medical officer were found to be associated with having at least 50% of the recommended infrastructure / basic amenities and equipment. Continuing medical education, funding and security were identified by the health workers as key enablers of service delivery. In conclusion, despite a focus on expanding primary care in Enugu State, significant gaps exist that need to be closed for PHC to make significant contributions towards achieving universal healthcare, core to achieving the health-related Sustainable Development Goal agenda.

Challenges to implementing electronic trial data collection in primary care: a qualitative study

BACKGROUND

Within-consultation recruitment to primary care trials is challenging. Ensuring procedures are efficient and self-explanatory is the key to optimising recruitment. Trial recruitment software that integrates with the electronic health record to support and partially automate procedures is becoming more common. If it works well, such software can support greater participation and more efficient trial designs. An innovative electronic trial recruitment and outcomes software was designed to support recruitment to the Runny Ear randomised controlled trial, comparing topical, oral and delayed antibiotic treatment for acute otitis media with discharge in children. A qualitative evaluation investigated the views and experiences of primary care staff using this trial software.

METHODS

Staff were purposively sampled in relation to site, role and whether the practice successfully recruited patients. In-depth interviews were conducted using a flexible topic guide, audio recorded and transcribed. Data were analysed thematically.

RESULTS

Sixteen staff were interviewed, including GPs, practice managers, information technology (IT) leads and research staff. GPs wanted trial software that automatically captures patient data. However, the experience of getting the software to work within the limited and complex IT infrastructure of primary care was frustrating and time consuming. Installation was reliant on practice level IT expertise, which varied between practices. Although most had external IT support, this rarely included supported for research IT. Arrangements for approving new software varied across practices and often, but not always, required authorisation from Clinical Commissioning Groups.

CONCLUSIONS

Primary care IT systems are not solely under the control of individual practices or CCGs or the National Health Service. Rather they are part of a complex system that spans all three and is influenced by semi-autonomous stakeholders operating at different levels. This led to time consuming and sometimes insurmountable barriers to installation at the practice level. These need to be addressed if software supporting efficient research in primary care is to become a reality.

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed for the text domain to the structured EHR domain. Med-BERT is a contextualized embedding model pretrained on a structured EHR dataset of 28,490,650 patients. Fine-tuning experiments showed that Med-BERT substantially improves the prediction accuracy, boosting the area under the receiver operating characteristics curve (AUC) by 1.21-6.14% in two disease prediction tasks from two clinical databases. In particular, pretrained Med-BERT obtains promising performances on tasks with small fine-tuning training sets and can boost the AUC by more than 20% or obtain an AUC as high as a model trained on a training set ten times larger, compared with deep learning models without Med-BERT. We believe that Med-BERT will benefit disease prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.

Routine Health Information Systems in the European Context: A Systematic Review of Systematic Reviews

(1) Background: The aim of this study is to provide a better understanding of the requirements to improve routine health information systems (RHISs) for the management of health systems, including the identification of best practices, opportunities, and challenges in the 53 countries and territories of the WHO European region. (2) Methods: We conducted an overview of systematics reviews and searched the literature in the databases MEDLINE/PubMed, Cochrane, EMBASE, and Web of Science electronic databases. After a meticulous screening, we identified 20 that met the inclusion criteria, and RHIS evaluation results were presented according to the Performance of Routine Information System Management (PRISM) framework. (3) Results: The reviews were published between 2007 and 2020, focusing on the use of different systems or technologies and aimed to analyze interventions on professionals, centers, or patients' outcomes. All reviews examined showed variability in results in accordance with the variability of interventions and target populations. We have found different areas for improvement for RHISs according to the three determinants of the PRISM framework that influence the configuration of RHISs: technical, organizational, or behavioral elements. (4) Conclusions: RHIS interventions in the European region are promising. However, new global and international strategies and the development of tools and mechanisms should be promoted to highly integrate platforms among European countries.

Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

OBJECTIVE

To identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.

DESIGN

A systematic review of published studies.

DATA SOURCES

Pubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.

STUDY SELECTION

Observational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.

RESULTS

6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.

CONCLUSIONS

Comparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Embedding "Smart" Disease Coding Within Routine Electronic Medical Record Workflow: Prospective Single-Arm Trial

BACKGROUND

Electronic medical record (EMR) chronic disease measurement can help direct primary care prevention and treatment strategies and plan health services resource management. Incomplete data and poor consistency of coded disease values within EMR problem lists are widespread issues that limit primary and secondary uses of these data. These issues were shared by the McMaster University Sentinel and Information Collaboration (MUSIC), a primary care practice-based research network (PBRN) located in Hamilton, Ontario, Canada.

OBJECTIVE

We sought to develop and evaluate the effectiveness of new EMR interface tools aimed at improving the quantity and the consistency of disease codes recorded within the disease registry across the MUSIC PBRN.

METHODS

We used a single-arm prospective trial design with preintervention and postintervention data analysis to assess the effect of the intervention on disease recording volume and quality. The MUSIC network holds data on over 75,080 patients, 37,212 currently rostered. There were 4 MUSIC network clinician champions involved in gap analysis of the disease coding process and in the iterative design of new interface tools. We leveraged terminology standards and factored EMR workflow and usability into a new interface solution that aimed to optimize code selection volume and quality while minimizing physician time burden. The intervention was integrated as part of usual clinical workflow during routine billing activities.

RESULTS

After implementation of the new interface (June 25, 2017), we assessed the disease registry codes at 3 and 6 months (intervention period) to compare their volume and quality to preintervention levels (baseline period). A total of 17,496 International Classification of Diseases, 9th Revision (ICD9) code values were recorded in the disease registry during the 11.5-year (2006 to mid-2017) baseline period. A large gain in disease recording occurred in the intervention period (8516/17,496, 48.67% over baseline), resulting in a total of 26,774 codes. The coding rate increased by a factor of 11.2, averaging 1419 codes per month over the baseline average rate of 127 codes per month. The proportion of preferred ICD9 codes increased by 17.03% in the intervention period (11,007/17,496, 62.91% vs 7417/9278, 79.94%; χ²₁=819.4; P<.001). A total of 45.03% (4178/9278) of disease codes were entered by way of the new screen prompt tools, with significant increases between quarters (Jul-Sep: 2507/6140, 40.83% vs Oct-Dec: 1671/3148, 53.08%; χ²₁=126.2; P<.001).

CONCLUSIONS

The introduction of clinician co-designed, workflow-embedded disease coding tools is a very effective solution to the issues of poor disease coding and quality in EMRs. The substantial effectiveness in a routine care environment demonstrates usability, and the intervention detail described here should be generalizable to any setting. Significant improvements in problem list coding within primary care EMRs can be realized with minimal disruption to routine clinical workflow.

Quality of Life in Patients with Gynecological Cancers: A Web-Based Study

Introduction:

Gynecological cancers are common in adult women. One of the most important goals in the management of these patients is to improve quality of life, along with survival as a traditional outcome. The aim of this study was to evaluate quality of life in gynecological cancers in Iran.

Methods:

This cross-sectional study was performed on a sample of patients with gynecological cancers including uterine, ovarian, cervical, and vulvovaginal attending a teaching hospital affiliated to Tehran University of Medical Sciences between 2014 and 2019. The data was collected by a web-based platform with validated self-administered questionnaires including demographic information, the EORTC QLQ-C30 and the Hospital Anxiety and Depression (HADS). The data were analyzed using appropriate tests.

Results:

In all 251 patients were studied. The mean age of patients was 52.8±12.4 years and 43% had uterine, 30% had ovarian, 25% had cervical, and 2% had vulvovaginal cancer. The mean global quality of life score as measured by the EORTC QLQ-C30 was 59.8 ± 24.9. Women with ovarian cancer had the highest and women with cervical cancer had the lowest global quality of life score. There were significant differences in emotional, cognitive and global quality of life by cancer diagnosis (p <0.05). Although not significant, overall physical, role, cognitive and social functioning was found to be better in women who had been treated with surgery. The mean anxiety and depression score were 8.7± 5.0 and 7.1 ± 5.2, respectively.

Conclusion:

The results demonstrated that patients with gynecological cancers had a low quality of life, and experience higher anxiety and depression.

Harnessing the potential of electronic general practice pathology data in Australia: An examination of the quality use of pathology for type 2 diabetes patients

BACKGROUND

Despite the importance of pathology testing in diagnosis and disease monitoring, there is little in-depth research about pathology test ordering in general practice and how it impacts patient outcomes. This is in part due to the limited availability of high-quality data. With the now-widespread use of electronic software in general practice comes the potential for electronic patient data to be used for research leading to better understanding of general practice activities, including pathology testing.

OBJECTIVES

This study aimed to examine the usefulness of electronic general practice pathology data to: (1) identify patients' characteristics, (2) monitor quality of care, (3) evaluate intervention effects, (4) identify variations in patient care, and (5) measure patient outcomes. An exemplar study evaluating kidney function testing in type 2 diabetes mellitus (type 2 diabetes) compared to guidelines was used to demonstrate the value of pathology data.

MATERIALS AND METHODS

De-identified electronic data from approximately 200 general practices in Victoria were extracted using Outcome Health's Population Level Analysis & Reporting (POLAR) Aurora research platform. Our study population included patients ≥18 diagnosed with type 2 diabetes before July 2016. Data from July 2016 to June 2018 were used to i) determine frequency of kidney function tests (KFT), and ii) identify whether antihypertensive medications were prescribed for abnormal KFT results.

RESULTS

There were 20,514 active patients with type 2 diabetes identified from the data. The age and gender standardised estimate of diabetes prevalence was 4.9%, consistent with Australian estimates (5.2%). Sociodemographic features of prevalence, including higher prevalence in older males, were also consistent with previous Australian estimates. Kidney function testing was performed annually, as recommended by guidelines, in 75.7% of patients, with higher annual testing observed in patients managed under general practice incentive programs (80.1%) than those who were not (72.2%). Antihypertensive medications were prescribed as recommended in 77.4% of patients with suspected microalbuminuria or macroalbuminuria based on KFT results.

DISCUSSION

Evaluations using data from diabetes patients in this study illustrate the value of electronic data for identifying patients with the condition of interest (e.g. type 2 diabetes) along with sociodemographic characteristics. This allows for the ability to undertake analyses on pathology testing factors and the identification of variation compared to guidelines, which has a potential to ensure quality of care. Its potential to identify associations with incentive programs further demonstrates the advantages of the data's longitudinal nature. These include the ability to assess temporal order and time interval of tests as a marker of quality of monitoring and evaluate intervention effects on a cohort over time. Finally, analyses on antihypertensive medication prescribing in patients with suspected micro/macroalbuminuria exemplified the electronic data's usefulness in monitoring patient outcomes, such as appropriate prescribing based on pathology test results.

CONCLUSIONS

Electronic general practice data is an important resource which can provide valuable insights about the quality use of pathology. There are clear benefits to patients for better monitoring, and consequent better outcomes, and to inform policymakers about the best ways to channel resources to enhance the quality of care.

Pelvic floor disorders in women who consult primary care clinics: development and validation of case definitions using primary care electronic medical records

BACKGROUND

To date, there has been no validated method to identify cases of pelvic floor disorders in primary care electronic medical record (EMR) data. We aimed to develop and validate symptom-based case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse in women, for use in primary care epidemiologic or clinical research.

METHODS

Our retrospective study used EMR data from the Southern Alberta Primary Care Research Network (SAPCReN) and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in southern Alberta. Trained researchers remotely reviewed a random sample of EMR charts of women aged 18 years or older from 6 rural and urban clinics to validate case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and estimated SAPCReN prevalence as appropriate.

RESULTS

Charts of 900 women were included. Sensitivity was 81.9% (95% confidence interval [CI] 75.1-87.2) for urinary incontinence, 61.2% (95% CI 46.2-74.5) for fecal incontinence, and 51.8% (95% CI 40.6-62.8) for pelvic organ prolapse. Corresponding specificity values were 71.9% (95% CI 68.4-75.1), 99.2% (95% CI 98.2-99.6) and 98.8% (95% CI 97.7-99.4), PPVs 40.6% (95% CI 35.4-46.0), 81.1% (95% CI 64.3-91.4) and 81.1% (95% CI 67.6-90.1), and NPVs 94.4% (95% CI 92.1-96.1), 97.8% (95% CI 96.5-98.6) and 95.3% (95% CI 93.6-96.6). The SAPCReN-observed prevalence for urinary incontinence was 29.7% (95% CI 29.3-30.0), but the adjusted prevalence was 2.97%.

INTERPRETATION

The case definition for urinary incontinence met our standard for validity (sensitivity and specificity > 70%), and the case definitions for fecal incontinence and pelvic organ prolapse had PPVs greater than 80%. The urinary incontinence definition may be used in epidemiologic research, and those for fecal incontinence and pelvic organ prolapse may be used in quality-improvement studies or creation of disease registries. Our symptom-based case definitions could also be adapted for research in other EMR settings.

Evaluation of automatic annotation by a multi-terminological concepts extractor within a corpus of data from family medicine consultations

INTRODUCTION

Research in family medicine is necessary to improve the quality of care. The number of publications in general medicine remains low. Databases from Electronic Medical Records can increase the number of these publications. These data must be coded to be used pertinently. The objective of this study was to assess the quality of semantic annotation by a multi-terminological concept extractor within a corpus of family medicine consultations.

METHOD

Consultation data in French from 25 general practitioners were automatically annotated using 28 different terminologies. The data extracted were classified into three groups: reasons for consulting, observations and consultation results. The first evaluation led to a correction phase of the tool which led to a second evaluation. For each evaluation, the precision, recall and F-measure were quantified. Then, the inter- and intra-terminological coverage of each terminology was assessed.

RESULTS

Nearly 15,000 automatic annotations were manually evaluated. The mean values for the second evaluation of precision, recall and F-measure were 0.85, 0.83 and 0.84 respectively. The most common terminologies used were SNOMED CT, SNOMED 3.5 and NClt. The terminologies with the best intra-terminological coverage were ICPC-2, DRC and CISMeF Meta-Terms.

CONCLUSION

A multi-terminological concepts extractor can be used for the automatic annotation of consultation data in family medicine. Integrating such a tool into general practitioners' business software would be a solution to the lack of routine coding. Developing the use of a single terminology specific to family medicine could improve coding, facilitate semantic interoperability and the communication of relevant information.

How We Should Assess the Delivery of End-Of-Life Care in General Practice? A Systematic Review

BACKGROUND

The majority of end-of-life (EOL) care occurs in general practice. However, we still have little knowledge about how this care is delivered or how it can be assessed and supported.

AIM

(i) To review the existing evaluation tools used for assessment of the delivery of EOL care from the perspective of general practice; (ii) To describe how EOL care is provided in general practice; (iii) To identify major areas of concern in providing EOL care in this context.

DESIGN

A systematic review.

DATA SOURCES

Systematic searches of major electronic databases (Medline, EMBASE, PsycINFO, and CINAHL) from inception to 2017 were used to identify evaluation tools focusing on organizational structures/systems and process of end-of-life care from a general practice perspective.

RESULTS

A total of 43 studies representing nine evaluation tools were included. A relatively restricted focus and lack of validation were common limitations. Key general practitioner (GP) activities assessed by the evaluation tools were summarized and the main issues in current GP EOL care practice were identified.

CONCLUSIONS

The review of evaluation tools revealed that GPs are highly involved in management of patients at the EOL, but there are a range of issues relating to the delivery of care. An EOL care registration system integrated with electronic health records could provide an optimal approach to address the concerns about recall bias and time demands in retrospective analyses. Such a system should ideally capture the core GP activities and any major issues in care provision on a case-by-case basis.